Canonical covariate columns

This file is the authoritative register of covariate column names used in nlmixr2lib models. Every covariate referenced inside a model’s model() block must have an entry here. The register is seeded from a full audit of inst/modeldb/ and extended whenever a new paper introduces a covariate that isn’t yet registered.

How to use this register

1. Before adding a covariate to a new model, search this file (by canonical name and by source alias) for the concept you need.
2. If the canonical name exists, use it exactly. Document any source-paper rename in the model’s covariateData[[name]]$source_name field.
3. If the source paper uses an alias listed under an existing canonical name, use the canonical name and note in covariateData[[name]]$notes whether the values must be transformed (e.g., SEXM -> SEXF inverts values; the effect coefficient sign / reference category must be inverted as well).
4. If the covariate is not in this register at all, propose a new entry with a canonical name, description, units, type, scope, and source aliases. Verify with the user before committing. The addition is part of the model’s PR.
5. Do not modify existing model files when you discover an alias; simply document the mapping in the register. Retrofitting existing models is a separate effort.

Scope: general vs specific

Each entry has a Scope: field declaring whether it is general (any model may use it without warning) or specific (only the models listed under Example models may use it; other usage is flagged by checkModelConventions()). This prevents accidentally reusing a covariate name whose meaning is tied to a particular paper.

• Scope: general – the covariate has a stable, paper-independent meaning and any future model may use it. Examples: WT, AGE, SEXF, CREAT, ADA_POS, CRP, CRCL.
• Scope: specific – the covariate’s semantics depend on a particular study’s design (a specific study indicator, a drug-product variant, a composite race grouping, a tumor-type decomposition, etc.). If a new paper needs the same concept with different semantics, register a new canonical name; if the concept matches, extend the Example models list (and consider promoting to general).

When adding or updating an entry, choose the most conservative scope: if in doubt, start with specific and promote when a second model legitimately ratifies the name.

Case convention

Covariate column names should be ALL CAPS. Current non-all-caps canonical names are dilution and nonECZTRA (both scope: specific), preserved from their source files with “future rename” notes. New entries should default to all caps.

Entry schema

- name: <CANONICAL_NAME>
  description: <one-sentence definition>
  units: <unit string, or "(binary)" / "(categorical)">
  type: continuous | binary | categorical | count
  scope: general | specific
  reference_category: <the 0 group for binary/categorical, or NULL>
  source_aliases:
    - <ALIAS_NAME> (<transformation if any>) -- used in <model.R>
  example_models:
    - <model.R>
  notes: <free text>

---

Demographics

WT (canonical for body weight (baseline or time-varying))

• Description: Body weight (baseline or time-varying).
• Units: kg
• Type: continuous
• Scope: general
• Reference category: n/a – used with allometric scaling (WT / ref_wt)^exponent. Reference weights observed: 70 kg (adults), 75 kg, 84.8 kg, 56 kg (Kloprogge 2014 quinine cohort typical), 5 kg (infants), 25 kg (Wang 2012 Chinese pediatric epilepsy cohort median).
• Source aliases:
  • WEIG – weight column abbreviation used by Wang 2012 (Acta Pharmacol Sin 33:845-851); same biological quantity in kg, no value transformation. Used in Wang_2012_levetiracetam.R.
• Example models: Clegg_2024_nirsevimab.R, Hu_2026_clesrovimab.R, Zhu_2017_lebrikizumab.R, Kovalenko_2020_dupilumab.R, CarlssonPetri_2021_liraglutide.R, Cirincione_2017_exenatide.R, Grimm_2023_gantenerumab.R, Grimm_2023_trontinemab.R, Kyhl_2016_nalmefene.R, Soehoel_2022_tralokinumab.R, Xie_2019_agomelatine.R, PK_2cmt_mAb_Davda_2014.R, phenylalanine_charbonneau_2021.R, Chua_2025_mirikizumab.R, Jackson_2022_ixekizumab.R, Kotani_2022_astegolimab.R, Ma_2020_sarilumab_anc.R, Ma_2020_sarilumab_das28crp.R, Moein_2022_etrolizumab.R, Tiraboschi_2025_amlitelimab.R, Robbie_2012_palivizumab.R, Bajaj_2017_nivolumab.R, Quartino_2019_trastuzumab.R, Wang_2020_ontamalimab.R, Fau_2020_isatuximab.R, Okada_2025_rocatinlimab.R, Kunisawa_2014_olprinone.R, Xu_2020_daratumumab.R (reference 78.6 kg; power exponents 0.451 on linear CL and 0.375 on V1), Dirks_2008_cetuximab.R (reference 60 kg; additive linear-deviation effect 0.0083 per kg on V1 of a two-compartment Michaelis-Menten model for cetuximab in SCCHN; source column WGT), Struemper_2017_belimumab.R (reference 67 kg; fixed allometric exponents 0.75 on CL and Q, 1.00 on Vc, 0.8 on Vp; baseline-only, source column BWT), MedellinGaribay_2015_gentamicin.R (linear (not allometric) weight scaling on both CL and Vc: CL = theta1 * BW + theta5 * (CRCL/75), Vc = theta2 * BW; no reference weight used because the scaling is linear, not divisive; source column BW; cohort mean 6.4 +/- 2.2 kg, infants 1-24 months), Wahlby_2004_pefloxacin.R (time-varying; reference 65 kg; linear-exp form on V with coefficient 0.014), Decker_2024_baricitinib.R (baseline-only, source column WTE = weight at entry; reference 74 kg carried from the upstream adult rheumatoid-arthritis model rather than taken from the pediatric cohort; exponents fixed at 0.75 on all clearance arms and 1 on both volumes).
• Notes: Universal. Verify time-varying vs. baseline-only against the source paper.

AGE (canonical for subject age)

• Description: Subject age in years.
• Units: years
• Type: continuous
• Scope: general
• Reference category: n/a
• Source aliases: none.
• Example models: Archary_2019_lamivudine.R, Budha_2023_tislelizumab.R, Chakraborty_2012_canakinumab.R, Chen_2020_luspatercept.R, Conrado_2014_alzheimer.R, Diepstraten_2013_propofol.R, Gandhi_2021_abatacept.R, Goel_2016_Sonidegib.R, Hennig_2013_tobra.R, Hong_2025_datopotamab.R, Ide_2020_elotuzumab.R, Koopman_2023_factorix.R, Kuchimanchi_2024_dostarlimab.R, Kunarajah_2017_doxorubicin.R, Kyhl_2016_nalmefene.R, Lahu_2010_roflumilast.R, Li_2006_meropenem.R, Li_2017_cediranib.R, Li_2019_abatacept.R, Lin_2024_casirivimab.R, Martinez_2019_alirocumab.R, Melhem_2022_dostarlimab.R, Mulyukov_2018_ranibizumab.R, NA_NA_tte_gompertz.R, NA_NA_tte_lognormal.R, Retlich_2015_linagliptin.R, Rosario_2015_vedolizumab.R, Svensson_2016_bedaquiline.R, Thakre_2022_risankizumab.R, Wu_2024_inotuzumab.R, Yassen_2025_asundexian.R, Yu_2022_ofatumumab.R, Zhong_2026_abatacept.R, Zhou_2021_belimumab.R, Zhu_2017_lebrikizumab.R.
• Notes: Zhu 2017 normalizes as AGE/40.

AGE_GE70 (canonical for age >= 70 years indicator)

• Description: 1 = subject is aged 70 years or older at baseline, 0 = subject is younger than 70 years. Time-fixed at study entry per subject. Used when the source paper reports an age effect as a threshold indicator (>= 70 y vs < 70 y) rather than as a continuous covariate on AGE.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (< 70 years).
• Source aliases:
  • AGE >= 70 – derived from the continuous AGE column via AGE_GE70 = as.integer(AGE >= 70); used in Overgaard_2016_liraglutide.R (Overgaard 2016 Fig. 2 / Table S1 covariate label “Age >= 70 years”).
• Example models: Overgaard_2016_liraglutide.R (multiplicative log-scale effect on CL/F, coefficient -0.10 per Table S1).
• Notes: Sibling to the ECOG_GE1 / ECOG_GE2 binary threshold family; captures a paper-defined age band as a decomposed binary indicator rather than a continuous AGE term. When the source paper describes the age band as “>= 70 y” (as in Overgaard 2016), use AGE_GE70; for other thresholds (e.g., >= 65 y, >= 75 y) register a sibling canonical (AGE_GE65, AGE_GE75). Use the AGE_GT<n> spelling instead when the paper’s threshold is strictly greater than the cut point (see AGE_GT65). The underlying continuous AGE may still be recorded alongside for downstream re-derivation.

AGE_GT65 (canonical for age > 65 years indicator)

• Description: 1 = subject is older than 65 years at baseline, 0 = subject is 65 years old or younger. Time-fixed at study entry per subject. Used when the source paper reports an age effect as a threshold indicator (> 65 y vs <= 65 y) rather than as a continuous covariate on AGE. This is the conventional geriatric cut point in anti-infective and oncology popPK.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (<= 65 years).
• Source aliases:
  • AGE_GROUP (coded 1 = age <= 65 y, 2 = age > 65 y) – derived from the continuous AGE column via AGE_GT65 = as.integer(AGE > 65), equivalently AGE_GT65 = AGE_GROUP - 1; used in Zakria_2026_voriconazole.R (Zakria 2026 Table 2 rows “CL-AGE_GROUP 1” / “CL-AGE_GROUP 2”).
• Example models: Zakria_2026_voriconazole.R (additive-fractional effect on CL, coefficient -0.519 per Table 2 and Eq 2).
• Notes: Sibling to AGE_GE70 and to the ECOG_GE1 / ECOG_GE2 binary threshold family. The GT (strictly greater) rather than GE (greater or equal) spelling is load-bearing: a subject aged exactly 65 years belongs to the reference group under AGE_GT65 but to the indicator group under a hypothetical AGE_GE65. Match the spelling to the inequality the source paper prints – Zakria 2026 dichotomises as “<= 65 years” versus “> 65 years”, so AGE_GT65 is correct and AGE_GE65 would misclassify the 65-year-olds. Register AGE_GE65 separately if a paper genuinely uses the “>= 65 y” band. The underlying continuous AGE may still be recorded alongside for downstream re-derivation.

LBM (canonical for lean body mass)

• Description: Lean body mass.
• Units: kg
• Type: continuous
• Scope: general
• Reference category: n/a
• Source aliases:
  • LBW (lean body weight) – synonym; same biological quantity (total body weight minus body fat). Hemophilia popPK literature typically uses LBW (Hume or James formula) where mAb / general literature uses LBM. Used in Garmann_2017_BAY81_8973.R (reference 51.1 kg).
• Example models: Kyhl_2016_nalmefene.R (reference 56.28 kg, exponent 0.626 on CL), Garmann_2017_BAY81_8973.R (alias LBW; reference 51.1 kg, exponents 0.610 on CL and 0.950 on Vc), Schoemaker_2017_brivaracetam.R (alias LBW; paediatric cohort, reference 50 kg adult typical value, fixed theoretical allometric exponents 0.750 on CL/F and 1.00 on V/F), Lee_2025_levofloxacin.R (James formula from sex, total body weight, and height; reference 47.91 kg cohort median, range 37.7-60.3; power exponent 1.75 on the PERIPHERAL volume V2 rather than on CL or Vc, which is unusual for this column – the source screened total body weight, FFM, and NFM as alternatives and retained none).
• Notes: The body-composition formula varies by source paper (James, Boer, Hume) and materially changes the value at a given weight and height; record the formula the source used in covariateData[[LBM]]$notes. The James formulae are LBM (female) = 1.07 * TBW - 148 * (TBW / height)^2 and LBM (male) = 1.1 * TBW - 128 * (TBW / height)^2, with TBW in kg and height in cm.

FFM (canonical for fat-free mass)

• Description: Fat-free mass derived from body weight, height, and sex via the Janmahasatian et al. formula (Clin Pharmacokinet 2005;44:1051-1065).
• Units: kg
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (FFM / ref)^exponent. Reference values observed: 40.69 kg (Zhou 2021 belimumab pooled adult+pediatric SLE), 45 kg (Aguiar 2021, Crohn’s disease cohort median), 50 kg (Sinha 2026 oxcarbazepine, the “reference adult” fat-free mass).
• Source aliases:
  • PKWT (“pharmacokinetic weight”) – used in Sinha_2026_oxcarbazepine.R. Same biological quantity in kg with no value transformation, but PKWT is a composite fat-free-mass metric: it equals FFM for subjects aged >= 3 years and total body weight (WT) for children younger than 3 years. The WT fallback exists because the Al-Sallami et al. paediatric FFM equation was developed in children >= 3 years, so Sinha 2026 Methods 2.3.2 assumes a WT-equivalent FFM below that age. A downstream user computing the column for a cohort that includes children under 3 years must apply the fallback; above 3 years PKWT and FFM are the same quantity.
• Example models: Zhou_2021_belimumab.R (reference 40.69 kg; exponents 0.673 on CL and 0.891 on V1), Aguiar_2021_ustekinumab.R (reference 45 kg; power exponents 0.598 on CL, 0.590 on Vc, 0.586 on Vp), Sinha_2026_oxcarbazepine.R (alias PKWT; reference 50 kg; exponent 1 fixed on oxcarbazepine clearance, 0.671 on MHD-metabolite clearance, and a shared 0.752 on both central volumes; paediatric cohort spanning 44 days to 20.9 years including 52% with obesity, where the FFM-based descriptor outperformed total body weight by ~7 AIC points), Rolsma_2025_cefepime.R (alias LBW; reference 33 kg; linear normalisation (FFM / 33) on the central volume with no exponent reported, in children and adults with cystic fibrosis).
• Notes: Distinct from LBM (lean body mass) which is sometimes computed by the Boer or Hume formulae. When the source paper reports the body-composition formula it used (e.g., Janmahasatian for FFM), record it in covariateData[[FFM]]$notes. FFM is preferred over total body weight when scaling monoclonal-antibody PK because mAb distribution is largely confined to extracellular fluid; muscle / lean tissue tracks extracellular volume better than total weight in heavier patients. Paediatric extractions should record which FFM equation applies over which age band: Janmahasatian et al. (Clin Pharmacokinet 2005;44:1051-1065) is an adult formula, and Al-Sallami et al. (Clin Pharmacokinet 2015;54:1169-1178) supplies the paediatric age-correction multiplier a + (1 - a)/(1 + (PNA/b)^-c) applied to the adult form, with a = 0.88, b = 13.4, c = 12.7 for males and a = 1.11, b = 7.1, c = 1.1 for females. Note that the female numerator (1 - 1.11) is NEGATIVE; Sinha 2026 ESM1 Eq. S4 prints it as +0.11, which is a transcription error – the multiplier must converge to 1 at adolescence so the paediatric form reduces to the adult one.

IBW (canonical for ideal body weight)

• Description: Ideal body weight in kg, typically derived from height and sex using the Devine formula or its variants. Time-fixed at baseline unless the source paper states otherwise. Used in size-normalisation of clearance / dose-rate in adult popPK models where the source paper reports IBW as the preferred size descriptor over total body weight (e.g., when overweight subjects pull clearance scaling away from the typical pattern).
• Units: kg
• Type: continuous
• Scope: specific
• Reference category: n/a – used with a linear ratio (IBW / ref) for clearance / dose-rate normalisation. Reference values observed: 60 kg (Holford 1992 tacrine adult Alzheimer’s population mean IBW).
• Source aliases:
  • IBW – standard abbreviation used directly in Holford 1992.
• Example models: Holford_1992_tacrine.R (reference 60 kg; tacrine “clearance” relative to IBW = 60 enters as the dose-rate normalisation factor (60 / IBW) driving the tacrine effect-compartment input; the Holford-Peace Devine variant is documented in covariateData[[IBW]]$notes), VanWart_2004_garenoxacin.R (reference 64.2 kg; additive linear-deviation effect 0.764 mL/min per (IBW - 64.2) kg on CL/F, plus an in-model obesity flag derived as WT > 1.3 * IBW per Van Wart 2004’s >130% IBW obesity definition), Shoji_2011_pregabalin.R (reference 62 kg; power-of-ratio scaling on CL/F (exponent 0.354) and V/F (exponent 0.819) per Shoji 2011 Table 3 final model; the IBW formula is the Devine-family variant cited as reference [12] of the paper), Dirks_2008_cetuximab.R (reference 64 kg; additive linear-deviation effect 0.0108 per kg on Vmax of a two-compartment Michaelis-Menten model for cetuximab in SCCHN; the source dataset supplied IBW pre-computed, so the Devine-family variant used is not identified in the paper), Stoschus_2025_phenobarbital.R (reference 68.8 kg, the cohort median; classical allometric scaling of a one-compartment model, exponent 1 on V and 0.75 on CL, in critically ill adults with refractory / superrefractory status epilepticus. IBW was the only covariate retained of the demographic, clinical, laboratory, and comedication set screened. The source computed IBW with the formula of Brower et al. (Stoschus 2025 reference 25) but does not reproduce the formula, so the variant is not identifiable from the paper alone).
• Notes: Specific scope until a second adult-popPK model ratifies the name; at that point promote to general. Per-model covariateData[[IBW]]$notes should record the formula the source paper used. The Holford-Peace 1992 variant is: men IBW (kg) = 52 + 0.75 * (height_cm - 152); women IBW (kg) = 49 + 0.67 * (height_cm - 152). The classic Devine 1974 formula is: men 50 + 2.3 * (height_in - 60); women 45.5 + 2.3 * (height_in - 60). Other variants (Robinson 1983, Miller 1983, Hamwi 1964) exist; the per-paper choice should be recorded so a user simulating against IBW can match the source’s derivation. When the source dataset supplies IBW pre-computed, the column name is typically IBW directly. When only HT + SEXF are provided, the user must compute IBW externally using the source-paper formula before passing it to the model.

HT (canonical for body height at baseline)

• Description: Subject body height at baseline. Time-fixed unless the source paper states otherwise.
• Units: cm
• Type: continuous
• Scope: general
• Reference category: n/a – used with a linear-deviation form (HT - ref) or with a power-style scaling. Reference values observed: 167 cm (Naik 2016, vortioxetine adult MDD/GAD population median); 165 cm (Zhang 2018 flurbiprofen and Angeli 2016 healthy non-menopausal women).
• Source aliases:
  • HGT – height (cm) abbreviation appearing in some NONMEM control streams.
  • HEIGHT – spelled-out form (used by Angeli 2016).
• Example models: Naik_2016_vortioxetine.R (reference 167 cm; linear-additive effect 0.40 L/hr per (HT - 167) cm on CL/F, retained over weight and BMI in stepwise selection because it produced the larger reduction in CL IIV), Zhang_2018_flurbiprofen.R (reference 165 cm; linear-multiplicative effect 1 + theta_height * (HT - 165) on the effect-compartment equilibration rate Ke alongside a paired linear-multiplicative WT effect), Angeli_2016_iron_hepcidin.R (reference 165 cm; power-law multiplier (HT / 165)^32.70 on the hepcidin post-menses rebound parameter krel_hep; the very large exponent reflects the narrow height range across the cohort, 158-173 cm at the 10th-90th percentile), Wu_2014_FEV1_asthma.R (per-observation HT in cm enters directly into the exponential FEV1 disease-progression model: FEV1 = exp(theta_age*AGE + theta_ht*HT - theta_int) + ... with theta_ht = 0.0169 1/cm; paediatric population, ages 5-13 y).
• Notes: Height is sometimes retained as a size covariate when allometric scaling on weight performs less well; it is also an input to BSA, BMI, FFM, and LBM derivations, so a model that retains HT alongside one of those derived covariates should document the dependency in covariateData[[HT]]$notes. Promoted from specific to general on 2026-06-03 with the Angeli 2016 iron / hepcidin extraction (the third adult-popPK model to register HT, satisfying the original promotion condition documented when the canonical was introduced).

BSA (canonical for body surface area)

• Description: Body surface area (typically computed by DuBois, Mosteller, or Haycock from height and weight).
• Units: m^2
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (BSA / ref)^exponent or with linear centering (1 + slope * (BSA - ref)). Reference values observed: 1.70 m^2 (Yamada 2025, adult oncology), 1.11 m^2 (Park 2023, paediatric HSCT cohort median).
• Source aliases: none.
• Example models: Yamada_2025_zolbetuximab.R (reference 1.70 m^2; power scaling, exponents 1.06 on clearances and 0.968 on volumes), Roepcke_2023_rezafungin.R (reference 1.90 m^2 = the pooled-cohort median; separately estimated exponents 0.882 on CL, 1.56 on the central volume V1, and 1.17 on the shared peripheral volume V23; BSA replaced body weight and BMI, which the paper notes were highly correlated with it and would have fit comparably; the BSA formula used is unspecified in the source), Park_2023_mycophenolic_acid.R (reference 1.11 m^2; centered-linear scaling Vd/F = 89.83 * (1 + 0.854 * (BSA - 1.11)) on the apparent volume of distribution in paediatric HSCT recipients; founding centered-linear example), Yao_2025_flurbiprofen_r.R (reference 2.6 m^2 = the cohort median; power exponent 1.37 on the apparent central volume Vc of R(-)-flurbiprofen; BSA formula unstated and the tabulated values are NOT reproducible from the source’s own height and weight – see Notes).
• Notes: Oncology mAbs dosed by BSA (mg/m^2) often use BSA in place of body weight for allometric-style scaling. Document the BSA computation formula (DuBois / Mosteller / Haycock) the source paper used; if unstated, record “unspecified.” Sanity-check the tabulated BSA against the source’s own height and weight before using it as a normalisation constant. Yao 2025 reports a median BSA of 2.6 m^2 (range 2.0-3.2) for a cohort whose median height and weight are 1.61 m and 70 kg, which give about 1.76 m^2 by DuBois or 1.79 m^2 by Mosteller – the tabulated values run roughly 1.45x high and no published BSA formula reproduces them. Because a covariate exponent is estimated against whatever BSA scale the source used, a model carrying such an effect must be driven with BSA on that same scale (and its virtual cohort must sample BSA from the reported distribution rather than recomputing it from height and weight); silently substituting a correctly computed BSA would shift every predicted volume.

BMI (canonical for body mass index)

• Description: Body mass index at baseline.
• Units: kg/m^2
• Type: continuous
• Scope: general
• Reference category: n/a – used with a linear-deviation form (1 + e * (BMI - ref)) or a power form ((BMI / ref)^e). Document the reference value in covariateData[[BMI]]$notes.
• Source aliases: none known.
• Example models: Chua_2025_mirikizumab.R (reference 24.75 kg/m^2; linear-deviation effect on logit of bioavailability), NA_NA_lidocaine.R (DDMODEL00000281; binary stratification at threshold 27.93 kg/m^2 adding +0.939 to the GX rate constant K30 in the BMI > 27.93 cohort), Struemper_2017_belimumab.R (kg/m^2, reference 24.7; power exponent -0.610 on Vc; baseline-only, source column BBMI).
• Notes: Universal clinical-trial demographic. Derived as WT / (height_m)^2; assume time-fixed at baseline unless the source paper states otherwise.

BMIZ (canonical for body-mass-index z-score (age- and sex-standardised))

• Description: Age- and sex-standardised body-mass-index z-score (number of standard deviations above or below the reference-population mean BMI for the subject’s age and sex). Distinct from raw BMI (kg/m^2): BMIZ is unitless and centred at 0 in the reference population, so the reference value used in linear-deviation effects is 0 (not a population BMI in kg/m^2). Time-varying when the source paper carries a per-visit z-score; document baseline-vs-time-varying status in covariateData[[BMIZ]]$notes.
• Units: unitless (z-score; standard-deviation units)
• Type: continuous
• Scope: specific
• Reference category: n/a – used with a linear-deviation form (1 + e * (BMIZ - 0)) so the reference is 0 (population mean for the subject’s age/sex). Effect coefficients are interpreted as fractional change per 1 z-score-unit deviation from the reference.
• Source aliases:
  • BMI – when a paper uses the column name BMI for what is actually a z-score (e.g., the Harun 2019 NMTRAN control stream column BMI is documented in the dataset header as “body-mass index z-score”). The canonical column is BMIZ; the source-paper column name is recorded in covariateData[[BMIZ]]$source_name.
• Example models: Harun_2019_cysticFibrosis.R (time-varying per-visit BMI z-score; linear-deviation effect on baseline FEV1% predicted with reference 0 and coefficient +0.0382 per z-score unit).
• Notes: Distinct from BMI (raw kg/m^2 used in adult populations). Paediatric and adolescent studies routinely report BMI as a z-score relative to a growth reference (WHO 2007 Growth Reference for school-aged children, CDC 2000, etc.); document the reference standard the source paper used in covariateData[[BMIZ]]$notes. Specific scope until a second paediatric model ratifies the name; at that point promote to general.

SEXF (canonical for sex)

• Description: Biological sex indicator, 1 = female, 0 = male.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (male).
• Source aliases:
  • SEXM (values inverted: SEXF = 1 - SEXM; effect coefficient sign and reference category both invert) – used in CarlssonPetri_2021_liraglutide.R.
  • SEX with "M"/"F" strings – derive SEXF = as.integer(SEX == "F").
  • SEX with 1=male / 2=female numeric coding – derive SEXF = as.integer(SEX == 2). Used in Netterberg_2017_docetaxel.R and NA_NA_miridesap.R (DDMODEL00000262 source bundle; Sahota 2015 NONMEM convention).
  • FEM (1 = female, 0 = male; same orientation as canonical, no transformation) – used in Guiastrennec_2016_gastric_emptying.R.
• Example models: Zhu_2017_lebrikizumab.R (canonical), CarlssonPetri_2021_liraglutide.R (alias SEXM), Bajaj_2017_nivolumab.R (male-indicator source; effect applied as exp(coef * (1 - SEXF)) to preserve the paper’s female-reference CL_REF / VC_REF), Fau_2020_isatuximab.R (exponential effect on Vc; reference category 0 = male), Netterberg_2017_docetaxel.R (multiplicative effect on baseline ANC: BACOV *= (1 + theta * SEXF); source column SEX with 1 = male, 2 = female encoding, decomposed via SEXF = as.integer(SEX == 2)), NA_NA_miridesap.R (DDMODEL00000262 / Sahota 2015; multiplicative effect on baseline SAP via SAP_BASE_ref * (1 + e_sexf_sap0 * SEXF) with e_sexf_sap0 = -0.30; female baseline is ~30% lower than male), Xu_2020_daratumumab.R (additive shift on V1 (1 + e_sexf_vc * SEXF) with e_sexf_vc = -0.205: female V1 is 20.5% lower than male, reference category 0 = male), Guiastrennec_2016_gastric_emptying.R (multiplicative +40.7% strengthening of the caloric-feedback slope SLPCAL on gastric emptying in females; SLPCAL_eff = SLPCAL * (1 + 0.407 * SEXF)), Wada_2023_sparsentan.R (male-indicator source with a FEMALE reference subject, so the published +0.139 log-scale male effect on CL/F is applied as exp(e_sexf_cl * (1 - SEXF)) to preserve the verbatim coefficient while keeping the canonical 1 = female orientation – the same construction as Bajaj_2017_nivolumab.R; men had ~15% higher CL/F).
• Notes: When translating a model that used SEXM, flag the sign/reference-category inversion to the user.

PREG (canonical for pregnancy status indicator)

• Description: 1 = pregnant, 0 = non-pregnant. Time-fixed per subject in trial cohorts that enrol pregnant and non-pregnant women in parallel; not a time-varying flag.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (non-pregnant).
• Source aliases: none known; source NONMEM control streams typically use PREG directly.
• Example models: Birgersson_2019_artesunate.R (multiplicative effect on dihydroartemisinin clearance; the published structural CLM = 190 L/h is reported with the source-paper reference category PREG = 1, so the model file applies the effect via (1 + e_preg_cl_dha * (1 - PREG)) with e_preg_cl_dha = -0.214 to preserve verbatim source values; non-pregnant women have ~21% lower CLM relative to pregnant women).
• Notes: Use this canonical for adult clinical-trial models that test a pregnancy-vs-non-pregnancy contrast (typical settings: malaria-in-pregnancy PK, antiviral-in-pregnancy PK). Trimester or gestational-age stratification within the pregnant cohort should use a separate canonical (e.g., gestational-age weeks via GA or a trimester indicator, ratified separately when needed). The canonical convention is reference category 0 (non-pregnant) following the broader pharmacology default; source papers that use the pregnant cohort as the reference (Birgersson 2019) preserve their published structural values via a (1 - PREG) form on the effect coefficient. Ratified canonically on 2026-05-07.

CHILD (canonical for child age-cohort indicator)

• Description: 1 = subject is a child, 0 = not a child. In non-human models this is the general juvenile-versus-adult age-cohort indicator (1 = juvenile animal, 0 = adult animal); the concept is the same binary immature-versus-mature contrast, only the species-specific age cutoff changes.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (not child, i.e., adult baseline).
• Source aliases:
  • PED – used in the Schoemaker 2018 LEV / BRV pediatric extrapolation (DDMODEL00000239) as the pediatric-vs-adult indicator that gates the Markov-amplitude term, the overdispersion IIV, and the four pediatric offsets on log baseline rate / mixture / placebo / Emax / EC50.
  • adult_calve – used in Winter_2024_oxytetracycline_cattle.R (and in the raw data set distributed with Winter 2024 as code_adult_calve) as the calf-vs-adult-cattle indicator on all three volumes and all three clearances. Same coding as the canonical: 0 = adult, 1 = calf.
• Example models: CarlssonPetri_2021_liraglutide.R, Schoemaker_2018_levetiracetam.R (DDMODEL00000239), Winter_2024_oxytetracycline_cattle.R.
• Notes: Age-group indicator used alongside ADOLESCENT; paper’s age cutoffs must be captured in covariateData[[CHILD]]$notes. That requirement is load-bearing for veterinary and preclinical models, where the cutoff is species-specific and bears no relation to the human paediatric ranges (Winter 2024 defines a calf as under 6 months of age, or any animal the original study authors declared to be a calf).

ADOLESCENT (canonical for adolescent age-cohort indicator)

• Description: 1 = subject is an adolescent, 0 = not.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0.
• Example models: CarlssonPetri_2021_liraglutide.R.
• Notes: Paired with CHILD. Document age cutoffs.

FM (canonical for fat mass)

• Description: Fat mass in kg derived as the complement of fat-free mass, FM = TBW - FFM, where FFM is computed by the Janmahasatian et al. (2005) formula (or, equivalently, by direct measurement via bioelectrical-impedance analysis / DXA when the source paper specifies it). Time-fixed at baseline.
• Units: kg
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (FM / ref)^exponent. Reference values observed: 19 kg (Robarge 2017 efavirenz; cohort median calculated FM).
• Source aliases: FAT, FATMASS.
• Example models: Robarge_2017_efavirenz.R (reference 19 kg; exponent 1 fixed on V_p/F).
• Notes: Natural complement of FFM. Use both in the same model when the paper scales different disposition parameters by FFM vs FM (e.g., Robarge 2017 efavirenz: CL/F by FFM, V_p/F by FM, motivated by lipophilic partitioning into adipose tissue). When the source paper reports the body-composition formula it used (Janmahasatian for FFM is the most common; direct DXA / BIA also acceptable), record it in covariateData[[FM]]$notes.

VISCERAL_ABDOMINAL_FAT (canonical for visceral abdominal fat area from imaging)

• Description: Visceral abdominal fat area measured from a single computed-tomographic (CT) slice at the L2 / L3 vertebral level (standard IVGTT / metabolic-imaging protocol; Jensen et al. 1995). Captures the metabolically active visceral adipose-tissue depot that drives insulin resistance independently of subcutaneous fat. Time-fixed at baseline.
• Units: cm^2 (single CT slice). Document per-model via covariateData[[VISCERAL_ABDOMINAL_FAT]]$units when a paper uses a different anatomical level (L3 / L4, L4 / L5) or a different summary form (volumetric L1-L5 cm^3, MRI-derived).
• Type: continuous
• Scope: specific
• Reference category: n/a – used with mean-centred linear-in-log form log(SI) = log(SI_ref) + theta * (VISCERAL_ABDOMINAL_FAT - ref). Reference values observed: 141.8 cm^2 / CT-slice (Denti 2010 Table 1 pooled-population mean across 204 healthy adults aged 18-87).
• Source aliases:
  • VAF – universal abbreviation in the IVGTT / glucose-metabolism literature (Denti 2010, Basu 2003, Basu 2006). The canonical column is VISCERAL_ABDOMINAL_FAT; the source paper’s column name is recorded in covariateData[[VISCERAL_ABDOMINAL_FAT]]$source_name.
• Example models: Denti_2010_glucoseMinimal.R (introduces canonical; reference 141.8 cm^2 / CT-slice; linear-in-log effect coefficient -0.00208 per cm^2 deviation on insulin sensitivity lsi).
• Notes: Specific scope because the value reported is tied to the imaging modality (single-slice CT at L2 / L3 in the founding example). Volumetric CT, multi-slice averages, and MRI-derived volumetric adiposity from future papers can ratify under the same canonical with the assay method and units recorded per-model in covariateData[[VISCERAL_ABDOMINAL_FAT]]$notes. Distinct from BODYFAT_PCT (whole-body adiposity fraction by DEXA) and from FFM / LBM (fat-free mass / lean body mass). The H3-name uses underscores to preserve readability; future authors may abbreviate the source column as VAF in their own datasets and declare source_name = "VAF". Operator-ratified 2026-06-07 via the Denti 2010 extraction sidecar.

BODYFAT_PCT (canonical for percent total body fat (whole-body adiposity fraction))

• Description: Percent total body fat, measured as the fraction of total body mass classified as fat tissue by an imaging assay (typically dual-energy X-ray absorptiometry, DEXA). Expressed as a percentage (0-100). Time-fixed at baseline unless the source paper states otherwise. Captures whole-body adiposity, distinct from regional adiposity (visceral, abdominal, subcutaneous) and from fat-free mass.
• Units: % (percent; 0 = no body fat, 100 = all body fat). Document per-model via covariateData[[BODYFAT_PCT]]$units when a paper uses a fractional 0-1 scale.
• Type: continuous
• Scope: general
• Reference category: n/a – used with mean-centred linear-in-log form log(VD) = log(VD_ref) + theta * (BODYFAT_PCT - ref), or as the direct determinant of an adipose-compartment volume in whole-body PBPK. Reference values observed: 32.39 % (Denti 2010 Table 1 pooled-population mean across 204 healthy adults); 25 % (Levitt PKQuest standard human, i.e. 17.5 kg adipose of a 70 kg total, used by Pei_2023_tacrolimus_pbpk.R).
• Source aliases:
  • %TBF – paper symbol in the IVGTT / glucose-metabolism literature (Denti 2010, Basu 2003). The leading % character is dataset-incompatible (NMTRAN / R column-name rules); the canonical column is BODYFAT_PCT and the paper’s symbol is recorded in covariateData[[BODYFAT_PCT]]$source_name.
  • PTBF – numeric-safe rendering of the paper symbol used in some derived datasets.
• Example models: Denti_2010_glucoseMinimal.R (introduces canonical; reference 32.39 %; linear-in-log effect coefficient -0.0101 per percentage-point deviation on glucose distribution volume lvd), Pei_2023_tacrolimus_pbpk.R (whole-body PBPK organ-volume scaling: the adipose compartment weight is set directly as WT * BODYFAT_PCT / 100 and the remaining lean organ weights and blood flows scale by WT * (1 - BODYFAT_PCT / 100) / 52.5 kg; Pei 2023 Methods 2.5 names “the proportion of adipose tissue” as one of the two determinants of organ volume and Table 5 reports its local sensitivity).
• Notes: Promoted from Scope: specific to Scope: general on 2026-08-05 alongside the Pei 2023 tacrolimus PBPK extraction. The original specific scope reflected the founding example’s DEXA assay, but percent total body fat is an assay-independent body-composition quantity and the second registered use (a whole-body PBPK adipose-compartment volume) is unrelated to how the value was measured; the assay method belongs in the per-model covariateData[[BODYFAT_PCT]]$notes, not in the scope. Bioimpedance- or skinfold-derived %BF from future papers ratifies under the same canonical on the same terms. Distinct from FFM (fat-free mass in kg) and from VISCERAL_ABDOMINAL_FAT (regional visceral adiposity in cm^2 / CT slice). The negative effect coefficient on glucose distribution volume captures the physiological observation that glucose distributes preferentially into lean rather than adipose tissue, so per-kg distribution volume decreases as fat fraction rises. Operator-ratified 2026-06-07 via the Denti 2010 extraction sidecar.

WT_BASE (canonical for per-subject baseline body weight (time-fixed, paired with time-varying WT))

• Description: Per-subject baseline body weight, time-fixed. Used when a source paper applies Wahlby 2004’s extended covariate-model split (Br J Clin Pharmacol 2004;58(4):367-377) and the data column carries the subject’s baseline weight as a constant alongside a time-varying WT column. Operationally, WT_BASE is equal to WT at the subject’s first observation and remains constant for all subsequent records; the within-subject delta is computed in model() as (WT - WT_BASE).
• Units: kg
• Type: continuous
• Scope: specific
• Reference category: n/a – typically used as (WT_BASE - ref) in an exponential or linear-deviation effect on a structural parameter, where ref is the cohort median baseline weight.
• Source aliases:
  • BWT (baseline weight) – Wahlby 2004 source-column convention; used in Wahlby_2004_pefloxacin.R.
• Example models: Wahlby_2004_pefloxacin.R (reference 65 kg; saturating “up to median WT” qualifier from Wahlby 2004 Methods plateaus the effect above 65 kg, encoded as min(WT_BASE, 65) - 65).
• Notes: Specific scope because the BCOV/DCOV decomposition pattern is paper-defined: WT_BASE exists conceptually as a per-subject snapshot of baseline weight that the original modeller chose to enter separately from the time-varying weight column. Promote to general if a second paper ratifies the same baseline-weight column with consistent semantics. Distinct from time-fixed adult-cohort weight (where the per-subject weight is just constant by data design); use WT_BASE only when the source paper deliberately separates the BCOV from the time-varying COV. Ratified canonically alongside the Wahlby 2004 extraction.

BSA_BASE (canonical for per-subject baseline body surface area (time-fixed))

• Description: Per-subject baseline body surface area, time-fixed. Used when a source paper enters the subject’s baseline BSA as a constant alongside a time-varying BSA column under Wahlby 2004’s extended covariate-model decomposition (Br J Clin Pharmacol 2004;58(4):367-377). Time-fixed at the subject’s first observation.
• Units: m^2
• Type: continuous
• Scope: specific
• Reference category: n/a – typically used as a linear scaling factor on a volume parameter.
• Source aliases:
  • BBSA (baseline BSA) – Wahlby 2004 source-column convention; used in Wahlby_2004_gentamicin.R.
• Example models: Wahlby_2004_gentamicin.R (replaces time-varying BSA in the final-model V1 equation V1 = 8.63 * BSA_BASE * (ALB/34)^-0.41 because BBSA was the better predictor than time-varying BSA, RSE 2.5% vs 110% on delta-BSA).
• Notes: Specific scope because the BCOV/DCOV split is a paper-defined modelling choice (Wahlby 2004 demonstrated that BBSA was the relevant predictor for gentamicin V1 because delta-BSA was uninformative). Promote to general if a second paper ratifies the baseline-BSA column with consistent semantics. Ratified canonically alongside the Wahlby 2004 extraction.

BLOOD_GROUP_O (canonical for ABO blood group O indicator)

• Description: 1 = subject has ABO blood group O (either O-positive or O-negative Rh subtype); 0 = subject has ABO blood group A, B, or AB (non-O). Time-fixed per subject. Enters factor VIII (FVIII) and von Willebrand factor (VWF) population PK models as a surrogate for baseline VWF plasma concentration: blood group O individuals have ~25% lower circulating VWF than non-O individuals due to accelerated VWF clearance by the ASGR / CLEC4M receptor system (VWF’s ABH glycan antigens are recognized differently by clearance receptors depending on the ABO glycosyltransferase phenotype). Because VWF binds and stabilizes circulating FVIII, lower VWF in blood group O individuals translates to faster FVIII clearance.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (non-O ABO blood group: A, B, or AB).
• Source aliases:
  • blood group (paper-prose 0/1 indicator, 1 = O) – used in Hazendonk_2016_factor_viii.R (Hazendonk 2016 Table 5: 1.26 ^ blood_group on CL, so blood group O subjects have 26% higher FVIII clearance than non-O).
• Example models: Hazendonk_2016_factor_viii.R (multiplicative effect on CL: (1 + 0.26 * BLOOD_GROUP_O) – equivalent to 1.26 ^ BLOOD_GROUP_O when binary; reference category 0 = non-O ABO blood group).
• Notes: General scope because ABO blood group is a universally recorded clinical variable that recurs in FVIII / VWF / VWF-related-protein PK models (Hazendonk 2016 Discussion cites the well-established 25% lower baseline VWF in blood group O as the mechanism; also documented in Gill 1987, Miller 1985). The canonical is orthogonal to VWF (the actual plasma VWF concentration): when both are available, papers typically retain VWF and drop BLOOD_GROUP_O because VWF captures the effect more directly (e.g., Nestorov 2014 uses VWF directly; Hazendonk 2016 uses BLOOD_GROUP_O because VWF measurements were only available for ~50% of the cohort). Founding example: Hazendonk 2016. Ratified canonically on 2026-07-11 alongside the Hazendonk 2016 perioperative FVIII extraction.

Pediatric / maturation

PAGE (canonical for postmenstrual age)

• Description: Postmenstrual age in months (GA_weeks / 4.35 + postnatal_months). Time-varying.
• Units: months
• Type: continuous
• Scope: general
• Example models: Clegg_2024_nirsevimab.R, Robbie_2012_palivizumab.R.

PNA (canonical for postnatal age)

• Description: Postnatal age (chronological since birth). Time-varying.
• Units: months
• Type: continuous
• Scope: general
• Source aliases:
  • PNA – used in Zhao 2018 (paper Methods ‘Population pharmacokinetic-pharmacogenetic modelling’ and Table 2 report PNA in DAYS; the canonical PNA carries months, so Zhao 2018’s F_PNA = (PNA_days / 38)^0.472 is reparameterised inside model() as F_PNA = (PNA_months / 1.249)^0.472 using the conversion PNA_months = PNA_days / 30.4375 and reference 1.249 months = 38 days / 30.4375).
• Example models: Hu_2026_clesrovimab.R, Zhao_2018_omeprazole.R (power effect on the formation clearance of 5-hydroxy-omeprazole: (PNA / 1.249)^0.472; PNA reference 1.249 months / 38 days from Zhao 2018 Table 2 cohort median).

GA (canonical for gestational age at birth)

• Description: Gestational age at birth. Time-fixed per subject.
• Units: weeks
• Type: continuous
• Scope: general
• Example models: Hu_2026_clesrovimab.R, Clegg_2024_nirsevimab.R (folded into PAGE).

WT_BIRTH (canonical for birth weight)

• Description: Body weight at birth. Time-fixed per subject. Distinct from current body weight (WT), which is time-varying.
• Units: kg
• Type: continuous
• Scope: general
• Reference category: n/a – used with linear-deviation (1 + e * (WT_BIRTH - ref)) or power scaling. Reference value observed: 2.59 kg (Voller 2017 newborn cohort).
• Source aliases:
  • BWEIGHT – used in Voller_2017_phenobarbital.R (Voller 2017 source data column for birth weight in kg).
  • Bwb – used in Wu_2025_paracetamol.R (Wu 2025 source-paper symbol for birth weight in kg; same biological quantity, no value transformation).
• Example models: Voller_2017_phenobarbital.R (linear-deviation effect on CL: clbw = 1 + 0.369 * (WT_BIRTH - 2.59)), Wu_2025_paracetamol.R (power-law scaling of PTNA CL_birth and GFR_birth terms with reference 1.75 kg).
• Notes: Time-fixed at birth; characterises pre-/term-newborn cohorts. Pairs with GA (gestational age at birth) when both are reported. The conventional clinical-PK abbreviation BWT is intentionally NOT used as the canonical name because it is already used across the codebase (Gandhi 2021, Li 2019, Chen 2022, Wojciechowski 2022, Lu 2019) as a source-name alias for body weight (WT). The WT_BIRTH form keeps the WT root consistent with the existing body-weight canonical and avoids the BWT ambiguity.

AGE_DPF (canonical for zebrafish-larval age in days post-fertilization)

• Description: Age of a zebrafish (Danio rerio) larva in days post-fertilization (dpf). Time-fixed per subject in destructive-sampling designs (each larva is harvested at exactly one observation, so its dpf is fixed at the value assigned at exposure-start).
• Units: days post-fertilization (dpf)
• Type: continuous
• Scope: specific
• Reference category: n/a – used in van Wijk 2019 with a step-form effect on the absorption rate (IF (AGE_DPF > 3) k12 = k12_3 * (1 + e_age_dpf_k12)) and a per-day power-form effect on the elimination rate (k25 = k25_3 * (1 + e_age_dpf_k25)^(AGE_DPF - 3)). Reference age is 3 dpf (the youngest cohort in the study).
• Source aliases:
  • AGE – van Wijk 2019 NONMEM column. Renamed to canonical AGE_DPF because the human-PK canonical AGE denotes subject age in years; the zebrafish dpf semantic is incompatible and would silently corrupt any future model that mixed them. Same orientation, no value transformation.
• Example models: vanWijk_2019_paracetamol.R.
• Notes: Specific scope because the meaning is bounded to zebrafish-larval-development PK studies. Distinct from canonical AGE (human age in years), PNA (postnatal age in months), PAGE (postmenstrual age in months), and GA (gestational age in weeks) – none of those are appropriate for a non-mammalian organism whose developmental clock is anchored at fertilization rather than birth. Integer values 3, 4, 5 in the van Wijk 2019 dataset, but treated as a continuous covariate in the elimination-rate power form. Future zebrafish-PK or other non-mammalian-developmental-age models should reuse this canonical only when the covariate is indeed dpf-anchored; other developmental-time conventions (e.g., somite-stage, hpf, dph) would warrant separate canonicals. Ratified canonically on 2026-05-07.

ASPHYXIA (canonical for perinatal asphyxia indicator (low 5-minute Apgar score))

• Description: Binary indicator of perinatal asphyxia at birth defined by a 5-minute Apgar score < 5; 1 = 5-minute Apgar score was less than 5 (i.e., the neonate met the paper’s perinatal-asphyxia criterion), 0 = 5-minute Apgar score was 5 or higher. Time-fixed per subject (an event at birth, not a time-varying physiologic state). Used by neonatal popPK models that report a structural-PK shift in subjects who experienced perinatal asphyxia relative to those who did not.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no perinatal asphyxia; 5-minute Apgar >= 5). Effect form in Grasela 1985 is a linear-deviation increment on the volume of distribution: V = V_typical * (1 + e_asphyxia_vc * ASPHYXIA) with e_asphyxia_vc = 0.13 (+13% V in subjects with 5-minute Apgar < 5; p < 0.05).
• Source aliases:
  • None registered (Grasela 1985 abstract names the covariate asphyxia in prose; the abstract does not report the source NONMEM column label).
• Example models: Grasela_1985_phenobarbital.R (Grasela & Donn 1985; preterm-neonate phenobarbital popPK; +13% on V when 5-minute Apgar < 5; no detected effect on CL).
• Notes: Distinct from the continuous Apgar score itself (which has not been registered as a canonical because no model in nlmixr2lib uses the continuous form as of this entry). The < 5 cutoff at 5 minutes is the perinatal-asphyxia convention Grasela 1985 used; future papers that adopt a different Apgar cutoff (e.g., < 7 at 1 minute, or a different time point) should register a separate canonical (e.g., APGAR1_LT7) rather than reusing ASPHYXIA with relaxed semantics. Ratified canonically alongside the Grasela 1985 phenobarbital extraction.

Mother-infant dyad partner

Ratified 2026-08-06 with the library’s first lactation / mother-to-infant transfer model (Wattanakul_2024_primaquine.R / Wattanakul_2024_primaquine_motherinfant.R; operator sidecar oare_PMC11078975 request-001 / response-001, question q3, option A).

In a dyad model the modelled SUBJECT is the mother, so the general WT, AGE, and PAGE canonicals already denote her. Describing the breastfed infant with those same names would put two different people’s demographics on one record under one canonical name, and nothing in the column name would say which person it referred to. The _INFANT suffix marks a column as belonging to the dyad PARTNER rather than to the modelled subject. It parallels the infant_<canonical> compartment namespace in compartment-names.md.

Use these columns only in a genuine dyad model, i.e. one that carries maternal and infant quantities simultaneously. A standalone paediatric popPK model whose subject is the infant uses the plain WT / AGE / PAGE canonicals, because there is no second person to disambiguate against.

WT_INFANT (canonical for breastfed-infant body weight in a mother-infant dyad model)

• Description: Body weight of the breastfed infant paired with the modelled lactating subject. Distinct from WT, which in a dyad model is the mother’s body weight.
• Units: kg
• Type: continuous
• Scope: general
• Reference category: n/a – used both to size the maternal breast-milk compartment (milk ingested per feed = daily milk intake per kg x WT_INFANT / feeds per day) and, in a full dyad model, to scale the infant’s clearances and volumes allometrically from the mother’s, (WT_INFANT / WT)^0.75 on clearance and (WT_INFANT / WT) on volume.
• Source aliases:
  • INFWT – NONMEM $INPUT column name in Wattanakul_2024_primaquine.R / Wattanakul_2024_primaquine_motherinfant.R (INFWT ; INFANT BODY WEIGHT (KG)); same quantity in kg, no value transformation.
• Example models: Wattanakul_2024_primaquine.R (enters only through the breast-milk compartment volume V_M = (0.15 L/kg/day * WT_INFANT) / feeds-per-day; cohort median 6.8 kg, range 4.13-10.8, giving milk-compartment volumes of 0.062-0.162 L), Wattanakul_2024_primaquine_motherinfant.R (additionally scales the infant’s apparent clearances and volumes from the mother’s estimates).
• Notes: Baseline in the founding models, but time-varying in principle – infant weight changes materially over a multi-month lactation period, and dyad simulations that sweep infant age (Wattanakul 2024 uses WHO weight-for-age curves with z-scores -3 to +3 from birth to 24 months) pair each AGE_INFANT with the corresponding WT_INFANT. Verify baseline vs. time-varying against the source paper.

AGE_INFANT (canonical for breastfed-infant postnatal age in a mother-infant dyad model)

• Description: Postnatal age of the breastfed infant paired with the modelled lactating subject. Distinct from AGE, which in a dyad model is the mother’s age.
• Units: months
• Type: continuous
• Scope: general
• Reference category: n/a – typically enters an enzyme-maturation function on the infant’s clearance. When the maturation function is written in postmenstrual age, the model derives PMA internally by adding an assumed term gestation to AGE_INFANT rather than requiring a separate column, because individual gestational ages are rarely reported in lactation studies; the assumed gestation must be stated in the model file.
• Source aliases:
  • INFAGE – NONMEM $INPUT column name in Wattanakul_2024_primaquine_motherinfant.R (INFAGE ; INFANT AGE (MONTHS)); same quantity in months, no value transformation.
• Example models: Wattanakul_2024_primaquine_motherinfant.R (drives monoamine-oxidase-A maturation on the infant’s primaquine clearance, MF = PMA / (TM50 + PMA) with PMA = AGE_INFANT + 9.2 months for assumed full-term gestation of 40 weeks and TM50 = 7.6 months; cohort median 5.0 months, range 1.6-21.7).
• Notes: Months, not years – lactation cohorts are dominated by infants under two years old, where a year scale loses resolution, and paediatric maturation functions are conventionally written in months or weeks of postmenstrual age. This matches the units of the general PAGE canonical. Distinct from PAGE: AGE_INFANT is postnatal, so a model that needs postmenstrual age must add the gestational term explicitly.

Nutritional status

MAL_NOURISH (canonical for malnutrition status indicator)

• Description: 1 = subject is malnourished at study entry per a paper-defined anthropometric criterion (e.g., WHO height-for-age and weight-for-age Z-scores both < -2.0 in Tikiso 2021); 0 = not malnourished. Subject-level baseline indicator; the time-decaying recovery during nutritional supplementation is carried by the paired T_NUT_SUPP column rather than as a time-varying value of MAL_NOURISH.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (not malnourished).
• Source aliases:
  • MAL – used in Tikiso_2021_abacavir.R (the dataset’s paper-defined indicator, 1 = malnourished, 0 = not malnourished).
  • NUT – used in Catalan-Latorre_2018_taurine_rat.R (the dataset’s paper-defined indicator, 1 = undernourished UN, 0 = well-nourished WN).
  • Group – used in Kir_2025_atenolol_rat_pbpk.R and Kir_2025_metoprolol_rat_pbpk.R (the Monolix regressor named Group that selects the control vs malnourished parameter set).
• Example models: Tikiso_2021_abacavir.R (gates the time-decaying malnutrition effect: mal_decay = MAL_NOURISH * exp(-T_NUT_SUPP * log(2) / 12.2), which then drives multiplicative shifts of +115% on F and -64% on CL at the start of nutritional supplementation, decaying with a 12.2-day half-life).
  • Tikiso_2021_abacavir.R (gates the time-decaying malnutrition effect: mal_decay = MAL_NOURISH * exp(-T_NUT_SUPP * log(2) / 12.2), which then drives multiplicative shifts of +115% on F and -64% on CL at the start of nutritional supplementation, decaying with a 12.2-day half-life).
  • Catalan-Latorre_2018_taurine_rat.R (static baseline indicator – no T_NUT_SUPP pairing because there was no nutritional rehabilitation phase in the preclinical Wistar-rat study; MAL_NOURISH = 1 reduces the saturable tubular secretion Vmax of taurine by 9.4% relative to well-nourished animals).
  • Kir_2025_atenolol_rat_pbpk.R (static baseline indicator in a preclinical Sprague-Dawley mPBPK model; no T_NUT_SUPP pairing because there was no nutritional rehabilitation phase. Unusually, MAL_NOURISH here gates ABSORPTION rather than disposition: it switches all three sequential zero-order absorption rates, the presence and end time of the third absorption window, the blood-to-plasma ratio, and the proportional residual-error magnitude. The structural disposition parameters fd1, Kp1 and CL were deliberately shared across nutrition groups).
  • Kir_2025_metoprolol_rat_pbpk.R (companion to the atenolol model above, from the same paper; MAL_NOURISH switches both zero-order absorption rates, the end time of the second absorption window, the blood-to-plasma ratio, the additive residual-error magnitude, and the fraction of hepatic intrinsic clearance operating in the oral arm).
• Notes: Specific scope because the malnutrition definition (WHO Z-score thresholds in Tikiso 2021; end-of-adaptation body weight below 80% of the well-nourished mean AND serum albumin below 23 g/L in Catalan-Latorre 2018; a 5% protein isocaloric diet fed for 17-20 days, confirmed by significant falls in body weight, serum albumin and total cholesterol, in Kir 2025; mid-upper arm circumference, weight-for-height vs height-for-age, etc.) is paper-defined; per-model covariateData[[MAL_NOURISH]]$notes must document the criterion used. Pairs with T_NUT_SUPP (days on nutritional supplementation) when the model uses a time-decaying recovery function; otherwise MAL_NOURISH alone serves as a static baseline indicator. Distinct from generic body-weight Z-scores (which are continuous anthropometric metrics rather than a binarised malnutrition indicator).

MUAC (canonical for mid-upper arm circumference)

• Description: Mid-upper arm circumference, the continuous anthropometric measurement of the circumference of the upper arm at its midpoint. A standard WHO nutritional-status measure in children aged 6-59 months; the WHO severe-acute-malnutrition threshold is MUAC < 115 mm. Time-fixed at admission in the models that use it, though it is measurable repeatedly during nutritional rehabilitation.
• Units: mm
• Type: continuous
• Scope: general
• Reference category: n/a
• Source aliases:
  • MUAC – used in Chotsiri_2019_lumefantrine.R (Chotsiri 2019 Table 1 and Table 2 report MUAC in mm and the covariate coefficient per cm; the model therefore converts MUAC / 10 inside model()).
• Example models: Chotsiri_2019_lumefantrine.R (continuous exponential effect on the relative bioavailability of oral lumefantrine: fmuac = exp(e_muac_f * (MUAC / 10 - muac_ref)) with e_muac_f = -log(1 - 0.254) per cm and muac_ref = 13.0 cm, i.e. bioavailability falls 25.4% per 1 cm reduction in MUAC).
• Notes: Store the raw measurement in millimetres – the unit WHO uses for the SAM threshold and the unit source tables almost always tabulate – and convert inside model() when a paper publishes its coefficient per centimetre. Distinct from the binary MAL_NOURISH: MAL_NOURISH is a paper-defined malnourished/not-malnourished indicator, whereas MUAC is the underlying continuous measurement and is one of the criteria papers use to set MAL_NOURISH. A model that retains a continuous MUAC effect must use MUAC; binarising it onto MAL_NOURISH would discard the dose-response. Where a paper reports both, carry both columns. Also distinct from the weight-for-height / weight-for-age / height-for-age z-scores (WHZ / WAZ / HAZ families), which are population-standardised scores rather than a raw circumference.

T_NUT_SUPP (canonical for time on nutritional supplementation)

• Description: Time elapsed since the start of nutritional / refeeding supplementation, in days. 0 at the start of supplementation; increases with time. Used by population PK models that describe the time-varying recovery of PK parameters during nutritional rehabilitation in malnourished cohorts.
• Units: days
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as the argument of an exponential decay function exp(-T_NUT_SUPP * log(2) / T_half) whose half-life T_half is an estimated parameter (12.2 days in Tikiso 2021).
• Source aliases:
  • TNUTRI – used in Tikiso_2021_abacavir.R (the dataset’s paper-defined column for days since start of nutritional supplementation; same orientation as the canonical, 0 = start of supplementation, increasing with time on supplementation).
• Example models: Tikiso_2021_abacavir.R (paired with MAL_NOURISH; drives the recovery decay exp(-T_NUT_SUPP * log(2) / 12.2) of the malnutrition effect on F and CL).
• Notes: Specific scope because the underlying recovery dynamics are tied to the nutritional-rehabilitation protocol of the source study. For non-malnourished subjects (MAL_NOURISH == 0) the value is irrelevant because the malnutrition effect is gated by MAL_NOURISH; supply 0 as a default. For fully-recovered malnourished subjects, supply a large value (e.g., >= 100 days, well beyond the 12.2-day Tikiso 2021 half-life) so the decay function reaches near zero and the effect vanishes.

DRINK_OGTT (canonical for oral-glucose-tolerance-test-only drink indicator)

• Description: Binary indicator that the postprandial test drink is glucose-only (oral glucose tolerance test, OGTT). 1 = the drink is glucose-only with no fat content (e.g., 25 / 75 / 125 g OGTT); 0 = otherwise (water, or any drink containing fat). Per-occasion (per-test-drink-administration) covariate – a single subject in a crossover challenge protocol receives different drink types across occasions, so the indicator varies per dose-record rather than per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (water or fat-containing drink). Mutually exclusive with DRINK_FAT – both indicators cannot be 1 simultaneously.
• Source aliases: paper narrative “glucose solution” / “OGTT” cohort labels driving the gastric-emptying-onset T50OGTT selection in Guiastrennec 2016.
• Example models: Guiastrennec_2016_gastric_emptying.R (selects the OGTT-specific half-onset time T50OGTT = 15.7 min for the gastric-emptying delay Hill function; water is recovered when DRINK_OGTT = DRINK_FAT = 0 with the onset factor pinned to 1).
• Notes: Specific scope because the OGTT / fat-containing partition is tied to the Guiastrennec 2016 postprandial-challenge design (Studies B and C OGTT arms). Set to 1 for Study B’s 25 / 75 / 125 g OGTT drinks and the Study C 75 g OGTT arm; set to 0 for Study A water and for all fat-containing drinks. Pairs with DRINK_FAT: the two indicators jointly select the appropriate gastric-emptying-delay T50 parameter (T50OGTT vs T50Fat) for the Hill onset function. Ratified canonically alongside the Guiastrennec 2016 gastric-emptying / CCK / GBE extraction.

DRINK_FAT (canonical for fat-containing test-drink indicator)

• Description: Binary indicator that the postprandial test drink contains fat (any nonzero fat content). 1 = the drink contains fat (e.g., the Study C low / medium / high-fat isocaloric drinks, or the Study D medium-high-fat drink); 0 = otherwise (water or glucose-only OGTT drinks). Per-occasion covariate – a single subject in a crossover challenge protocol receives different drink types across occasions.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (water or glucose-only drink). Mutually exclusive with DRINK_OGTT – both indicators cannot be 1 simultaneously.
• Source aliases: paper narrative “low-fat” / “medium-fat” / “high-fat” / “medium-high-fat” cohort labels driving the gastric-emptying-onset T50Fat selection in Guiastrennec 2016.
• Example models: Guiastrennec_2016_gastric_emptying.R (selects the fat-specific half-onset time T50Fat = 23.1 min for the gastric-emptying delay Hill function).
• Notes: Specific scope because the fat-containing partition is tied to the Guiastrennec 2016 postprandial-challenge design. Pairs with DRINK_OGTT: the two indicators jointly select the appropriate gastric-emptying-delay T50 parameter (T50OGTT vs T50Fat) for the Hill onset function. Ratified canonically alongside the Guiastrennec 2016 gastric-emptying / CCK / GBE extraction.

GLN_BL (canonical for baseline (pre-dose) endogenous plasma L-glutamine concentration)

• Description: Per-subject pre-dose plasma concentration of endogenous L-glutamine, measured before the first study dose. L-glutamine is the most abundant free amino acid in plasma and is present at several hundred umol/L in every subject, so an exogenous oral dose is superimposed on a large endogenous pool. In popPK models of administered L-glutamine the baseline plays a dual role: it is subtracted from every measured concentration to isolate the exogenous increment being modelled (so it defines the zero of the modelled concentration scale), and it enters the clearance covariate equation, because a higher endogenous pool competes with exogenous glutamine for metabolism and other elimination pathways.
• Units: umol/L
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as a power (Eq. 5) term normalised to a standard baseline, (GLN_BL / GLN_BL_standard)^theta. Sadaf 2024 uses GLN_BL_standard = 683 umol/L. Record the per-model normalisation constant in covariateData[[GLN_BL]]$notes, since it is a modelling choice rather than a physiological constant.
• Source aliases:
  • Glu_BSL – used in Sadaf_2024_glutamine.R (Sadaf 2024 Table 2; the paired standard value is written Glu_BSLstardard in the table footnote, a typographical variant of “standard”). Note the Glu stem here abbreviates glutamine, not glutamate: Sadaf 2024 measured both analytes and screened baseline L-glutamate separately (it was not retained), so a future extraction of that paper’s sibling analyte must not reuse this alias.
• Example models: Sadaf_2024_glutamine.R (power effect on apparent oral clearance: cl = 78.5 * (WT/70)^0.75 * (GLN_BL/683)^-0.96, Sadaf 2024 Table 2 theta2 = -0.96; the same per-subject value is the quantity subtracted from each measurement before fitting).
• Notes: Specific scope until a second L-glutamine or amino-acid supplementation model ratifies the canonical. Member of the <ANALYTE>_BL baseline-concentration family (HGB_BL, FERRITIN_BL, INS_BL, PKK_BL, TRACP5B_BL, TRAST_BL); the closest structural precedent is AT_BL_UDL, which likewise carries the endogenous baseline of the very substance being dosed and likewise serves both as a power covariate normalised to a reference and as the endogenous anchor of the observation scale. Distinct from AA_* general amino-acid panel entries (none registered at the time of writing) and from the dosed amount, which is DOSE_GLN_GKG. Time-fixed per subject in Sadaf 2024, which verified that the pre-dose baseline did not drift across the four study visits; a study in which the endogenous pool does shift over time should carry it as time-varying and say so in the model’s covariateData notes. Ratified canonically alongside the Sadaf 2024 L-glutamine sickle-cell-disease extraction.

Pregnancy / hormonal status

EGA (canonical for maternal estimated gestational age during pregnancy)

• Description: The mother’s estimated gestational age at the time of an observation or dose, in weeks. Time-varying across a pregnancy (though usually effectively constant within a single treatment cycle). EGA = 0 is the non-pregnant anchor: semi-physiological pregnancy models are constructed so that every gestational relation collapses to its non-pregnant value at EGA = 0, which makes the covariate a continuous pregnant-vs-non-pregnant contrast rather than a within-pregnancy-only stratifier.
• Units: weeks
• Type: continuous
• Scope: general
• Reference category: n/a – EGA = 0 is the non-pregnant reference value, not a category. Enters as the argument of the gestational physiology polynomials (serum albumin, alpha-1-acid glycoprotein, GFR, haematocrit, CYP3A4 activity, plasma volume, extracellular water, total body water), each of which is written so that it returns the non-pregnant value at EGA = 0.
• Source aliases:
  • EGA – Janssen 2023 symbol for estimated gestational age; same orientation and units, no transformation.
  • GAWK, GESTAGE, GAWEEK – common NONMEM column spellings for maternal gestational age in weeks; same orientation, no transformation.
• Example models: Janssen_2023_docetaxel.R, Janssen_2023_paclitaxel.R, Janssen_2023_doxorubicin.R, Janssen_2023_epirubicin.R (semi-physiological enriched cytotoxic models in which EGA drives all ten gestational physiology relations reprinted as Janssen 2023 Eqs 1-17).
• Notes: Distinct from GA (gestational age at birth), which is a time-fixed maturity descriptor of the neonate used in paediatric models – EGA describes the mother and varies over her pregnancy. Also distinct from PREG (binary pregnancy status) and from TPP (time postpartum, which is 0 during pregnancy and increases after delivery). PREG gives a step contrast; EGA gives the continuous within-pregnancy trajectory that PREG explicitly defers to (see the PREG entry’s note that “trimester or gestational-age stratification within the pregnant cohort should use a separate canonical … ratified separately when needed”). When a source paper reports trimesters rather than weeks, record the week value used per trimester in covariateData[[EGA]]$notes rather than introducing a trimester-indicator canonical.

TPP (canonical for time postpartum (time after delivery))

• Description: Time elapsed since delivery, in weeks. 0 during pregnancy and at delivery; increases after delivery. Used by popPK models that describe the time-varying postpartum recovery of pregnancy-induced PK changes (e.g., renal blood flow, hepatic enzyme activity) that return gradually to the prepregnant baseline over weeks to months rather than instantaneously at delivery.
• Units: weeks
• Type: continuous
• Scope: general
• Reference category: n/a – enters as the argument of a sigmoidal recovery function (TPP^gamma) / (TPP^gamma + T50^gamma) whose T50 (weeks-to-50%-recovery) and gamma (shape parameter) are estimated. For pregnant visits (TPP = 0) the sigmoid evaluates to 0 and the pregnancy-state PK parameter is unchanged; for far-postpartum visits (TPP >> T50) the sigmoid approaches 1 and the parameter reaches its asymptotic non-pregnant value.
• Source aliases:
  • TPP – de Kock 2017 NONMEM column for time after delivery in weeks; same orientation, no transformation; assigned 0 for samples collected during pregnancy.
• Example models: deKock_2017_sulfadoxinePyrimethamine.R (sigmoidal effect on sulfadoxine CL with asymptotic fractional change -0.757, T50 = 6.35 weeks, gamma = 4.90; the sigmoid approaches its asymptote ~13 weeks postpartum, consistent with the literature for return of GFR and renal blood flow to prepregnant values within 6-12 weeks postpartum).
• Notes: Paired with PREG (pregnancy status indicator) when the source paper models pregnancy as a step contrast on one PK parameter and as a sigmoidal time-decay on another. During pregnancy TPP = 0; after delivery TPP > 0. Document the postpartum sampling window in covariateData[[TPP]]$notes per model. Distinct from T_NUT_SUPP (time on nutritional supplementation, days) and GA (gestational age at birth, weeks) – those are different timescale covariates anchored to different events. Ratified canonically on 2026-05-18 alongside the de Kock 2017 sulfadoxine/pyrimethamine extraction.

TERM_BIRTH (canonical for term-vs-preterm birth indicator)

• Description: Binary indicator of term-vs-preterm birth status; 1 = term birth (>= 37 weeks gestation), 0 = preterm birth (< 37 weeks gestation). Time-fixed per subject. In Allegaert 2015 (paracetamol PK in young women) the indicator is used to select between two typical-value clearances for the sulphate-formation pathway.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (preterm). Effect form is the symmetric CL = TERM_BIRTH * theta_term + (1 - TERM_BIRTH) * theta_preterm selection – so neither category is “the multiplicative reference”, and both per-stratum clearances are estimated parameters.
• Source aliases:
  • TERM (Allegaert 2015 NONMEM column; same orientation, no transformation) – used in Allegaert_2015_paracetamol.R.
• Example models: Allegaert_2015_paracetamol.R.
• Notes: Distinct from GA (continuous gestational age in weeks): TERM_BIRTH is the binarized version with the conventional 37-week cutoff. Use GA when the source paper carries gestational age as a continuous covariate; use TERM_BIRTH only when the paper itself dichotomizes. Do not derive TERM_BIRTH from GA programmatically inside model() – the term-cutoff convention belongs in data assembly, not the model file.

CONMED_BIRTHCONTROL (canonical for oral-contraceptive use indicator)

• Description: Binary indicator of oral hormonal contraceptive use; 1 = currently taking an oral contraceptive (estrogen-progestin or progestin-only pill), 0 = not on hormonal contraception. Time-varying as women cycle on/off contraception across study occasions.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no oral contraceptive). Effect form in Allegaert 2015 is multiplicative: CL_glucuronide *= theta_CONMED_BIRTHCONTROL when CONMED_BIRTHCONTROL == 1, with theta_CONMED_BIRTHCONTROL = 1.46 (estrogen-driven UGT2B7 induction).
• Source aliases:
  • BC (Allegaert 2015 NONMEM column; same orientation, no transformation) – used in Allegaert_2015_paracetamol.R.
  • OC (Csajka 2005 NONMEM column; same orientation, no transformation) – used in Csajka_2005_ephedrine_caffeine.R.
• Example models: Allegaert_2015_paracetamol.R, Csajka_2005_ephedrine_caffeine.R, Angeli_2016_iron_hepcidin.R (multiplicative effect on iron elimination kout_iron; women on oral contraception have ~18% lower iron elimination, consistent with the paper’s narrative that contraception limits menstrual blood loss; e_conmed_birthcontrol_kout_iron = -0.20).
• Notes: Specific scope because the canonical encoding pools all oral contraceptive types (combined / progestin-only) into a single binary; future models that need to distinguish formulations should register a finer-grained canonical (e.g., CONMED_BIRTHCONTROL_COMBINED, CONMED_BIRTHCONTROL_PROGESTIN). The full-word canonical name was chosen over a shorter BC_USE form for clarity in source traces.

DIS_EOPE (canonical for early-onset pre-eclampsia indicator)

• Description: Binary indicator of early-onset pre-eclampsia (eoPE); 1 = eoPE diagnosed before 34 weeks gestation, 0 = not eoPE. Time-fixed per subject within the gestational PK study window. Used by population PK models that compare drug disposition in pregnant women with vs without early-onset pre-eclampsia.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no eoPE). Effect form in Schoenmakers 2025 is multiplicative on CL: CL_eoPE = CL * ThetaPE^DIS_EOPE with ThetaPE = 0.617 (38% reduction in betamethasone CL when eoPE is present); encoded in nlmixr2 as the log-additive shift cl <- exp(lcl + etalcl + e_eope_cl * DIS_EOPE) with e_eope_cl = log(0.617).
• Source aliases:
  • PE – common abbreviation in obstetric pharmacology papers when the cohort restriction is to early-onset PE only; used in Schoenmakers_2025_betamethasone.R (paper notation: eoPE / ThetaPE).
• Example models: Schoenmakers_2025_betamethasone.R (multiplicative effect on CL, encoded via the log-additive form on lcl; reduces apparent betamethasone clearance from 15.6 L/h to 9.6 L/h).
• Notes: Distinct from a broader PREECL indicator that would pool early-onset, late-onset and postpartum pre-eclampsia. The “early-onset” specifier corresponds to diagnosis before 34 weeks gestation, the conventional clinical cutoff (Phipps 2019 Nat Rev Nephrol). Future papers that enrol mixed early-/late-onset cohorts or that report PE status without the 34-week stratification should register a separate canonical (e.g., PREECL for any-onset PE, or LOPE for late-onset PE) rather than reusing DIS_EOPE with relaxed semantics. Distinct from PREG (pregnancy status indicator): DIS_EOPE is a complication-of-pregnancy stratifier within a pregnant cohort, whereas PREG discriminates pregnant-vs-non-pregnant subjects. Ratified canonically on 2026-05-11 alongside the Schoenmakers 2025 betamethasone extraction.

SCORE_HIGHAM (canonical for Higham pictorial blood-loss assessment chart score)

• Description: Higham’s score (Pictorial Blood-loss Assessment Chart, PBAC), a semi-quantitative pictorial scoring of menstrual blood loss summed across all sanitary protection items used during a menstrual period. Higher scores correspond to heavier menstrual bleeding; a per-menstrual-period score >= 100 is the conventional clinical threshold for menorrhagia (Higham 1990 BJOG). Time-fixed per subject when computed as the mean of the last few cycles preceding the study (the Angeli 2016 use), time-varying when scored per cycle.
• Units: (score; unitless)
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as a power-law multiplier (SCORE_HIGHAM / tHIGHAM)^exponent on hepcidin synthesis and elimination rate constants in Angeli 2016, with the reference value tHIGHAM set near the population median (96.6 in the Angeli 2016 HEPMEN cohort).
• Source aliases:
  • HIGHAM – prior canonical name (pre-2026-06-19 standardization audit).
  • HIGHAM – prior canonical name (pre-2026-06-19 SCORE_ family standardization).
  • HiS – Angeli 2016 paper notation; same orientation, no transformation.
• Example models: Angeli_2016_iron_hepcidin.R (per-subject mean of the three most recent cycles’ Higham scores; enters ksynH and koutH via power-law effects with exponents 0.66 and 0.83 respectively).
• Notes: Specific scope because the Higham / PBAC score is a reproductive-health-specific instrument. Promote to general if a second menstrual-cycle or iron-status paper registers the same instrument. Distinct from generic blood-loss quantities reported in mL (which would warrant a separate MENSTRUAL_BLOOD_LOSS_ML canonical). The threshold for menorrhagia is 100 (Higham 1990 BJOG 97:734); the Angeli 2016 cohort had a mean of 96.6 with SD 60.5 (Table I), so most subjects sat near the menorrhagia threshold. Ratified canonically on 2026-06-03 alongside the Angeli 2016 iron and hepcidin extraction. Renamed from HIGHAM to SCORE_HIGHAM on 2026-06-19 per the canonical-register standardization audit (clinical-instrument SCORE_ family normalization).

DLOSS (canonical for menstrual-period duration)

• Description: Per-subject length of the menstrual period (days of menstrual bleeding) used as a time bound on the increased-elimination phase in joint iron / hepcidin turnover models. Time-fixed per subject within a single menstrual cycle (the Angeli 2016 use); set to the individual’s observed menses length over the study cycle rather than estimated from the data.
• Units: days
• Type: continuous
• Scope: specific
• Reference category: n/a – enters the model as the upper bound of the loss-phase indicator window t < DLOSS. Reference values observed: Angeli 2016 inclusion criterion required menses length between 3 and 5 days (Methods p. 491); the per-subject value was fixed to the observed individual menses length.
• Source aliases:
  • dloss – Angeli 2016 paper notation; same orientation, no transformation.
• Example models: Angeli_2016_iron_hepcidin.R (per-subject menses length in days; bounds the loss phase during which kloss adds to both kout_iron and kout_hep).
• Notes: Specific scope because the menses-length covariate is meaningful only inside joint iron / hepcidin turnover models that include a discrete loss-phase indicator. Promote to general if a second reproductive-cycle PK / PD paper uses an equivalent per-subject menses-length time-bound. Distinct from TPP (time postpartum, weeks; unbounded postpartum time after delivery) and from a hypothetical CYCLE_LENGTH covariate (full menstrual cycle length in days, which would warrant a separate canonical). Ratified canonically on 2026-06-03 alongside the Angeli 2016 iron and hepcidin extraction.

DAY_DELIVERY (canonical for day-of-delivery indicator)

• Description: Binary indicator that the PK sample was collected on the day of delivery (the labour-and-childbirth occasion). 1 = sample taken on the day of delivery; 0 = otherwise (pregnant non-delivery, or non-pregnant). In Hirt 2007 the indicator is mutually exclusive with PREG = 1: per the paper, on the day of delivery the coding was PREG = 0 and DAY_DELIVERY = 1, so the two indicators jointly partition women into non-pregnant (PREG = 0, DAY_DELIVERY = 0), pregnant non-delivery (PREG = 1, DAY_DELIVERY = 0), and delivery (PREG = 0, DAY_DELIVERY = 1) cohorts. Time-fixed per sampling occasion.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (not day of delivery).
• Source aliases:
  • DEL – Hirt 2007 NONMEM symbol for the day-of-delivery indicator; same orientation, no value transformation.
• Example models: Hirt_2007_nelfinavir.R (multiplicative effect 1 + 1.92 * DAY_DELIVERY applied to both maternal nelfinavir clearance CL_Nm_No and distribution volume V; gates a body-weight power effect (WT/73)^2.81 on CL_Nm_No within the delivery cohort only).
• Notes: Distinct from PREG (pregnant non-delivery, 1 = pregnant; see entry above), TPP (time postpartum, a continuous time-since-delivery covariate for postpartum recovery), and TERM_BIRTH (term-vs-preterm birth at any postnatal time). Use DAY_DELIVERY when the source paper carries a contrasted day-of-delivery cohort against pregnant non-delivery women in the same PK analysis (placental-transfer / labour-PK studies). Specific scope until a second model ratifies the name; promote to general when a future placental-transfer or labour-PK paper uses the same indicator. Ratified canonically alongside the Hirt 2007 nelfinavir extraction.

Vital signs

HR (canonical for heart rate)

• Description: Subject heart rate, in beats per minute. Captured in popPK studies where hemodynamic state modifies hepatic blood flow and hence clearance of high-extraction-ratio drugs (e.g., propofol). May be time-varying when serial intra-operative or intensive-monitoring values are recorded; many studies summarise as the per-subject median across the observation window and treat the covariate as time-fixed. Document baseline-vs-time-varying status in covariateData[[HR]]$notes per model.
• Units: beats/min
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (HR / ref)^exponent or linear-deviation forms (1 + e * (HR - ref)). Reference values observed: 158 beats/min (Ngamprasertwong 2016; population reference encoded in the Table 2 equation CL = theta1 * (HR/158)^theta2).
• Source aliases:
  • HR – same orientation as the canonical, no value transformation; used in Ngamprasertwong_2016_propofol_sheep.R (per-subject median HR over the propofol-infusion observation window, treated as time-fixed in line with the cohort-typical sheep hemodynamic state).
• Example models: Ngamprasertwong_2016_propofol_sheep.R (power effect on maternal propofol clearance: CL_indiv = theta1 * (HR/158)^theta2 with theta2 = 0.764; clearance increases with heart rate, plausibly reflecting heart-rate-driven increases in hepatic blood flow that govern propofol’s high hepatic-extraction-ratio elimination).
• Notes: General scope because heart rate is a universally applicable vital sign suitable for any model where hemodynamic state modulates clearance. Future models can use a different reference HR (typical adult human is ~70 beats/min vs the sheep cohort 158 beats/min); document the reference in covariateData[[HR]]$notes. Distinct from HR_BAND or HRV (not yet registered) which would be a heart-rate-band stratifier or heart-rate variability metric, respectively. Ratified canonically on 2026-05-23 alongside the Ngamprasertwong 2016 propofol maternal-fetal sheep extraction.

SBP (canonical for systolic blood pressure)

• Description: Subject systolic blood pressure, in mmHg. Captured at baseline or serially during a study. Universal vital sign used in popPK / disease-progression models where hemodynamic state serves as either (a) a prognostic covariate on non-PK endpoints (overall survival, tumor dynamics), or (b) a covariate on clearance for drugs whose disposition is sensitive to blood pressure. Document baseline-vs-time-varying status per model in covariateData[[SBP]]$notes.
• Units: mmHg
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (SBP / ref)^exponent, linear-deviation forms (1 + e * (SBP - ref)), or additive-log forms beta * log(SBP). Reference values observed: 120 mmHg (Terranova 2022 avelumab JAVELIN Gastric 100 cohort median; also the population-adult clinical normal midpoint).
• Source aliases:
  • Log systolic blood pressure – Terranova 2022 Table S2 OS TTE coefficient label; enters as log(SBP) * beta (an additive-log form on log-median OS).
  • SBP – source-paper column name; same orientation, no value transformation.
• Example models: Terranova_2022_TGD_OS_gastric.R (additive-log effect on log-median OS: log_median_OS += 0.532 * log(SBP), screened by ML and retained in the parametric TTE model despite the credible interval including zero, in keeping with the paper’s hypothesis-generating scope).
• Notes: General scope because systolic BP is a universally applicable vital sign. Sibling of HR (heart rate, beats/min) and DBP_REL (relative change in diastolic BP from baseline, unitless). The two BP siblings are physiologically related but independent covariate columns: some source papers report SBP only, some report DBP or DBP-change only, some report both. SBP here is the raw absolute value; if a future model needs a relative-change (SBP_REL) or a diastolic sibling (DBP) it should register a new sibling canonical rather than overloading this entry. Ratified canonically on 2026-07-24 alongside the Terranova 2022 avelumab JAVELIN Gastric 100 extraction (agcand_13066655 sidecar request-001 q1=A).

BODYTEMP (canonical for body temperature)

• Description: Subject body temperature (typically axillary or oral) at the relevant clinical observation. Captured at study admission in acute-infection PK studies (fever as a marker of acute illness severity); may be time-varying when serial temperature measurements are recorded across visits. Document baseline-vs-time-varying status in covariateData[[BODYTEMP]]$notes per model.
• Units: degC
• Type: continuous
• Scope: general
• Reference category: n/a – used with linear-deviation forms (1 + e * (BODYTEMP - ref)) or exponential forms exp(e * (BODYTEMP - ref)). Reference values observed: 36.9 degC (Kloprogge 2013 lumefantrine; pooled-cohort median in Ugandan pregnant + non-pregnant women with uncomplicated P. falciparum malaria), 37.2 degC (Kloprogge 2014 quinine; pregnant Ugandan women with uncomplicated P. falciparum malaria, cohort median at admission).
• Source aliases:
  • TEMP – common short form in malaria / infectious-disease NONMEM control streams; used in Kloprogge_2013_lumefantrine.R and Kloprogge_2014_quinine.R (same orientation as the canonical, no value transformation).
• Example models: Kloprogge_2013_lumefantrine.R (linear-deviation effect on mean absorption transit time MTT: MTT_indiv = TVMTT * (1 + e_bodytemp_mtt * (BODYTEMP - 36.9)) with e_bodytemp_mtt = 0.165 per degC; mean transit time increases ~16.5% per degC over 36.0-39.8 degC, plausibly reflecting reduced gut motility / prolonged absorption in feverish malaria patients), Kloprogge_2014_quinine.R (exponential effect on elimination clearance: CL_indiv = TVCL * exp(e_bodytemp_cl * (BODYTEMP - 37.2)) with e_bodytemp_cl = -0.243 per degC, centered at the cohort median; clearance decreases ~21.6% per degC increase in admission body temperature over 36.0-38.9 degC, reflecting reduced metabolic CYP3A4 activity during acute febrile malaria).
• Notes: General scope because body temperature is a universally applicable vital sign; the Kloprogge 2013 reference value 36.9 degC is cohort-specific (Ugandan malaria cohort median) and future models should document their own reference in covariateData[[BODYTEMP]]$notes. Units are degrees Celsius; convert from Fahrenheit (degF) at data-assembly time, not inside model(). Distinct from BODYTEMP_FEBRILE (not yet registered) which would be a binary fever indicator if a future paper dichotomises at the conventional 37.5 / 38.0 degC threshold. Ratified canonically on 2026-05-16 alongside the Kloprogge 2013 lumefantrine extraction.

PACO2 (canonical for arterial blood carbon dioxide tension (PaCO2))

• Description: Arterial blood partial pressure of carbon dioxide, a routinely-measured blood-gas variable. Elevated in hypercapnia and respiratory acidosis (chronic obstructive pulmonary disease, restrictive lung disease, hypoventilation, severe asthma exacerbation). Used in population-PK analyses of theophylline and other drugs whose hepatic metabolism is sensitive to acid-base status / pulmonary disease severity. Typically captured at study admission via an automatic blood-gas analyzer on an arterial puncture sample; document baseline-vs-time-varying status in covariateData[[PACO2]]$notes per model.
• Units: mmHg
• Type: continuous
• Scope: general
• Reference category: n/a – used as a continuous covariate in exponential / log-linear effect forms exp(e_paco2_param * PACO2) or centered exp(e_paco2_param * (PACO2 - ref)). Reference values observed: 42.5 mmHg (Yano 1993 stable chronic airway obstruction cohort mean; also the laboratory normal-range midpoint of 35-45 mmHg in adults).
• Source aliases:
  • PaCO2 – the standard physiological abbreviation; used directly in Yano 1993 Table I and Eqs 7-8.
• Example models: Yano_1993_theophylline.R (log-linear effect on both CL and Vd of theophylline: CL = exp(lcl + e_hepimp_cl * HEPIMP + e_paco2_cl * PACO2 + etalcl) with e_paco2_cl = 0.0233 per mmHg, and Vd = exp(lvc + e_paco2_vc * PACO2 + etalvc) with e_paco2_vc = 0.00934 per mmHg; both estimated by forward-selection likelihood-ratio testing).
• Notes: Companion to arterial PaO2, arterial blood pH, serum bicarbonate, and base excess in respiratory-physiology covariate panels (not yet registered; future models retaining these as final covariates should propose them as new canonicals at extraction time). Yano 1993 found PaCO2 to be the most informative blood-gas covariate when entered in a forward-selection competition against PaO2, pH, hematocrit, and albumin. General scope because PaCO2 is a routinely-measured laboratory variable suitable for any model where pulmonary disease severity or acid-base status modulates drug disposition. Ratified canonically on 2026-06-07 alongside the Yano 1993 theophylline extraction.

PEEP (canonical for positive end expiratory pressure)

• Description: Positive end expiratory pressure (PEEP) applied at the end of the expiratory phase during positive-pressure mechanical ventilation. Airway pressure setting maintained above atmospheric to prevent alveolar collapse; range 0-20 mmHg (0-25 cmH2O) in adult ICU cohorts. Time-varying within an ICU stay as ventilator settings are adjusted; source papers may use it as an admission value or a time-varying covariate – document per-model in covariateData[[PEEP]]$notes. Higher PEEP raises intrathoracic pressure, decreases venous return and cardiac output, and can compromise hepatic blood flow (total hepatic blood flow decreases 4%, 12%, 32% at PEEP 10, 15, 20 mmHg respectively).
• Units: mmHg (the unit reported in Swart 2004 Table 3). Some ICU literature uses cmH2O (1 cmH2O = 0.736 mmHg); when the source paper uses cmH2O the convention should be documented in covariateData[[PEEP]]$notes and values converted to mmHg for consistency across models.
• Type: continuous
• Scope: specific
• Reference category: n/a – typically used with centred linear scaling. Reference values observed: 5 mmHg (Swart 2004 lorazepam CL formula centring value; representative of routine “protective” PEEP in mechanically-ventilated ICU adults).
• Source aliases:
  • PEEP – printed column label in Swart 2004 Table 3 and formula label in Table 4; used in Swart_2004_lorazepam.R.
• Example models: Swart_2004_lorazepam.R (centred linear effect on CL for non-alcohol-abuse subjects with reference 5: cl = 4.13 - (PEEP - 5) * 0.417 L/h; each 1 mmHg above 5 lowers CL by 0.417 L/h, capturing decreased hepatic blood flow at higher PEEP; alcohol-abuse subjects instead take a flat CL = 0.74 L/h with no PEEP effect).
• Notes: Specific scope because PEEP is meaningful only for mechanically-ventilated cohorts. Distinct from MECH_VENT (binary invasive-mechanical-ventilation status) which is a treatment-status flag, not a quantitative pressure – MECH_VENT cannot substitute for PEEP because every subject in a PEEP-covariate model already has MECH_VENT = 1. The Swart 2004 rationale (Discussion) links PEEP to hepatic blood flow, so PEEP as a covariate is expected to matter most for low-hepatic-extraction-ratio drugs (lorazepam ER ~ 0.06). Future ICU popPK papers reusing PEEP with the same quantitative encoding can extend Example models; scope may be promoted to general after multiple papers. Per-model covariateData[[PEEP]]$notes must document whether the value is admission PEEP, time-varying PEEP, or a cohort summary. Ratified canonically on 2026-07-26 alongside the Swart 2004 lorazepam / midazolam extraction.

CARDIAC_OUTPUT (canonical for cardiac output)

• Description: Subject cardiac output, in litres of blood pumped per minute. Captured in popPK / physiological-PK studies where hemodynamic state modulates drug distribution (volumes) and / or clearance, most often via uptake or elimination at organs whose effective flow is set by cardiac output (lung uptake for inhaled anesthetics, hepatic flow for high-extraction drugs such as propofol, fentanyl). May be time-fixed per subject (cohort-typical or per-subject median over the observation window) or time-varying when serial intra-operative measurements are recorded; document baseline-vs-time-varying status in covariateData[[CARDIAC_OUTPUT]]$notes per model.
• Units: L/min
• Type: continuous
• Scope: general
• Reference category: n/a – used most commonly with linear-deviation forms (param + e * (CARDIAC_OUTPUT - ref)) or power scaling (CARDIAC_OUTPUT / ref)^exponent. Reference values observed: 5 L/min (Hendrickx 2006; population centering for the WT=70 / CARDIAC_OUTPUT=5 typical-adult anchor in the Hendrickx covariate equations).
• Source aliases:
  • CO – common short form in inhaled-anesthetic and hemodynamic popPK NONMEM control streams; same orientation as the canonical, no value transformation.
• Example models: none yet (canonical pre-registered ahead of the Hendrickx 2006 inhaled-anesthetic task; see notes).
• Notes: General scope because cardiac output is a universally applicable hemodynamic measurement. Distinct from HR (heart rate, beats/min) – the two are physiologically related (CARDIAC_OUTPUT = HR * stroke_volume) but they are independent covariate columns and a paper may report either or both. Distinct from BFR (blood flow rate through the extracorporeal dialysis circuit, mL/min) which is dialysis-circuit-specific. Distinct from ECMO_PUMP_SPEED (ECMO centrifugal-pump rotational speed) which characterises an externally-imposed cardiac-output augmentation rather than the patient’s intrinsic cardiac output. The full-word CARDIAC_OUTPUT canonical was chosen over the bare CO source-data abbreviation per operator instruction (Hendrickx 2006 sidecar response, task frompeople-744); CO is preserved as the recognised source alias so paper NONMEM control streams using the bare two-letter form translate without value transformation. Pre-registered on 2026-06-10 ahead of the Hendrickx 2006 (BMC Anesthesiology 6:7, doi:10.1186/1471-2253-6-7) inhaled-anesthetic extraction; the Hendrickx 2006 model files were not built because the source data were generated by the Gas Man(R) physiological simulator rather than measured in real patients (the paper fits empirical 3-compartment mammillary parameters to Gas Man-simulated alveolar-concentration curves for four inhaled anesthetics, with no IIV or residual error parameters reported), but the canonical is registered so future real-patient hemodynamic / inhaled-anesthetic popPK papers can use it without re-litigating the name.

DBP_REL (canonical for relative change in diastolic blood pressure from baseline)

• Description: Relative (fractional) change in diastolic blood pressure from the per-subject baseline, unitless (e.g., 0.10 = +10% above baseline). Typically a time-varying driver simulated from an upstream diastolic-blood-pressure indirect-response model as DBP_REL(t) = (dbp(t) - dbp0) / dbp0, then consumed as a covariate by a downstream model. Distinct from a raw diastolic-BP value (mmHg); this is the normalized deviation.
• Units: fraction (unitless; positive when dBP is elevated above baseline)
• Type: continuous
• Scope: specific
• Reference category: n/a – enters a log-linear hazard term exp(e_dbprel_haz * DBP_REL).
• Source aliases:
  • DBPREL – Hansson 2013 e85 NONMEM column for the relative dBP change; same orientation, no value transformation.
• Example models: Hansson_2013_sunitinib_os.R (time-varying relative dBP change, simulated from Hansson_2013_sunitinib_dbp and entered as an overall-survival hazard modulator: a greater relative dBP increase lowers the hazard).
• Notes: Ratified 2026-06-28. Specific scope because the value semantics are tied to the upstream Hansson 2013 indirect-response dBP model’s relative-change definition; a future model needing the same relative-dBP-change driver should add itself to the Example models list (and consider promotion) rather than reuse the name with different semantics. Structurally analogous to RCFB1MAX (relative change from baseline in SUVmax driving the Schindler 2016 OS hazard). The related raw-state PD output is the dbp compartment in compartment-names.md; the placebo-arm indicator paired with that model family is PLACEBO.

QTC_BL (canonical for subject’s heart-rate-corrected QT interval at pre-dose baseline)

• Description: Subject’s pre-dose baseline QT interval corrected for heart rate, treated as a per-subject time-fixed covariate. Used in concentration-DeltaQTc PD models to capture each subject’s intrinsic QTc level entering the linear-mixed-effects intercept as a centered term e_qtc_bl_e0 * (QTC_BL - QTC_BL_ref). The QT correction method (Bazett, Fridericia, individual, study-population-specific) is model-specific and documented per-model in covariateData[[QTC_BL]]$notes; the canonical name does NOT carry the correction method, on the same precedent as HGB_BL (units / convention documented per-model) and IGG (units documented per-model). Distinct from HR (heart rate at observation, beats/min) and BODYTEMP (vital-sign covariate): QTC_BL is a derived per-subject ECG summary statistic, not a per-observation vital-sign reading.
• Units: ms (the standard ECG / QT interval unit; document the correction method per-model in covariateData[[QTC_BL]]$notes).
• Type: continuous
• Scope: general
• Reference category: n/a – enters as a centered linear deviation e_qtc_bl_e0 * (QTC_BL - QTC_BL_ref). Reference values observed: 390 ms (Darpo 2014; rounded standard reflecting the male-dominated 28 men / 11 women cohort with day-0 QTcI ranges 384-394 ms (men) / 398-412 ms (women) and similar QTcF ranges; the specific cohort median was not quoted in the paper). Future popPK-DeltaQTc models centering on a different reference (e.g., 400 ms in older / sicker cohorts where QTc is elevated) should document the per-model reference in covariateData[[QTC_BL]]$notes.
• Source aliases:
  • baseline QTcI – used in Darpo_2014_racSotalol_QTcI.R (day-0 individually-corrected QT interval, ms; values transformed via centering against the cohort-median reference inside model()).
  • baseline QTcF – used in Darpo_2014_racSotalol_QTcF.R (day-0 Fridericia-corrected QT interval, ms; same covariate canonical, correction method documented per-model).
• Example models: Darpo_2014_racSotalol_QTcI.R, Darpo_2014_racSotalol_QTcF.R (PD-only linear mixed-effects E-R model for DeltaQTcI / DeltaQTcF after single 160 mg oral rac-sotalol; QTC_BL is each subject’s pre-dose day-0 mean QTc by the corresponding correction method, time-fixed for the analysis).
• Notes: General scope because pre-dose QTc baseline is a universally applicable ECG-derived per-subject covariate in any concentration-DeltaQTc model. The QT correction method (Bazett, Fridericia, individual, study-population-specific) is model-specific and is the per-model documentation responsibility of covariateData[[QTC_BL]]$notes; do NOT register parallel canonicals like QTCI_BL or QTCF_BL for different correction methods (the unit, role, and centering pattern are identical – only the precise QT-to-QTc conversion formula differs upstream of the model). Future concentration-DeltaQTc papers using a non-zero centering reference different from the Darpo 2014 rounded standard 390 ms should document the per-model reference in covariateData[[QTC_BL]]$notes. Ratified canonically on 2026-06-30 alongside the Darpo 2014 rac-sotalol concentration-QTc extraction.

Organ and lesion volumes (ORGVOL_<ORGAN> family)

Per-subject measured volumes of a named anatomical compartment, in mL, supplied to a model as an input rather than derived from body size. Physiologically based models need these whenever a paper images an organ or a lesion per patient instead of scaling it from a reference individual: the measured volume then sets that compartment’s vascular, interstitial and intracellular sub-volumes, its serum flow (flow density x volume) and its permeability-surface-area product (PS density x volume), and multiplies a binding-site density to give the compartment’s total receptor pool.

Naming convention: ORGVOL_<ORGAN>, all caps, where <ORGAN> is the anatomical compartment as the model names it. New members are added by registering an entry below; the family convention itself does not need re-ratifying. Keep <ORGAN> at the granularity the model actually resolves – register ORGVOL_KIDNEY for a model with one kidney compartment, not ORGVOL_KIDNEY_LEFT / ORGVOL_KIDNEY_RIGHT, unless the model genuinely carries them separately.

Relationship to TUM_VOL. TUM_VOL is aggregate tumour burden – total tumour volume summed over every lesion, in mm^3, used as a size stratifier or as a TGI initial condition. The ORGVOL_TUMOR<n> members are something different: the volume of one individually delineated lesion that the model carries as its own compartment, in mL, on exactly the same footing as an organ. A model that resolves individual lesions needs one column per lesion and TUM_VOL supplies only one; a model that needs total burden should use TUM_VOL and not sum the ORGVOL_TUMOR<n> columns, because the lumped-remainder compartment is an assumption rather than a measurement. Both may legitimately appear in the same dataset.

Ratified canonically on 2026-08-14 (sidecar oare_PMC11791192 request-001 q3, escalated for the same reason as the AE_<EVENT> family) alongside the Golzaryan 2025 whole-body 177Lu-PSMA I&T PBPK extraction, which supplies every founding member below.

ORGVOL_SALGLAND (canonical for measured total salivary gland volume)

• Description: Measured total volume of the salivary glands. The founding source delineates the left plus right parotid glands only; a model that includes the submandibular or sublingual glands should say so in covariateData[[ORGVOL_SALGLAND]]$notes rather than registering a separate canonical.
• Units: mL
• Type: continuous
• Scope: general
• Reference category: n/a – used directly as a physiologic input that sets a compartment’s sub-volumes, its perfusion and its permeability-surface-area product; not a covariate-effect coefficient.
• Source aliases:
  • `Salivary Glands (measured volume) – Golzaryan 2025 Table S1 column header; reported in mL, no value transformation.`
• Example models: Golzaryan_2025_lu177psmaIT_pbpk.R.
• Notes: Founding member of the ORGVOL_<ORGAN> family. The salivary glands are a dose-limiting organ at risk in PSMA-targeted radioligand therapy, so their volume is measured per patient rather than scaled from body size. Cohort range 17-54 mL in five mCRPC patients.

ORGVOL_TUMOR1 (canonical for measured individually delineated tumour lesion 1 volume)

• Description: Measured volume of the first of the individually delineated tumour lesions that a model carries as its own compartment. Lesion numbering follows the source paper.
• Units: mL
• Type: continuous
• Scope: general
• Reference category: n/a – used directly as a physiologic input that sets a compartment’s sub-volumes, its perfusion and its permeability-surface-area product; not a covariate-effect coefficient.
• Source aliases:
  • `Tumor 1 (measured volume) – Golzaryan 2025 Table S1 column header; reported in mL, no value transformation.`
• Example models: Golzaryan_2025_lu177psmaIT_pbpk.R.
• Notes: Founding member of the ORGVOL_<ORGAN> family; see the family note above for the distinction from TUM_VOL. Lesion indices are positional labels from the source, not anatomical sites, so a model that names its lesions anatomically should register named members (ORGVOL_TUMOR_BONE, …) instead of numbered ones. Cohort range 0.5-4 mL in five mCRPC patients.

ORGVOL_TUMOR2 (canonical for measured individually delineated tumour lesion 2 volume)

• Description: Measured volume of the second of the individually delineated tumour lesions that a model carries as its own compartment.
• Units: mL
• Type: continuous
• Scope: general
• Reference category: n/a – used directly as a physiologic input that sets a compartment’s sub-volumes, its perfusion and its permeability-surface-area product; not a covariate-effect coefficient.
• Source aliases:
  • `Tumor 2 (measured volume) – Golzaryan 2025 Table S1 column header; reported in mL, no value transformation.`
• Example models: Golzaryan_2025_lu177psmaIT_pbpk.R.
• Notes: Founding member of the ORGVOL_<ORGAN> family. Extend the family with ORGVOL_TUMOR3 and beyond as models resolve more lesions. Cohort range 1-34 mL in five mCRPC patients.

ORGVOL_TUMORREST (canonical for measured lumped remaining-tumour-lesion volume)

• Description: Volume assigned to the single compartment that represents every tumour lesion a model does not delineate individually. Unlike the other members of this family this is an assumption rather than a measurement, because the lesions it stands for were not imaged individually; record how the source arrived at it in covariateData[[ORGVOL_TUMORREST]]$notes.
• Units: mL
• Type: continuous
• Scope: general
• Reference category: n/a – used directly as a physiologic input that sets a compartment’s sub-volumes, its perfusion and its permeability-surface-area product; not a covariate-effect coefficient.
• Source aliases:
  • `R_TU,Rest,0 / [R_TU,Rest,0] – Golzaryan 2025 Table S2 states the lumped compartment through its total binding sites at an assumed 266 nmol/L density, which is the same quantity divided by that density.`
• Example models: Golzaryan_2025_lu177psmaIT_pbpk.R.
• Notes: Founding member of the ORGVOL_<ORGAN> family. Registered as its own canonical rather than as another ORGVOL_TUMOR<n> because its provenance is categorically different: it is an assumed residual-disease burden, and a downstream user must be able to exclude it when summing measured lesion volumes. The founding source assumes either 10 mL or 50 mL per patient.

Renal / hepatic function

URINE_FLOW (canonical for instantaneous urine flow rate)

• Description: Instantaneous urine flow rate (mL/h) measured over the urine collection interval that includes the current observation. Time-varying.
• Units: mL/h
• Type: continuous
• Scope: specific
• Reference category: n/a – used with a centered-linear-effect form CL_renal = base + theta_URINE_FLOW * (URINE_FLOW - URINE_FLOW_ref) with URINE_FLOW_ref = 100 mL/h in Allegaert 2015. A value of 0 is a sentinel for “no urine collected during the interval” (i.e., the urine pathway contribution is dropped); the linear-effect term is gated by URINE_FLOW > 0 and not extrapolated below the centering reference.
• Source aliases:
  • UF (Allegaert 2015 NONMEM column; same orientation, no transformation) – used in Allegaert_2015_paracetamol.R.
• Example models: Allegaert_2015_paracetamol.R.
• Notes: Specific scope because the centered-linear effect form with the URINE_FLOW == 0 sentinel-zero rule reflects an Allegaert-specific convention rather than a universally-agreed-upon parameterization. A second model that uses a different effect form (e.g., direct URINE_FLOW / URINE_FLOW_ref proportional scaling, no zero-sentinel) should register its own canonical (e.g., URINE_FLOW_PROP) rather than reusing URINE_FLOW with conflicting semantics. The full-word canonical name was chosen over the bare UF source-data abbreviation for clarity in source traces.

URINE_VOL_24H (canonical for 24-hour residual diuresis (total urine volume per day))

• Description: Total urine volume collected over a 24-hour interval, in mL per 24 h. Used as an indicator of preserved native renal function in CRRT-dependent / oliguric / anuric critically ill patients, where it characterises the residual non-CRRT renal-excretion pathway. Time-fixed per study day in the founding example (a single 24-hour summary collected by nursing staff during the day of PK sampling). Distinct from URINE_FLOW (instantaneous urine flow rate in mL/h measured per collection interval).
• Units: mL/24h
• Type: continuous
• Scope: general
• Reference category: n/a – used with an additive linear effect CL = CL_base + e_URINE_VOL_24H_cl * (URINE_VOL_24H / 100) centered at 100 mL/24h (the anuria cutoff), or as a binary preserved-diuresis gate (URINE_VOL_24H > 100) multiplying a renal-clearance arm. Clinical cutoffs used in the founding example: anuria < 100 mL/24h, oliguria 100-500 mL/24h, preserved diuresis > 500 mL/24h.
• Source aliases:
  • residual diuresis (Ulldemolins 2015 prose); typical NONMEM column abbreviation DIUR. Same value orientation, no transformation.
• Example models: Ulldemolins_2015_meropenem.R (critically ill adults with septic shock and continuous renal replacement therapy; reference 100 mL/24h; additive linear effect 0.22 L/h per (URINE_VOL_24H / 100) on meropenem total CL on top of the CRRT-mediated baseline 3.68 L/h), Huppe_2023_fosfomycin.R (critically ill adults with renal insufficiency on continuous venovenous hemodialysis; used as the binary preserved-diuresis gate (URINE_VOL_24H > 100) that switches the renal clearance arm off entirely in anuric patients, per the source’s own definition of preserved diuresis as residual diuresis exceeding 100 mL/24h).
• Notes: Promoted from specific to general alongside the Huppe 2023 fosfomycin extraction, the second model to retain a 24-hour urine-volume covariate with consistent semantics. The two registered effect forms are both multiplicative-or-additive in the same 100 mL/24h anuria cutoff and so share this entry: Ulldemolins 2015 uses the additive linear base + slope * (URINE_VOL_24H / 100), and Huppe 2023 uses the binary gate (URINE_VOL_24H > 100) on a renal-clearance arm. Distinct from URINE_FLOW because (a) URINE_VOL_24H is a 24-hour cumulative volume (mL/24h), not an instantaneous rate (mL/h), and (b) the additive-linear effect form base + slope * (URINE_VOL_24H / 100) is structurally different from the centered-linear-effect-with-sentinel-zero form used by URINE_FLOW. A future model that uses a proportional or multiplicative effect form on a 24-hour volume should evaluate whether the semantics still match this entry (proportional / multiplicative forms are compatible); a centered-linear-with-sentinel form (analogous to the URINE_FLOW convention) would diverge and should register a sibling canonical. The full-word URINE_VOL_24H form was chosen over the shorter URINE_24H per operator instruction (sidecar request 001 of task frompeople-536). Ratified canonically on 2026-06-27 alongside the Ulldemolins 2015 meropenem extraction.

TER_MAG3 (canonical for tubular extraction rate measured by 99mTc-MAG3 renography)

• Description: Tubular extraction rate of 99mTc-mercaptoacetyltriglycine (MAG3), the whole-kidney tubular secretion clearance measured by MAG3 renography. Not BSA-normalized and not a filtration measurement: MAG3 is handled predominantly by active proximal-tubular secretion, whereas creatinine clearance and 51Cr-EDTA / iohexol clearance measure glomerular filtration. Papers that use TER as their renal-function input typically convert it to a filtration rate with a published regression before it enters the model.
• Units: mL/min
• Type: continuous
• Scope: general
• Reference category: n/a – used as a direct model input, not as a covariate-effect coefficient.
• Source aliases:
  • TER – the bare symbol used by Golzaryan 2025 Table S1 and by the upstream Kletting 2016 PLoS One Table 1 (“TER = tubular extraction rate as determined with the 99mTc Mag3 method”); same quantity in mL/min, no value transformation.
• Example models: Golzaryan_2025_lu177psmaIT_pbpk.R (Golzaryan 2025 whole-body 177Lu-PSMA I&T PBPK; per-patient TER of 136-252 mL/min is converted to the 51Cr-EDTA glomerular filtration rate as GFR = TER / 3 * 20 / 15 per Table S2, then scaled to peptide molecular size by the sieving ratio phi = 0.66 to give the filtration flow used by the kidney sub-model – founding example).
• Notes: Deliberately NOT folded into CRCL. CRCL pools creatinine-based and tracer-measured estimates of glomerular filtration, normalized to 1.73 m^2; TER_MAG3 is non-normalized tubular extraction, a physiologically distinct process measured by a different tracer, and the two are related only through a cohort-specific regression. Reusing CRCL would require an undocumented conversion at data-ingestion time and would silently mix two renal processes under one column. The tracer is named in the canonical because the number is uninterpretable without it – a tubular extraction rate measured with 131I-OIH is not numerically interchangeable with a MAG3 one. Ratified canonically on 2026-08-14 (sidecar oare_PMC11791192 request-001 q2) alongside the Golzaryan 2025 extraction.

CRCL (canonical for BSA-normalized renal function (creatinine-based estimate OR tracer-measured GFR))

• Description: BSA-normalized renal function expressed in mL/min/1.73 m^2. Accepts either (a) a creatinine-based estimate – MDRD- or CKD-EPI-estimated glomerular filtration rate, or a measured creatinine clearance that has been BSA-normalized as 1.73 x CrCl / BSA – or (b) a tracer-measured glomerular filtration rate using an exogenous filtration marker (iohexol clearance, inulin clearance, 99mTc-DTPA clearance, 51Cr-EDTA clearance), which is the clinical gold standard for measured GFR. All variants enter popPK models with the same operational role (a covariate on clearance) and the same units; the per-model covariateData[[CRCL]]$description and notes must state which assay the source paper used.
• Units: mL/min/1.73 m^2
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (CRCL / ref)^exponent or with linear centering (1 + slope * (CRCL - ref)). Reference values observed: 76 mL/min/1.73 m^2 (Hamren 2008, iohexol-clearance-measured GFR), 80 mL/min/1.73 m^2 (Cirincione 2017, MDRD eGFR), 90 mL/min/1.73 m^2 (Li 2019, calculated GFR; Bajaj 2017, CKD-EPI eGFR), 100 mL/min/1.73 m^2 (Xu 2019, measured-CrCl BSA-normalized), 141 mL/min/1.73 m^2 (Jung 2024, bedside Schwartz eGFR in critically ill children, whose renal function skews supranormal).
• Source aliases:
  • eGFR – estimated glomerular filtration rate; the estimating equation varies by cohort and must be documented per model. MDRD variant used in Cirincione_2017_exenatide.R and Kotani_2022_astegolimab.R; creatinine-based CKD-EPI variant in Bajaj_2017_nivolumab.R; the cystatin-C-based CKD-EPI variant in Patel_2025_eteplirsen.R, where creatinine is explicitly rejected as a renal marker because it is a by-product of the muscle breakdown that defines Duchenne muscular dystrophy (cystatin C is produced by all nucleated cells and is therefore independent of muscle mass – see CYSC); the paediatric bedside Schwartz variant 0.413 x height (cm) / Scr (mg/dL) in Jung_2024_vancomycin.R.
  • EGFR – all-caps variant.
  • CRCL_BSA – BSA-normalized creatinine clearance (measured CrCl / BSA x 1.73); used in Xu_2019_sarilumab.R.
  • 1.73*CrCl/BSA – the formula form appearing in Xu 2019 Eq. for Vm.
  • cGFR – calculated/estimated GFR, BSA-normalized; used in Li_2019_abatacept.R.
  • CLCR – source-paper column name; underlying assay form varies. Used in Delattre_2010_amikacin.R (raw Cockcroft-Gault, NOT BSA-normalized; median 55.5 mL/min in critically ill septic adults) and in MedellinGaribay_2015_gentamicin.R (Schwartz formula CLCR = K * length / SCr with K in {0.33, 0.45, 0.55}, BSA-normalized to mL/min/1.73 m^2). Document the assay form per model in covariateData[[CRCL]]$description.
  • CLCR,LBW – creatinine clearance computed on LEAN body weight rather than total body weight, raw mL/min and NOT BSA-normalized. Rolsma 2025 builds it with the Cockcroft-Gault equation substituting lean body weight for subjects over 12 years of age and the Schwartz bedside equation for subjects up to 12 years, so a single column spans a paediatric-to-adult cohort under two different estimating equations. Used in Rolsma_2025_cefepime.R (reference 77 mL/min) and Rolsma_2025_meropenem.R (reference 90 mL/min).
  • CLiohexol – plasma iohexol clearance, the clinical gold-standard tracer-measured glomerular filtration rate; used in Hamren_2008_tesaglitazar.R (tesaglitazar / acyl glucuronide interconversion popPK in subjects with varying renal function). Iohexol is an exogenous contrast agent cleared exclusively by glomerular filtration (no tubular secretion or reabsorption); its clearance is a direct measurement of GFR.
  • CLinulin – plasma inulin clearance, the historical gold standard for measured GFR; analogous to iohexol clearance but requires constant intravenous infusion of inulin.
  • CL_DTPA – plasma 99mTc-DTPA clearance, a radiolabelled tracer of glomerular filtration commonly used in clinical practice.
  • CL_EDTA – plasma 51Cr-EDTA clearance, another radiolabelled glomerular-filtration tracer.
• Example models: Cirincione_2017_exenatide.R (MDRD eGFR), Xu_2019_sarilumab.R (measured CrCl BSA-normalized), Kotani_2022_astegolimab.R (MDRD eGFR), Li_2019_abatacept.R (cGFR), Bajaj_2017_nivolumab.R (CKD-EPI eGFR, reference 90 mL/min/1.73 m^2), NA_NA_lidocaine.R (DDMODEL00000281; binary stratification at threshold 52.7 mL/min adding -0.319 to the GX rate constant K30 in the CRCL <= 52.7 cohort; the source .ctl does not state the BSA-normalisation method), Delattre_2010_amikacin.R (raw Cockcroft-Gault mL/min, NOT BSA-normalized; reference 55.5 mL/min population median; additive linear effect 1.42 L/h per (CRCL/55.5) on CL), MedellinGaribay_2015_gentamicin.R (Schwartz BSA-normalized CLCR; reference 75 mL/min/1.73 m^2 (population mean 76.7); additive linear effect 0.06 L/h per (CRCL/75) on CL in infants 1-24 months), Georges_2009_ceftazidime.R (raw MDRD-eGFR mL/min, NOT BSA-normalized; mean 121 mL/min; additive linear effect 0.024 L/h per mL/min on CL, no centering), Hamren_2008_tesaglitazar.R (iohexol-clearance-measured GFR; reference 76 mL/min/1.73 m^2; linear centered slope 0.99 %/(mL/min/1.73 m^2) on renal clearance of parent tesaglitazar AND simple linear normalised scaling (CRCL / 76) on the saturable Michaelis-Menten Vmax of acyl-glucuronide renal elimination; founding tracer-GFR example), Chen_2023_nemonoxacin.R (raw Cockcroft-Gault mL/min, NOT BSA-normalized; cohort 114.4 +/- 29.2 mL/min, median 116.2, range 50.7-200.7; additive linear effect 0.026 L/h per mL/min on a 10.3 L/h intercept with no centering, per Chen 2023 Eq. 8 CL = (10.3 + 0.026 x CLcr) x (BW/70)^0.75; source column CLcr), Wada_2023_sparsentan.R (raw Cockcroft-Gault mL/min, NOT BSA-normalized; reference 112 mL/min overall cohort median, range 26-361; power effect (CRCL / 112)^0.222 on CL/F, so lower renal function raises sparsentan exposure even though urinary excretion accounts for only 2.18% of the dose – the paper notes the association may be partly confounded by sex, which is a Cockcroft-Gault input and also a retained covariate on CL/F), Shu_2024_posaconazole.R (raw Cockcroft-Gault mL/min, NOT BSA-normalized; reference 103.81 mL/min cohort median, range 14.44-240.64 in Chinese HSCT recipients spanning ages 3-65; power effect (CRCL / 103.81)^0.68 on CL/F for posaconazole oral suspension, of which only about 13% of a dose is renally excreted – as with Wada 2023 the effect is more plausibly a marker of general physiological reserve than a mechanistic renal-elimination pathway, and the authors flag it as a departure from earlier posaconazole popPK models), Suzuki_2024_mycophenolic_acid.R (Cockcroft-Gault standardised to a fixed 70 kg body weight – i.e. mL/min/70 kg, NOT BSA-normalised and NOT a raw value; cohort 2.5th / 50th / 97.5th percentiles 6.6 / 52.8 / 148.8 in renal-transplant recipients at a single Japanese centre; enters as the ratio CRCL / 100 multiplying the renal arm of an additive renal-plus-non-renal apparent clearance, with body size handled separately by an allometric (WT/70)^0.75 term), Jung_2024_vancomycin.R (bedside Schwartz paediatric eGFR, BSA-normalized; reference 141 mL/min/1.73 m^2; power effect (CRCL / 141)^0.5259 on CL in critically ill children – a less-than-proportional renal effect, and the sole retained covariate on clearance), Nakai_2025_tranexamicAcid.R (raw Cockcroft-Gault mL/min computed with actual body weight, NOT BSA-normalized; reference 61.0 mL/min cohort median, range 21.8-147.5 in adults on cardiopulmonary bypass; power effect (CRCL / 61.0)^0.752 on CL; source column CLcr, and note that the paper’s summary equation misannotates the covariate as “CLcr (L/h)” while dividing by the mL/min median – the Discussion’s worked example resolves the ratio to mL/min), Rolsma_2025_cefepime.R, Rolsma_2025_meropenem.R (alias CLCR,LBW; raw Cockcroft-Gault-on-lean-body-weight / Schwartz-bedside mL/min, NOT BSA-normalized; references 77 and 90 mL/min respectively, both being the cohort typical value; through-origin linear normalisation (CRCL / ref) on the RENAL arm of an additive renal-plus-non-renal clearance, with the non-renal arm carrying the body-size covariate instead), Patel_2025_eteplirsen.R (cystatin-C-based CKD-EPI eGFR, BSA-normalized; reference 145 mL/min/1.73 m^2 cohort median, range 85.5-180 in boys with Duchenne muscular dystrophy; power effect (CRCL / 145)^1.60 on CL. Two features are unusual and worth carrying forward: the cohort is uniformly supranormal – no subject fell below 85.5 – so the model carries no information about renal impairment and the paper says so explicitly; and the estimating equation was chosen because creatinine is invalid in this disease, not merely as a convenience), Lee_2025_levofloxacin.R (raw Cockcroft-Gault mL/min, NOT BSA-normalized; reference 105.71 mL/min cohort median, observed range 74.8-113 in 12 healthy Korean adults; power effect (CRCL / 105.71)^0.901 on CL, the paper having tested and rejected an additional non-CrCl clearance component as negligible – note that the paper’s own Monte Carlo dosing simulations extrapolate this term from the fitted 74.8-113 range out to 10-170 mL/min), Zhang_2026_tebipenem.R (WEIGHT-normalized mL/min/kg, Sato 2008 paediatric predictive equations from serum creatinine; cohort mean 3.72 +/- 0.97, median 3.73, range 1.46-6.97; additive linear effect 0.104 (L/h/kg) per (mL/min/kg) on a 0.363 L/h/kg intercept with no centering, the whole bracket then multiplied by body weight to give CL/F in L/h – the founding weight-normalized example).
• Notes: All estimation methods (creatinine-based MDRD / CKD-EPI / measured CrCl, and tracer-based iohexol / inulin / DTPA / EDTA clearance) produce values in the same units and are operationally interchangeable as a covariate on clearance. Three distinct size normalisations appear across the example models and must be documented per model, because they are not interchangeable: BSA-normalised (mL/min/1.73 m^2, the canonical default), raw un-normalised (mL/min, e.g. Delattre_2010_amikacin.R, Chen_2023_nemonoxacin.R), and per-70-kg-body-weight (mL/min/70 kg, e.g. Suzuki_2024_mycophenolic_acid.R). Supplying a value on the wrong normalisation silently rescales the renal-function term, and in a model that also carries an allometric weight term it double-counts body size. Document the method explicitly in each model’s covariateData[[CRCL]]$description so future reviewers can trace the source assay; the assay choice affects accuracy of the absolute GFR value but does not change the covariate’s role as a linear / power scalar on renal clearance. Tracer-measured GFR is the clinical gold standard (unaffected by muscle mass, malnutrition, and tubular-secretion confounders that perturb creatinine-based estimates) but is rarely available outside research and renal-impairment-cohort studies; creatinine-based estimates dominate routine clinical popPK datasets. Tracer-measured-GFR scope codified on 2026-06-17 alongside the Hamren 2008 tesaglitazar extraction (per operator decision in sidecar request 001).

RENALFUNC_REL (canonical for relative renal function, 1 = normal)

• Description: Dimensionless per-subject renal function expressed as a FRACTION OF NORMAL, so that 1 = normal renal function, 0.5 = half of normal, and values above 1 describe supranormal filtration. This is the organ-function scalar of the Groningen / Medimatics MwPharm and Edsim++ modelling tradition, where it lives in a dedicated organ-function object (the ORG / P object) and multiplies the renal arm of a decomposed clearance: CLi = CL * (fe * RENALFUNC_REL + (1 - fe) * HEPFUNC_REL), with the paper-named parameter fe supplying the renal share of total clearance. Compute it as measured GFR / reference normal GFR (or as the ratio of any consistent renal-function measure to its own normal reference) – the ratio, not the absolute measurement, is what this column carries.
• Units: (dimensionless)
• Type: continuous
• Scope: general
• Reference category: n/a – the column IS the multiplier and enters the model directly; 1 denotes normal renal function. No centering or power transform is applied.
• Source aliases:
  • RF – Visscher 2025 Methods section 2.3 and Edsim++ ORG object symbol for relative renal function. Same orientation and scale, no value transformation. NOTE: the bare two-letter name RF must NOT be used as the canonical, because it is already recorded in this register as a rejected alias of the rheumatoid-factor column.
  • RENAL_FUNCTION_FRACTION, GFR_REL, GFR/GFR_normal – descriptive forms of the same ratio.
• Example models: Visscher_2025_parathyroidHormone.R (founding example; single-patient rhPTH(1-84) PK-PD in postsurgical hypoparathyroidism. Multiplies the renal fraction fe = 0.3 of the 39.93 L/h population clearance per Visscher 2025 Eq. 3. Held at 1 in the validation vignette because the source states that the equation corrects for the variations in GFR but reports NO numeric RF values and NO reference-GFR denominator from which the ratio could be recovered – and Table S3 omits the 24-h urine volume that the paper’s own GFR equation needs, so the per-interval ratio cannot be reconstructed from the supplement either.).
• Notes: Ratified 2026-08-20 alongside the Visscher 2025 rhPTH(1-84) extraction (sidecar request-001 / response-001, question q1, operator answer A). Genuinely distinct from every pre-existing renal-function entry, which is why it needed its own canonical rather than an alias: CRCL and CRCL_BASE are ABSOLUTE renal function in their own units (mL/min/1.73 m^2) and converting one to a relative multiplier requires a reference denominator that a source paper often does not state; KBF is an absolute organ blood flow; and RENALIMP_MILD / RENALIMP_MOD / RENALIMP_SEV are CATEGORICAL impairment bands that cannot express a continuous multiplier. A model that reports an absolute GFR should use CRCL; use RENALFUNC_REL only when the source’s own parameterisation is the dimensionless fraction-of-normal. Sister canonical to HEPFUNC_REL; the two are normally used together, since the point of the parameterisation is to let the renal and non-renal arms of clearance vary independently. Expect reuse: the relative-organ-function scalar is the standard parameterisation of the MwPharm / Edsim++ family (Edsim++ author N.C. Punt is a co-author of the founding paper, whose reference list cites a sibling model from the same group and framework).

HEPFUNC_REL (canonical for relative liver function, 1 = normal)

• Description: Dimensionless per-subject hepatic function expressed as a FRACTION OF NORMAL, so that 1 = normal liver function and 0.5 = half of normal metabolic capacity. Companion to RENALFUNC_REL in the MwPharm / Edsim++ organ-function (ORG / P) object, multiplying the non-renal (hepatic-metabolic) remainder of a decomposed clearance: CLi = CL * (fe * RENALFUNC_REL + (1 - fe) * HEPFUNC_REL).
• Units: (dimensionless)
• Type: continuous
• Scope: general
• Reference category: n/a – the column IS the multiplier and enters the model directly; 1 denotes normal liver function.
• Source aliases:
  • LF – Visscher 2025 Methods section 2.3 and Edsim++ ORG object symbol for relative liver function. Same orientation and scale, no value transformation.
  • HEP_FUNCTION_FRACTION, LIVERFUNC_REL – descriptive forms of the same ratio.
• Example models: Visscher_2025_parathyroidHormone.R (founding example; multiplies the hepatic remainder 1 - fe = 0.7 of total clearance. Held at 1 because Visscher 2025 Methods states explicitly that “LF is the relative liver function (equal to 1 in this model)” – the patient had normal hepatic function and no hepatic-impairment data inform the parameter. Retained in the model rather than folded into CL so the paper’s own renal / hepatic decomposition of clearance stays visible and user-perturbable.).
• Notes: Ratified 2026-08-20 alongside the Visscher 2025 rhPTH(1-84) extraction (same sidecar as RENALFUNC_REL). Distinct from the HEPIMP / HEPIMP_MILD / HEPIMP_MOD / HEPIMP_SEV / HEPIMP_MODSEV family, which are binary Child-Pugh-style impairment INDICATORS, and from the underlying laboratory measures AST / ALT / BILI / ALB: this column is a continuous fraction-of-normal metabolic-capacity multiplier, not a category or a lab value. A source that reports a Child-Pugh class should use the HEPIMP_* family; use HEPFUNC_REL only when the source’s own parameterisation is the dimensionless fraction-of-normal. Sister canonical to RENALFUNC_REL. A value of exactly 1 is a legitimate and common entry – it records that the source’s structural decomposition of clearance exists and was exercised at normal hepatic function, which is information a downstream user needs in order to perturb it.

KBF (canonical for native kidney (renal) blood flow)

• Description: Whole-organ blood flow perfusing the kidneys, used as a physiological system input in mechanistic kidney / renal PBPK models, where it sets the peritubular vascular flow that carries drug past the tubular epithelium. May be measured (para-aminohippurate or isovalerylglycine renal clearance, phase-contrast MRI, Doppler ultrasound) or assumed from a population physiological default. Distinct from the extracorporeal-circuit flows BFR / DFR / Q_CVVH / QBL / QEFF, which describe dialysis or ECMO hardware rather than native renal perfusion, and from CARDIAC_OUTPUT (whole-body hemodynamic output, of which renal blood flow is roughly one fifth).
• Units: mL/min (raw whole-organ flow, NOT BSA-normalized; the Huang & Isoherranen mechanistic kidney framework consumes it internally as L/h via the 1 mL/min = 0.06 L/h conversion)
• Type: continuous
• Scope: general
• Reference category: n/a – enters the model directly as the peritubular vascular flow, not as a ratio or centered deviation. Healthy-adult physiological default in the founding framework: 60 L/h (= 1000 mL/min), scaled proportionally with GFR in the population version of the model (Huang & Isoherranen 2020 distributed MATLAB code, Q_kidney0 = 60 * Proportional_Reduction). Granda 2024 replaces that population default with a per-subject measurement, which is the paper’s central methodological contribution.
• Source aliases:
  • Isovalerylglycine CL_r – Granda 2024 Table 3 column heading; the renal clearance of the endogenous solute isovalerylglycine used as the KBF surrogate (high extraction ratio, low protein binding, clearance more than fourfold GFR). Same orientation and units, no value transformation.
  • Q_kidney / Qk / Q_kidney0 – Huang & Isoherranen 2018 / 2020 and Chang 2023 symbol for the same quantity, expressed in L/h (divide by 0.06 to obtain the canonical mL/min).
  • RBF / RPF – renal blood flow / renal plasma flow shorthand common in renal-physiology and PAH-clearance literature. RPF is PLASMA flow and must be converted to blood flow (KBF = RPF / (1 - haematocrit)) before use.
• Example models: Granda_2024_kynurenicacid_pbpk.R, Granda_2024_tenofovir_pbpk.R, Granda_2024_oseltamivircarboxylate_pbpk.R (all three: per-subject KBF estimated from measured isovalerylglycine renal clearance, mL/min; cohort mean 575 +/- 387, range 76-1692 across 27 adults spanning CKD stages 1-5; enters as the peritubular blood flow qk <- KBF * 0.06 in the 35-state mechanistic kidney PBPK).
• Notes: Ratified 2026-08-05 alongside the Granda 2024 mechanistic-kidney-PBPK extraction (sidecar request 001, operator answer A). General scope because native kidney blood flow is a paper-independent physiological quantity that any renal-PBPK or renal-physiology model may use. The canonical name states the quantity rather than a paper symbol, per the standing canonical-naming rule. Note the physiological constraint the founding models enforce: secretory clearance cannot exceed kidney blood flow, so a subject whose measured secretory-marker clearance exceeds their measured KBF cannot be fitted (Granda 2024 participants 38, 41 and 45, whose fitted secretory clearance therefore railed at the 1000 L/h search bound); a model consuming KBF should surface that violation rather than silently saturating. The companion per-subject quantity in Granda 2024 – unbound intrinsic secretory clearance – is deliberately NOT registered as a covariate: it is a fitted model parameter (lclintsec), not a measurement, per the same sidecar decision.

CREAT (canonical for serum creatinine)

• Description: Serum creatinine concentration (baseline or time-varying).
• Units: umol/L or mg/dL – document the unit used in each model via covariateData[[CREAT]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (CREAT / ref)^exponent.
• Source aliases:
  • CRE (umol/L, reference 70.73) – used in Thakre_2022_risankizumab.R.
  • SCR – common clinical-PK abbreviation; also Llanos-Paez 2020 source column for the patient’s individual serum creatinine.
• Example models: Thakre_2022_risankizumab.R, Hennig_2013_tobra.R (umol/L; paired with CREAT_REF for the SCR_mean / SCR ratio used in the Hennig 2013 renal-function factor), Llanos_2017_gentamicin.R (umol/L; standardized per-patient against CREAT_REF rather than a fixed cohort reference), Llanos-Paez_2020_gentamicin.R (umol/L; used as the patient’s SCR_i in the renal-function ratio (CREAT_REF / CREAT)^0.58 on CL).
• Notes: CREAT chosen over the shorter CRE/SCR as the NONMEM/clinical-PK convention that is unambiguous. Per-model reference values must be documented in covariateData[[CREAT]]$notes.

CREAT_REF (canonical for sex/age/size-expected normal-mean serum creatinine)

• Description: Externally-computed reference serum creatinine for the individual (the expected normal SCR for a healthy person of the same sex, age and body size). Used as the numerator of a ratio against the patient’s measured CREAT to define a renal-function factor on clearance.
• Units: umol/L or mg/dL – must match the unit of the paired CREAT column. Document via covariateData[[CREAT_REF]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – used as (CREAT_REF / CREAT)^exponent so that a patient with measured SCR equal to the population-expected normal SCR has factor 1.
• Source aliases:
  • SCR_mean – used in Hennig 2013 (Eq. 5: f_SCR = (SCR_mean / SCR)^theta_SCR); also Llanos-Paez 2020 paper notation for the Ceriotti 2008 age/sex-matched physiological mean SCR.
  • Scrmean – Llanos-Paez 2017 paper notation; computed from Ceriotti et al. 2008 age- and sex-stratified medians (Clin Chem 54:559-566, doi:10.1373/clinchem.2007.099648).
  • SCR_standardised – Germovsek 2018 paper notation; PMA-adjusted standardisation of raw SCR per the paper’s reference 28 (a previously-developed Standing-style PMA stratification).
  • CCR,adj – Ruhs 2012 paper notation; age- and gender-adjusted reference creatinine derived from the paper’s reference [23] (a Schwartz-style paediatric maturation adjustment); the main text does not give the explicit formula.
• Example models: Hennig_2013_tobra.R, Llanos_2017_gentamicin.R (umol/L; computed externally per Ceriotti et al. 2008), Llanos-Paez_2020_gentamicin.R (umol/L; ratio (CREAT_REF / CREAT)^0.58 multiplies the maturation-scaled CL), Germovsek_2018_meropenem.R (umol/L; ratio (CREAT_REF / CREAT)^0.40 multiplies the maturation-scaled CL for renal meropenem clearance in neonates and young infants; PMA-stratification reference per Germovsek 2018 Methods reference 28), Ruhs_2012_methotrexate.R (mg/dL; age- and gender-adjusted CCR,adj per the paper’s reference [23]; ratio (CREAT_REF / CREAT)^0.314 multiplies the BSA-scaled MTX CL in paediatric ALL patients).
• Notes: Specific scope because the formula used to derive the reference value is paper-defined (Hennig 2013 cites a combination of Ceriotti 2008, Junge 2004 and Johansson 2011 reference-interval relationships; Llanos-Paez 2017 and 2020 both use Ceriotti 2008); a future paper that uses a different reference-SCR derivation (e.g., a CKD-EPI-style adult-only reference, or a Schwartz-derived paediatric-only reference) should pin its formula in covariateData[[CREAT_REF]]$notes so that a user assembling a virtual cohort can reproduce it. When no covariate data are available to compute CREAT_REF, set CREAT_REF = CREAT so the renal-function factor evaluates to 1 (matching the Hennig 2013 ‘covariate set to 1 for missing data’ rule). Ratified canonically on 2026-05-08 alongside the Hennig 2013 tobramycin extraction.

BUN (canonical for blood urea nitrogen)

• Description: Blood urea nitrogen concentration (baseline or time-varying). Reflects the nitrogenous waste burden being cleared by the kidneys; rises with reduced glomerular filtration, dehydration, increased protein catabolism, or GI bleeding. Distinct from CREAT (the other commonly-reported renal-function marker) because urea reabsorption is flow-dependent in the renal tubule, so BUN is sensitive to volume status as well as GFR.
• Units: mg/dL or mmol/L – document the unit used in each model via covariateData[[BUN]]$units (1 mmol/L urea ~= 2.80 mg/dL BUN).
• Type: continuous
• Scope: general
• Reference category: n/a – used either as a linear / hinge effect on a PK parameter, or with power scaling (BUN / ref)^exponent. Reference values observed: 7 mg/dL (Hall 2017 MARS hinge knot for ka; not a population median); 4.2 mmol/L (Chen 2017 cohort median for the tacrolimus power-of-ratio CL effect).
• Source aliases: none known.
• Example models: Hall_2017_dapsone.R (mg/dL; population median 13 mg/dL [range 7-28]; enters the MARS-based covariate model on the absorption rate constant via the basis function BF1 = max(0, BUN - 7), which interacts with a weight hinge to drive Ka), Chen_2017_tacrolimus.R (mmol/L; cohort median 4.2 mmol/L [range 1.7-10.4]; enters CL/F via standard power-of-ratio scaling (BUN/4.2)^1.42 in low-dose oral tacrolimus for Chinese myasthenia-gravis patients).
• Notes: Promoted to general scope on 2026-06-03 alongside the Chen 2017 tacrolimus extraction, the second model registering BUN. Hall 2017 enters BUN only through a piecewise-linear MARS hinge (max(0, BUN - 7) * max(0, 63.7 - WT)), not as a power scaling – the per-model covariateData[[BUN]]$notes documents this is part of a machine-learning-driven hinge model, not a standard popPK covariate transform. Chen 2017 uses the conventional centred power-of-ratio form (BUN/ref)^exponent referenced to the cohort median; the positive exponent there is interpreted via a urea-driven protein-carbamylation mechanism that reduces albumin binding of the ~99%-protein-bound tacrolimus. Ratified canonically on 2026-05-18 alongside the Hall 2017 dapsone extraction.

UACR (canonical for urine albumin-to-creatinine ratio)

• Description: Urine albumin-to-creatinine ratio (UACR), a renal-damage biomarker quantifying albuminuria. UACR is the spot-urine albumin concentration divided by the spot-urine creatinine concentration; it approximates 24-hour urinary albumin excretion (in mg/day) because creatinine excretion is roughly constant at ~1 g/day in an adult. UACR is the principal albuminuria measure in KDIGO chronic-kidney-disease (CKD) staging, with thresholds A1 (< 30 mg/g, normal-to-mildly increased), A2 (30-300 mg/g, moderately increased), and A3 (> 300 mg/g, severely increased). Distinct from the function markers CRCL / CREAT / BUN: UACR captures upstream glomerular albumin leak (kidney damage), whereas CRCL / CREAT / BUN capture downstream filtration rate (kidney function). Both axes are routinely reported together in diabetic-kidney-disease (DKD) and CKD popPK / PD studies. Time-fixed at baseline by default; document baseline-vs-time-varying status per model via covariateData[[UACR]]$notes.
• Units: mg/g (urine albumin in mg per gram of urine creatinine). Equivalent to mg/mmol scaled by 8.84 (1 mg/g ~= 0.113 mg/mmol). Document the unit used in each model via covariateData[[UACR]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – used either as a centred linear effect on (UACR - ref) or as a power scaling (UACR / ref)^exponent. Reference values observed: 800 mg/g (Goulooze 2022 serum-potassium analysis centring value) and 850 mg/g (Goulooze 2022 UACR / eGFR analysis centring value); both are close to the FIDELIO-DKD cohort median of 852 mg/g.
• Source aliases:
  • UACR0 – Goulooze 2022 paper convention for baseline UACR (the 0 suffix denotes the baseline / pre-randomisation value).
• Example models: Goulooze_2022_finerenone.R (centred linear effects on Emax and on the disease-progression slope TSLOPE, both centred at 800 mg/g; coefficients 9.31e-5 g/mg on Emax and 1.14e-3 g/mg on TSLOPE per Goulooze 2022 Table 1), Goulooze_2022_finerenone_uacr.R (power scaling (UACR / 850)^0.877 on the baseline of the UACR disease-progression state), Goulooze_2022_finerenone_egfr.R (same scaling on the embedded UACR sub-model baseline, plus (UACR / 850)^0.306 on the magnitude of the inter-individual variability of the eGFR decline rate – a covariate on a variance term rather than on a typical value).
• Notes: Promoted from specific to general scope alongside the Goulooze 2022 UACR / eGFR extraction, which was the second paper to register the column (as the original entry anticipated). Distinct from the uacr COMPARTMENT, which is the modelled UACR time course; this column is the time-fixed observed baseline. Ratified canonically alongside the Goulooze 2022 finerenone extraction.

SOD (canonical for serum sodium concentration)

• Description: Serum (or plasma) sodium concentration. Most commonly captured at study entry as a baseline biochemistry value, but can be time-varying when serial electrolytes are recorded. Reflects fluid / electrolyte balance; hyponatraemia is common in severe acute illness, severe malnutrition, GI losses, SIADH and adrenal insufficiency, while hypernatraemia accompanies dehydration and excessive sodium intake.
• Units: mmol/L (equivalent to mEq/L for sodium).
• Type: continuous
• Scope: general
• Reference category: n/a – used with a centered-linear-deviation form (1 + e_sod_<param> * (SOD - ref)). Reference values observed: 136 mmol/L (Thuo 2011 ciprofloxacin; cohort median in Kenyan children with severe malnutrition, normal-range lower bound).
• Source aliases:
  • Na+ / NA / SODIUM – common source-paper printed forms; renamed to canonical SOD when assembling input data. Used in Thuo_2011_ciprofloxacin.R (the paper writes “Na+ (mmol/L)” in Table 1 and structural-model equations).
• Example models: Thuo_2011_ciprofloxacin.R (linear centered-deviation effects on apparent CL and apparent Vc: 1 + 0.0368*(SOD - 136) and 1 + 0.0291*(SOD - 136); reference 136 mmol/L is the cohort median).
• Notes: General scope because serum sodium is a universally applicable serum-electrolyte covariate. Reference value is paper-specific (cohort median); future models should document their own reference in covariateData[[SOD]]$notes. Distinct from any “sodium content of dosed formulation” concept (e.g., sodium-rich oral rehydration solution) – that would warrant a separate canonical (DOSE_NA_MGML, etc.) if a future model retains it. Ratified canonically on 2026-05-21 alongside the Thuo 2011 ciprofloxacin extraction.

POT (canonical for serum potassium concentration)

• Description: Serum (or plasma) potassium concentration. Most commonly captured at study entry as a baseline biochemistry value, but can be time-varying when serial electrolytes are recorded. Central to mineralocorticoid-receptor-antagonist, potassium-sparing-diuretic and RAAS-inhibitor programmes, where hyperkalaemia is the dose-limiting toxicity and drives titration and discontinuation rules; KDIGO and ESC hyperkalaemia thresholds are 5.5 and 6.0 mmol/L. Direct sibling of SOD (serum sodium). Distinct from the serumK COMPARTMENT, which is the modelled potassium time course when potassium is the PD output rather than an explanatory covariate.
• Units: mmol/L (equivalent to mEq/L for potassium).
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (POT / ref)^exponent or linear centring (1 + slope * (POT - ref)). Reference values observed: 4.4 mmol/L (Goulooze 2022 FIDELIO-DKD, the cohort median).
• Source aliases:
  • K0 – Goulooze 2022 convention for observed baseline serum potassium (the 0 suffix denotes the baseline / pre-randomisation value).
  • K, K+, POTAS, SERUM_K – common source-paper printed forms.
• Example models: Goulooze_2022_finerenone_egfr.R (power scaling (POT / 4.4)^-1.38 on DSLOPE, the slope of the acute finerenone effect on eGFR; ESM Table S2 theta_K0,DSLOPE = -1.38, reproducing the paper’s Table 2 range of +31.9% to -18.4% over the 3.6-5.1 mmol/L 5th-95th percentile band).
• Notes: General scope because serum potassium is a universally applicable serum-electrolyte covariate, matching the scope granted to SOD. Reference value is paper-specific (cohort median); future models should document their own reference in covariateData[[POT]]$notes. Ratified canonically alongside the Goulooze 2022 finerenone UACR / eGFR extraction.

Renal-replacement-therapy (RRT) family – section-header policy

All RRT-related canonicals follow the RRT_<MODALITY>_<KIND> shape, where MODALITY is the specific RRT type (HEMODIAL for intermittent hemodialysis, CRRT for continuous / extended modalities, PERIT_DIAL for peritoneal dialysis) and KIND is STATUS for subject-level treatment-status indicators (time-fixed within the analysis window) or ACTIVE for per-time-point session gates (time-varying within subject, 1 only during an active session). The 2026-06-19 canonical-register standardization audit renamed the prior HEMODIAL, HEMODIALYSIS, and CRRT_STATUS canonicals into this family to make the modality + kind contrast explicit at the column name; pre-2026-06-19 names are preserved as source_aliases so existing data CSVs continue to work for one release cycle.

RRT_HEMODIAL_STATUS (canonical for intermittent-hemodialysis treatment-status indicator)

• Description: 1 = the subject was undergoing intermittent hemodialysis during the modeled period; 0 = no intermittent hemodialysis. Treatment-status flag rather than a measured renal-function value; used as a multiplicative covariate on PK parameters that change with chronic dialysis (typically CL and Vc – intermittent hemodialysis decreases vancomycin-class CL and reduces interstitial volume overload, lowering Vc). Per-subject indicator in the source data; in Goti 2018 it is treated as time-fixed at the subject level (the cohort either was or was not receiving intermittent hemodialysis during the admission), and Goti 2018 explicitly notes that actual hemodialysis-session timing was not used because of documentation limitations.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no intermittent hemodialysis).
• Source aliases:
  • HEMODIAL – prior canonical name (pre-2026-06-19 RRT__ standardization).
  • DIAL – used in Goti_2018_vancomycin.R (binary indicator on CL and Vc in a 2-compartment vancomycin popPK model). Goti 2018 Methods notes the indicator was created for the routine-TDM cohort (n = 336 hemodialysis subjects of 1812 total) and that all hemodialysis procedures were intermittent and used high-flux membranes.
• Example models: Goti_2018_vancomycin.R (multiplicative factors on CL and Vc: 0.7^RRT_HEMODIAL_STATUS on CL and 0.5^RRT_HEMODIAL_STATUS on Vc, so dialysis subjects have 30% lower CL and 50% lower central volume than non-dialysis subjects), VanWart_2025_telavancin.R (used purely as a gate, not as an effect in its own right: it restricts the additive T_POST_HEMODIAL > 48 h central-volume increase to the 8 chronic-kidney-disease-stage-5 subjects, while the dialysis CLEARANCE arm of the same model is gated by the time-varying RRT_HEMODIAL_ACTIVE instead – the clearest available illustration of the STATUS-versus-ACTIVE split).
• Notes: Specific to intermittent hemodialysis (IHD). Distinct from peritoneal dialysis (PD) and from continuous renal replacement therapy (CRRT), each of which has different drug-extraction kinetics and would warrant its own canonical (RRT_PERIT_DIAL_STATUS, RRT_CRRT_STATUS) if a future paper retains them as covariates. Goti 2018 treats RRT_HEMODIAL_STATUS as time-fixed per subject because session-level dialysis timing was not reliably documented in the source EHR data; a future paper that resolves drug clearance during versus between dialysis sessions would use a time-varying form (RRT_HEMODIAL_ACTIVE) or a separate per-session covariate. When pairing RRT_HEMODIAL_STATUS with CRCL, note that the Cockcroft-Gault CRCL of an anuric hemodialysis patient is by convention very low or set per institution to a small floor value (Goti 2018 truncated CRCL > 150 mL/min to 150 mL/min and corrected SCr < 1 mg/dL in elderly subjects); residual renal function in hemodialysis subjects is highly variable and the dialysis indicator captures the bulk PK shift on top of the CRCL covariate. Ratified canonically on 2026-05-16 alongside the Goti 2018 vancomycin extraction. Renamed from HEMODIAL to RRT_HEMODIAL_STATUS on 2026-06-19 per the canonical-register standardization audit (RRT family normalization).

RRT_CRRT_STATUS (canonical for continuous / extended renal-replacement-therapy treatment-status indicator)

• Description: 1 = the subject was undergoing a continuous or extended (long-session) extracorporeal renal-replacement-therapy modality during the modeled period (continuous venovenous hemofiltration CVVH / CVVHF, continuous venovenous hemodiafiltration CVVHDF, sustained low-efficiency dialysis SLED, extended daily diafiltration EDD-f, or similar slow-clearance / long-duration extracorporeal therapies); 0 = no such therapy. Treatment-status flag rather than a measured renal-function value; used as a multiplicative or piecewise covariate on PK parameters that change when slow extracorporeal solute removal is active (typically CL). Per-subject indicator in the source data; in Shekar 2014 it is treated as time-fixed at the subject level (the cohort either was or was not receiving RRT during the entire PK sampling period; all RRT patients were on CVVH or EDD-f continuously / daily during sampling) – the indicator captures the subject-level RRT status, not session-level on/off timing.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no continuous / extended RRT).
• Source aliases:
  • CRRT_STATUS – prior canonical name (pre-2026-06-19 RRT__ standardization).
  • RRT – used in Shekar_2014_meropenem.R (binary indicator selecting between the RRT-fixed-CL term and the CrCL-driven non-RRT CL term in a 2-compartment meropenem popPK model). Shekar 2014 Methods describes the RRT cohort as mixed CVVH (control RRT subjects, true CRRT) and EDD-f (ECMO RRT subjects, extended daily diafiltration; pharmacokinetically CRRT-like for slow-clearance solutes such as meropenem) and the model treats the modalities as a single binary covariate without distinguishing them.
• Example models: Shekar_2014_meropenem.R (piecewise CL: TVCL = exp(lcl) * RRT_CRRT_STATUS + e_crcl_cl * CRCL_in_Lh * (1 - RRT_CRRT_STATUS), with CRCL in raw Cockcroft-Gault mL/min converted to L/h inside model(); 5/11 ECMO patients and 5/10 controls were on RRT).
• Notes: Distinct from RRT_HEMODIAL_STATUS (intermittent hemodialysis IHD only) and from RRT_HEMODIAL_ACTIVE (per-time-point session gate in within-subject time-varying dialysis-clearance models such as Liesenfeld 2013 dabigatran). Anticipated as a future canonical RRT_PERIT_DIAL_STATUS for peritoneal dialysis. Shekar 2014 ratification uses a mixed CVVH + EDD-f cohort because the source paper treats them identically as a single binary RRT covariate; a future paper that retains modality as a separate covariate (e.g. CVVH vs SLED vs CVVHDF) would either reuse RRT_CRRT_STATUS with finer per-modality columns layered on top, or warrant its own modality-specific canonical (RRT_CVVH_STATUS, RRT_SLED_STATUS, etc.). When pairing RRT_CRRT_STATUS with CRCL, note that Cockcroft-Gault CrCL is conventionally not defined / not reported for RRT-dependent subjects; Shekar 2014 records CrCL only for non-RRT subjects and the model formula switches off the CrCL term when RRT_CRRT_STATUS = 1. Ratified canonically on 2026-05-18 alongside the Shekar 2014 meropenem extraction. Renamed from CRRT_STATUS to RRT_CRRT_STATUS on 2026-06-19 per the canonical-register standardization audit (RRT family normalization).

ALB (canonical for serum albumin)

• Description: Serum albumin concentration.
• Units: g/L (SI; canonical as of 2026-06-19). US-convention papers report in g/dL; convert via ALB_gL = ALB_gdL * 10 on data ingestion. Models calibrated to g/dL values must apply an inline conversion in model() (e.g., alb_gdL <- ALB * 0.1 # SI g/L -> US-convention g/dL) so the structural coefficients stay aligned with their original calibration.
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (ALB / ref)^exponent.
• Source aliases:
  • BALB (baseline albumin) – used in Zhou_2021_belimumab.R. Maps directly to ALB; baseline-vs-time-varying status documented in per-model notes.
  • HSA (human serum albumin) – used in Fauchet_2015_lopinavir_unbound.R where the column name follows the paper’s protein-binding-equation notation distinguishing serum albumin from alpha-1 acid glycoprotein. Maps directly to canonical ALB; no value transformation. Reported in g/L; converted to umol/L inside model() via molecular weight 66500 g/mol for the K_HSA linear-binding term.
• Example models: Fasanmade_2009_infliximab.R (g/dL, reference 4.1), Thakre_2022_risankizumab.R (g/L, reference 45), Chua_2025_mirikizumab.R, Moein_2022_etrolizumab.R, Tiraboschi_2025_amlitelimab.R, Yamada_2025_zolbetuximab.R, Li_2019_abatacept.R (g/dL, reference 4.0; the Li 2019 Methods states ‘mg/dL’ which is a publication typo – see the model’s covariateData[[ALB]]$notes), Quartino_2019_trastuzumab.R (g/dL, reference 4; source column ALBU; negative exponent -0.998 on linear CL), Wang_2020_ontamalimab.R (g/L, reference 39), Zhou_2021_belimumab.R (g/L, reference 40; baseline-only, source column BALB), Okada_2025_rocatinlimab.R (g/L, reference 44; source column ALBU; power exponent -1.30 on linear CL), Xu_2020_daratumumab.R (g/L, reference 37.0; power exponent -1.149 on linear CL), Struemper_2017_belimumab.R (g/L, reference 41; baseline-only, source column BALB; power exponent -0.736 on linear CL), Fauchet_2015_lopinavir_unbound.R (g/L; source column HSA; enters the saturable-binding submodel via a linear K_HSA * [ALB] * Cunbound term with K_HSA = 0.036 L/umol, not as a power scaling on CL), Roepcke_2023_rezafungin.R (g/dL, reference 3.2; power exponent -0.708 on the shared peripheral volume V23 rather than on CL; model() applies the inline alb_gdL <- ALB * 0.1 SI-to-US conversion required by this entry’s Units note. Roepcke 2023 Table 2 prints the exponent magnitude without a sign and the footnote-a equation supplies the minus; the negative orientation is corroborated by Table 3, where the low-albumin patient cohort has the larger steady-state volume of distribution), Zhang_2025_abemaciclib_qsp.R (g/L, reference 31 = the source paper’s 3.1 g/dL mean in breast-cancer patients; ALB does NOT scale a clearance or a volume here – it scales the fraction unbound of four analytes through the paper’s Eq 2, fup = 1/(1 + B*ALB), so the effect is on the free concentration driving target engagement rather than on a disposition parameter).
• Notes: Ratified canonically on 2026-04-19 after cross-model review. Canonical units standardized to g/L (SI) on 2026-06-19 per the canonical-register standardization audit. The per-model covariateData[[ALB]]$units field is load-bearing for model-file source-trace; effect-coefficient magnitude is meaningless without the unit. Per-model conversion to US-convention g/dL (where the structural coefficients were calibrated) is required in model() via an inline alb_gdL <- ALB * 0.1 line - see the per-model notes for the conversion factor used. Conversion factor: 1 g/dL = 10 g/L.

TPRO (canonical for total serum protein)

• Description: Total serum protein concentration (sum of albumin + globulins; baseline or time-varying).
• Units: g/L (SI; canonical as of 2026-06-19). US-convention papers report in g/dL; convert via TPRO_gL = TPRO_gdL * 10 on data ingestion. Models calibrated to g/dL must apply an inline conversion in model() (e.g., tpro_gdL <- TPRO * 0.1). 1 g/dL = 10 g/L.
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (TPRO / ref)^exponent. Reference value observed: 74 g/L (Frey 2010 pooled-cohort median).
• Source aliases:
  • PROT – Frey 2010 abbreviation in the final-model equation.
  • TP – common clinical-chemistry abbreviation.
• Example models: Frey_2010_tocilizumab.R (g/L, reference 74; exponent -1.1 on V1).
• Notes: Distinct from ALB (serum albumin, the largest single component of total protein). Frey 2010 retains both TPRO and ALB on V1 as separate covariates with opposite signs (TPRO negative, ALB positive) and notes there is no clear mechanistic explanation; the joint effect may reflect serum-volume modifications. TPRO ratified canonically on 2026-04-28 alongside the Frey 2010 extraction.

FU (canonical for fraction unbound of parent drug in plasma supplied as a data column)

• Description: Fraction of the parent drug unbound in plasma, supplied to the model as a data column rather than as an internal parameter. Two established usages: (a) an individual subject’s measured fraction unbound, typically determined by ultrafiltration of a per-subject ex-vivo plasma sample (occasionally by equilibrium dialysis) – the founding Hamren 2008 usage; and (b) a scenario-level fraction unbound that a mechanistic / PBPK model varies across simulated patient types, where the paper assigns one fu value per scenario rather than per subject – the Abdullah-Koolmees 2024 usage. In both cases the value is an input the user supplies, not a quantity the model derives. Distinct from binding-protein concentrations (ALB / AAG / TPRO), which would derive fu indirectly through a binding equation – FU is the fraction itself.
• Units: % (percent; e.g., 0.1 = 0.1% fraction unbound = 0.001 expressed as a fraction) OR unitless fraction, depending on the source paper’s convention. The per-model covariateData[[FU]]$units field documents which, and MUST be set; effect-coefficient magnitudes and Kp scalings are meaningless without the unit.
• Type: continuous
• Scope: general
• Reference category: n/a – used with linear centering (1 + slope * (FU - ref)), with power scaling (FU / ref)^exponent, or directly as a multiplicative physiological factor (PBPK Kp = Kpu * FU and unbound-drug clearance terms). Reference values observed: 0.1% (Hamren 2008 tesaglitazar cohort median imputed for three subjects with missing fu measurements; range 0.06-0.2%); 0.42 unitless (Abdullah-Koolmees 2024 voriconazole, healthy / DDI scenarios at plasma albumin 40 g/L, with 0.49 in the ICU scenarios at albumin 30 g/L).
• Source aliases:
  • f_u – Hamren 2008 paper notation (italicised fu).
  • FRAC_UNBOUND – common NONMEM column-name form when the value is reported as a unitless fraction.
  • fup – Abdullah-Koolmees 2024 supplement R source (fup, fup_icu, fup_ddi); unitless fraction, same orientation as the canonical.
• Example models: Hamren_2008_tesaglitazar.R (%, reference 0.1%; linear centered effect 555 %/unit fu on the parent metabolic clearance CLmt – per-subject fu measurements at day 42 by ultrafiltration drive the realised CLmt range from approximately 1.5 to 3.0 L/h across the cohort fu range 0.06-0.2%; founding example), AbdullahKoolmees_2024_voriconazole_pbpk.R (unitless fraction, scenario-level; enters twice in a whole-body PBPK – once converting every tissue Kpu to a tissue:plasma Kp via Kp = Kpu * FU, and once selecting the unbound drug presented to saturable hepatic CYP metabolism and to renal clearance).
• Notes: Rare in popPK literature – only papers that do per-subject ultrafiltration measurements report the per-subject form. Most popPK papers either (a) assume a single fixed population-typical fu, in which case use a parameter-side fu paper-named parameter rather than this covariate, or (b) parameterise the effect through measured binding-protein concentrations (ALB, AAG, TPRO). Use FU when the fu value is a per-record model input the user is expected to supply – either because the paper measured it per subject, or because the paper varies it across named simulation scenarios. Promoted from specific to general alongside the Abdullah-Koolmees 2024 voriconazole whole-body PBPK extraction, the second model to use FU with consistent “fraction unbound of parent in plasma, supplied as data” semantics across two distinct mechanistic uses. Ratified canonically on 2026-06-17 alongside the Hamren 2008 tesaglitazar extraction (per operator decision in sidecar request 001).

CSF_TPRO (canonical for cerebrospinal-fluid total protein)

• Description: Total protein concentration measured in cerebrospinal fluid (baseline or time-varying). CNS-compartment analogue of TPRO; used in popPK models of CSF-penetrating drugs as a surrogate for blood-brain-barrier integrity (elevated CSF protein indicates inflammation or barrier breakdown and typically correlates with increased CNS penetration of small-molecule drugs).
• Units: g/L (SI; canonical as of 2026-06-19). US-convention reporting in mg/dL converts via CSF_TPRO_gL = CSF_TPRO_mgdL * 0.01. 1 g/L = 100 mg/dL.
• Type: continuous
• Scope: general
• Reference category: n/a – enters either as an additive term on the logit-scale CSF uptake / barrier parameter, or as a power scaling (CSF_TPRO / ref)^exponent on a penetration fraction. Reference value observed: 1.2 g/L (Germovsek 2018 typical-infant value; sick-neonate cohort median).
• Source aliases:
  • CSF_protein – Germovsek 2018 paper notation.
  • CSFPROT / CSF_PROT – compact column-name forms common in NONMEM control streams.
• Example models: Germovsek_2018_meropenem.R (g/L, reference 1.2; additive on the logit CSF barrier parameter with coefficient theta_CSFproteins = -0.17 per g/L deviation from 1.2; ratified canonically on 2026-05-21 alongside the Germovsek 2018 meropenem extraction).
• Notes: Distinct from TPRO (serum total protein) – the two are biologically independent because the blood-brain barrier prevents free equilibration of serum protein into CSF. Normal CSF protein is approximately 0.15-0.45 g/L in healthy adults; sick neonates and meningitis patients can reach several g/L. The covariate is typically time-varying because CSF protein evolves over the course of CNS inflammation; missing values are commonly imputed to the cohort median when the source paper does not report a per-sample CSF protein measurement.

IGG (canonical for serum immunoglobulin G)

• Description: Serum total immunoglobulin G concentration (baseline or time-varying). Used in mAb PK analyses as a competition-for-FcRn-recycling covariate on therapeutic-mAb clearance – high endogenous IgG is hypothesized to displace the therapeutic mAb from FcRn salvage and increase its catabolic clearance.
• Units: g/L (SI; canonical as of 2026-06-19). US-convention papers report in mg/dL; convert via IGG_gL = IGG_mgdL * 0.01 on data ingestion. 1 g/L = 100 mg/dL. Models calibrated to mg/dL must apply an inline conversion in model() (e.g., igg_mgdL <- IGG * 100).
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (IGG / ref)^exponent. Reference values observed: 14.8 g/L (Zhou 2021), 9.65 g/L (Yang 2021).
• Source aliases:
  • BIGG (baseline IgG) – used in Zhou_2021_belimumab.R.
  • IGGBL (baseline IgG) – used in Yang_2021_cemiplimab.R.
• Example models: Zhou_2021_belimumab.R (g/L, reference 14.8; baseline-only; exponent 0.293 on CL), Yang_2021_cemiplimab.R (g/L, reference 9.65; small positive exponent 0.184 on shared CL/Q), Struemper_2017_belimumab.R (g/L, reference 13.7; baseline-only; exponent 0.347 on CL).
• Notes: Mechanistically meaningful for monoclonal-antibody PK because endogenous IgG competes with the therapeutic mAb for FcRn-mediated recycling. The per-model covariateData[[IGG]]$units field is load-bearing (1 g/L ~= 100 mg/dL). Baseline-vs-time-varying status documented in covariateData[[IGG]]$notes. Distinct from lIgG0 / IgG-as-a-state in mechanistic FcRn-competition TMDD models (e.g., Valenzuela_2025_nipocalimab.R), where IgG is a dynamic state, not a baseline covariate; use IGG only when the source paper treats IgG as a static (baseline) covariate column.

IGM (canonical for serum immunoglobulin M)

• Description: Serum total immunoglobulin M (IgM) concentration (baseline). Used in IgRT population-PK analyses as a proxy for B-cell antibody-producing capacity / humoral function – IgM is the first antibody produced after B-cell activation, so circulating IgM reflects ongoing B-cell activity prior to class-switching to IgG.
• Units: g/L (SI; canonical as of 2026-06-19). US-convention reporting in mg/dL converts via IGM_gL = IGM_mgdL * 0.01. 1 g/L = 100 mg/dL.
• Type: continuous
• Scope: specific
• Reference category: n/a – used with power scaling (IGM / ref)^exponent. Reference values observed: 0.21 g/L (Cheng 2026, pooled PID + SAD pediatric cohort median).
• Source aliases: none known.
• Example models: Cheng_2026_immunoglobulin.R (g/L, reference 0.21; baseline-only; power exponent 0.11 on baseline IgG (CBAS) – IgM enters as a humoral-capacity proxy that informs the endogenous-IgG baseline rather than directly modifying clearance).
• Notes: IgM is the immune-globulin class produced by activated B cells before class-switching, so it remains detectable in patients with hypogammaglobulinaemia who still have residual B-cell function. Scope: specific because the relevance of IgM as a covariate depends on the paper’s mechanistic interpretation (in Cheng 2026 it acts on the endogenous-IgG baseline; future use cases may differ). Promote to general if a second paper retains IgM with consistent semantics. Ratified canonically on 2026-04-28.

TBILI (canonical for total bilirubin)

• Description: Total serum bilirubin concentration.
• Units: umol/L (SI; canonical as of 2026-06-19). US-convention papers report in mg/dL; convert via TBILI_umolL = TBILI_mgdL * 17.1 on data ingestion. 1 mg/dL = 17.1 umol/L. Models calibrated to mg/dL must apply an inline conversion in model() (e.g., tbili_mgdL <- TBILI / 17.1 # SI umol/L -> US-convention mg/dL).
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (TBILI / ref)^exponent.
• Source aliases:
  • BIL (legacy NONMEM short label for total bilirubin) – used in NA_NA_lidocaine.R (DDMODEL00000281; binarised at threshold 0.53 mg/dL with BIL_HIGH = as.integer(BIL > 0.53)).
  • BILT (Urien 2005 capecitabine paper’s NONMEM short label for “total bilirubin”) – used in Urien_2005_capecitabine.R (umol/L, reference 8.8; power scaling on the capecitabine non-transformation CL10 and on the 5’-DFUR -> 5-FU rate constant K34).
• Example models: Yamada_2025_zolbetuximab.R (mg/dL, reference 0.38; small positive exponent 0.0347 on V1), NA_NA_lidocaine.R (mg/dL, source column BIL; binary effect at threshold 0.53 mg/dL on the GX elimination rate constant K30), Urien_2005_capecitabine.R (umol/L, reference 8.8; source column BILT; positive exponent +0.32 on capecitabine non-transformation CL10 and negative exponent -0.36 on the 5’-DFUR -> 5-FU rate constant K34).
• Notes: Hepatic-function marker. Unit varies by paper (US convention mg/dL, SI convention umol/L; 1 mg/dL ~= 17.1 umol/L). The per-model covariateData[[TBILI]]$units field is load-bearing.

DBIL (canonical for direct (conjugated) bilirubin)

• Description: Direct (conjugated) serum bilirubin concentration. Distinct from TBILI: direct bilirubin is the water-soluble glucuronide-conjugated fraction processed by hepatocytes and excreted in bile, so a rise in DBIL specifically flags impaired biliary excretion / cholestasis or intrahepatic shunting, whereas total bilirubin also captures unconjugated (indirect) hyperbilirubinaemia from haemolysis or Gilbert-type conjugation defects.
• Units: umol/L (SI; canonical as of 2026-06-19). US-convention papers report in mg/dL; convert via DBIL_umolL = DBIL_mgdL * 17.1 on data ingestion. 1 mg/dL = 17.1 umol/L. Models calibrated to mg/dL must apply an inline conversion in model() (e.g., dbil_mgdL <- DBIL / 17.1).
• Type: continuous
• Scope: specific
• Reference category: n/a – used with power scaling (DBIL / ref)^exponent. Reference values observed: 2.6 umol/L (Chen 2015 voriconazole Chinese ICU cohort population median).
• Source aliases: none known.
• Example models: Chen_2015_voriconazole.R (umol/L, reference 2.6; negative exponent -0.40 on CL: CL = TVCL * (DBIL / 2.6)^-0.40).
• Notes: Hepatic-function / cholestasis-specific marker. Unit varies by paper (US convention mg/dL, SI convention umol/L; 1 mg/dL ~= 17.1 umol/L). Distinct entry from TBILI because direct vs total are not interchangeable: total = direct + indirect, and the two fractions track different pathophysiologic processes. Scope kept specific pending a second model that ratifies DBIL with consistent semantics; promote to general once corroborated.

AST (canonical for aspartate aminotransferase)

• Description: Serum aspartate aminotransferase activity (baseline or time-varying).
• Units: U/L (SI; canonical as of 2026-06-19; IU/L is used interchangeably). Document per-model via covariateData[[AST]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (AST / ref)^exponent.
• Source aliases:
  • SGOT (serum glutamic-oxaloacetic transaminase; the legacy clinical-chemistry name for AST) – used in Quartino_2019_trastuzumab.R.
• Example models: Lu_2014_trastuzumabemtansine.R (U/L, reference 27; small positive exponent 0.071 on CL), Quartino_2019_trastuzumab.R (IU/L, reference 24; source column SGOT; positive exponent 0.205 on linear CL).
• Notes: Hepatic-function marker. Commonly reported alongside ALT and TBILI; register a separate ALT canonical if a future paper requires it. SGOT is the older lab-reporting name; values and units are identical to AST.

ALT (canonical for alanine aminotransferase)

• Description: Serum alanine aminotransferase activity (baseline or time-varying).
• Units: U/L (SI; canonical as of 2026-06-19; IU/L is used interchangeably). Document per-model via covariateData[[ALT]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (ALT / ref)^exponent.
• Source aliases:
  • SGPT (serum glutamic-pyruvic transaminase; the legacy clinical-chemistry name for ALT, paralleling SGOT -> AST) – used in NA_NA_lidocaine.R (DDMODEL00000281; binarised at threshold 11 with SGPT_HIGH = as.integer(SGPT > 11)).
• Example models: Nikanjam_2019_siltuximab.R (U/L, reference 19; small negative exponent -0.096 on CL), Melhem_2022_dostarlimab.R (U/L, reference 18; small negative exponent -0.0585 on CL, time-varying), NA_NA_lidocaine.R (source column SGPT; binary effects at threshold 11 on the GX rate constant K30 and on the 2,6-xylidide rate constant K40).
• Notes: Hepatic-function marker. Commonly reported alongside AST and TBILI. Ratified canonically on 2026-04-24. SGPT is the older lab-reporting name; values and units are identical to ALT.

ALP (canonical for alkaline phosphatase)

• Description: Serum alkaline phosphatase activity (baseline or time-varying). Liver-function / cholestasis marker; often used in popPK covariate models either as a continuous concentration with power scaling or as a binary above/below upper-limit-of-normal (ULN) indicator. When binarized inline, document the ULN threshold used (typically ~120 U/L for adults; varies by lab, age, and sex).
• Units: U/L (SI; canonical as of 2026-06-19; IU/L is used interchangeably). Document per-model via covariateData[[ALP]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (ALP / ref)^exponent, with a linear-deviation form, or binarized inline as alp_high <- (ALP > uln) for a binary >ULN indicator.
• Source aliases: none known.
• Example models: Gupta_2016_lenvatinib.R (binarized inline as alp_high <- (ALP > 120); the source paper enters ALP as a 0/1 NONMEM indicator with ALP = 1 when the ratio ALP/ULN > 1; multiplicative effect on CL/F: 0.883^alp_high), Wada_2023_sparsentan.R (continuous power scaling on CL/F normalised to the 68 U/L overall cohort median: (ALP / 68)^-0.208; elevated alkaline phosphatase lowers apparent clearance, and ALKP was the only one of four hepatic markers – ALKP, ALT, AST, total bilirubin – retained after backward elimination in a study where hepatic impairment itself was not tested as a covariate).
• Notes: Liver-function / cholestasis marker; routine clinical-chemistry covariate. Commonly tested alongside ALT / AST / GGT / TBILI. Ratified canonically alongside the Gupta 2016 lenvatinib extraction.

PHOS (canonical for serum phosphate)

• Description: Serum inorganic phosphate concentration, reported clinically as elemental phosphorus (baseline or time-varying). A routine serum-chemistry analyte and the pharmacodynamic biomarker of fibroblast growth factor receptor (FGFR) inhibition: FGFR1 blockade suppresses FGF23 signalling in the proximal tubule, reducing urinary phosphate excretion and raising serum phosphate, so on-treatment hyperphosphatemia is an on-target class effect of the FGFR-inhibitor drug class. Used both as a baseline covariate (predicting the on-treatment rise) and, in its change-from-baseline form, as an exposure surrogate.
• Units: mmol/L (SI; canonical). US-convention papers report in mg/dL; convert via PHOS_mmolL = PHOS_mgdL * 0.3229 on data ingestion (elemental phosphorus, atomic mass 30.974 g/mol, so 1 mg/dL = 0.32285 mmol/L and 1 mmol/L = 3.0975 mg/dL). Models calibrated against mg/dL values must either apply an inline conversion in model() (e.g., phos_mgdL <- PHOS * 3.0975) or carry the coefficient rescaled to per-mmol/L units, with the conversion shown in the model file – the same treatment ALB receives (see the ALB entry). Adult reference range is roughly 2.5-4.5 mg/dL (0.81-1.45 mmol/L).
• Type: continuous
• Scope: general
• Reference category: n/a – entered so far in an uncentered linear form theta * PHOS. Reference values observed: baseline serum phosphate was not tabulated by Gong 2023, but the observed quartile boundaries of the change from baseline at 13.5 mg once daily were 0.5, 2.1, 2.7, 3.5, and 6.3 mg/dL (0.16, 0.68, 0.87, 1.13, and 2.03 mmol/L; Gong 2023 Figure 3a).
• Source aliases:
  • PHOS – the NONMEM $INPUT column name in the Gong 2023 Appendix S1 control stream, in mg/dL. Maps directly to the canonical name; unit conversion to SI mmol/L is required.
  • PO4 / PHOSPHORUS / IPHOS (inorganic phosphorus) – common clinical-laboratory reporting names for the same analyte.
• Example models: Gong_2023_pemigatinib_phosphate.R (mmol/L; uncentered linear coefficient -0.5730 mg/dL per mmol/L on the change-from-baseline endpoint, rescaled from the published -0.185 per mg/dL; the model’s endpoint is retained in the paper’s mg/dL so Table S2 remains usable verbatim).
• Notes: Distinct from ALP (alkaline phosphatase, an enzyme activity) and from CPK (creatine phosphokinase) despite the shared word stem – PHOS is the electrolyte. Housed in the renal / hepatic function section because phosphate homeostasis is renally regulated and because source papers report it inside the same serum-chemistry covariate panel as ALB / ALP / ALT / AST / TBILI (Gong 2023 tests exactly that panel); the register has no separate electrolyte section. When a source paper models the change from baseline in serum phosphate as an endpoint rather than as a covariate, that is a model output, not a covariate column – register only the baseline (or time-varying) input value here. Ratified canonically alongside the Gong 2023 pemigatinib extraction (operator decision, sidecar request 001 q1: canonical units SI mmol/L, matching the 2026-06-19 SI standardization applied to ALB).

GGT (canonical for gamma-glutamyltransferase)

• Description: Serum gamma-glutamyltransferase activity (baseline or time-varying); hepatic / cholestatic biliary-enzyme marker.
• Units: U/L (SI; canonical as of 2026-06-19; IU/L is used interchangeably). Document per-model via covariateData[[GGT]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – used with linear-deviation form 1 + theta * (GGT - ref) or power scaling (GGT / ref)^exponent. Reference values observed: 33 U/L (Retlich 2015 popPK linagliptin median), 32.3 U/L (Retlich 2015 popPK/PD linagliptin median).
• Source aliases: none known.
• Example models: Retlich_2015_linagliptin.R (U/L, reference 33; linear-deviation effect on linagliptin CL with coefficient -0.0339 % per U/L deviation. The PK/PD layer uses GGT (reference 32.3 U/L) as a piecewise covariate on baseline DPP-4 activity BSL with a linear-deviation effect below GGT = 175 U/L and a constant +21.3% effect above the threshold).
• Notes: Liver-function / cholestasis marker; routine clinical-chemistry covariate. Commonly tested alongside ALT / AST / ALP / TBILI. The piecewise above/below-threshold form in Retlich 2015 reflects empirical saturation of the GGT-vs-DPP-4-activity relationship at extreme values. Ratified canonically alongside the Retlich 2015 linagliptin extraction.

LDH (canonical for serum lactate dehydrogenase)

• Description: Serum lactate dehydrogenase activity (baseline or time-varying). General-purpose marker of tissue / cellular turnover; in oncology PK analyses it is interpreted as a disease-burden / cell-turnover proxy.
• Units: U/L (SI; canonical as of 2026-06-19; IU/L is used interchangeably). Document per-model via covariateData[[LDH]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (LDH / ref)^exponent or with an additive linear-on-log form exp(coef * (log(LDH) - log(ref))) (algebraically equivalent to (log(LDH) / log(ref))^coef). Reference values observed: 217 U/L (Sanghavi 2020).
• Source aliases:
  • BLDH (baseline LDH) – used in Sanghavi_2020_ipilimumab.R.
• Example models: Sanghavi_2020_ipilimumab.R (linear-on-log form on CL with reference 217 U/L; coefficient 0.703), NA_NA_lidocaine.R (DDMODEL00000281; binary stratification at threshold 195 U/L switching the typical-value baseline of the 2,6-xylidide rate constant K40).
• Notes: Universal lab marker. Sanghavi 2020 log-transforms LDH because the distribution is heavily right-skewed (range 74-6,245 U/L over a median of 217); other papers may use a simple (LDH/ref)^exponent form. Document the functional form in covariateData[[LDH]]$notes.

AMYL (canonical for blood amylase activity)

• Description: Blood (serum or plasma) total amylase activity (baseline or time-varying). Routine clinical-chemistry enzyme, conventionally read as a pancreatic / salivary marker, but amylase is also cleared renally and therefore accumulates as glomerular filtration falls. In population PK analyses of renally-impaired cohorts it is used in the latter role – as a surrogate for uremic burden rather than for pancreatic disease – so the direction of any effect should be interpreted against the paper’s stated mechanism, not assumed to be pancreatic.
• Units: U/L (SI; canonical, matching the AST / ALT / ALP / GGT / LDH enzyme family; IU/L is used interchangeably). Document per-model via covariateData[[AMYL]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (AMYL / ref)^exponent. Reference values observed: 59.5 IU/L (Kim 2025 healthy-subject median).
• Source aliases:
  • Amyl – Kim 2025 Table 1 / Table 2 and NONMEM $INPUT column abbreviation.
• Example models: Kim_2025_evogliptin.R (IU/L, reference 59.5; positive exponent 0.363 on relative bioavailability F1, paired with TRIG; both markers rise with worsening renal impairment and jointly express the paper’s finding that uremia inhibits CYP3A4-mediated first-pass metabolism and so raises oral bioavailability).
• Notes: Routine clinical-chemistry covariate; follows the bare-abbreviation naming of the established enzyme canonicals AST, ALT, ALP, GGT, and LDH. Distinct from lipase, which is the companion pancreatic enzyme and was recorded but not tested in Kim 2025 – register a separate LIPASE canonical if a future paper retains it. Because amylase can move for either pancreatic or renal reasons, covariateData[[AMYL]]$notes should state which interpretation the source paper intends. Ratified canonically alongside the Kim 2025 evogliptin extraction.

HEPIMP_MILD (canonical for mild hepatic impairment indicator)

• Description: 1 = mild hepatic impairment per the National Cancer Institute Organ Dysfunction Working Group (NCI ODWG) criteria, 0 = normal hepatic function or non-mild category. NCI ODWG mild = total bilirubin <= ULN with AST > ULN, OR total bilirubin > 1.0xULN to <= 1.5xULN with any AST.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (normal hepatic function; the moderate / severe categories are typically pooled into the reference for population PK analyses where mild impairment is the only category with non-trivial sample size).
• Source aliases:
  • HEPIMP (with values 1 = mild / 0 = others) – used in Lin_2024_casirivimab.R.
  • Child-Pugh A – used in Desai_2016_isavuconazole.R and in the Luo_2024_*_pbpk.R CES1 semi-PBPK family, where the three HEPIMP_MILD / HEPIMP_MOD / HEPIMP_SEV indicators are mutually exclusive and select the Child-Pugh A / B / C physiology columns respectively, with all three 0 selecting the normal-hepatic-function column.
• Example models: Lin_2024_casirivimab.R (NCI ODWG mild; multiplicative fractional change on CL), Lu_2022_patritumab.R (NCI ODWG mild; paired with HEPIMP_MOD_OR_MISSING; multiplicative fractional effect 0.706 on CLDXd for mild impairment vs the normal-hepatic-function reference), Desai_2016_isavuconazole.R (Child-Pugh A; log-additive shift e_hepimp_mild_cl = log(1.55 / 2.54) = -0.494 on CL and e_hepimp_mild_q = log(38.8 / 33.678) = +0.142 on Q vs the healthy reference, paired with HEPIMP_MOD for the parallel Child-Pugh B stratum).
• Notes: Use this column when a model dichotomizes hepatic-impairment status as “mild vs. others” (i.e., normal + the rare moderate/severe cases pooled into the reference). For models that test moderate or severe as separate categories, register additional canonicals HEPIMP_MOD / HEPIMP_SEV rather than overloading this entry.

HEPIMP_MOD_OR_MISSING (canonical for composite moderate-or-data-missing hepatic impairment indicator)

• Description: 1 = moderate hepatic impairment per the NCI ODWG criteria OR baseline hepatic-function data missing/unknown; 0 = normal hepatic function or any other (non-moderate, non-missing) category. Composite indicator used by source papers that pool the moderate-impairment subgroup with patients whose hepatic-function data are missing because both subgroups are individually too small to estimate as separate effects.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (normal hepatic function; mild impairment is typically captured by a separate HEPIMP_MILD indicator paired with this column, so all-zero corresponds to NCI ODWG group 1 = normal).
• Source aliases:
  • HEPIMP_MOD_MISSING – prior canonical name (pre-2026-06-19 OR-disambiguation standardization).
  • HEPATIC / HEPATIC_MOD_MISSING – informal NONMEM names for the composite group.
• Example models: Lu_2022_patritumab.R (paired with HEPIMP_MILD; multiplicative fractional effect 0.532 on CLDXd; the composite group pools n = 6 moderate-impairment patients with n = 6 missing/unknown patients per Lu 2022 Table S5).
• Notes: Specific scope because the composition of the “moderate or missing” group is paper-defined and the missing/unknown subgroup may have a different distribution of true hepatic-function status across studies. Use only when the source paper explicitly pools the moderate-impairment cases with missing-data cases under a single coefficient; for models that estimate moderate impairment separately (without pooling missing data), use the HEPIMP_MOD canonical instead. Ratified canonically on 2026-04-28. Renamed from HEPIMP_MOD_MISSING to HEPIMP_MOD_OR_MISSING on 2026-06-19 per the canonical-register standardization audit (operator decision: insert OR so the composite-group meaning is explicit in the name; the prior form could be misread as “moderate, with missing-data flag” rather than “moderate OR missing pooled”).

B2M (canonical for serum beta-2-microglobulin)

• Description: Serum beta-2-microglobulin concentration. Low-molecular-weight (~12 kDa) protein freely filtered at the glomerulus and reabsorbed in the proximal tubule; serum levels rise with renal impairment, with increased plasma-cell turnover in multiple myeloma, and with broader lymphoid-cell turnover. Used in oncology PK analyses both as a renal-function proxy and as a tumor-burden / disease-severity covariate.
• Units: mg/L
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (B2M / ref)^exponent. Reference values observed: 3.90 mg/L (Fau 2020 multiple-myeloma cohort median).
• Source aliases: none; B2M is the universal abbreviation.
• Example models: Fau_2020_isatuximab.R (mg/L, reference 3.90; exponent 0.343 on the steady-state linear clearance CLinf).
• Notes: In multiple myeloma B2M is part of the International Staging System (ISS); in routine PK analyses it is interpreted simultaneously as a renal-function and disease-burden marker. Document the interpretation per-model via covariateData[[B2M]]$notes.

CYSC (canonical for serum cystatin C)

• Description: Serum cystatin C concentration. Low-molecular-weight (~13 kDa) protease-inhibitor protein produced at a near-constant rate by all nucleated cells; freely filtered at the glomerulus and almost entirely reabsorbed and catabolized in the proximal tubule, so serum cystatin C reflects glomerular filtration rate (GFR) more directly than serum creatinine. Used as a renal-function covariate on the clearance of renally-eliminated drugs, especially in populations where creatinine-based estimates of GFR are unreliable (low muscle mass, normal-creatinine concentrations masking impaired GFR, ICU patients).
• Units: mg/L
• Type: continuous
• Scope: general
• Reference category: n/a – used with either power scaling (CYSC / ref)^exponent (Chung 2013) or centred-linear scaling on the reciprocal 1 + e * (1/CYSC - ref_inv) (Viberg 2006). Reference values observed: 0.91 mg/L (Chung 2013 vancomycin Korean adults with SCr <= 1.2 mg/dL; cohort median); 1.32 mg/L equivalent to 1/CYSC = 0.758 (mg/L)^-1 (Viberg 2006 cefuroxime adult patients with broad renal-function range; population-typical).
• Source aliases:
  • Cystatin C / cystatin – Chung 2013 paper narrative and Table 2 footnote.
  • CysC – Viberg 2006 paper narrative and Table 4 footnote.
• Example models: Chung_2013_vancomycin.R (mg/L, reference 0.91; power exponent -0.780 on CL: CL_pop * (CYSC / 0.91)^-0.780), Viberg_2006_cefuroxime.R (mg/L; centred-linear effect on 1/CYSC with coefficient 1.43 per (mg/L)^-1 and reference 0.758 (mg/L)^-1 on CL: CL_pop * (1 + 1.43 * (1/CYSC - 0.758))).
• Notes: Cystatin C is freely filtered at the glomerulus and is not secreted by the renal tubule (unlike creatinine), so it is less sensitive to muscle mass, body composition, and tubular-secretion blockers. Reference ranges 0.57-0.97 mg/L for adult females and 0.65-1.10 mg/L for adult males (Chung 2013 Methods; Roche Cobas 6000 particle-enhanced immunoturbidimetric assay). Distinct from CREAT (serum creatinine) – the two are commonly reported alongside each other and can enter the same model as separate covariates (as in Chung 2013, where CYSC explains 62% of CL variability vs SCr 13%). The functional form (power on CYSC vs centred-linear on 1/CYSC) is paper-specific and lives in the model file; the canonical column is the underlying biomarker concentration in mg/L.

HEPIMP (canonical for hepatic-impairment indicator (NCI ODWG classification))

• Description: Baseline hepatic-impairment indicator per the National Cancer Institute Organ Dysfunction Working Group (NCI ODWG) classification: 1 = mild or worse hepatic impairment (group >= 2 = mild, moderate, or severe), 0 = normal hepatic function (group 1).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (normal hepatic function, NCI ODWG group 1).
• Source aliases:
  • BHPTGRPN (categorical: 1 = normal, 2 = mild, 3 = moderate, 4 = severe; 9999 = missing) – used in Lu_2019_polatuzumab.R. Decompose: HEPIMP = as.integer(BHPTGRPN > 1.5 & BHPTGRPN != 9999).
  • HEP_IMP – retired canonical name; replaced by HEPIMP for consistency with the HEPIMP_MILD family.
• Example models: Lu_2019_polatuzumab.R (multiplicative effect on FRAC_NS = 1.19, applied as 1.19^HEPIMP).
• Notes: NCI ODWG classification (Ramalingam SS et al., J Clin Oncol 2010;28:4507) groups subjects by total bilirubin and AST: group 1 = normal, group 2 = mild (TBILI <= ULN and AST > ULN, or TBILI > 1-1.5 x ULN), group 3 = moderate (TBILI > 1.5-3 x ULN), group 4 = severe (TBILI > 3 x ULN). Source papers typically pool groups 2-4 versus group 1 for a binary indicator because the impaired-liver subgroups are individually small. If a future model needs finer resolution (separate effects for mild vs moderate-or-worse), add a parallel HEPIMP_MOD canonical rather than overloading this one.

NASF (canonical for nonalcoholic steatohepatitis severity score combining NAS and fibrosis staging)

• Description: Integer composite severity score for nonalcoholic steatohepatitis (NASH), summing the NAFLD activity score (NAS: steatosis 0-3 + hepatocyte ballooning 0-2 + lobular inflammation 0-3, total 0-8) and the fibrosis staging score (0 = absent, 1 = perisinusoidal / pericellular, 2 = periportal, 3 = bridging, 4 = cirrhosis). Total range 0-12; healthy subjects without biopsy-confirmed NASH are assigned NASF = 0 by convention. Scores below 5 reflect a benign form of NAFLD; scores >= 5 reflect biopsy-confirmed NASH.
• Units: (count, 0-12)
• Type: count
• Scope: specific
• Reference category: n/a – used with a linear effect on log(NASF / 4) for NASF >= 4 and zero contribution for NASF < 4 (so NASF = 4 and any NASF < 4 reduce to the typical-value reference). The cutoff of 4 distinguishes patients with a benign form of NAFLD from those with biopsy-confirmed NASH (Pierre 2017 Methods ‘Covariate analysis’ and references 31 and 34).
• Source aliases:
  • NASF – used in Pierre_2017_morphine.R (Pierre 2017 Methods ‘Covariate analysis’).
• Example models: Pierre_2017_morphine.R (linear effect on log(NASF / 4) for NASF >= 4 with coefficient -0.628 on M3G clearance: CL_M3G_i = CL_M3G_pop * (1 + e_nasf_cl_m3g * log(NASF / 4)) for NASF >= 4 and CL_M3G_i = CL_M3G_pop for NASF < 4; higher NASF reduces M3G clearance via reduced biliary excretion and increased basolateral efflux of M3G into systemic circulation).
• Notes: The NAFLD activity score (NAS) component is the histology score described by Bondini 2007 / Kleiner 2005 and references therein; the fibrosis staging is the Brunt / NASH-CRN system. The combined NASF score is the noninvasive staging proposed by Santiago-Rolon 2015 (Proc R Health Sci J 34:189-194) and used by Angulo 2007 as the NAFLD Fibrosis Score cutoff. Scope: specific because the precise cutoff (NASF >= 4) and the linear-on-log functional form are Pierre 2017’s modeling choice; future papers may model NASF or its components differently. Distinct from HEPIMP* (NCI ODWG oncology-trial hepatic-impairment categories) and from continuous liver enzymes (ALT, AST, ALP) – NASF is a biopsy-derived disease-severity ordinal specific to NAFLD / NASH. Ratified canonically on 2026-05-18 alongside the Pierre 2017 morphine extraction.

HEPIMP_SEV (canonical for severe hepatic impairment indicator)

• Description: 1 = severe hepatic impairment, 0 = normal hepatic function or less-than-severe category. The classification scheme that defines “severe” is paper-specific and must be documented in per-model covariateData[[HEPIMP_SEV]]$notes. Two schemes are commonly encountered:
  • NCI ODWG group 4: total bilirubin > 3 x ULN with any AST (Ramalingam SS et al., J Clin Oncol 2010;28:4507).
  • Child-Pugh Class C: composite score 10-15 across bilirubin, albumin, INR, ascites, and encephalopathy.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (any non-severe category: normal, mild, or moderate; the model typically uses HEPIMP_SEV alongside other severity-specific indicators that partition the non-severe pool further).
• Source aliases:
  • Child-Pugh Class C – used in vanderWalt_2013_dapagliflozin.R (covariate effects on CLP_M15 and V2M; the paper dichotomizes severe hepatic impairment per the Child-Pugh classification).
• Example models: vanderWalt_2013_dapagliflozin.R (Child-Pugh Class C; multiplicative fractional effects -0.422 on the dapagliflozin -> D3OG metabolic clearance and +1.33 on the D3OG central volume of distribution; paper text “With severe HI (Child-Pugh Class C), CLP M15 decreased by 41% and V2M increased by 134%”), Comisar_2025_rimegepant.R (multiplicative fractional effect on CL/F = -0.423 per Comisar 2025 Table 3, a 42.3% clearance decrease giving a 1.7-fold rise in simulated steady-state AUCtau, which supports the label recommendation to avoid rimegepant in severe hepatic impairment; paired with HEPIMP_MOD (-0.229) as two mutually exclusive indicators sharing a normal-OR-mild reference group; classification scheme not stated in the paper).
• Notes: Use this column when a model dichotomizes severe hepatic impairment as a separate indicator from milder categories. The classification scheme (NCI ODWG vs Child-Pugh vs other) is paper-specific and must be documented per-model. For composite “moderate-or-severe” pooled indicators, use the parallel HEPIMP_MODSEV canonical rather than overloading this entry. Companion to HEPIMP_MILD (mild only) and HEPIMP_MODSEV (moderate + severe pooled); the SKILL.md anticipates each severity level as its own canonical when the source paper tests them as separate covariates.

HEPIMP_MODSEV (canonical for composite moderate-or-severe hepatic impairment indicator)

• Description: 1 = moderate or severe hepatic impairment, 0 = normal hepatic function or mild impairment. Composite indicator used by source papers that pool the moderate and severe subgroups because the severe subgroup alone is too small to support a separate covariate-effect estimate. Distinct from HEPIMP_MOD_OR_MISSING (which pools moderate cases with missing-data cases, not with severe cases). The classification scheme that defines the cut points is paper-specific and must be documented in per-model covariateData[[HEPIMP_MODSEV]]$notes. Two schemes are commonly encountered:
  • NCI ODWG groups 3-4 pooled: total bilirubin > 1.5 x ULN with any AST.
  • Child-Pugh Class B or C pooled: composite score >= 7.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (normal hepatic function or mild impairment; the indicator is mutually exclusive with HEPIMP_MILD, so all-zero on both indicators corresponds to the normal-function reference and HEPIMP_MILD=1 with HEPIMP_MODSEV=0 corresponds to mild-only).
• Source aliases:
  • Child-Pugh Class B,C – used in vanderWalt_2013_dapagliflozin.R (covariate effects on V3P and CLM; the paper dichotomizes moderate-or-severe hepatic impairment per the Child-Pugh classification).
• Example models: vanderWalt_2013_dapagliflozin.R (Child-Pugh Class B or C; multiplicative fractional effects -0.600 on the dapagliflozin peripheral volume of distribution V3P and -0.293 on the D3OG renal clearance CLM; paper text “Moderate or severe HI (Child-Pugh Class B or C) decreased CLM and the peripheral volume of distribution of dapagliflozin (V3P) by 29 and 60%, respectively”).
• Notes: Use this column when a model pools moderate-and-severe hepatic impairment under a single coefficient (typically because the severe subgroup alone is too small to estimate as its own effect). The classification scheme (NCI ODWG vs Child-Pugh vs other) is paper-specific and must be documented per-model. Companion to HEPIMP_MILD (mild only) and HEPIMP_SEV (severe only). Distinct from HEPIMP_MOD_OR_MISSING (which pools moderate cases with subjects whose hepatic-function data are missing/unknown, not with severe cases). The composite mod-or-sev pooling is a different load-bearing convention than the mod-or-missing pooling, so the two canonicals must remain separate.

LIVER_RESECT_MAJOR (canonical for major hepatic-resection indicator)

• Description: 1 = major hepatic resection (three or more liver segments resected), 0 = minor hepatic resection (two liver segments resected). Time-fixed per subject, describing the surgical exposure rather than a chronic hepatic-impairment state. Distinct from the HEPIMP_* family (which classifies chronic liver disease by NCI ODWG / Child-Pugh criteria) because major hepatic resection produces a transient reduction in metabolic capacity that recovers within 2-3 weeks in patients with normal underlying liver function (Ollier 2015 Discussion citing Fortner et al., Ann Surg 1987).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (minor resection; two liver segments)
• Source aliases:
  • NSH – used in Ollier_2015_ropivacaine.R (Ollier 2015 paper notation, dichotomised: NSH = 0 for 2 segments, NSH = 1 for 3, 4 or 5 segments per Methods ‘Covariate model’).
• Example models: Ollier_2015_ropivacaine.R (multiplicative exponential effect on free-ropivacaine clearance: Cl = exp(lcl + etalcl) * exp(beta_NSH * LIVER_RESECT_MAJOR) with beta_NSH = log(620/1310) = -0.7480; Cl drops from 1310 L/h at LIVER_RESECT_MAJOR = 0 to 620 L/h at LIVER_RESECT_MAJOR = 1, a 53% reduction).
• Notes: Specific scope pending a second liver-resection-popPK model to ratify the name. The dichotomisation at >= 3 segments reflects the Ollier 2015 analytical choice; a future model that parameterises resection extent as a continuous count of segments should introduce a separate canonical rather than repurpose LIVER_RESECT_MAJOR. Do not confuse with the HEPIMP_* family: HEPIMP_MILD / HEPIMP_MOD / HEPIMP_SEV etc. describe chronic hepatic impairment classified from labs (bilirubin, AST) or Child-Pugh scores, whereas LIVER_RESECT_MAJOR is a surgical-exposure covariate that acts on drug clearance for the transient postoperative window.

RENALIMP_MOD (canonical for moderate renal impairment indicator)

• Description: 1 = moderate renal impairment, 0 = normal renal function, mild renal impairment, or any non-moderate category. Binary categorical indicator used by source papers that test renal-impairment status as a discrete covariate rather than via continuous creatinine clearance (CRCL). The classification scheme that defines “moderate” is paper-specific and must be documented in per-model covariateData[[RENALIMP_MOD]]$notes. Two schemes are commonly encountered:
  • Cockcroft-Gault CrCl 30-50 mL/min (Morris 2011 ZP-006 study eligibility criteria).
  • FDA / EMA labeling: CrCl 30-59 mL/min (US FDA Guidance for Industry: PK in Patients with Impaired Renal Function, 2020).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (normal renal function or mild renal impairment; severe and end-stage categories are typically pooled with the reference or excluded from the source cohort).
• Source aliases:
  • RENAL (binary 1 = moderate / 0 = healthy or mild) – used in Morris_2011_telapristone.R (Morris 2011 Methods “Covariate Analysis”, paragraph after Equation for the proportional covariate model).
• Example models: Morris_2011_telapristone.R (proportional fractional effect on the telapristone absorption rate constant Ka; coefficient -0.744 yielding Ka = Ka_typ * (1 + e_renalimp_mod_ka * RENALIMP_MOD), i.e., a 74% decrease in Ka in moderate-renal-impaired subjects vs the reference healthy/mild-renal cohort), CarlssonPetri_2018_semaglutide.R.
• Notes: Use this column when a model dichotomizes moderate renal impairment as a separate indicator (typically because severe / ESRD subjects are not in the source cohort and the moderate group is the only renal-impairment stratum with non-trivial sample size). The classification scheme (Cockcroft-Gault vs MDRD vs CKD-EPI; mL/min vs mL/min/1.73 m^2) is paper-specific and must be documented per-model. Companion to CRCL (continuous Cockcroft-Gault clearance), RENALIMP_MILD and RENALIMP_SEV (parallel canonicals reserved for future extractions following the HEPIMP_* family precedent). The Morris 2011 Discussion notes that the effect is “not directly attributed to renal impairment (in terms of decrease glomerular filtration rate/changes in creatinine clearance)” but “attributed to some other disease precluding/resulting in renal impairment or other pathophysiologic states induced by renal impairment (e.g., delayed gastric emptying as a result of diabetes)”, so the binary indicator captures the cohort allocation rather than a mechanistic GFR effect. Ratified canonically on 2026-06-09 alongside the Morris 2011 telapristone extraction.

RENALIMP_MILD (canonical for mild renal impairment indicator)

• Description: 1 = mild renal impairment, 0 = normal renal function or any non-mild category. Binary categorical indicator used by source papers that test renal-impairment status as a discrete covariate rather than via continuous creatinine clearance (CRCL). The classification scheme that defines “mild” is paper-specific and must be documented in per-model covariateData[[RENALIMP_MILD]]$notes. A common scheme (used by Carlsson Petri 2018 and mirrored in FDA / EMA labeling guidance for renal-impairment PK) is eGFR 60-89 mL/min/1.73 m^2 as mild versus eGFR >= 90 mL/min/1.73 m^2 as normal.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (normal renal function; the moderate / severe categories are typically encoded as separate RENALIMP_MOD / RENALIMP_SEV indicators alongside this one, with all three set to 0 for the normal-renal-function reference).
• Source aliases:
  • RENAL (multi-level categorical with mild as one level; decompose to binary indicators) – used in CarlssonPetri_2018_semaglutide.R (Carlsson Petri 2018 Methods ‘Renal impairment groups were categorized [by estimated glomerular filtration rate (eGFR)] as normal function (eGFR >= 90 ml/min/1.73 m^2), mild (eGFR = 60-89 ml/min/1.73 m^2), moderate (eGFR = 30-59 ml/min/1.73 m^2), or severe (eGFR < 30 ml/min/1.73 m^2) renal impairment’).
• Example models: CarlssonPetri_2018_semaglutide.R (multiplicative CL/F ratio 0.948 for mild renal impairment vs normal reference, encoded as e_renal_mild_cl^RENALIMP_MILD per Table S3).
• Notes: Sister canonical to RENALIMP_MOD and RENALIMP_SEV; the trio decomposes a four-level renal-function classification (normal / mild / moderate / severe) into three binary indicators with normal as the implicit reference. Follows the HEPIMP_* family precedent (mild / moderate / severe as parallel binary canonicals). Companion to continuous CRCL. Founded alongside the CarlssonPetri_2018_semaglutide extraction where all three renal-impairment categories were tested simultaneously.

RENALIMP_SEV (canonical for severe renal impairment indicator)

• Description: 1 = severe renal impairment, 0 = normal renal function or any non-severe category. Binary categorical indicator used by source papers that test renal-impairment status as a discrete covariate rather than via continuous creatinine clearance (CRCL). The classification scheme that defines “severe” is paper-specific and must be documented in per-model covariateData[[RENALIMP_SEV]]$notes. A common scheme (used by Carlsson Petri 2018 and mirrored in FDA / EMA labeling guidance for renal-impairment PK) is eGFR < 30 mL/min/1.73 m^2 as severe.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (normal renal function; typically paired with RENALIMP_MILD and RENALIMP_MOD so the trio of indicators is mutually exclusive and normal renal function is the shared reference).
• Source aliases:
  • RENAL (multi-level categorical with severe as one level; decompose to binary indicators) – used in CarlssonPetri_2018_semaglutide.R.
• Example models: CarlssonPetri_2018_semaglutide.R (multiplicative CL/F ratio 0.920 for severe renal impairment vs normal reference, encoded as e_renal_sev_cl^RENALIMP_SEV per Table S3; the Discussion notes the severe stratum came only from SUSTAIN 6 and might be confounded by trial effect, and that a dedicated renal-impairment clinical pharmacology trial found no clinically relevant effect).
• Notes: Sister canonical to RENALIMP_MILD and RENALIMP_MOD; the trio decomposes a four-level renal-function classification (normal / mild / moderate / severe) into three binary indicators with normal as the implicit reference. Follows the HEPIMP_* family precedent. Companion to continuous CRCL. Founded alongside the CarlssonPetri_2018_semaglutide extraction.

RENALIMP_ESRD (canonical for end-stage renal disease indicator)

• Description: 1 = end-stage renal disease (ESRD), 0 = any renal function above ESRD. Fourth and most severe member of the RENALIMP_* binary renal-function family, for source papers that carry ESRD as its own discrete covariate rather than letting continuous CRCL extrapolate into the anuric range. The threshold that defines ESRD is paper-specific and must be documented in per-model covariateData[[RENALIMP_ESRD]]$notes; a common scheme (used by Xie 2025 and mirrored in FDA / EMA renal-impairment labeling guidance) is CrCL < 15 mL/min.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (any renal function above ESRD; typically paired with RENALIMP_MILD / RENALIMP_MOD / RENALIMP_SEV so the set of indicators is mutually exclusive and normal renal function is the shared reference).
• Source aliases:
  • ESRD – used in Xie_2025_aztreonam_avibactam.R (Table S3 row ESRD on CL_AVI).
• Example models: Xie_2025_aztreonam_avibactam.R (proportional shift of -0.923 on avibactam clearance, leaving 7.7% of the reference value; aztreonam carries no ESRD term).
• Notes: Sister canonical to RENALIMP_MILD, RENALIMP_MOD and RENALIMP_SEV, completing the four-level family; general scope for the same reason those carry it, namely that the renal-impairment strata have a standard regulatory definition any future model can reuse. Two traps worth documenting per model. First, an ESRD indicator commonly replaces rather than multiplies the continuous CRCL relationship – Das 2024 prints its avibactam power arm as applying when “nCrCL < 80 mL/min and not ESRD”, and multiplying the two in Xie 2025 would give a physiologically impossible ~0.7% of reference clearance instead of 7.7%. Second, papers routinely define the ESRD stratum on raw CrCL (mL/min) while the model’s continuous renal covariate is the BSA-normalized CRCL (mL/min/1.73 m^2), so the column is generally not derivable from CRCL by a threshold; supply it from the raw value. Distinct from RRT_HEMODIAL_STATUS / RRT_HEMODIAL_ACTIVE, which record dialysis treatment rather than the underlying renal-function stratum – a model may carry both, and Xie 2025 makes them mutually exclusive because dialysis patients take a separate absolute clearance. Ratified canonically on 2026-08-17 alongside the Xie 2025 aztreonam-avibactam extraction.

CPK (canonical for serum creatine phosphokinase / creatine kinase)

• Description: Serum creatine phosphokinase (also called creatine kinase, CK) activity (baseline or time-varying). Skeletal-muscle / cardiac-muscle injury and turnover marker; in macrophage-targeted PK/PD analyses (axatilimab, anti-CSF-1R) it is interpreted as a Kupffer-cell / tissue-macrophage clearance surrogate because Kupffer cells participate in the elimination of circulating muscle-derived enzymes.
• Units: U/L (SI; canonical as of 2026-06-19; IU/L is used interchangeably). Document per-model via covariateData[[CPK]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (CPK / ref)^exponent. Reference values observed: 63 U/L (Yang 2024 axatilimab; pooled-cohort median).
• Source aliases:
  • BLCPK (baseline CPK) – informal usage in Yang 2024.
• Example models: Yang_2024_axatilimab.R (baseline-only covariate on baseline NCMC concentration BL_NCMC with power exponent 0.376; reference 63 U/L).
• Notes: Muscle-origin enzyme distinct from AST / ALT (hepatic) and LDH (general tissue turnover). Yang 2024 uses CPK alongside AST and LDH as tracked safety biomarkers. Per-model covariateData[[CPK]]$notes should document baseline-vs-time-varying status and the clinical interpretation in the source population (skeletal-muscle injury, macrophage-clearance surrogate, or both). Distinct from any model state variable representing CPK time-course dynamics – covariate column is the pre-dose laboratory observation.

MYO (canonical for plasma myoglobin)

• Description: Plasma or serum myoglobin concentration, an oxygen-binding haem protein of skeletal and cardiac muscle used clinically as a muscle-damage / muscle-mass marker. Baseline or time-varying; document which via per-model covariateData[[MYO]]$notes.
• Units: ng/mL (equivalently ug/L; SI)
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (MYO / ref)^exponent. Reference values observed: 25 ng/mL (Zhao 2025 paracetamol; pooled-cohort mean).
• Source aliases:
  • myoglobin – spelled-out form used in Zhao_2025_paracetamol.R (Table 1 baseline characteristics, Table 2 covariate rows).
• Example models: Zhao_2025_paracetamol.R (power exponent -1.10 on the paracetamol leftover clearance CL_p/F, normalised to the pooled mean of 25 ng/mL; baseline medians 34 ng/mL in healthy controls vs 17 ng/mL in spinal-muscular-atrophy patients, with the paper quoting a normal range of 15-75 ng/mL, and the effect carrying dOFV = 18.5 as the strongest covariate in that analysis).
• Notes: Sibling of the existing muscle / tissue-turnover markers CPK and LDH, and distinct from both – myoglobin is a protein mass concentration in ng/mL while CPK and LDH are enzyme activities in U/L, and Zhao 2025 screens all three independently (only myoglobin is retained). Scope: general because the concept is assay-standard and paper-independent, matching the other routine plasma clinical-chemistry analytes ALB, CRP, CREAT, HGB, ALT, AST, TBILI, ALP, LDH, and CPK. In muscle-wasting populations myoglobin is low because muscle mass is low, so a negative exponent on a clearance term should be read as a muscle-mass surrogate rather than as muscle injury; the direction of the clinical interpretation is paper-specific and belongs in per-model notes. Distinct from any model state variable representing a myoglobin time course – the covariate column is the laboratory observation.

DIAL (DEPRECATED – superseded by RRT_HEMODIAL_ACTIVE)

• Description: Deprecated entry. The 2026-06-09 audit renamed this canonical to HEMODIALYSIS, and the 2026-06-19 audit then renamed it again to the current RRT_HEMODIAL_ACTIVE (see that entry below for full documentation). The DIAL form is retained ONLY as a source_alias of RRT_HEMODIAL_ACTIVE so that pre-2026-06-09 data CSVs continue to load.
• Notes: Do not use for new models. See RRT_HEMODIAL_ACTIVE for the current canonical.

BFR (canonical for blood flow rate through the extracorporeal circuit during dialysis)

• Description: Instantaneous blood flow rate through the extracorporeal circuit during an active dialysis session. Time-varying within subject; meaningful only when DIAL = 1 – in the interdialytic period the value is sentinel and the Michaels-equation term is gated off by DIAL.
• Units: mL/min
• Type: continuous
• Scope: general
• Reference category: n/a – enters the Michaels equation together with DFR and a hemodialyzer mass-transfer-area coefficient. Values investigated in the ratification source were 200, 300, and 400 mL/min (Liesenfeld 2013 Methods, Study Design; Table 1).
• Source aliases: none known.
• Example models: Liesenfeld_2013_dabigatran.R.
• Notes: Pairs with DIAL (binary on/off gate) and DFR (dialysate flow rate). Ratified canonically on 2026-05-16 alongside the Liesenfeld 2013 dabigatran extraction.

DFR (canonical for dialysate flow rate through the extracorporeal circuit during dialysis)

• Description: Instantaneous dialysate flow rate through the extracorporeal circuit during an active dialysis session. Time-varying within subject; meaningful only when DIAL = 1.
• Units: mL/min
• Type: continuous
• Scope: general
• Reference category: n/a – enters the Michaels equation together with BFR. The ratification source fixed DFR at 700 mL/min throughout (Liesenfeld 2013 Methods, Study Design) and additionally simulated 500 mL/min (Methods, Simulations).
• Source aliases: none known.
• Example models: Liesenfeld_2013_dabigatran.R.
• Notes: Pairs with DIAL and BFR. Ratified canonically on 2026-05-16 alongside the Liesenfeld 2013 dabigatran extraction.

VASCACC_AVF1N (canonical for single-needle arteriovenous-fistula vascular-access indicator)

• Description: 1 = the haemodialysis session was performed through an arteriovenous fistula cannulated in a SINGLE-needle setup (one needle alternately draws and returns blood, so a fraction of already-dialysed blood is recirculated); 0 = any other access type. Per-subject (or per-session) indicator describing how the patient is cannulated for extracorporeal therapy, as distinct from the circuit-setting covariates BFR / DFR / Q_CVVH (flow rates) and the therapy-presence flags RRT_HEMODIAL_STATUS / RRT_HEMODIAL_ACTIVE. Access type matters pharmacokinetically because it changes the effective solute presentation to the dialyser: a single-needle fistula recirculates blood, so the nominal pump blood flow rate overstates the flow of undialysed blood reaching the membrane.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (tunnelled dialysis catheter, TDC), shared with the sister canonical VASCACC_AVF2N; the pair decomposes the three-level access classification (TDC / AVF single-needle / AVF double-needle) into two binary indicators with TDC as the implicit reference (both indicators 0).
• Source aliases:
  • CVC0SN1DN2 (three-level integer selector, 0 = TDC, 1 = single-needle AVF, 2 = double-needle AVF; decompose to binary indicators) – used in Kong_2025_piperacillin_tazobactam.R (ESM NONMEM $PK: LGT_TAZ = THETA(18); IF(CVC0SN1DN2.EQ.1) LGT_TAZ = THETA(19); IF(CVC0SN1DN2.EQ.2) LGT_TAZ = THETA(20)).
  • AVF 1N – Kong 2025 Table 1 / Table 3 row label (“AVF 1N single-needle arteriovenous fistula”).
• Example models: Kong_2025_piperacillin_tazobactam.R (selects the tazobactam dialyser extraction ratio: 73.9% for single-needle AVF versus the 80.1% TDC reference and 73.5% for double-needle AVF, Table 3; retained in backward elimination at p < 0.001, dOFV -22.88).
• Notes: Sister canonical to VASCACC_AVF2N. Decomposed-binary-indicator encoding follows the register’s settled convention for multi-level categoricals (RENALIMP_MILD / RENALIMP_MOD / RENALIMP_SEV, the parallel HEPIMP_* family, and RACE_<GROUP>); an integer selector column was explicitly rejected because it would imply an ordering between access types that does not exist. Kong 2025 additionally applied a time-averaged blood flow rate for single-needle sessions when computing BFR (Methods 2.4), so a model that carries both VASCACC_AVF1N and BFR should document in covariateData notes whether the recirculation correction has already been folded into the supplied BFR values. Ratified canonically on 2026-08-20 (sidecar request-001 / response-001 q2, option A) alongside the Kong 2025 piperacillin/tazobactam ESKD haemodialysis extraction. General scope because vascular access type is a universally recorded haemodialysis patient characteristic and is a plausible determinant of effective dialyser clearance in any ESKD antibiotic popPK.

VASCACC_AVF2N (canonical for double-needle arteriovenous-fistula vascular-access indicator)

• Description: 1 = the haemodialysis session was performed through an arteriovenous fistula cannulated in a DOUBLE-needle setup (separate arterial-draw and venous-return needles, so there is no single-needle recirculation); 0 = any other access type. Per-subject (or per-session) indicator describing how the patient is cannulated for extracorporeal therapy; see the sister entry VASCACC_AVF1N for the family rationale and the distinction from the circuit-flow and therapy-presence covariates.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (tunnelled dialysis catheter, TDC), shared with VASCACC_AVF1N; both indicators 0 selects the TDC reference level.
• Source aliases:
  • CVC0SN1DN2 (three-level integer selector, level 2; decompose to binary indicators) – used in Kong_2025_piperacillin_tazobactam.R.
  • AVF 2N – Kong 2025 Table 1 / Table 3 row label (“AVF 2N double-needle arteriovenous fistula”).
• Example models: Kong_2025_piperacillin_tazobactam.R (selects the tazobactam dialyser extraction ratio: 73.5% for double-needle AVF versus the 80.1% TDC reference, Table 3).
• Notes: Sister canonical to VASCACC_AVF1N; the two are mutually exclusive (a subject with both set to 1 is invalid). Ratified canonically on 2026-08-20 (sidecar request-001 / response-001 q2, option A) alongside the Kong 2025 piperacillin/tazobactam ESKD haemodialysis extraction. Kong 2025 found the access-type effect statistically significant but clinically limited (Discussion: “the difference in extraction ratio between the vascular access types were limited and therefore not expected to be clinically relevant”), and the analogous access-type effect on the PIPERACILLIN extraction ratio did NOT survive backward elimination (dOFV +11.26), so piperacillin keeps a single 64.0% extraction ratio.

ECMO_PUMP_SPEED (canonical for extracorporeal-membrane-oxygenation centrifugal-pump rotational speed)

• Description: Rotational speed of the extracorporeal-membrane-oxygenation (ECMO) centrifugal blood pump during VA-ECMO or VV-ECMO support. Continuous covariate; treated as time-fixed per subject in Yang 2017 (the per-subject pump speed reported in the source data was the prevailing speed during the PK sampling window; pump-speed adjustments during sampling were not modelled as time-varying). For future models that resolve session-level changes in pump speed, the covariate is naturally time-varying and the per-model covariateData[[ECMO_PUMP_SPEED]]$notes should document the time resolution.
• Units: RPM (revolutions per minute)
• Type: continuous
• Scope: general
• Reference category: n/a – enters as a power-centered effect (ECMO_PUMP_SPEED / ref)^exponent. The reference value is paper-specific (median pump speed in the source cohort): Yang 2017 uses 2350 RPM (cohort median; Table 1 / Results: “median ECMO pump speeds of 2350 RPM”).
• Source aliases: none known.
• Example models: Yang_2017_remifentanil.R (power effect on remifentanil CL: (ECMO_PUMP_SPEED / 2350)^2.04; higher pump speed associated with higher CL, hypothesised mechanism is increased spontaneous drug degradation at high centrifugal-pump shear).
• Notes: Distinct from blood flow rate (BFR, mL/min) and from dialysate flow rate (DFR, mL/min) which characterise renal-replacement-therapy circuits. ECMO circuits use a centrifugal pump whose rotational speed sets the cardiac-output augmentation; the resulting blood flow rate (LPM) is a separate measured quantity that depends on circuit resistance and patient hemodynamics. Yang 2017 tested both ECMO pump speed (RPM) and ECMO flow rate (LPM) and only pump speed was retained as a significant covariate (ECMO flow rate was not significantly associated with PK parameters). Ratified canonically on 2026-05-23 alongside the Yang 2017 remifentanil extraction.

Q_CVVH (canonical for continuous venovenous hemofiltration circuit flow rate during ECMO)

• Description: Flow rate through the continuous venovenous hemofiltration (CVVH) filter when it is placed parallel to the ECMO circuit. The filter contributes solute removal that adds to the drug’s apparent CL whenever CVVH is active; the column is zero in subjects whose ECMO circuit has no CVVH filter or whose CVVH is currently off. Continuous covariate; naturally time-varying although the source paper treats the per-subject median as time-fixed (the median Q_CVVH per individual did not vary much during sampling).
• Units: mL/min
• Type: continuous
• Scope: general
• Reference category: n/a – enters as a power-centered effect (Q_CVVH / ref)^exponent with the source paper additionally gating the effect to 1 when Q_CVVH = 0 (no CVVH active). The reference value is paper-specific (cohort median during CVVH operation): Ahsman 2010 uses 193 mL/min (cohort median; Table 1).
• Source aliases:
  • QCVVH – used in Ahsman_2010_cefotaxime.R (paper notation, subscript dropped).
• Example models: Ahsman_2010_cefotaxime.R (power effect on DACT metabolite CL: (Q_CVVH / 193)^0.72 when CVVH active, 1 otherwise).
• Notes: Distinct from ECMO_PUMP_SPEED (the centrifugal-pump rotational speed setting cardiac-output augmentation) and from BFR / DFR (blood-flow / dialysate-flow rates characterising standalone hemodialysis circuits): Q_CVVH is the slow-clearance flow through a CVVH filter integrated INTO an ECMO circuit. Distinct from RRT_CRRT_STATUS (binary on / off indicator for continuous RRT) which captures presence-of-therapy rather than its rate. Future ECMO + CVVH popPK extractions that quantify CVVH flow should reuse this canonical and extend the example list. Ratified canonically alongside the Ahsman 2010 cefotaxime extraction.

T_ECMO (canonical for time since ECMO cannulation start (beginning of extracorporeal circulation))

• Description: Wall-clock time elapsed since the patient was cannulated onto ECMO (the beginning of extracorporeal circulation). Zero before cannulation; positive and monotonically increasing throughout the ECMO support period. Time-varying. Whether the value continues to increase after decannulation, resets at decannulation, or is censored at decannulation is paper-specific and must be documented in covariateData[[T_ECMO]]$notes; when the source model uses a saturating Hill / Emax form (Kleiber 2017) it does not matter whether T_ECMO continues past decannulation because the effect is already saturated.
• Units: hour
• Type: continuous
• Scope: general
• Reference category: n/a – enters as a sigmoidal Emax multiplier 1 + Emax * T_ECMO^gamma / (T50_EC^gamma + T_ECMO^gamma) (Kleiber 2017 Eq 9) or a saturating half-life form (Ahsman 2010 Eq 10-style). At T_ECMO = 0 (pre-ECMO baseline) the Hill ratio evaluates to 0 and the multiplier collapses to 1.
• Source aliases:
  • tec / t_EC – used in Kleiber_2017_clonidine.R (paper notation, Eq 9 and Table 3).
• Example models: Kleiber_2017_clonidine.R (sigmoidal Emax effect on V: V x (1 + 0.55 * T_ECMO^18.5 / (51.7^18.5 + T_ECMO^18.5)); V approaches 1.55 * V_pop * (WT/70) on full ECMO and recovers to V_pop * (WT/70) before ECMO).
• Notes: Conceptually the on-ECMO mirror of T_POST_ECMO – T_ECMO measures time since cannulation, T_POST_ECMO measures time since decannulation. A model that uses BOTH typically gates each by an additional ECMO-on indicator or by the natural floor (T_ECMO = 0 before cannulation, T_POST_ECMO = 0 before-and-during ECMO). Kleiber 2017 uses only T_ECMO; Ahsman 2010 uses only T_POST_ECMO. Distinct from on-ECMO duration covariates (per-run summary of total ECMO hours) and from ECMO_PUMP_SPEED (instantaneous centrifugal-pump rotational speed). Anticipated by the existing T_POST_ECMO entry’s Notes (which referenced t_EC as “tested but not retained” in Ahsman 2010); ratified canonically alongside the Kleiber 2017 clonidine extraction where the covariate is retained.

T_POST_ECMO (canonical for time after ECMO decannulation (end of extracorporeal circulation))

• Description: Wall-clock time elapsed since the patient was decannulated from ECMO (the end of extracorporeal circulation). Zero before and during ECMO support; becomes positive after decannulation and increases linearly with time. Naturally time-varying. Used as a power-centred recovery-time covariate on PK parameters whose values change as the patient transitions off ECMO back to native circulation.
• Units: hour
• Type: continuous
• Scope: general
• Reference category: n/a – enters as a power-centered effect (T_POST_ECMO / ref)^exponent with the source paper additionally gating the effect to 1 when T_POST_ECMO = 0 (before or during ECMO). The reference value is paper-specific: Ahsman 2010 uses 100 h (Appendix).
• Source aliases:
  • tEND – used in Ahsman_2010_cefotaxime.R (paper notation, subscript dropped).
• Example models: Ahsman_2010_cefotaxime.R (power effect on parent CTX CL: (T_POST_ECMO / 100)^0.16 when post-ECMO, 1 otherwise; power effect on DACT metabolite CL: (T_POST_ECMO / 100)^0.53 when post-ECMO, 1 otherwise).
• Notes: Conceptually a time-since-event covariate similar to TPP (time postpartum) and T_ENTRY (per-subject study-entry time) but anchored to ECMO decannulation. Distinct from t_EC (time since ECMO START / beginning of extracorporeal circulation, which the source paper tested but did not retain) and from on-ECMO duration covariates. The gating-at-zero convention is structural: during ECMO the recovery-time covariate is undefined / not yet started, so the (0 / ref)^exponent expression would collapse pathologically and the implementer must short-circuit the multiplier to 1. Future ECMO popPK extractions that include a post-decannulation recovery phase should reuse this canonical and extend the example list. Ratified canonically alongside the Ahsman 2010 cefotaxime extraction.

T_POST_HEMODIAL (canonical for time since the end of the last intermittent-hemodialysis session)

• Description: Wall-clock time elapsed since the END of the subject’s most recent intermittent-hemodialysis (IHD) session. Resets to zero at the end of each session and increases through the interdialytic interval, so it is naturally time-varying within subject. Undefined for subjects never dialysed; set to 0 for them and pair the effect with RRT_HEMODIAL_STATUS so the value is inert outside the dialysis-dependent stratum. Captures interdialytic physiology that the on/off session gate cannot – principally fluid accumulation between sessions, which expands the central volume of distribution.
• Units: hour
• Type: continuous
• Scope: general
• Reference category: n/a – enters either as a threshold indicator (T_POST_HEMODIAL > <cut>) or, if a future paper resolves it, as a continuous centred / power effect. VanWart_2025_telavancin.R uses the threshold form at 48 h.
• Source aliases: none known; Van Wart 2025 describes the covariate in prose (“PK samples were collected more than 48 hours after the last active IHD session”) rather than naming a data-set column.
• Example models: VanWart_2025_telavancin.R (48-hour threshold gating a fixed additive central-volume increase of +1.55 L in dialysis-dependent subjects: vc <- ... + e_t_post_hemodial_vc * RRT_HEMODIAL_STATUS * (T_POST_HEMODIAL > 48); Van Wart 2025 attributes the rise to “fluid depletion during IHD and accumulation between sessions”, telavancin plasma concentrations being higher immediately off IHD and falling thereafter).
• Notes: Completes the intermittent-hemodialysis covariate set as the INTERDIALYTIC time axis: RRT_HEMODIAL_STATUS says whether the subject is dialysis-dependent at all, RRT_HEMODIAL_ACTIVE says whether a session is running right now, and T_POST_HEMODIAL says how long it has been since the last one ended. The RRT_HEMODIAL_STATUS entry anticipated exactly this column (“a future paper that resolves drug clearance during versus between dialysis sessions would use a time-varying form (RRT_HEMODIAL_ACTIVE) or a separate per-session covariate”). Named in the T_<event> time-since-event family alongside T_POST_ECMO (time after ECMO decannulation), T_ECMO, T_CPB and T_NUT_SUPP, which is the same shape: time measured from the end of an extracorporeal therapy. Distinct from FILT_AGE_HI, which also uses a 48-hour threshold but measures the age of the DIALYSER MEMBRANE while a therapy is running (declining dialysis efficiency from membrane fouling), not the time since a session ended (interdialytic fluid accumulation) – the two thresholds coincide numerically and mean opposite things, so do not substitute one for the other. General scope because time since the last dialysis session is a paper-independent quantity any IHD popPK model may use. A future paper that retains the effect as a continuous function rather than a threshold should reuse this column and record the functional form in per-model covariateData[[T_POST_HEMODIAL]]$notes. Ratified canonically on 2026-08-17 alongside the Van Wart 2025 telavancin extraction.

FILT_AGE_HI (canonical for dialysis-filter-age above-48-hour indicator)

• Description: Binary indicator that the in-use extracorporeal renal-replacement-therapy (CVVHDF / CVVH / SLED / EDD-f / IHD) hemofilter or dialyser membrane has been in continuous use for more than 48 hours at the time of dosing / sampling. 1 = filter is older than 48 h; 0 = filter is within the first 48 h of use (the fresh-filter reference). Subject-level binary in the source data (Patel 2011 had 1 of 10 patients on a > 48 h filter at the start of CVVHDF treatment; the rest had fresh filters), but the covariate is naturally time-varying within subject as the filter ages and is replaced – record the per-model time resolution in covariateData[[FILT_AGE_HI]]$notes.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (fresh filter, less than 48 h in use). Models that apply a multiplicative reduction to the dialysis clearance arm should encode the effect as cl_dialysis * e_filt_age_hi_<arm>^FILT_AGE_HI so the FILT_AGE_HI = 0 case recovers the fresh-filter baseline.
• Source aliases: none known; Patel 2011 retains the effect via the parameter ffCL_CVVHDF (a multiplicative factor on the CVVHDF clearance arm) rather than naming a data-set column. When ratified the column was assigned the canonical name FILT_AGE_HI in the model file.
• Example models: Patel_2011_fluconazole.R (multiplicative factor e_filt_age_hi_cl_renal^FILT_AGE_HI on the CVVHDF clearance arm: e_filt_age_hi_cl_renal = 0.368, so > 48 h filters give 36.8% of fresh-filter CVVHDF efficiency; bootstrap 95% CI 0.326-0.426 from Patel 2011 Table 2; Delta-OBJ = -11.46 vs base model).
• Notes: Distinct from RRT_CRRT_STATUS (subject-level binary indicator that ANY continuous / extended renal-replacement therapy is occurring) and from RRT_HEMODIAL_ACTIVE (per-time-point on/off gate for the dialysis-clearance term in within-subject time-varying dialysis-clearance models). FILT_AGE_HI gates WITHIN the CRRT-on subset by membrane age – meaningful only when RRT_CRRT_STATUS = 1 or RRT_HEMODIAL_ACTIVE = 1. The > 48 h threshold is paper-specific (Patel 2011 Methods note that membrane fouling and clot formation in the hemofilter pores progressively reduce dialysis efficiency, with the inflexion typically observed near 48 h). Future extractions that retain a finer filter-age structure (continuous filter_age_hours or multiple thresholds) should reuse FILT_AGE_HI for the binary > 48 h case and add a separate canonical for the continuous form. Scope kept specific pending a second model that ratifies the same > 48 h threshold; promote to general once corroborated. Ratified canonically on 2026-06-09 alongside the Patel 2011 fluconazole extraction.

FILT_SA_MED (canonical for medium-haemofilter membrane-surface-area indicator)

• Description: Binary indicator that the in-use extracorporeal renal-replacement-therapy haemofilter is the manufacturer’s MEDIUM membrane-surface-area size, within a paper’s discrete filter-size range. 1 = medium filter; 0 = the small (reference) filter, the large filter, or no extracorporeal therapy. Membrane surface area is the physical determinant of the diffusive/convective solute-transfer capacity of the filter, so papers that stock only a few filter sizes retain size as a categorical covariate on the extracorporeal clearance arm rather than as a continuous area. Paired with FILT_SA_LARGE; the two indicators are mutually exclusive and both are 0 for the small reference filter.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0, meaning the smallest filter the source paper stocked (0.2 m^2 in Butragueno-Laiseca 2025). Encode the effect as cl_hemodialysis * e_filt_sa_med_<arm>^FILT_SA_MED * e_filt_sa_large_<arm>^FILT_SA_LARGE so that the small-filter case recovers the reference multiplier of 1.
• Source aliases:
  • FILT, theta_FILT – Butragueno-Laiseca 2025 Table 2 notation for the three-level categorical covariate that these indicators decompose.
  • Filter surface area (m2) – Butragueno-Laiseca 2025 Table 1 row label.
• Example models: ButraguenoLaiseca_2025_teicoplanin.R (multiplicative factor e_filt_sa_med_cl_hemodialysis = 3.58 on the CKRT clearance arm; Butragueno-Laiseca 2025 Table 2, RSE 13%, SIR 95% CI 2.78-4.40).
• Notes: The absolute surface areas that the size labels denote are paper-specific and MUST be recorded in per-model covariateData[[FILT_SA_MED]]$notes – in the founding source, small = 0.2 m^2, medium = 0.6 m^2, large = 1.2 m^2, assigned by patient weight band (small 3-10 kg, medium 10-30 kg, large 30-60 kg). Because size is assigned by weight in paediatric practice, filter size and body weight are strongly collinear and a paper may describe one as a surrogate for the other; record which the model actually uses. Distinct from BSA (body surface area, a patient property) and from FILT_AGE_HI (membrane age, not size). Meaningful only when RRT_CRRT_ACTIVE = 1 (or the equivalent hemodialysis gate); the whole extracorporeal arm is gated off otherwise. A source paper that reports a genuinely continuous membrane surface area, or more than three sizes, should add a continuous canonical rather than extending the indicator set indefinitely. Butragueno-Laiseca 2025 also tested effluent flow and blood flow on the extracorporeal clearance arm and could not retain them alongside filter size because of strong mutual correlation, so filter size is doing the work those flows would otherwise do. Scope kept specific pending a second model that ratifies the same three-level decomposition.

FILT_SA_LARGE (canonical for large-haemofilter membrane-surface-area indicator)

• Description: Binary indicator that the in-use extracorporeal renal-replacement-therapy haemofilter is the manufacturer’s LARGE membrane-surface-area size. 1 = large filter; 0 = the small (reference) filter, the medium filter, or no extracorporeal therapy. The large-size partner of FILT_SA_MED; see that entry for the full rationale.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0, meaning the smallest filter the source paper stocked (0.2 m^2 in Butragueno-Laiseca 2025).
• Source aliases:
  • FILT, theta_FILT – Butragueno-Laiseca 2025 Table 2 notation.
  • Filter surface area (m2) – Butragueno-Laiseca 2025 Table 1 row label.
• Example models: ButraguenoLaiseca_2025_teicoplanin.R (multiplicative factor e_filt_sa_large_cl_hemodialysis = 5.04 on the CKRT clearance arm; Butragueno-Laiseca 2025 Table 2, RSE 14%, SIR 95% CI 3.80-6.26; only 2 of the 12 CKRT patients used a large filter).
• Notes: Mutually exclusive with FILT_SA_MED. See the FILT_SA_MED entry for the reference-category encoding, the requirement to record the paper-specific absolute surface areas in per-model notes, the distinction from BSA and FILT_AGE_HI, and the collinearity with body weight and circuit flows. Scope kept specific pending a second ratifying model.

RRT_HEMODIAL_ACTIVE (canonical for hemodialysis-active indicator (time-varying per-session gate))

• Description: Within-subject time-varying indicator for whether an extracorporeal renal-replacement-therapy session (intermittent hemodialysis, hemofiltration, or hemodiafiltration) is currently running. 1 during the session; 0 in the interdialytic interval and in non-dialysed subjects.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no dialysis running). Models that compose an additional cl_hemodialysis term should gate it by RRT_HEMODIAL_ACTIVE = 1 so that the interdialytic clearance reduces to the intrinsic body clearance.
• Source aliases:
  • HEMODIALYSIS – prior canonical name (2026-06-09 – 2026-06-19); the 2026-06-19 RRT family standardization renamed it to RRT_HEMODIAL_ACTIVE to align with the sibling RRT_HEMODIAL_STATUS / RRT_CRRT_STATUS shape.
  • DIAL – legacy form (used in Liesenfeld 2013, Jacobs 2016, Veinstein 2013 prior to the 2026-06-09 rename to the more explicit HEMODIALYSIS).
  • IHD_ON, HD_ACTIVE, RRT_ACTIVE – variant abbreviations used in adjacent ESRD / CRRT popPK literature.
• Example models:
  • Liesenfeld_2013_dabigatran.R (Michaels-equation gate; cl_total <- cl + RRT_HEMODIAL_ACTIVE * Michaels(BFR, DFR, KoA); the dialysis arm is derived from blood flow rate, dialysate flow rate, and a hemodialyzer mass-transfer-area coefficient).
  • Jacobs_2016_colistin.R (additive on/off gate of fixed device-level hemodialysis-clearance constants for CMS and colistin; cl_tot <- cl + RRT_HEMODIAL_ACTIVE * cl_hd_cms).
  • Veinstein_2013_gentamicin.R (additive arm with an estimated primary ini() parameter; cl_total <- cl + RRT_HEMODIAL_ACTIVE * cl_hemodialysis).
  • VanWart_2025_telavancin.R (same additive-arm shape as Veinstein 2013, with IIV on the dialysis arm: cl_hemodialysis <- exp(lcl_hemodialysis + etalcl_hemodialysis) * RRT_HEMODIAL_ACTIVE, CL_DL = 1.77 L/h; Van Wart 2025 states CL_DL “was estimated only during those periods where intermittent hemodialysis (IHD) was active and was fixed to a value of zero when IHD was not operative”). Paired in the same model with the interdialytic T_POST_HEMODIAL on central volume.
• Notes: Distinct from a renal-impairment indicator (subject-level baseline class) and from RRT_HEMODIAL_STATUS (the subject-level treatment-status indicator) – RRT_HEMODIAL_ACTIVE is the per-time-point gate that turns the dialysis-clearance term on and off WITHIN a single hemodialysis subject’s record. Pair with BFR and DFR when the dialysis clearance depends on flow rates; pair with a filter-specific mass-transfer coefficient (estimated lkoa in the model, not a covariate) when the Michaels parameterisation is used. Pair with lcl_hemodialysis (canonical) when the dialysis arm is a primary estimated structural parameter rather than a derived expression. Ratified canonically on 2026-05-16 (as DIAL), renamed to HEMODIALYSIS on 2026-06-09 alongside the Veinstein 2013 gentamicin extraction, then renamed again to RRT_HEMODIAL_ACTIVE on 2026-06-19 per the canonical-register standardization audit (RRT family normalization: the RRT_<modality>_<kind> pattern makes the modality + per-session-vs-subject-level distinction explicit at the column name).

RRT_CRRT_ACTIVE (canonical for continuous-renal-replacement-therapy-active indicator (time-varying gate))

• Description: Within-subject time-varying indicator for whether a continuous or extended extracorporeal renal-replacement-therapy modality is currently running – continuous venovenous hemodialysis (CVVHD), hemofiltration (CVVH), hemodiafiltration (CVVHDF), sustained low-efficiency dialysis (SLED), or extended daily diafiltration (EDD-f). 1 while the therapy is running; 0 while it is interrupted and in subjects never treated. Completes the RRT_<modality>_<kind> family as the continuous-modality counterpart of RRT_HEMODIAL_ACTIVE.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no continuous RRT running). Models that compose an additional dialysis-clearance term should gate it by RRT_CRRT_ACTIVE = 1 so that clearance during an interruption reduces to the intrinsic body clearance.
• Source aliases:
  • CVVHD – Huppe 2023 prose and figure legends.
  • CRRT_ON, CVVH_ACTIVE – variant abbreviations used in adjacent critical-care popPK literature.
• Example models: Huppe_2023_fosfomycin.R (Michaels-equation gate on the dialysis clearance arm: cl_cvvhd <- RRT_CRRT_ACTIVE * 60 * Michaels(BFR, DFR, K0A); each of the 15 patients contributed one PK profile during CVVHD and one after CVVHD was interrupted for tubing-system replacement, so the covariate genuinely varies within subject), Zhang_2025_fluconazole.R (gates the covariate-supplied extracorporeal clearance: clcrrt <- QEFF * RRT_CRRT_ACTIVE, reproducing the supplement’s $PK block IF (CRRTYN.EQ.0) CL_crrt = 0 / IF (CRRTYN.EQ.1) CL_crrt = CLCRRT. Every subject in the model-building dataset was on CRRT, so the gate does not vary within that dataset; it carries the paper’s off-CRRT simulation arm – the 0 mL/kg/h row of Tables 3 and 4 – and the intermittent-session scenarios of the companion Shiny application).
• Notes: Distinct from RRT_CRRT_STATUS, which is the SUBJECT-LEVEL treatment-status flag for the same modality family (Shekar 2014 records whether a patient was on RRT at all during sampling, not when a session was running). Distinct from RRT_HEMODIAL_ACTIVE, which is the per-session gate for INTERMITTENT hemodialysis (Liesenfeld 2013, Jacobs 2016, Veinstein 2013). The intermittent-versus-continuous split is retained at the ACTIVE level for the same reason the register already retains it at the STATUS level: the two modality classes have materially different drug-extraction kinetics – intermittent hemodialysis runs high blood and dialysate flows for a few hours, whereas continuous modalities run flows an order of magnitude lower around the clock, so a coefficient or mass-transfer coefficient calibrated on one does not transfer to the other. Pair with BFR and DFR when the dialysis clearance is derived from circuit flow rates via the Michaels equation, and with lkoa as the estimated filter mass-transfer-area coefficient in model() (not a covariate). Note that BFR and DFR are canonically both mL/min, while continuous-modality papers commonly report dialysate flow in L/h – convert on ingestion and document any in-model() back-conversion, as Huppe_2023_fosfomycin.R must do.

HEPIMP_MOD (canonical for moderate hepatic impairment indicator)

• Description: 1 = moderate hepatic impairment, 0 = normal hepatic function or any other (non-moderate) category. The classification scheme that defines “moderate” is paper-specific and must be documented in per-model covariateData[[HEPIMP_MOD]]$notes. Two schemes are commonly encountered:
  • NCI ODWG group 3: total bilirubin > 1.5-3 x ULN with any AST (Ramalingam SS et al., J Clin Oncol 2010;28:4507).
  • Child-Pugh Class B: composite score 7-9 across bilirubin, albumin, INR, ascites, and encephalopathy.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (any non-moderate category: normal, mild, or severe; the model typically uses HEPIMP_MOD alongside HEPIMP_MILD and/or HEPIMP_SEV to partition the non-moderate pool further).
• Source aliases:
  • HEP2 – used in Desai_2016_isavuconazole.R (paper’s liver-function index HEP2 for moderate Child-Pugh B; same orientation as the canonical).
• Example models: Desai_2016_isavuconazole.R (Child-Pugh Class B; log-additive shift e_hepimp_mod_cl = log(1.326 / 2.54) = -0.650 on CL and e_hepimp_mod_q = log(63.554 / 33.678) = +0.635 on Q vs the healthy reference, paired with HEPIMP_MILD for the parallel Child-Pugh A stratum; encodes the paper’s three-typical-value form CL = theta_g * exp(eta_jCL) as a healthy-reference baseline plus group-specific log shifts), Comisar_2025_rimegepant.R (multiplicative fractional effect on CL/F = -0.229 per Comisar 2025 Table 3, a 22.9% clearance decrease giving a 1.3-fold rise in simulated steady-state AUCtau; paired with HEPIMP_SEV (-0.423) as two mutually exclusive indicators whose shared reference group is normal-OR-mild hepatic function, because mild impairment was screened and found not statistically significant; the paper does not state whether its mild / moderate / severe classification follows Child-Pugh or NCI ODWG).
• Notes: Use this column when a model dichotomizes moderate hepatic impairment as a separate indicator from milder or more-severe categories. The classification scheme (NCI ODWG vs Child-Pugh vs other) is paper-specific and must be documented per-model. Companion to HEPIMP_MILD (mild only) and HEPIMP_SEV (severe only). Distinct from HEPIMP_MOD_OR_MISSING (pools moderate cases with missing-data cases) and HEPIMP_MODSEV (pools moderate with severe). Anticipated by the HEPIMP_MILD entry’s Notes: “For models that test moderate or severe as separate categories, register additional canonicals HEPIMP_MOD / HEPIMP_SEV rather than overloading this entry.” HEPIMP_SEV was registered separately; HEPIMP_MOD completes the parallel.

CRCL_BASE (canonical for per-subject baseline creatinine clearance (time-fixed))

• Description: Per-subject baseline renal function (creatinine clearance or BSA-normalized eGFR), time-fixed. Used when a source paper enters the subject’s baseline renal-function value as a separate constant column alongside a time-varying CRCL column under Wahlby 2004’s extended covariate-model decomposition (Br J Clin Pharmacol 2004;58(4):367-377). The within-subject delta is computed in model() as (CRCL - CRCL_BASE).
• Units: mL/min or mL/min/1.73 m^2 – match the units of the paired CRCL column and document per-model via covariateData[[CRCL_BASE]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – used either as (CRCL_BASE - ref) in a linear or linear-exp effect, or as the ratio (CRCL_BASE / ref), where ref is a cohort or upstream-analysis median baseline renal function. Reference values observed: 71.7 mL/min (Wahlby 2004 BCLC median), 93 mL/min/1.73 m^2 (Decker 2024, median eGFR of the upstream adult rheumatoid-arthritis analysis).
• Source aliases:
  • BCLC (baseline creatinine clearance) – Wahlby 2004 source-column convention; used in Wahlby_2004_gentamicin.R (raw mL/min) and Wahlby_2004_pefloxacin.R (concept tested but not retained in final model).
  • baseline eGFR – Decker 2024 source-table wording for the time-fixed bedside-Schwartz pediatric eGFR entering the renal-clearance arm as (baseline eGFR / 93); used in Decker_2024_baricitinib.R.
• Example models: Wahlby_2004_gentamicin.R (raw mL/min; reference 71.7 mL/min = BCLC median; coefficient 0.0098 per mL/min on CL alongside paired DCLC effect 0.0068 per mL/min), Decker_2024_baricitinib.R (bedside-Schwartz pediatric eGFR in mL/min/1.73 m^2; enters the apparent renal clearance arm multiplicatively as the ratio (CRCL_BASE / 93) rather than as a centred linear deviation, paired with the delta effect 0.00586 per mL/min/1.73 m^2 on (CRCL - CRCL_BASE); the reference 93 is the median eGFR of the upstream adult analysis, not a statistic of the pediatric cohort, whose mean baseline eGFR is 119).
• Notes: Promoted to general scope on 2026-08-04 alongside the Decker 2024 baricitinib extraction, the second model registering the column with consistent Wahlby-style BCOV/DCOV semantics. The two ratifying papers differ in the functional form applied to the baseline arm – Wahlby 2004 uses a centred linear deviation (CRCL_BASE - 71.7), Decker 2024 a multiplicative ratio (CRCL_BASE / 93) – and in the underlying assay (Cockcroft-Gault-style raw CrCl vs bedside-Schwartz pediatric eGFR); both are within scope because the column’s role is identical (the time-fixed baseline half of a time-varying renal-function covariate). Document the assay, the units, and the functional form per model in covariateData[[CRCL_BASE]]$notes. Distinct from time-fixed adult-cohort CRCL (where per-subject CRCL is just constant by data design); use CRCL_BASE only when the source paper deliberately separates the BCOV from the time-varying COV. Ratified canonically alongside the Wahlby 2004 extraction.

TBILI_BASE (canonical for per-subject baseline total bilirubin (time-fixed))

• Description: Per-subject baseline total serum bilirubin, time-fixed. Used when a source paper enters the subject’s baseline TBILI as a constant column alongside the time-varying TBILI column under Wahlby 2004’s extended covariate-model decomposition (Br J Clin Pharmacol 2004;58(4):367-377). The within-subject delta is computed in model() as (TBILI - TBILI_BASE).
• Units: mg/dL or umol/L – match the units of the paired TBILI column and document per-model via covariateData[[TBILI_BASE]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – typically used as (TBILI_BASE - ref) in a linear or linear-exp effect.
• Source aliases:
  • BBIL (baseline total bilirubin) – Wahlby 2004 source-column convention; used in Wahlby_2004_pefloxacin.R and Wahlby_2004_paclitaxel_myelosuppression.R.
• Example models: Wahlby_2004_pefloxacin.R (umol/L; centered at 25 umol/L; coefficient -0.0068 per umol/L on CL via exp[e * (TBILI_BASE - 25)]), Wahlby_2004_paclitaxel_myelosuppression.R (umol/L; used together with TBILI as the within-subject delta TBILI - TBILI_BASE entering the Slope-DBIL effect).
• Notes: Specific scope because the BCOV/DCOV decomposition is paper-defined. Promote to general if a second paper ratifies. Distinct from DBIL (direct/conjugated bilirubin, a clinical-chemistry concept) – TBILI_BASE is a methodologic baseline-snapshot column, not a separate biomarker. Ratified canonically alongside the Wahlby 2004 extraction.

ALP_BASE (canonical for per-subject baseline alkaline phosphatase (time-fixed))

• Description: Per-subject baseline serum alkaline phosphatase, time-fixed. Used when a source paper enters the subject’s baseline ALP as a constant column alongside the time-varying ALP column under Wahlby 2004’s extended covariate-model decomposition (Br J Clin Pharmacol 2004;58(4):367-377). The within-subject delta (or log-ratio) is computed in model() from ALP and ALP_BASE.
• Units: U/L (IU/L; interchangeable). Match the units of the paired ALP column and document per-model via covariateData[[ALP_BASE]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – typically used inside a log-ratio log(ALP / ALP_BASE) or a linear delta (ALP - ALP_BASE).
• Source aliases:
  • BALKP (baseline alkaline phosphatase) – Wahlby 2004 source-column convention; used in Wahlby_2004_voriconazole.R.
• Example models: Wahlby_2004_voriconazole.R (IU/L; population median 136 IU/L; paired with time-varying ALP to form log(ALP/ALP_BASE), the paper’s log(DALKP) term, with coefficient 0.59 on CL inside Eq 7 Final-Model column).
• Notes: Specific scope because the BCOV/DCOV decomposition is paper-defined. Promote to general if a second paper ratifies. Ratified canonically alongside the Wahlby 2004 extraction.

PERIT_DIAL (canonical for peritoneal-dialysis treatment-status indicator)

• Description: 1 = the subject was undergoing chronic peritoneal dialysis (PD) during the modeled period; 0 = not on peritoneal dialysis. Treatment-status flag rather than a measured renal-function value; used as a multiplicative covariate on PK parameters that change between dialysis modalities (typically central volume Vc – chronic peritoneal dialysis is associated with higher extracellular fluid volume than intermittent hemodialysis, so plasma volume markers are larger in PD subjects). Per-subject indicator in the source data; typically time-fixed at the subject level (chronic dialysis modality is a months-to-years-stable patient characteristic, not a per-session attribute).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (not on peritoneal dialysis). In a chronic-dialysis cohort comparing PD vs HD modalities, the reference category corresponds to the HD subjects; PERIT_DIAL = 1 selects the PD subgroup.
• Source aliases:
  • DIA – used in Takama_2007_darbepoetin.R (dialysis-modality indicator: DIA = 0 for hemodialysis, DIA = 1 for peritoneal dialysis; maps directly onto PERIT_DIAL with no value transformation). Takama 2007 Table 4 and Methods describe the covariate equation V1 = theta_V1 * [1 + theta_V1_WT * (WT - 54) + theta_V1_DIA * DIA] where theta_V1_DIA = 0.170 increments V1 by +17% in PD subjects relative to HD subjects.
• Example models: Takama_2007_darbepoetin.R (+17% multiplicative-deviation effect on central volume V1; chronic-dialysis cohort comparing 63 HD vs 68 PD Japanese adult patients).
• Notes: Distinct from RRT_HEMODIAL_STATUS (intermittent-hemodialysis IHD indicator; RRT_HEMODIAL_STATUS = 1 selects HD subjects) and from RRT_CRRT_STATUS (continuous / extended RRT indicator). In a chronic-dialysis cohort where every subject is on either HD or PD, RRT_HEMODIAL_STATUS and PERIT_DIAL are perfectly anti-correlated (PERIT_DIAL = 1 - RRT_HEMODIAL_STATUS); the choice of which to use as the covariate is dictated by which modality the source paper treats as the reference category. Takama 2007 took HD as the reference and reports a +17% effect on V1 for PD, so PERIT_DIAL is the natural canonical for that paper (V1 increases when PERIT_DIAL = 1). Anticipated in the RRT_HEMODIAL_STATUS register-entry notes (“would warrant its own canonical (RRT_PERIT_DIAL_STATUS, RRT_CRRT_STATUS) if a future paper retains them as covariates”) and ratified canonically on 2026-06-12 alongside the Takama 2007 darbepoetin alfa extraction. Future audit may rename this to RRT_PERIT_DIAL_STATUS for consistency with the RRT__ family; left unchanged for now to avoid disturbing the Takama 2007 ingestion before broader peritoneal-dialysis coverage in the registry.

ECMO_STATUS (canonical for extracorporeal-membrane-oxygenation treatment-status indicator)

• Description: 1 = the subject was supported by extracorporeal membrane oxygenation (veno-venous VV-ECMO or veno-arterial VA-ECMO) during the modeled period; 0 = no ECMO support. Treatment-status flag rather than a measured circuit quantity; used as a multiplicative covariate on the PK parameters that shift when a patient is cannulated – most often the central volume, which expands because the extracorporeal circuit adds several litres of circulating fluid for a hydrophilic drug to distribute into, and sometimes clearance. Completes the extracorporeal-support covariate family as the presence-of-therapy counterpart of the circuit-quantity columns ECMO_PUMP_SPEED and Q_CVVH and of the time-since-event columns T_ECMO / T_POST_ECMO.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (not receiving ECMO).
• Source aliases:
  • ECMO – the bare source-column form used by both ratifying papers; same orientation (1 = on ECMO), no value transformation. Used in Kang_2020_cefpirome.R and Valadez_2025_cefepime.R.
• Example models: Kang_2020_cefpirome.R (power-form multipliers on both parameters, CL = 5.71 * 0.487^(CREAT/1.6) * 1.41^ECMO_STATUS and V1 = 2.74 * 4.22^ECMO_STATUS, in VA-ECMO patients sampled both during support and after weaning), Valadez_2025_cefepime.R (exponential multiplier on the central volume only, Vd = V1 * (WT/70) * exp(1.043 * ECMO_STATUS), a 2.8-fold expansion; an ECMO effect on CL was tested and rejected for imprecision, and the cohort excluded renal-replacement patients so the indicator is not confounded by CRRT).
• Notes: Time resolution is paper-specific and must be documented in each model’s covariateData[[ECMO_STATUS]]$notes. Kang 2020 treats it as time-varying within subject – PK samples were drawn both during ECMO and after successful weaning, so the per-record indicator switches at decannulation. Valadez 2025 treats it as a subject-level indicator because the source does not resolve cannulation / decannulation timing. Distinct from ECMO_PUMP_SPEED (continuous centrifugal-pump rotational speed in RPM, which quantifies the degree of support rather than its presence) and from T_ECMO / T_POST_ECMO (time since cannulation / decannulation, which model a gradual onset or recovery rather than a step change); a paper that reports both presence and a circuit quantity should register both columns. Distinct from RRT_CRRT_STATUS – ECMO and continuous renal replacement therapy are separate extracorporeal circuits that frequently co-occur, and conflating them is the confounder Valadez 2025 was designed to remove by excluding RRT patients entirely. When pairing ECMO_STATUS with CRCL, note that the two ratifying papers disagree on whether ECMO alters clearance (Kang 2020 retains a 1.41-fold CL increase; Valadez 2025 rejects an ECMO-on-CL term as a null effect with CV > 200%), so the covariate’s effect on CL should never be assumed from class precedent. Registered on 2026-08-16 alongside the Valadez 2025 cefepime extraction, which closed a gap: Kang_2020_cefpirome.R had been using the name since its own extraction without a register entry.

Hematology

HGB (canonical for hemoglobin)

• Description: Blood hemoglobin concentration.
• Units: g/L or g/dL – document the unit used in each model via covariateData[[HGB]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (HGB / ref)^exponent.
• Source aliases: none; HGB is the common NONMEM / clinical-PK abbreviation.
• Example models: Yamada_2025_zolbetuximab.R (g/L, reference 118; exponent -0.374 on V1).
• Notes: Unit varies by paper (SI g/L, US g/dL; 1 g/dL = 10 g/L). The per-model covariateData[[HGB]]$units field is load-bearing.

HGB_BL (canonical for per-subject baseline hemoglobin concentration (initial-condition use))

• Description: Pre-treatment (or per-subject anchor-time) hemoglobin concentration used as the initial condition for a hemoglobin state variable in indirect-response / turnover anaemia models. Distinct from HGB (which is the time-varying or baseline-only hemoglobin value when entering a covariate effect on a structural PK parameter): HGB_BL is a static per-subject covariate that supplies the steady-state baseline for an Hb-state ODE, typically together with a kin = kout * HGB_BL synthesis-rate-from-baseline coupling so the Hb state starts at steady state. Parallel to FERRITIN_BL (baseline ferritin) and WT_BASE (baseline weight).
• Units: g/L, g/dL, or mmol/L – document the unit used in each model via covariateData[[HGB_BL]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – subject-level baseline supplied as a covariate column. Reference values observed: 8.3 mmol/L (Mulder 2025 chronic-HEV SOT cohort median).
• Source aliases:
  • HBBASE – used in Mulder_2025_ribavirin.R.
• Example models: Mulder_2025_ribavirin.R (mmol/L; initial condition for the hb_state compartment; per-subject kin = kout * HGB_BL so the Hb state is at steady state pre-RBV; the source paper substitutes the cohort typical (median) baseline for the one subject without an individual baseline value).
• Notes: Specific scope because the initial-condition idiom is paper-defined (Mulder 2025 specifies kin per subject from baseline rather than estimating a typical Hb0). Promote to general if a second paper ratifies the same baseline-Hb-as-initial-condition pattern. The unit varies widely across papers (SI g/L, US g/dL, Dutch / European convention mmol/L; 1 g/dL = 10 g/L; 1 mmol/L of tetrameric haemoglobin ~ 16.114 g/L); the per-model covariateData[[HGB_BL]]$units field is load-bearing.

WBC (canonical for white blood cell count)

• Description: Total white blood cell count (baseline or time-varying). In chronic lymphocytic leukaemia (CLL) populations the value is elevated because circulating leukaemic B-cells make up the majority of the count, so WBC can serve as a biomarker of target-cell burden rather than general hematology.
• Units: 10^9 cells/L (equivalent to 10^3 cells/uL). Document per-model via covariateData[[WBC]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (WBC / ref)^exponent. Reference values observed: 10 x 10^9/L (Mould 2007, typical CLL Vmax normalization).
• Source aliases: none known (WBC is the universal clinical-PK abbreviation).
• Example models: Mould_2007_alemtuzumab.R (reference 10 x 10^9/L; exponent 0.194 on Vmax), Dirks_2008_cetuximab.R (reference 6.8 x 10^9/L; additive linear-deviation effect 0.0216 per 10^9/L on Vmax; baseline only – cetuximab does not deplete circulating leukocytes, so WBC is treated as a static covariate).
• Notes: Time-varying in treatment studies where the drug depletes the leukaemic clone (e.g., alemtuzumab in CLL): WBC must be supplied at every observation time in the event dataset. In diseases where WBC is not therapeutically targeted the column can be treated as a baseline-only covariate; record the per-model convention in covariateData[[WBC]]$notes.

NLR (canonical for neutrophil-to-lymphocyte ratio)

• Description: Ratio of absolute neutrophil count to absolute lymphocyte count from a complete blood count with differential. Used as a peripheral inflammation marker. May be reported as baseline only or as a time-varying covariate.
• Units: ratio (unitless)
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (NLR / ref)^exponent or exponential effects. Reference values observed: 2.11 (Lin 2024, median in pooled COVID-19 + non-infected cohort).
• Source aliases: none; NLR is the universal abbreviation in clinical-PK and inflammation-biomarker literature.
• Example models: Lin_2024_casirivimab.R (time-varying; reference 2.11; small positive exponent +0.029 on CL).
• Notes: Document baseline-vs-time-varying status in covariateData[[NLR]]$notes. Although it derives from WBC differential counts, register it as its own canonical because the ratio (not the absolute counts) is what the model uses.

HCT (canonical for hematocrit)

• Description: Hematocrit – packed red blood cell volume fraction (baseline or time-varying).
• Units: % (volume fraction times 100). Document per-model via covariateData[[HCT]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (HCT / ref)^exponent. Reference values observed: 45 % (Nestorov 2014, study-population median for severe hemophilia A adults).
• Source aliases: none; HCT is the universal NONMEM / clinical-PK abbreviation. Note that source papers report hematocrit on either the percent scale (45) or the volume-fraction scale (0.45); the canonical column is percent, so a paper reporting fractions must have both its per-subject values and its reference value multiplied by 100 before they are recorded against this column (Zhou_2025_tacrolimus.R does exactly this: paper median 0.265 -> canonical reference 26.5 %).
• Example models: Nestorov_2014_factorviii.R (reference 45 %, exponent -0.419 on V1), Zhou_2025_tacrolimus.R (reference 26.5 % = the Zhou 2025 cohort median of 0.265 rescaled to percent, exponent -0.71 on tacrolimus CL/F; a negative exponent on clearance rather than on volume – see Notes).
• Notes: Higher HCT (more red-cell volume) leaves a smaller plasma fraction within total body volume; for plasma-restricted distribution (e.g., factor VIII activity, which circulates in plasma) the central volume of distribution decreases as HCT rises, so the exponent is negative. Document baseline-vs-time-varying status in covariateData[[HCT]]$notes. Distinct from HGB (mass concentration of hemoglobin); the two correlate but enter different mechanistic relationships. Two distinct mechanisms put HCT on different parameters with the same negative sign. (a) On volume, via the plasma-fraction argument above (Nestorov 2014 factor VIII). (b) On apparent clearance of an erythrocyte-partitioned drug: tacrolimus is extensively bound to red blood cells, so whole-blood clearance falls as HCT rises because a larger share of the measured whole-blood drug is sequestered in erythrocytes and unavailable to hepatic metabolism. Because the assay measures whole blood, the negative exponent on CL/F is an artefact-free consequence of the partitioning, not a change in intrinsic clearance (Zhou 2025 Discussion: ‘Recipients with low levels of HCT have more free tacrolimus in the plasma, resulting in a corresponding increase in clearance’). The exponent magnitude is reproducible across independent lung-transplant cohorts: -0.71 (Zhou 2025) versus -0.868 (Cai et al., cited in Zhou 2025 Discussion). Because the effect enters as a ratio (HCT / ref)^exponent, a model that mistakenly mixes the percent and fraction scales between the column and the reference is off by a factor of 100^exponent – a 24-fold error at exponent -0.71 – so the scale of both must be checked together, not separately.

THB_MASS (canonical for total hemoglobin mass)

• Description: Subject-level total hemoglobin mass in grams. Plasma-volume-independent quantity measured by the optimised CO-rebreathing method (Schmidt 2005, Pottgiesser 2008); distinct from HGB (mass concentration in plasma) and HCT (volume fraction). Used in erythropoiesis / RBC-regeneration models as the steady-state set point Base that drives the negative-feedback term and seeds the steady-state initial conditions of the precursor compartments.
• Units: g
• Type: continuous
• Scope: specific
• Reference category: n/a – subject-level baseline supplied as a covariate column. Reference values observed: 885.42 g (Tetschke 2018 Table 1 example subject); Pottgiesser 2008 cohort mean ~870 g across 28 estimable adult-male volunteers.
• Source aliases: Base (Tetschke 2018 paper symbol).
• Example models: Tetschke_2018_erythropoiesis.R (reference 885.42 g; Pottgiesser 2008 dataset of 29 healthy adult male volunteers).
• Notes: Specific scope because total hemoglobin mass requires the optimised CO-rebreathing method to obtain (Schmidt 2005), which is a specialised technique not present in routine clinical labs; promote to general if a second model registers this quantity. Distinct from HGB (g/L or g/dL plasma concentration) and HCT (RBC volume fraction): THB_MASS is the absolute body-pool mass and is not perturbed by short-term plasma-volume fluctuations (Pottgiesser 2008 Section 3.2 explicitly motivates the choice of mass over concentration). Sex-dimorphic: typical value in adult males is meaningfully higher than in adult females; document the sex composition of the population in covariateData[[THB_MASS]]$notes.

LYMPH_ABS (canonical for absolute peripheral-blood lymphocyte count (total))

• Description: Total absolute peripheral-blood lymphocyte count (all lymphocyte subsets pooled: T, B, and NK cells). Baseline or time-varying; document the time resolution per model via covariateData[[LYMPH_ABS]]$notes. Used as a continuous covariate on clearance and other PK parameters when the source paper carries the count as an exogenous exposure covariate rather than as a modelled PD state. Common in allo-HSCT / GvHD-prophylaxis popPK studies where circulating-lymphocyte burden is a mechanistic driver for lymphocyte-targeting biologics (integrin blockers, T-cell / B-cell-depleting mAbs).
• Units: cells/uL (equivalent to 10^9 cells/L = K/uL x 1000; document the per-paper reporting unit in covariateData[[LYMPH_ABS]]$units). Reporting conventions vary: some papers report as K/uL (i.e., 10^3 cells/uL, numerically identical to 10^9 cells/L) and some as cells/uL – both encode the same numerical value once the K/uL suffix is expanded. Prefer cells/uL as the canonical unit for consistency with CD4_ABS, CD19_ABS, and NEUT.
• Type: continuous
• Scope: general
• Reference category: n/a – typically enters as a power term (LYMPH_ABS / ref)^exponent against a paper-specific reference. Reference values observed: 100 cells/uL (Waterhouse 2024, 0.1 K/uL, exponent -0.0180 on CL; the paper further replaces any zero lymphocyte counts with 0.01 K/uL = 10 cells/uL to avoid a log-of-zero in the power form).
• Source aliases:
  • LYMPH – Waterhouse 2024 NM-TRAN column, time-varying absolute lymphocyte count in K/uL.
  • ALC – clinical laboratory abbreviation for “absolute lymphocyte count”; common in hematology / transplant papers. Same semantics; no value transformation.
• Example models: Waterhouse_2024_vedolizumab.R (time-varying absolute lymphocyte count on CL: cl *= (LYMPH_ABS / 100)^-0.0180; reference 100 cells/uL = 0.1 K/uL per paper Table 2; zeros replaced with 10 cells/uL to avoid log(0)).
• Notes: Distinct from CD4_ABS (a subset – CD4+ T-lymphocytes only), CD19_ABS (a subset – CD19+ B-lymphocytes only), and NLR (a ratio, neutrophil-to-lymphocyte). LYMPH_ABS is the total pooled lymphocyte count and is the correct canonical for papers that measure “absolute lymphocyte count” without further flow-cytometry subsetting. Distinct from WBC (total white blood cell count, of which lymphocytes are one differential fraction). Zero-value handling: allo-HSCT / lymphocyte-depleting-therapy cohorts routinely have observed lymphocyte counts of exactly 0 K/uL at some time points; source papers commonly substitute a small floor (e.g., Waterhouse 2024 uses 0.01 K/uL) before evaluating a power-form covariate effect, per Waterhouse 2024 Supplement Equation S2. Document any per-paper zero-floor convention in covariateData[[LYMPH_ABS]]$notes. Ratified canonically on 2026-07-25 alongside the Waterhouse 2024 vedolizumab extraction.

NEUT (canonical for absolute neutrophil count)

• Description: Absolute neutrophil count, typically as a baseline covariate (entered via centred-deviation (NEUT - ref) or power scaling (NEUT / ref)^exponent) or, in semi-mechanistic myelosuppression models, as a per-subject initial-condition value for the proliferation, transit, and circulating compartments.
• Units: cells/mm^3 (equivalent to cells/uL; i.e., the same value reported in 10^9/L x 1000). Document per-model via covariateData[[NEUT]]$units if the source paper uses a different unit (e.g., 10^9 cells/L for Ozawa_2007_docetaxel.R per the paper’s Table-3 reporting unit).
• Type: continuous
• Scope: general
• Reference category: n/a – used in centred-deviation form exp(coef * (NEUT - ref)), in power scaling (NEUT / ref)^exponent, or as a direct per-subject initial-condition assignment in semi-mechanistic Friberg-family models. Reference values observed: 4133 cells/mm^3 (BAST PTTE 2017 simulated cohort median; NA_NA_tte_gompertz.R Event 1 base hazard model); 5 x 10^9/L (Ozawa 2007 typical Japanese cancer cohort, used as the initial condition for the proliferation, transit, and circulation compartments).
• Source aliases:
  • BASE – per-subject baseline ANC supplied as a NONMEM data column (used in Ozawa_2007_docetaxel.R; Appendix I $INPUT).
  • ANC – the absolute-neutrophil-count source-column spelling (10^9/L); time-varying hazard modulator in Hansson_2013_sunitinib_os.R (the overall-survival Weibull hazard term exp(e_anc_haz * ANC + ...)). No value transformation beyond the per-paper unit (document 10^9/L in covariateData[["ANC"]]$units).
• Example models: NA_NA_tte_gompertz.R (BAST PTTE 2017 / DDMODEL00000243 Event 1 hazard model; centred at NEUT = 4133/mm^3; coefficient -1.56e-4 on the NONMEM rescaled scale, equivalent to exp(-1.56e-4 * (NEUT - 4133)) on the hazard), Ozawa_2007_docetaxel.R (Friberg-extension myelosuppression PD; per-subject baseline ANC supplied via the NEUT column, used as the initial condition for the proliferating, three transit, and circulating compartments per the Methods text ‘Circ (t = 0) was fixed at its observed value’), Hansson_2013_sunitinib_os.R (time-varying ANC as ANC, simulated from the upstream Hansson 2013 myelosuppression model and consumed as an OS-hazard predictor).
• Notes: General scope because absolute neutrophil count is a routine clinical-laboratory measurement that recurs across cytotoxic-chemotherapy myelosuppression models (centred-deviation hazard models, Friberg-family per-subject baseline initial conditions, and time-varying ANC outputs). Promoted to general scope on 2026-05-10 to support Ozawa_2007_docetaxel.R. The NEUT canonical units are cells/mm^3, but the reporting unit 10^9 cells/L (numerically NEUT_per_mm3 / 1000) is also common in oncology papers; per-model covariateData[[NEUT]]$units documents the per-paper unit. Distinct from WBC (total white blood cell count, of which neutrophils are the largest fraction in healthy adults) – NEUT is a specific differential-count subfraction. Also distinct from NLR (neutrophil-to-lymphocyte ratio), which is a derived ratio.

FERRITIN_BL (canonical for baseline serum ferritin concentration)

• Description: Pre-treatment (or per-subject anchor-time) serum ferritin concentration. Two complementary uses across iron-related models: (1) as the per-subject initial condition for a ferritin state variable in iron-overload disease-progression models (ferritin(0) <- FERRITIN_BL; Bellanti 2015), and (2) as a static per-subject covariate entering a power-law multiplier on hepcidin turnover rate constants in iron-status / menstrual-cycle turnover models (Angeli 2016). Distinct from a state-output ferritin trajectory: FERRITIN_BL is a static per-subject covariate (one value, supplied at simulation start); a state-output ferritin would evolve over time per a disease ODE.
• Units: ug/L (clinical reporting convention in iron-related papers; numerically equivalent to ng/mL). Document per-model via covariateData[[FERRITIN_BL]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – subject-level baseline supplied as a covariate column. Reference values observed: 2260 ug/L (Bellanti 2015 thalassaemia cohort median; range 393-8500 ug/L across 27 transfusion-dependent beta-thalassaemia major patients); 53 ug/L (Angeli 2016 healthy non-menopausal women; mean 53.14, range at 10th-90th percentile 18.6-97.4 ug/L).
• Source aliases: none known.
• Example models: Bellanti_2015_deferoxamine.R (ug/L; initial condition for the ferritin compartment; n=27 transfusion-dependent beta-thalassaemia major paediatric / adolescent cohort, median 2260, range 393-8500), Angeli_2016_iron_hepcidin.R (ug/L; per-subject end-of-cycle baseline used as a power-law multiplier (FERRITIN_BL / 53)^exponent on hepcidin elimination kout_hep (exponent -0.60) and on the hepcidin post-menses rebound krel_hep (exponent -1.95); higher baseline ferritin -> slower hepcidin elimination and a smaller rebound, consistent with iron-regulatory feedback).
• Notes: Iron storage protein; elevated in transfusional iron overload (beta-thalassaemia major, sickle-cell disease on chronic transfusion, MDS on transfusion support) and in some inflammatory states (acute-phase reactant); near normal in healthy adults (typically ~30-300 ug/L). Promoted from specific to general on 2026-06-03 with the Angeli 2016 iron / hepcidin extraction (the second model to register FERRITIN_BL with consistent baseline-ferritin semantics across two distinct mechanistic uses).

RBC (canonical for red blood cell (erythrocyte) count)

• Description: Red blood cell (erythrocyte) count (baseline or time-varying).
• Units: 10^12 cells/L (clinical reporting convention; equivalent to 10^6 cells/uL). Document per-model via covariateData[[RBC]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – used with linear-deviation (RBC - ref) or power scaling (RBC / ref)^exponent. Reference values observed: 4.40 x 10^12/L (Zhang 2012 healthy Chinese adult cohort median across the four dosing groups in Table 1); 3.7 x 10^12/L (Jiang 2023 postoperative Chinese GIST cohort central value, Table 1).
• Source aliases:
  • RBC* – used in Zhang_2012_bivalirudin.R (the asterisk in Zhang 2012 Table 1 denotes the per-subject baseline demographic value).
• Example models: Zhang_2012_bivalirudin.R (linear-deviation effect on EC50: EC50_i = theta_EC50 * exp(eta_EC50) * (1 + 1.70 * (RBC - 4.40))), Jiang_2023_imatinib.R (power scaling on apparent oral clearance: CL/F = 9.72 * (RBC / 3.7)^0.49 * ...; the paper attributes the positive association to imatinib binding to and distributing into red blood cells, and reports that halving RBC lowers CL/F by 29%).
• Notes: Universal CBC component, distinct from HGB (hemoglobin mass concentration), HCT (hematocrit volume fraction), and WBC (white blood cell count). Document time-varying-vs-baseline-only status in covariateData[[RBC]]$notes. RBC and HCT correlate via mean corpuscular volume (HCT ~ RBC * MCV / 10); models that retain both should record the dependency in notes.

Coagulation / hemostasis biomarkers

AT_BL_UDL (canonical for per-subject baseline plasma antithrombin activity)

• Description: Per-subject baseline (predose) plasma antithrombin (AT) activity level, expressed as units of AT activity per deciliter. Because the Stachrom chromogenic AT assay is calibrated to pooled normal plasma, units/dL is numerically equivalent to % of normal (100 units/dL = 100% of normal). Time-fixed per subject: the single predose value measured before the first AT-concentrate dose. Used both as a covariate on the volume of distribution ((AT_BL_UDL / 60)^exponent) and as an additive endogenous baseline in the observation equation to represent ongoing endogenous AT production alongside dosed exogenous AT (Dansirikul B3 method).
• Units: units/dL (equivalent to % of normal on chromogenic anti-FIIa or anti-FXa AT-activity assays).
• Type: continuous
• Scope: specific
• Reference category: n/a – used with power scaling (AT_BL_UDL / ref)^exponent on VD in the source paper’s final model. Reference values observed: 60 units/dL (Moffett 2017 cohort baseline mean 59 +/- 17, cohort median approximately 60). Normal healthy paediatric range 85-130 units/dL; the cohort baseline mean 59 is depressed relative to healthy range because the patients under study had consumptive AT depletion secondary to critical illness / concurrent UFH.
• Source aliases:
  • BASE – used in Moffett_2017_antithrombin.R (Moffett 2017 Table 3 baseline antithrombin activity covariate). The paper reports the covariate value as units/dL; the same numerical value is equivalent to % of normal on the STA-Stachrom AT chromogenic assay.
• Example models: Moffett_2017_antithrombin.R (power effect on VD: VD_dL = 67.9 * (WT/70)^1 * (AT_BL_UDL / 60)^(-0.389); reference 60 units/dL from Moffett 2017 Table 3. Also enters the observation equation as an additive endogenous baseline: Cc_measured = central / vc + AT_BL_UDL, per Dansirikul 2008 B3 method described in Moffett 2017 Methods “Data handling”).
• Notes: Specific scope until a second paediatric or adult AT / anticoagulation-supplementation model ratifies the canonical. Distinct from INR_BASE (baseline international normalized ratio, an integrated coagulation-cascade readout) and from other coagulation biomarkers (VWF, FVIIIRECENT) which measure different clotting-cascade components. The dual role – both a VD covariate AND an additive constant in the observation equation – reflects the Dansirikul B3 method’s approach to modelling exogenous drug administration on top of an endogenous baseline. When simulating, the same subject-level AT_BL_UDL value must be supplied for both effects; the additive baseline term captures the ongoing endogenous production of AT during the drug-derived-concentration decay window. Ratified canonically on 2026-06-21 alongside the Moffett 2017 antithrombin extraction.

INR_BASE (canonical for baseline international normalized ratio)

• Description: Pre-medication baseline INR (international normalized ratio of prothrombin time). Time-fixed per subject (measured once, before the first warfarin dose). Used directly in the warfarin K-PD INR equation as an additive constant (INR = INR_BASE + inrmax * (1 - (coag_s3 + coag_l3)/2) per Xia 2024 supplement Section 1.1) so the simulated INR returns to the subject-specific baseline when the drug is removed.
• Units: (unitless ratio; INR has no units)
• Type: continuous
• Scope: general
• Reference category: n/a – subject-specific baseline. Default simulation value documented per-model in covariateData[[INR_BASE]]$notes; the Xia 2024 simulation uses the total-cohort mean of 1.13 (Table 1).
• Source aliases:
  • INR_BASE, BL_INR, INRBASE – pre-medication INR column in NONMEM data sets; document the source-column name per-model in covariateData[[INR_BASE]]$source_name.
• Example models: Xia_2024_warfarin.R (additive baseline in the INR observation equation; cohort mean 1.13, SD 0.59 per Xia 2024 Table 1), Ohara_2014_warfarin_s.R (additive baseline in Ohara 2014 Eq 3; cohort mean 1.05, SD 0.10 per Ohara 2014 Table 1).
• Notes: Distinct from a time-varying INR observation (the model’s observed INR variable). Healthy subjects with no anticoagulation typically have INR around 1.0; the Hamberg / Xia 2024 model treats deviations from 1.0 as a subject-specific covariate rather than an estimated parameter so the model returns to the observed baseline when warfarin is withdrawn. Ratified canonically on 2026-05-16 alongside the Xia 2024 warfarin extraction.

NPT_BASE (canonical for baseline normal (fully carboxylated) prothrombin concentration)

• Description: Pre-medication baseline plasma concentration of normal prothrombin (NPT) – the fully gamma-carboxylated, coagulation-competent fraction of coagulation factor II, measured before the first vitamin-K-antagonist dose. Time-fixed per subject. This is a mass concentration of a specific protein species, not a clotting-time-derived index: it is distinct from INR_BASE and PTR, which are both unitless ratios derived from prothrombin time. Warfarin lowers NPT by inhibiting the vitamin-K-dependent carboxylation step, so NPT falls while total (carboxylated plus des-carboxy) factor II is comparatively unchanged; an assay that measures only the carboxylated fraction, such as the carinactivase-1 method of Ohara 2014, is therefore required.
• Units: mg/L (equivalently ug/mL, the unit used in the source literature)
• Type: continuous
• Scope: general
• Reference category: n/a – subject-specific measured baseline. Where a model centers a covariate effect on NPT_BASE, document the centering value per-model; Ohara_2014_warfarin_s.R centers on the 119 mg/L cohort median given in Shi 2024 Table 3 footnote d.
• Source aliases:
  • NPT0, NPT_0, NPT0_individual – Ohara 2014 / Shi 2024; the baseline value of the NPT turnover pool.
  • normal prothrombin / normal prothrombin concentration – prose form in the Japanese and Taiwanese warfarin pharmacometrics literature.
• Example models: Ohara_2014_warfarin_s.R (load-bearing in three places at once: the initial condition of the npt state, the zero-order synthesis rate kin = kout * NPT_BASE, and the covariate on the INR exponent lambda = 3.48 * exp(0.00588 * (NPT_BASE - 119)); cohort mean 118.2 mg/L, SD 22.1, n = 99 per Ohara 2014 Table 1).
• Notes: In indirect-response vitamin-K-antagonist models NPT0 is measured data rather than an estimated parameter – Ohara 2014 fitted no inter-individual variance term for it and excluded from the PD analysis the three patients whose NPT0 was missing – so it belongs in the data as a covariate column, exactly like INR_BASE. The _BASE suffix follows the established <ANALYTE>_BASE family (WT_BASE, CRCL_BASE, TBILI_BASE, INR_BASE, …). The abbreviation NPT is retained over a descriptive alternative (PROTHROMBIN_BASE) or coagulation-factor nomenclature (FII_BASE) because it is the standard term in this literature and because “factor II” would overstate what the column holds: NPT is specifically the carboxylated subfraction, not total factor II. Ratified canonically on 2026-08-07 alongside the Ohara 2014 S-warfarin PK/PD extraction (operator sidecar oare_PMC11424055 request-001 q1).

PTR (canonical for prothrombin time ratio (PT relative to baseline))

• Description: Prothrombin time ratio – the ratio of measured prothrombin time to the pre-treatment baseline prothrombin time of the same subject (or trial-arm population mean). Unitless. Used as a time-varying input biomarker that drives downstream pharmacodynamic / event-rate models for direct oral factor-Xa inhibitors and related anticoagulant pharmacology.
• Units: (unitless ratio)
• Type: continuous
• Scope: specific
• Reference category: n/a – continuous. PTR = 1 corresponds to no FXa-inhibitor effect (placebo / pre-dose baseline) and reproduces the placebo log-odds in Yoshioka_2018_FXa_inhibitors_mbma. Must be > 0 because downstream PD equations evaluate log(PTR).
• Source aliases:
  • PTR, PT_RATIO, x – in Yoshioka 2018 Eq. 1 / Eq. 2 the symbol x is used for the PT ratio; document the source-column name per-model in covariateData[[PTR]]$source_name.
• Example models: Yoshioka_2018_FXa_inhibitors_mbma.R (model-based meta-analysis: per-arm population-mean PTR is input; outputs are per-arm event probability of ischemic stroke/SE and major bleeding).
• Notes: Distinct from INR_BASE (a time-fixed baseline INR scalar used as an additive constant in warfarin K-PD models). PTR is time-varying and must be supplied externally (typically computed from an upstream popPK -> PT-ratio model for the FXa inhibitor of interest, e.g., Girgis 2014 rivaroxaban, Leil 2014 / Chang 2016 apixaban, Krekels 2016 / Koretsune 2015 edoxaban). Yoshioka 2018 corrects all PT measurements to RecombiplasTin reagent equivalence per Gosselin 2016 before computing the ratio; downstream models that consume PTR should document the reagent-correction convention they assume. Scope: specific until a second model ratifies the canonical name.

VWF (canonical for von Willebrand factor concentration)

• Description: Plasma concentration (or activity) of von Willebrand factor (VWF) – the multimeric carrier protein that binds and protects circulating factor VIII (FVIII) from proteolytic degradation and rapid clearance. Used as a covariate on FVIII (and FVIII-Fc) clearance because the vast majority (>95%) of circulating FVIII is in complex with VWF.
• Units: IU/dL (equivalent to % of pooled normal plasma); document per-model via covariateData[[VWF]]$units. Some sources report VWF:Ag (antigen) versus VWF:RCo (ristocetin cofactor activity); record which assay was used in covariateData[[VWF]]$notes.
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (VWF / ref)^exponent. Reference values observed: 118 IU/dL (Nestorov 2014, study-population median).
• Source aliases: none; VWF is the universal abbreviation. Source papers may write vWF (lowercase v) or specify the assay (VWF:Ag).
• Example models: Nestorov_2014_factorviii.R (reference 118 IU/dL, exponent -0.343 on CL; VWF antigen).
• Notes: Higher VWF protects FVIII from clearance, so the exponent on CL is negative. VWF is time-varying within an individual (acute-phase response, age, blood group, etc.), but most published population PK models use baseline-only VWF when the within-subject dynamics are not characterized; document the per-model convention in covariateData[[VWF]]$notes.

FVIIIRECENT (canonical for most recently measured FVIII:C activity)

• Description: The patient’s most recently measured plasma factor VIII coagulant activity (FVIII:C), obtained at most 1 day prior to a desmopressin (DDAVP) test / treatment administration and in the absence of any treatment effect on the measurement. Used as a per-occasion covariate that indexes the patient’s current endogenous FVIII synthetic capacity, which the source paper found to be more predictive of DDAVP-triggered FVIII:C response than the alternative FVIII-lowest (ever-lowest measurement) covariate. Distinct from the model’s observed FVIII:C time profile after DDAVP – FVIIIRECENT is the single pre-dose anchor value.
• Units: IU/mL (1 IU/mL = 100% of pooled normal plasma FVIII activity; equivalent to 100 IU/dL).
• Type: continuous
• Scope: specific
• Reference category: n/a – used with power scaling (FVIIIRECENT / ref)^exponent. Reference value observed: 0.15 IU/mL (Schütte 2018, study-population median FVIII-recent in Table 1).
• Source aliases:
  • FVIII-recent, FVIII_recent, fviii_recent, FVIIIRECENT – the recently-measured FVIII:C column. Document the source-column name per-model in covariateData[[FVIIIRECENT]]$source_name.
• Example models: Schutte_2018_desmopressin.R (reference 0.15 IU/mL; exponents +0.74 on baseline FVIII, -0.61 on V1, -0.73 on CL; Schütte 2018 Table 2 final covariate model).
• Notes: Specific scope until a second nonsevere haemophilia A / DDAVP-response model registers the canonical. Time-fixed per desmopressin episode (one pre-dose measurement per occasion). The source paper additionally defines a binary missing-data branch with flat correction factors (1.2 / 1.1 / 0.78 on baseline FVIII / V1 / CL) when FVIIIRECENT was unavailable for a fitted subject; per the standing nlmixr2lib pattern, the missing branch is documented in the validation vignette deviations rather than encoded as a separate MISSING_FVIIIRECENT covariate, and simulation users are expected to supply FVIIIRECENT for every simulated patient. Distinct from FVIII-lowest (ever-lowest historical FVIII:C), which Schütte 2018 tested but did NOT retain in the final covariate model. Ratified canonically on 2026-05-30 alongside the Schütte 2018 desmopressin extraction.

DDIMER (canonical for plasma D-dimer concentration)

• Description: Plasma D-dimer protein concentration, the fibrin-degradation peptide produced by plasmin-mediated cleavage of cross-linked fibrin. Used in vascular / coagulation-pathology models as a circulating biomarker of fibrin turnover, intra-aneurysmal thrombus burden, or systemic fibrinolytic activity.
• Units: ng/mL
• Type: continuous
• Scope: specific
• Reference category: n/a – used either with log10-transformed proportional scaling log10(DDIMER) / median(log10(DDIMER)) (Sherer 2012) or with categorical strata (Sherer 2012 sensitivity analysis groups: <=150, 151-300, 301-900, >900 ng/mL). Reference values observed: 326 ng/mL (Sherer 2012 cohort median; log10 approx 2.513).
• Source aliases:
  • C^(D-dimer) – used in Sherer_2012_AAA.R (the symbol in Sherer 2012 Methods equation page 2).
• Example models: Sherer_2012_AAA.R (proportional log10-transformed covariate on the baseline AAA growth rate beta1 (e_ddimer_b1 = 0.90 mm/year) and on the first derivative of growth rate with size beta2 (e_ddimer_b2 = 0.37/year)).
• Notes: Specific scope until a second model registers the canonical. Time-fixed (baseline-only) in Sherer 2012 because the HIMS cohort had a single follow-up D-dimer measurement; the source paper flags this as a limitation. Cohort interquartile range 142-785 ng/mL; extrapolation outside this range is not validated by the source. The log10 transformation reflects Sherer 2012’s finding that “differences in AAA growth were predominantly driven by patients with the highest plasma D-dimer concentrations.” Distinct from the time-varying biomarker columns in indirect-response / TMDD models – DDIMER enters Sherer 2012 as a baseline regression covariate, not as a dynamic exposure / response variable. Ratified canonically on 2026-05-16 alongside the Sherer 2012 extraction.

AAA_DIAM (canonical for baseline abdominal aortic aneurysm diameter)

• Description: Abdominal aortic aneurysm (AAA) maximum infrarenal diameter, ascertained by ultrasound at study entry. Used in vascular disease-progression models as the per-subject baseline severity covariate that anchors the typical-value regression for individual-level growth parameters.
• Units: mm
• Type: continuous
• Scope: specific
• Reference category: n/a – used in proportional form AAA_DIAM / median(AAA_DIAM) so the effect coefficients represent the contribution at the cohort median. Reference value observed: 32.7 mm (Sherer 2012 cohort median; q1 30.8, q3 36.0).
• Source aliases:
  • Y(0) – used in Sherer_2012_AAA.R (the symbol in Sherer 2012 Methods equation page 2; the baseline screening ultrasound diameter).
• Example models: Sherer_2012_AAA.R (proportional covariate on all three individual-level parameters: e_aaadiam_b0 = 32.6 mm on baseline size beta0, e_aaadiam_b1 = 2.03 mm/year on baseline growth rate beta1, and e_aaadiam_b2 = 0.59/year on the first derivative of growth rate with size beta2).
• Notes: Specific scope until a second model registers the canonical. Time-fixed (baseline-only) per subject – the value is the single screening ultrasound diameter; the time-evolving AAA diameter during follow-up is the model’s observation, not the covariate. Sherer 2012 inclusion criterion (HIMS cohort): 30-49 mm small AAA, so extrapolation outside this range to <30 mm (non-aneurysmal aorta) or >=50 mm (surgical-referral threshold) is not validated by the source. Ratified canonically on 2026-05-16 alongside the Sherer 2012 extraction.

FIB (canonical for plasma fibrinogen concentration)

• Description: Plasma fibrinogen concentration (Clauss assay or equivalent). Acute-phase protein synthesised in the liver; values rise in inflammation, infection, trauma, and pregnancy and fall in disseminated intravascular coagulation, severe hepatic failure, and congenital afibrinogenaemia. Used as a per-subject (or time-varying) covariate that proxies hepatic acute-phase response or inflammatory burden on the clearance of drugs whose elimination correlates with these processes.
• Units: umol/L (Taubert 2016 reports values in umol/L; the more common clinical units are g/L or mg/dL – document per-model via covariateData[[FIB]]$units. Approximate conversion using a molecular weight of 340 kDa: 1 umol/L ~ 0.34 g/L.)
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (FIB / ref)^exponent. Reference values observed: 13.0 umol/L (Taubert 2016 patient-group-1 median; a critically-ill ICU cohort with elevated baseline fibrinogen).
• Source aliases:
  • Fibrinogen – the spelled-out column header in Taubert 2016 Table 1 and supplementary Table S3; same biological quantity, no value transformation.
• Example models: Taubert_2016_linezolid.R (umol/L; reference 13.0; positive power exponent 0.04 on CL; quantitatively a weak effect at the cohort range but selected by stepwise covariate modeling).
• Notes: Distinct from FVIIIRECENT and VWF (specific coagulation-factor activities used in haemophilia / DDAVP-response models) – FIB is the routine plasma-fibrinogen concentration used as an acute-phase / inflammatory marker in popPK analyses. Distinct from CRP, IL6, LDH, and other acute-phase / cell-turnover biomarkers; FIB may correlate with these but is a separate measurement. Ratified canonically on 2026-06-30 alongside the Taubert 2016 linezolid extraction.

Disease severity scores

SCORE_EASI (canonical for Eczema Area and Severity Index)

• Description: Eczema Area and Severity Index score (atopic-dermatitis severity composite; bounded continuous, scale 0-72 with higher values = more severe disease).
• Units: (score)
• Type: continuous
• Scope: general
• Reference category: n/a – healthy volunteers have SCORE_EASI = 0. Effect enters as an additive term in models that pool AD patients with HV (e.g., Tiraboschi_2025_amlitelimab.R).
• Source aliases:
  • EASI – prior canonical name (pre-2026-06-19 standardization audit).
  • BEASI (baseline SCORE_EASI) – used in Tiraboschi_2025_amlitelimab.R.
• Example models: Tiraboschi_2025_amlitelimab.R.
• Notes: When used as a time-invariant baseline covariate (BEASI), document in covariateData[[SCORE_EASI]]$notes. Canonical name is SCORE_EASI without the B prefix to match the AGE / WT / ALB pattern where baseline vs time-varying status is recorded in notes rather than the column name.

SCORE_MGADL (canonical for Myasthenia Gravis Activities of Daily Living score)

• Description: Myasthenia Gravis Activities of Daily Living score – eight-item patient-reported outcome measure (each item 0-3), total 0-24, higher values = greater symptom severity and functional limitation.
• Units: (score)
• Type: continuous
• Scope: general
• Reference category: n/a – healthy participants (no gMG) have SCORE_MGADL = 0 by definition. Effect enters as a baseline covariate on MG-ADL response parameters in gMG cohorts.
• Source aliases:
  • MGADL – prior canonical name (pre-2026-06-19 SCORE_ family standardization).
• Example models: Valenzuela_2025_nipocalimab.R (reference 7 points; power-form effect on IDecplacebo and on the slope between MG-ADL change and IgG reduction).
• Notes: Baseline-only in Valenzuela 2025 (the observation is the absolute change from baseline MG-ADL). When used time-varying (e.g., in pure PD models driven by disease-progression dynamics), document in covariateData[[SCORE_MGADL]]$notes. Canonical name is SCORE_MGADL without a BL prefix to match the SCORE_EASI / AGE / WT / ALB pattern.

SCORE_BCVA (canonical for best-corrected visual acuity)

• Description: Best-corrected visual acuity score measured on the Early Treatment Diabetic Retinopathy Study (ETDRS) chart, expressed as the number of letters read correctly (0-100; higher values = better vision). Used as a baseline severity covariate in ophthalmology PK/PD models of anti-VEGF treatment.
• Units: ETDRS letters (0-100)
• Type: continuous
• Scope: specific
• Reference category: n/a – used as a baseline input to set the initial condition of an indirect-response SCORE_BCVA state or as a power-form effect on response parameters. Reference value observed: 55 letters (Mulyukov 2018 narrative: mean study-population baseline SCORE_BCVA).
• Source aliases:
  • BCVA – prior canonical name (pre-2026-06-19 standardization audit).
  • BVA (baseline visual acuity) – used in Mulyukov_2018_ranibizumab.R.
• Example models: Mulyukov_2018_ranibizumab.R (baseline SCORE_BCVA used as the center for the initial-condition draw g0 = SCORE_BCVA + eta_g0).
• Notes: Ophthalmology-specific. Baseline-only in Mulyukov 2018 (carried once per subject and used only as the starting SCORE_BCVA for the indirect-response model). Canonical name drops the B prefix to match the SCORE_EASI / AGE / WT / ALB pattern (baseline-vs-time-varying status recorded in covariateData[[SCORE_BCVA]]$notes). Scope is specific until a second ophthalmology model ratifies the name; at that point promote to general.

PREV_AE_SCORE (canonical for previous-time-step ordinal adverse-event score)

• Description: Ordinal adverse-event score recorded at the immediately preceding observation time, used as a Markov-state covariate that conditions the current-time logit / probability calculation on the previous outcome. Integer 0..N where N is the maximum AE grade in the source-paper grading scheme; convention is that the value is 0 at the first observation (no prior AE).
• Units: (ordinal score; document per-model the scale in covariateData[[PREV_AE_SCORE]]$notes)
• Type: count
• Scope: general
• Reference category: n/a – typically used as a categorical conditioner (IF (PREV_AE_SCORE == 0) ...) or via piecewise-FPS indicator decomposition; the natural reference is PREV_AE_SCORE = 0 (no prior AE).
• Source aliases:
  • PREVSCOR – used in Girard_2012_pimasertib.R (CTCAE 0..3 ocular-AE score).
  • PDV – used in Yoshida_2024_fazpilodemab.R (GIAE grade 0 / 1 / 2-3 combined, carried via the NONMEM IF (EVID.EQ.0.AND.TYPE.EQ.1) PDV=DV idiom in the DTMM control stream).
• Example models: Girard_2012_pimasertib.R (Markov-state covariate that selects per-previous-score logit thresholds b01/b11/b21 and b02/b12/b22 and per-previous-score emax levels; reset to 0 at TIME = 0 per source IF (TIME.EQ.0) PREVSCOR=0), Yoshida_2024_fazpilodemab.R (Markov-state covariate that selects per-previous-grade DTMM intercepts b1_g{0,1,2}_ae / b2b1_g{0,1,2}_ae and per-previous-grade discontinuation intercepts b_dc_g{0,1,2}; pooled grades 2 and 3 into a single PREV_AE_SCORE = 2 category per the source paper Methods page 545).
• Notes: Scope promoted from specific to general on 2026-06-30 alongside the Yoshida 2024 fazpilodemab extraction (the second model to use this canonical). The ordinal scale itself is paper-specific (different AE-grading schemes, different number of categories, different grouping rules – Girard 2012 collapses CTCAE grades 1+2 into a single “1-2” category and treats grades >=3 as a third stratum; Yoshida 2024 pools CTCAE grades 2 and 3 into a single PREV_AE_SCORE = 2 category) and must be documented per-model in covariateData[[PREV_AE_SCORE]]$notes; the COVARIATE CONCEPT (previous-time-step ordinal AE grade as a Markov-state conditioner) is general. When assembling the simulation event table, set PREV_AE_SCORE = 0 at the first observation of every subject and update each subsequent observation to the previous observation’s sampled score – matching the NONMEM IF (TIME.EQ.0) PREVSCOR=0 / PREVSCOR = DV carry-forward idiom. Distinct from PAIN (continuous baseline pain score) and from SCORE_MGADL / SCORE_EASI (continuous severity scores not modelled as Markov states).

SCORE_CDR_SOB (canonical for Clinical Dementia Rating - Sum of Boxes score)

• Description: Clinical Dementia Rating scale - Sum of Boxes (CDR-SOB) score, the unweighted sum of the six CDR-box scores (memory, orientation, judgement and problem solving, community affairs, home and hobbies, personal care; each scored 0 / 0.5 / 1 / 2 / 3). Total ranges 0-18; higher values indicate more severe cognitive and functional impairment. Widely used as a primary efficacy endpoint in Alzheimer’s-disease (AD) clinical trials and as a disease-progression biomarker in mild-cognitive-impairment (MCI) / prodromal-AD populations.
• Units: (CDR-SOB units, 0-18 score)
• Type: continuous
• Scope: general
• Reference category: n/a – the source paper centres covariate effects on dataset medians (e.g., Delor 2013 uses SCORE_CDR_SOB / 2 in the DOT power-form and SCORE_CDR_SOB - 1 in the mixture-logit additive form).
• Source aliases:
  • CDR_SOB – prior canonical name (pre-2026-06-19 standardization audit).
  • CDR_bsl – used in Delor_2013_alzheimer.R (baseline CDR-SOB at study entry).
  • CDR – alternative bare-name often seen in ADNI / CAMD-style NONMEM datasets.
• Example models: Delor_2013_alzheimer.R (time-fixed baseline covariate; enters both the per-subject DOT power form ((SCORE_CDR_SOB / 2)^e_cdr_sob_dot with e_cdr_sob_dot = -0.072) and the per-subject slow-progression mixture-logit additive form (+ e_cdr_sob_slow * (SCORE_CDR_SOB - 1) with e_cdr_sob_slow = -1.27)).
• Notes: Canonical name is SCORE_CDR_SOB without a _BL suffix to match the SCORE_EASI / SCORE_MGADL / SCORE_BCVA pattern (baseline-vs-time-varying status recorded in covariateData[[SCORE_CDR_SOB]]$notes). The CDR sum-of-boxes form is distinct from the global CDR rating (CDR_GLOBAL, a 0 / 0.5 / 1 / 2 / 3 ordinal); the sum-of-boxes is preferred in disease-progression modelling for its finer granularity. Ratified canonically on 2026-05-16 alongside the Delor 2013 extraction.

SCORE_ADAS_COG (canonical for ADAS-cog total cognitive subscale score)

• Description: Alzheimer’s Disease Assessment Scale - cognitive subscale (ADAS-cog) total score. The total-11 form ranges 0-70; the modernised total-13 form ranges 0-85. Higher values = more cognitive impairment. The ADAS-cog is the most widely used cognitive endpoint in AD clinical trials and disease-progression modelling.
• Units: (ADAS-cog units; document the form (total-11 / total-13) per-model in covariateData[[SCORE_ADAS_COG]]$units)
• Type: continuous
• Scope: general
• Reference category: n/a – covariate effects typically centred on a dataset median (e.g., Delor 2013 centres on SCORE_ADAS_COG = 12.67).
• Source aliases:
  • ADAS_COG – prior canonical name (pre-2026-06-19 standardization audit).
  • ADAS_bsl – used in Delor_2013_alzheimer.R (baseline ADAS-cog total-11 at study entry).
  • ADAS, ADAS_COG_11, ADAS_COG_13 – alternative bare-name forms seen across ADNI / CAMD datasets.
• Example models: Delor_2013_alzheimer.R (time-fixed baseline covariate; ADAS-cog total-11 form; enters the per-subject DOT power form (SCORE_ADAS_COG / 12.67)^e_adas_cog_dot with e_adas_cog_dot = -0.0439).
• Notes: Canonical name is SCORE_ADAS_COG (no _BL suffix; baseline-vs-time-varying recorded in notes). The total-11 vs total-13 form must be documented per-model in covariateData[[SCORE_ADAS_COG]]$units because the same numeric SCORE_ADAS_COG value has different clinical interpretation across the two forms. Conrado 2014 uses ADAS-cog as the modelled observation (response variable) rather than as a baseline covariate; that model file therefore does not list SCORE_ADAS_COG in its covariateData. Ratified canonically on 2026-05-16 alongside the Delor 2013 extraction.

SCORE_MMSE (canonical for Mini Mental State Examination score)

• Description: Mini Mental State Examination total score (0-30; higher values = better cognitive function). A widely used cognitive-screening instrument in AD and MCI populations.
• Units: (SCORE_MMSE units, 0-30 score)
• Type: continuous
• Scope: general
• Reference category: n/a – covariate effects typically centred on a dataset median (e.g., Delor 2013 centres on SCORE_MMSE = 26).
• Source aliases:
  • MMSE – prior canonical name (pre-2026-06-19 standardization audit).
  • MMSE_bsl – used in Delor_2013_alzheimer.R (baseline SCORE_MMSE at study entry).
• Example models: Delor_2013_alzheimer.R (time-fixed baseline covariate; modifies the per-subject disease-progression acceleration parameter alpha via a power form (SCORE_MMSE / 26)^e_mmse_alpha with e_mmse_alpha = -2.01).
• Notes: Canonical name is SCORE_MMSE (no _BL suffix; baseline-vs-time-varying recorded in notes). SCORE_MMSE is the inverse-direction counterpart of SCORE_CDR_SOB / SCORE_ADAS_COG (SCORE_MMSE high = healthy; SCORE_CDR_SOB / SCORE_ADAS_COG high = impaired); covariate-effect coefficient signs are therefore typically opposite to those for SCORE_CDR_SOB / SCORE_ADAS_COG. Ratified canonically on 2026-05-16 alongside the Delor 2013 extraction.

SCORE_FAQ (canonical for Functional Assessment Questionnaire score)

• Description: Functional Assessment Questionnaire (Pfeffer SCORE_FAQ) total score: sum of ten functional-activities items (each scored 0 = normal to 3 = dependent), total 0-30; higher values = greater functional impairment. Used as a functional-status covariate alongside cognitive scores in AD and MCI populations.
• Units: (SCORE_FAQ units, 0-30 score)
• Type: continuous
• Scope: general
• Reference category: n/a – covariate effects typically centred on a dataset median (e.g., Delor 2013 centres on SCORE_FAQ = 1).
• Source aliases:
  • FAQ – prior canonical name (pre-2026-06-19 standardization audit).
  • FAQ_bsl – used in Delor_2013_alzheimer.R (baseline SCORE_FAQ at study entry).
• Example models: Delor_2013_alzheimer.R (time-fixed baseline covariate; enters the per-subject slow-progression mixture-logit additive form + e_faq_slow * (SCORE_FAQ - 1) with e_faq_slow = -0.341).
• Notes: Canonical name is SCORE_FAQ (no _BL suffix; baseline-vs-time-varying recorded in notes). Distinct from the cognitive scores (SCORE_CDR_SOB / SCORE_ADAS_COG / SCORE_MMSE): SCORE_FAQ measures instrumental activities of daily living rather than cognitive performance, and adds incremental information about disease-stage severity in MCI cohorts. Ratified canonically on 2026-05-16 alongside the Delor 2013 extraction.

SCORE_RHPNM (canonical for normalized hippocampal volume (head-size and age-adjusted))

• Description: Normalised hippocampal volume: the subject’s average left+right hippocampal volume divided by the value expected for a healthy subject of the same age and estimated intracranial volume (head size). 1.0 corresponds to the healthy reference; values below 1.0 indicate hippocampal atrophy. Derived from MRI volumetry and a healthy-subject regression on age and intracranial volume.
• Units: (unitless ratio; 1.0 = healthy reference)
• Type: continuous
• Scope: general
• Reference category: n/a – covariate effects typically centred on SCORE_RHPNM = 1 (the healthy reference, e.g., Delor 2013).
• Source aliases:
  • RHPNM – prior canonical name (pre-2026-06-19 standardization audit).
  • SCORE_RHPNM – used in Delor_2013_alzheimer.R (baseline normalized hippocampal volume; Delor 2013 derivation: RHPNMbsl_i = HIPVbsl_i / HPNMbsl_i where HPNMbsl_i = Age_i * (-26.6268 + EICVbsl_i * 0.0016 + 3340.4395)).
• Example models: Delor_2013_alzheimer.R (time-fixed baseline covariate; enters the per-subject slow-progression mixture-logit additive form + e_rhpnm_slow * (SCORE_RHPNM - 1) with e_rhpnm_slow = 7.5, a strongly positive effect indicating that less atrophic hippocampi (SCORE_RHPNM closer to 1) are associated with a higher probability of being in the slow-progressing subpopulation).
• Notes: Distinct from the raw hippocampal volume (which would be a HIPV canonical not yet registered; raw HIPV is confounded with head size and age, hence the need for the normalisation). The Delor 2013 paper notes that the same effect is only marginally significant with unnormalised HIPV (P = 0.02) but strongly significant with the normalised form (SCORE_RHPNM). The exact age / EICV regression coefficients are paper-specific and any future model adopting this canonical should re-derive the normalisation for its own population or document why the Delor 2013 regression is reused. Ratified canonically on 2026-05-16 alongside the Delor 2013 extraction.

SCORE_ROSS (canonical for modified Ross score of paediatric heart-failure severity)

• Description: Modified Ross score, a clinical heart-failure severity instrument for infants and young children. Integer sum of per-domain ordinal items across feeding / exertional, respiratory (tachypnoea, retractions), cardiac auscultation (S3, hepatomegaly), and perfusion domains. Scale 0-12 in the modified Reithmann / Ross / Connolly form used across paediatric heart-failure trials; lower values = milder heart failure, higher values = more severe. The paediatric analogue of the adult-only NYHA functional classification.
• Units: (score; integer 0-12)
• Type: count
• Scope: general
• Reference category: n/a – typically entered as an exponential effect exp(theta * (SCORE_ROSS - ref)) on a PK parameter, centred on a dataset median. Per-model reference value must be documented in covariateData[[SCORE_ROSS]]$notes. The exponential form is required over a power form because the score can legitimately be 0 (per Steichert 2025 Section 2.2.2 rationale).
• Source aliases:
  • Ross score – paper-prose form.
• Example models: Steichert_2025_enalapril_enalaprilat_pediatric.R (reference SCORE_ROSS = 4, the analysed-population weighted median; exponential effect theta = -0.15 (RSE 26.3%) on the apparent volume of distribution of enalaprilat Vd_ENAAT/F).
• Notes: Distinct from RACHS1 (Risk Adjustment for Congenital Heart Surgery, a perioperative surgery-risk category derived from procedure type) and from the adult-only NYHA classification. Count type rather than continuous because the score is an integer sum of per-domain ordinal items. Different modifications of the Ross score exist across paediatric cardiology (Reithmann 1989, Ross 1992 derivation; Connolly 2001, Laer / LENA 2015+ form); the specific modification used must be documented in covariateData[[SCORE_ROSS]]$notes per model.

SCORE_MADRS (canonical for Montgomery-Asberg Depression Rating Scale score)

• Description: Montgomery-Asberg Depression Rating Scale total score: ten clinician-rated items (apparent sadness, reported sadness, inner tension, reduced sleep, reduced appetite, concentration difficulties, lassitude, inability to feel, pessimistic thoughts, suicidal thoughts), each scored 0-6 at interview, total 0-60. Higher values = more severe depression. The standard severity instrument in antidepressant trials; remission is conventionally defined as a total score below 10.
• Units: (SCORE_MADRS units, 0-60 score)
• Type: continuous
• Scope: general
• Reference category: n/a – covariate effects are centred or normalised on a cohort value; record which assessment timepoint the column carries in covariateData[[SCORE_MADRS]]$notes. Reference value observed: 28.64 (the week-1 cohort value in Shigetome 2025).
• Source aliases:
  • MADRS_W1 – NONMEM $INPUT column name in Shigetome 2025 Text S3, carrying the score one week after starting treatment.
  • MADRS_BASE / MADRSW0 – the same instrument at baseline; screened but not retained in Shigetome 2025.
• Example models: Shigetome_2025_paroxetine_madrs.R (week-1 value; enters the time to half-maximal improvement as the added power term et50 + (SCORE_MADRS / 28.64)^2.38, so lower week-1 severity brings the improvement forward).
• Notes: Canonical name is SCORE_MADRS with no timepoint suffix, matching the SCORE_MMSE / SCORE_EASI / AGE / WT convention where baseline-versus-time-varying status and the specific assessment visit are recorded in covariateData[[SCORE_MADRS]]$notes rather than in the column name. Direction of severity is the same as SCORE_CDR_SOB / SCORE_ADAS_COG and the opposite of SCORE_MMSE (high = healthy), so covariate-effect signs are not transferable between them. When a model’s endpoint is derived from MADRS (e.g. an enhancement rate defined as the percentage reduction from the baseline score), the baseline score defines the endpoint and is not a covariate; only a separately-timed score such as the week-1 value belongs in this column. Distinct from the Hamilton Depression Rating Scale (HAM-D), Beck Depression Inventory and Quick Inventory of Depressive Symptomatology, which are different instruments on different scales and need their own canonicals.

ACUTE_MED_DAYS (canonical for baseline number of days/month of acute migraine medication use)

• Description: Baseline number of days per month on which acute migraine medication (triptans or ergot compounds) was used during the 28-day run-in period prior to first dose. Enters as a piecewise-linear shift on baseline migraine or moderate-to-severe headache days in migraine exposure-response models.
• Units: days/month
• Type: continuous
• Scope: specific
• Reference category: n/a – piecewise-linear shift with breakpoint at 5 d/mo: contributes 0 below 5 and slope * (ACUTE_MED_DAYS - 5) above 5 (Fiedler-Kelly 2020). The 5-day breakpoint reflects the clinical guideline for medication-overuse headache.
• Source aliases: “Baseline days/month of acute medications” – used in FiedlerKelly_2020_fremanezumab_em.R and FiedlerKelly_2020_fremanezumab_cm.R.
• Example models: FiedlerKelly_2020_fremanezumab_em.R (slope 0.438 d/d, episodic migraine), FiedlerKelly_2020_fremanezumab_cm.R (slope 0.460 d/d, chronic migraine).
• Notes: Specific scope because the variable is migraine-domain-bound. Time-fixed per subject (baseline-only). When future migraine E-R models register additional aliases or alternative breakpoints, document them per-model and consider promoting to general.

NEXAC12M (canonical for number of disease exacerbations in the 12 months before study entry)

• Description: Count of clinically significant disease exacerbations experienced in the 12 months immediately preceding study entry, supplied as a per-subject baseline covariate. Standard measure of pre-treatment disease instability in chronic airway disease (asthma, COPD) and other relapsing-remitting conditions; enters exposure-response models as a marker of how brittle the subject’s disease was before randomization. Time-fixed per subject.
• Units: count (events in the prior 12 months)
• Type: count
• Scope: specific
• Reference category: n/a – used with power scaling (NEXAC12M / ref)^exponent. Reference values observed: 1.0 (Zhang 2025, median of the pooled Phase 2b + Phase 3 uncontrolled moderate-to-severe asthma cohort; range 1-50).
• Source aliases:
  • PREEXAC (“number of prior exacerbations in the past year”) – used in Zhang_2025_dupilumab_fev1.R.
• Example models: Zhang_2025_dupilumab_fev1.R (power effect (NEXAC12M / 1.0)^-0.0411 on baseline pre-bronchodilator FEV1; subjects with more prior exacerbations start from a lower baseline lung function, though Zhang 2025 Table 4 shows the effect is not clinically meaningful – only -7% at the 95th-percentile count of 6).
• Notes: Distinct from [[HOSPRA]], which is a time-varying binary indicator of being hospitalised for a pulmonary exacerbation at the moment a spirometry value is recorded; NEXAC12M is a time-fixed baseline count over a fixed 12-month look-back window. Also distinct from the decomposed-band pattern used for baseline seizure counts (NSP3M_LT2 / NSP3M_7_50 / NSP3M_GT50): those source papers binned the count into categories, whereas Zhang 2025 uses the raw count continuously inside a power function, which cannot be expressed as band indicators. The exacerbation definition is disease- and protocol-specific (Zhang 2025: severe asthma exacerbations per the DRI12544 / EFC13579 protocols); document the source definition in covariateData[[NEXAC12M]]$notes per model. Register a differently-windowed canonical (e.g. a 6-month or 24-month look-back) rather than reusing this name if a future paper changes the window. Scope: specific until a second model ratifies the semantics; promote to general at that point.

DIS_AMYLOID_LOAD (canonical for systemic-amyloidosis whole-body amyloid load grade)

• Description: Ordinal whole-body amyloid load score in patients with systemic amyloidosis. Integer 0-3: 0 = no amyloid (healthy volunteers), 1 = small amyloid load, 2 = moderate amyloid load, 3 = large amyloid load. The grading combines organ-by-organ amyloid presence (liver, spleen, bone, adrenals, gut, heart) and SAP-scintigraphy uptake into a single per-patient severity grade at baseline. Time-fixed per subject.
• Units: (categorical 0-3)
• Type: categorical
• Scope: specific
• Reference category: 0 (no amyloid). Sahota 2015 Eq. 2 also treats category 1 as part of the reference for the V4 effect (categories 0 and 1 share a V4 multiplier of 1); only categories 2 and 3 carry a non-zero effect via the cumulative parameters e_amload2_vp_sap and e_amload2_vp_sap + e_amload3_vp_sap respectively.
• Source aliases:
  • AMLOAD – prior canonical name (pre-2026-06-19 DIS_ prefix standardization) and source-paper column in Sahota_2015_miridesap.R.
• Example models: NA_NA_miridesap.R (DDMODEL00000262; Sahota 2015 Eq. 2 multiplicative effect on SAP peripheral volume V4: V4 = V4_ref * (1 + e_amload2_vp_sap * I(DIS_AMYLOID_LOAD>=2) + e_amload3_vp_sap * I(DIS_AMYLOID_LOAD>=3)); reported effects e_amload2_vp_sap = 6.39 / e_amload3_vp_sap = 26.39 yielding ~7.4x V4 at moderate load and ~33.8x at large load), Sahota_2015_miridesap.R (paper-only extraction of the same Sahota 2015 final model with identical Eq. 2 effect on V4; values 6.39 / 26.39 taken from Table 2).
• Notes: Scope: specific because the grading scheme is amyloidosis-specific (Sahota 2015 Methods: “The whole body amyloid load covariate, AMLOAD, was a categorical score: 0 for no amyloid in healthy volunteers, 1 for small, 2 for moderate, and 3 for large”). The cumulative monotonic parameterisation in Sahota 2015 Eq. 2 encodes a positive-only step at each grade increment. Co-used with DIS_AMYLOID_LIVER for the binary hepatic-involvement modifier. Ratified canonically on 2026-05-15 alongside the DDMODEL00000262 / Sahota 2015 extraction. Renamed from AMLOAD to DIS_AMYLOID_LOAD on 2026-06-19 per the canonical-register standardization audit (operator decision to apply the DIS_<concept> prefix uniformly to disease-state indicators and to spell out amyloidosis rather than the shortened AM prefix that collides with chemistry symbols).

DIS_AMYLOID_LIVER (canonical for hepatic amyloid involvement indicator)

• Description: Binary indicator for the presence of amyloid in the liver as a separate organ involvement from the overall whole-body amyloid load. 1 = liver amyloid present at baseline; 0 = no liver amyloid. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no liver amyloid).
• Source aliases:
  • AMLIVER – prior canonical name (pre-2026-06-19 DIS_ prefix standardization) and source-paper column in Sahota_2015_miridesap.R.
• Example models: NA_NA_miridesap.R (DDMODEL00000262; Sahota 2015 Eq. 2 multiplicative effect on SAP intercompartmental clearance Q4: Q4 = Q4_ref * (1 + e_amliver_q4 * DIS_AMYLOID_LIVER); reported effect 4.01, yielding ~5x Q4 in patients with hepatic amyloid), Sahota_2015_miridesap.R (paper-only extraction of the same Sahota 2015 final model with identical Eq. 2 effect on Q4; value 4.01 from Table 2).
• Notes: Scope: specific because the covariate is amyloidosis-specific (Sahota 2015 Methods names AMLIVER alongside AMSPLEEN / AMHEART as organ-specific amyloid-involvement binary indicators; only AMLIVER was retained as a covariate in the final model). Used in combination with the global DIS_AMYLOID_LOAD grade so the model can express both general amyloid burden and the specific hepatic-clearance modifier (the SAP-CPHPC complex is cleared by the liver, motivating the hepatic-amyloid-specific Q4 effect). Ratified canonically on 2026-05-15 alongside the DDMODEL00000262 / Sahota 2015 extraction. Renamed from AMLIVER to DIS_AMYLOID_LIVER on 2026-06-19 per the canonical-register standardization audit (operator decision to apply the DIS_<concept> prefix uniformly to disease-state indicators and to spell out amyloidosis rather than the shortened AM prefix that collides with chemistry symbols).

DIS_ARF (canonical for binary acute-renal-failure indicator)

• Description: Binary indicator that the subject is in acute renal failure (equivalently, acute kidney injury) at the time of the modelled treatment. 1 = acute renal failure; 0 = normal renal function. Used when a population PK analysis pools subjects in acute renal failure with subjects whose renal function is intact and estimates a separate typical clearance in each stratum. Acute renal failure is commonly ascertained from urine output (anuria or oliguria) or from an acute creatinine rise, and in the critical-care popPK literature is frequently reported as a bare cohort yes/no flag with no accompanying creatinine-clearance value – which is exactly the situation this canonical exists to encode.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (normal renal function).
• Source aliases:
  • ARF – the Table 1 row label and the parameter-name suffix (CLbody_arf / CLbody_nrf) used in Zhang_2025_fluconazole.R.
  • URINE – the NONMEM $INPUT column in the Zhang 2025 supplement, a urine-output category whose level 4 denotes normal renal function; DIS_ARF = 1 - (URINE == 4).
• Example models: Zhang_2025_fluconazole.R (selects between two separately estimated residual body-clearance typical values sharing one IIV term: cl <- exp(lcl_arf * DIS_ARF + lcl_nrf * (1 - DIS_ARF) + etalcl), with lcl_arf = log(0.41 L/h) and lcl_nrf = log(1.25 L/h) from Zhang 2025 Table 2, reproducing the supplement’s $PK block CL = THETA(1)*EXP(ETA(1)) overridden by IF (URINE.EQ.4) CL = THETA(2)*EXP(ETA(1))).
• Notes: Distinct from the RENALIMP_MILD / RENALIMP_MOD / RENALIMP_SEV family, which are CHRONIC-impairment severity bands defined on creatinine clearance: those presuppose a measured CrCl and a stable baseline, whereas acute renal failure is an unstable acute state in which CrCl is neither measurable nor meaningful (the founding cohort is largely anuric or oliguric, so CrCl is near zero and carries no gradation). Do not substitute RENALIMP_SEV for DIS_ARF; the acute-versus-chronic distinction changes both the covariate’s ascertainment and the direction in which clearance is expected to recover. Distinct from URINE_FLOW (instantaneous mL/h) and URINE_VOL_24H (24-h residual diuresis), which are the continuous urine-output measurements from which an acute-renal-failure flag may be derived but which are not themselves the binary. Pairs naturally with RRT_CRRT_ACTIVE / RRT_HEMODIAL_ACTIVE and QBL / QEFF, because a cohort in acute renal failure is usually receiving renal replacement therapy and the model must then separate residual body clearance from extracorporeal clearance. Scope: specific; promote to general if a second paper pools acute-renal-failure with normal-renal-function subjects using the same bare-binary semantics. Registered in the DIS_<condition> family alongside the acute / critical-illness siblings DIS_CRITILL, DIS_ARDS, DIS_SEPSIS, and DIS_BURN_RECENT per operator decision (sidecar request-001, question 2, answered 2026-08-20). Ratified canonically alongside the Zhang 2025 fluconazole extraction.

Critical-illness severity

DIS_CRITILL (canonical for binary critical-illness / ICU-admission indicator)

• Description: Binary indicator that the subject is critically ill (equivalently, admitted to an intensive care unit) at the time of the modelled treatment. 1 = critically ill; 0 = not critically ill (ward inpatient or outpatient). Time-fixed per subject in the papers registered so far. Used when a population PK analysis pools critically ill and non-critically-ill subjects in one dataset and tests the contrast as a PK covariate – critical illness perturbs clearance and volume through capillary leak, altered protein binding, augmented or impaired renal clearance, and organ support, but is frequently reported as a bare yes/no cohort flag with no accompanying severity score.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (not critically ill).
• Source aliases:
  • critically ill – the covariate name used in Nguyen_2021_ganciclovir.R (Yang 2023 Table 3 footnote: “critically ill: 1 for critically ill patients and 0 for others”).
• Example models: Nguyen_2021_ganciclovir.R (multiplicative power factor on clearance: cl = 2.55 * (WT/11.7)^0.75 * (CRCL/167)^0.763 * 0.806^DIS_CRITILL, so a critically ill child has clearance 19.4% lower than a non-critically-ill child of the same weight and eGFR; Yang 2023 Table 3).
• Notes: Distinct from the continuous ICU severity scores SAPS_II and APACHE_II (which quantify how severely ill a patient is, and presuppose ICU admission), from ORG_FAIL_COUNT (integer count of failing organs), from MECH_VENT (a specific organ support), and from the ICU-admission-etiology indicators ICU_ADM_POLYTRAUMA / ICU_ADM_POSTSURG / ICU_ADM_MEDICAL (which classify why an already-admitted patient is in the ICU and are undefined for a non-ICU subject). DIS_CRITILL is the parent flag those etiology indicators presuppose: a dataset carrying any ICU_ADM_* indicator has DIS_CRITILL = 1 for those subjects. Do not derive DIS_CRITILL from ORG_FAIL_COUNT > 0 or from DIS_SEPSIS – a critically ill patient may have neither organ failure nor sepsis, and the founding paper reports the bare binary with no organ-failure count or sepsis diagnosis behind it. This canonical is only needed where a model contrasts critically ill against non-critically-ill subjects within one dataset; a study whose entire cohort is critically ill (e.g. Li_2021_ganciclovir.R, Krens_2020_ganciclovir.R, Horvatits_2014_ganciclovir.R) records critical illness as a population property in population$disease_state, not as a covariate column. Scope: specific; promote to general if a second paper pools critically ill with non-critically-ill subjects using the same bare-binary semantics. Registered as DIS_CRITILL in the DIS_<condition> family rather than under the ICU_ prefix per operator decision (sidecar request 001, question 1, 2026-07-30), because “critically ill” as papers use it is a clinical state rather than strictly an admission location. Ratified canonically alongside the Nguyen 2021 ganciclovir extraction.

ORG_FAIL_COUNT (canonical for number of organs failing in critically ill patients)

• Description: Integer count of failing organs in a critically ill patient at a given observation day, ascertained per-day and reported as the worst-of-day count. The count is decomposed into mutually exclusive strata 0 / 1 / 2 / 3 / >=4 (Vet 2016 used the strata 0, 1, 2, 3, and 4-or-5) that select per-stratum typical clearance values; the strata are not collapsed onto a single linear or power covariate effect because the underlying organ-failure mechanisms (cardiovascular, pulmonary, renal, hepatic, neurologic, hematologic) impair drug elimination heterogeneously.
• Units: (count)
• Type: categorical
• Scope: specific
• Reference category: 0 (no organs failing). Per-stratum typical CL values are estimated for ORG_FAIL_COUNT = 1, 2, 3, and >=4; ORG_FAIL_COUNT = 0 sets the baseline typical CL (frequently FIXED at the baseline value, as in Vet 2016).
• Source aliases:
  • ORGF – used in Vet_2016_midazolam.R (DDMODEL00000249 NMTRAN $INPUT column; values 0..>=4). Renamed to canonical ORG_FAIL_COUNT when assembling input data for the packaged model.
• Example models: Vet_2016_midazolam.R (per-stratum typical CL values: ORG_FAIL_COUNT=0 fixed at 1.6 L/h for a 5 kg child with CRP=32 mg/L; ORG_FAIL_COUNT=1 -> 1.29 L/h; ORG_FAIL_COUNT=2 -> 0.957 L/h; ORG_FAIL_COUNT=3 -> 0.842 L/h; ORG_FAIL_COUNT>=4 -> 0.678 L/h).
• Notes: Specific scope because the variable is critical-care-population-bound (PICU / ICU). Time-varying within subject (re-evaluated each ICU day). The Vet 2016 organ-failure ascertainment follows the Wilkinson 1987 paediatric multiple organ system failure (MOSF) criteria – operator-confirmed per-paper criteria should be documented in each model’s covariateData[["ORG_FAIL_COUNT"]]$notes. Decompose inside model() into binary indicators (orgf1 <- (ORG_FAIL_COUNT == 1), orgf2 <- (ORG_FAIL_COUNT == 2), orgf3 <- (ORG_FAIL_COUNT == 3), orgf_ge4 <- (ORG_FAIL_COUNT >= 4)) and select per-stratum CL with mutually-exclusive multiplicative-flag arithmetic. Ratified canonically on 2026-05-06.

SAPS_II (canonical for new Simplified Acute Physiology Score II at ICU admission)

• Description: New Simplified Acute Physiology Score II (SAPS II) at intensive-care-unit admission. Validated 17-item ICU severity-of-illness score (Le Gall, Lemeshow & Saulnier, JAMA 1993;270:2957-2963) computed from age, vital signs, laboratory values, type of admission, and chronic-disease history during the first 24 hours after ICU admission; higher scores indicate greater severity and a higher predicted hospital mortality. Range theoretically 0-163; in adult ICU cohorts admission scores typically span ~10-100 with cohort means in the 35-65 range. Time-fixed per ICU stay.
• Units: points
• Type: continuous
• Scope: specific
• Reference category: n/a – used with power scaling (SAPS_II / ref)^exponent. Reference value observed: 50 points (Abboud 2009 typical-subject reference for the septic-shock cohort, mean SAPS II = 64 +/- 23).
• Source aliases:
  • SAPS II (with whitespace, as printed in the source paper’s prose) – used in Abboud_2009_epinephrine.R.
• Example models: Abboud_2009_epinephrine.R (power exponent -0.67 on epinephrine CL with reference 50; higher SAPS II is associated with lower clearance), Polito_2016_fludrocortisone.R (power exponents +0.019 on apparent oral fludrocortisone CL/F and +0.036 on the absorption lag time Tlag, both with reference 53 = the cohort median SAPS II among the 14 patients with detectable plasma concentrations; higher SAPS II is associated with longer absorption lag and faster apparent oral clearance).
• Notes: Specific scope because the column is critical-care-population-bound (adult ICU) and the score’s clinical meaning depends on the SAPS-II derivation rules; future ICU models reusing the score with the same definition can extend Example models rather than registering a new canonical. Should not be confused with SAPS I or SAPS 3 (different scoring rules / item sets) – register those under separate canonicals if a future paper uses them. Ratified canonically on 2026-05-18 alongside the Abboud 2009 epinephrine extraction.

APACHE_II (canonical for Acute Physiology and Chronic Health Evaluation II score at ICU admission)

• Description: Acute Physiology and Chronic Health Evaluation II (APACHE II) score at intensive-care-unit admission. ICU severity-of-illness score (Knaus WA, Draper EA, Wagner DP, Zimmerman JE. Crit Care Med 1985;13(10):818-829) computed from the worst values of 12 acute-physiology variables during the first 24 hours after ICU admission, plus points for age and chronic health status. Integer range 0-71; higher scores indicate greater severity and higher predicted hospital mortality. Adult ICU cohorts typically span 5-40 with means in the 15-30 range. Time-fixed per ICU stay.
• Units: points
• Type: continuous
• Scope: specific
• Reference category: n/a – typically used with centred linear or power scaling. Reference values observed: 26 points (Swart 2004 midazolam learning-group mean APACHE II, used as the centring value in Q = 40.8 - (APACHE - 26) * 2.75).
• Source aliases:
  • APACHE II-score on admission to ICU – printed row label in Swart 2004 Table 1; used in Swart_2004_midazolam.R.
  • APACHE – shortened name in Swart 2004 Table 5 formula Q = 40.8 - (APACHE - 26) * 2.75.
• Example models: Swart_2004_midazolam.R (centred linear effect on Q with reference 26: q = 40.8 - (APACHE_II - 26) * 2.75; each 1-point increase above 26 lowers Q by 2.75 L/h, capturing decreased intercompartmental clearance in more severely ill patients).
• Notes: Specific scope because the score’s clinical meaning is critical-care-population-bound (adult ICU) and the Knaus 1985 derivation rules must apply. Distinct from SAPS_II (Le Gall 1993, 17-item Simplified Acute Physiology Score II) – APACHE II and SAPS II are different scoring systems with different item weights and calibration; a single ICU popPK paper may report one, the other, or both. Following the precedent set by SAPS_II Notes (“Should not be confused with SAPS I or SAPS 3 … register those under separate canonicals if a future paper uses them”), APACHE II gets its own canonical. Future ICU popPK papers using APACHE II can extend Example models rather than registering a new canonical; scope may be promoted to general after multiple corroborating papers. Ratified canonically on 2026-07-26 alongside the Swart 2004 lorazepam / midazolam extraction.

APACHE_II_SEV (canonical for elevated-APACHE-II severity stratum indicator)

• Description: 1 = the subject falls in the elevated-severity stratum of the APACHE II score, 0 = otherwise. Binary severity indicator DERIVED from the continuous APACHE_II score, for source papers that enter APACHE II severity as a discrete proportional shift rather than as a continuous scalar. The score threshold that defines the elevated stratum is paper-specific and must be documented in per-model covariateData[[APACHE_II_SEV]]$notes; where the source paper does not state it, say so explicitly rather than inventing one.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (not in the elevated-APACHE-II severity stratum).
• Source aliases:
  • APACHE II score – printed row label in Xie_2025_aztreonam_avibactam.R (Table S3 row APACHE II score on CL_AVI CL) and APACHE in Das 2024 supplementary Table 5 (APACHE effect on CL, CL*(1 + theta16)).
• Example models: Xie_2025_aztreonam_avibactam.R (proportional shift of -0.118 on avibactam clearance; aztreonam carries no APACHE term).
• Notes: Sibling of the continuous canonical APACHE_II, in the same relationship RENALIMP_SEV has to CRCL: APACHE_II holds the score in points, APACHE_II_SEV holds the derived stratum. Register both when a model needs the stratum, and do NOT overload APACHE_II with a 0/1 value – its declared type is continuous. Why binary rather than continuous in the founding model: neither Xie 2025 nor its predecessor Das 2024 prints the equation, but Das 2024 supplementary Table 5 writes the row as CL*(1 + theta16), which is the grammar Das uses for every binary population effect in that table (Population effect on Vc (cUTI), Vc*(1 + theta11)) and pointedly not the grammar Das uses for continuous covariates, which always carry the covariate symbol in the expression (AGE/35**theta14 on CL, (CrCL/80)**theta7). A raw continuous score is additionally impossible on arithmetic grounds: at a typical ICU APACHE II of 10, CL*(1 + (-0.118)*10) is negative. The founding model’s own notes record that the threshold is unstated in every source on disk, and its validation vignette holds the flag at the 0 reference throughout so that no reproduced published value depends on the inference. Scope specific until a second model ratifies it. Ratified canonically on 2026-08-17 alongside the Xie 2025 aztreonam-avibactam extraction.

LACT (canonical for serum lactate concentration)

• Description: Serum (or plasma / arterial-blood) L-lactate concentration. Routine acid-base / tissue-perfusion marker in critically ill patients; rising values reflect tissue hypoperfusion, anaerobic glycolysis, hepatic dysfunction, or metabolic disturbance. Distinct from LDH (lactate dehydrogenase enzyme activity), which is a related but mechanistically different cell-turnover marker. Time-varying within an ICU stay (often updated several times per day) although source papers may use it as a per-day or per-subject summary – document per-model in covariateData[[LACT]]$notes.
• Units: mmol/L (SI; equivalent to mEq/L; commonly reported as mg/dL = mmol/L * 9.0)
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (LACT / ref)^exponent. Reference values observed: 1.91 mmol/L (Taubert 2016 patient-group-1 day-1 median; a critically-ill ICU cohort spanning 0.53-10.7 mmol/L).
• Source aliases:
  • Lactate – the spelled-out column header in Taubert 2016 Table 1 and supplementary Table S3; same biological quantity, no value transformation.
• Example models: Taubert_2016_linezolid.R (mmol/L; reference 1.91; negative power exponent -0.21 on CL; lower lactate is associated with higher clearance, consistent with reduced cardiac output / impaired tissue perfusion at higher lactate).
• Notes: Distinct from LDH (serum lactate dehydrogenase enzyme activity in U/L); a popular point of confusion because both encode “lactate” in the lab-marker abbreviation. Critically-ill PK models that include both should keep the two columns separate. Taubert 2016 reports that lactate correlated significantly with the cardiovascular SOFA sub-score (i.e., lactate acts here as a perfusion / cardiac-output proxy more than as a direct biochemical driver of linezolid elimination). General scope because serum lactate is a universally applicable critical-care covariate. Ratified canonically on 2026-06-30 alongside the Taubert 2016 linezolid extraction.

RACHS1 (canonical for Risk Adjustment for Congenital Heart Surgery 1 (RACHS-1) category)

• Description: Integer 1-6 RACHS-1 surgical-risk category, ascertained pre-operatively from the type of congenital heart defect and the planned operation. Higher categories indicate greater perioperative risk of in-hospital mortality (Jenkins 2002 Pediatrics). Time-fixed per subject.
• Units: (categorical; 1-6 integer)
• Type: categorical
• Scope: specific
• Reference category: Paper-specific. Oualha 2014 uses RACHS-1 = 2 as the low-risk reference stratum on SV*SVR_max (categories 1 are absent from the cohort; 3 and 4 are pooled as the higher-risk stratum).
• Source aliases:
  • RACHS-1 – the publication’s printed form with a hyphen, not a valid R identifier; renamed to RACHS1 when assembling input data.
• Example models: Oualha_2014_epinephrine.R (decomposed inside model() into a binary indicator rachs1_high <- (RACHS1 >= 3) that selects an additive log-shift on SV*SVR_max from 0.44 to 0.26 for the high-risk pool).
• Notes: Specific scope because the variable is paediatric-cardiac-surgery-population-bound and the reference category depends on which RACHS-1 strata the cohort contains (Oualha 2014 has categories 2-4 only; a paper with categories 1-6 would need a different decomposition). Decompose inside model() into mutually exclusive binary indicators matching the source’s pooling (e.g., rachs1_high <- (RACHS1 >= 3)) and document the pooling rule in covariateData[["RACHS1"]]$notes.

CVP (canonical for central venous pressure)

• Description: Central venous pressure (CVP), measured in mmHg through a central venous catheter. Time-varying when monitored continuously; in cardiovascular Emax PD models it enters the mean-arterial-pressure equation (MAP = HR * SV*SVR + CVP) as an additive constant rather than a fitted covariate effect.
• Units: mmHg
• Type: continuous
• Scope: specific
• Reference category: n/a – used additively in the MAP equation. Cohort medians observed: 11 mmHg (Oualha 2014, range 8-15).
• Source aliases: none.
• Example models: Oualha_2014_epinephrine.R (enters Eq. 7 as the additive offset in MAP = HR * SV*SVR + CVP; the vignette defaults to the cohort median 11 mmHg when CVP is not supplied per subject).
• Notes: Specific scope because CVP is meaningful only for haemodynamic-PD models that resolve mean arterial pressure into its component cardiac-output and venous-return terms. The Oualha 2014 final model does not test CVP as a fitted covariate on PK or PD parameters; the column is used only as the structural offset in the MAP equation. Future haemodynamic models that fit a covariate effect on CVP itself can re-use the canonical name.

MORTRISK_HIGH (canonical for high-mortality-risk composite indicator)

• Description: 1 = subject meets the paper-defined “high mortality risk” composite criteria at study entry; 0 = subject is in the low or intermediate risk strata. Subject-level baseline indicator. The defining criteria are paper-specific: in Thuo 2011 (severe malnutrition cohort, following Berkley et al. Arch Dis Child 2003), a child is “high risk” if any one of the following is present: depressed conscious state, bradycardia (heart rate < 80 bpm), evidence of shock (capillary refill time >= 2 s, temperature gradient or weak pulse), or hypoglycaemia (blood glucose < 3 mmol/L); “intermediate risk” is any one of deep acidotic breathing, severe dehydration with diarrhoea, lethargy, hyponatraemia (Na < 125 mmol/L), or hypokalaemia (K < 2.5 mmol/L); “low risk” is none of the above. The canonical column collapses the intermediate and low strata into the 0 reference because the Thuo 2011 model only retains the high-vs-not-high contrast as a covariate on CL.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (low or intermediate mortality risk).
• Source aliases:
  • HIGHRISK / RISK (Thuo 2011 NONMEM notation; paper writes “high risk” in prose and structural equations as [1 + theta3 * (high risk)]) – used in Thuo_2011_ciprofloxacin.R.
• Example models: Thuo_2011_ciprofloxacin.R (multiplicative fractional effect on apparent CL: 1 + (-0.283) * MORTRISK_HIGH, i.e., a 28.3% reduction in apparent oral clearance for high-risk children; the standardised CL falls from 42.7 L/h/70 kg in low/intermediate-risk to 30.6 L/h/70 kg in high-risk; the paper attributes the contrast to delayed gastric emptying / impaired gut absorption in critically ill malnourished children).
• Notes: Specific scope because the underlying definition of “high mortality risk” is paper-defined (Thuo 2011 follows Berkley 2003’s three-stratum risk score for severely malnourished children). Distinct from SAPS_II (continuous adult-ICU severity score), RACHS1 (paediatric cardiac surgery risk category), and ORG_FAIL_COUNT (integer organ-failure count) – each is its own canonical with paper-specific scoring rules. Future paediatric-severe-malnutrition popPK papers that retain the Berkley 2003 three-stratum score (or a close variant) can reuse this canonical; per-model covariateData[[MORTRISK_HIGH]]$notes must document the exact criteria the source paper used. Ratified canonically on 2026-05-21 alongside the Thuo 2011 ciprofloxacin extraction.

MECH_VENT (canonical for invasive-mechanical-ventilation status indicator)

• Description: 1 = subject is receiving invasive mechanical ventilation during the modeled period; 0 = not. Treatment-status indicator (not a physiologic measurement) captured at study entry or as a time-fixed per-subject flag for the ICU admission. Captures the bulk fluid-distribution and hemodynamic shifts associated with positive-pressure ventilation – increased plasma renin activity, aldosterone, and antidiuretic hormone – which can change PK distribution volumes in critically ill patients.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (not mechanically ventilated).
• Source aliases:
  • mechanical ventilation – printed phrase in Georges 2009 Table 1 (no/yes count column); renamed to canonical MECH_VENT when assembling input data. Used in Georges_2009_ceftazidime.R.
• Example models: Georges_2009_ceftazidime.R (selector effect on V1 via the multiplicative log-effect form: vc = exp(lvc + e_mech_vent_vc * MECH_VENT + etalvc), with lvc = log(18.9) at MECH_VENT = 0 and e_mech_vent_vc = log(9.02 / 18.9) recovering TVV1 = 9.02 L at MECH_VENT = 1; Discussion attributes the V1 contraction to PPV-induced hormonal fluid shifts).
• Notes: Specific scope because the indicator is ICU-population-bound and the mode-of-ventilation conventions vary across cohorts (Georges 2009 uses a binary IMV/no-IMV partition without distinguishing non-invasive ventilation, tracheostomy delivery, or weaning trial state). Distinct from RRT_HEMODIAL_STATUS / RRT_CRRT_STATUS (renal-replacement-therapy treatment-status flags) and from SAPS_II (continuous severity score). When a future critically-ill popPK paper reuses MECH_VENT with the same definition (binary, time-fixed at ICU admission), extend the Example models list rather than registering a new canonical; promotion to general scope is appropriate after a second corroborating paper. Per-model covariateData[[MECH_VENT]]$notes must document whether the indicator is time-fixed at admission or re-evaluated as ventilation status changes during the ICU stay. Ratified canonically on 2026-06-10 alongside the Georges 2009 ceftazidime extraction.

ICU_ADM_POLYTRAUMA (canonical for ICU admission etiology indicator: polytrauma)

• Description: 1 = subject’s ICU admission etiology is polytrauma (severe multi-system blunt or penetrating trauma); 0 = otherwise. One of three mutually-exclusive ICU admission etiology indicators (ICU_ADM_POLYTRAUMA + ICU_ADM_POSTSURG + ICU_ADM_MEDICAL = 1 for every subject). Time-fixed per subject (the admission category is recorded once at ICU entry). Used as a categorical-stratum covariate on PK distribution parameters that differ between trauma-induced fluid shifts and elective surgical or medical patient profiles.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (other admission etiology – ICU_ADM_POSTSURG = 1 or ICU_ADM_MEDICAL = 1).
• Source aliases:
  • Admission for polytrauma – printed row label in Georges 2009 Table 1 (count column); renamed to canonical ICU_ADM_POLYTRAUMA when assembling input data. Used in Georges_2009_ceftazidime.R.
• Example models: Georges_2009_ceftazidime.R (selector effect on V2: TVV2 = theta6 = 57.1 L at ICU_ADM_POLYTRAUMA = 1; multiplicative log-effect form encodes the shift relative to the ICU_ADM_MEDICAL reference stratum as e_icu_adm_polytrauma_vp = log(57.1 / 13.6)).
• Notes: Specific scope because the polytrauma stratum definition is cohort-dependent (Georges 2009 partitions polytrauma vs postsurgical vs medical-reason at the time of ICU admission per Toulouse-cohort criteria; other ICU populations may use different boundaries – e.g., trauma + sepsis combination, isolated head injury, burns separated from polytrauma). The three ICU_ADM_* indicators must be mutually exclusive in the input data; the canonical mapping convention is that exactly one of them is 1 per subject. Distinct from MORTRISK_HIGH (severity score), SAPS_II (continuous severity), and ORG_FAIL_COUNT (organ-failure count) – all of which describe critical-illness severity rather than admission etiology. Future adult-ICU popPK papers that retain the same three-stratum etiology partition can extend the Example models list and the scope may be promoted to general after a second corroborating paper. Ratified canonically on 2026-06-10 alongside the Georges 2009 ceftazidime extraction.

ICU_ADM_POSTSURG (canonical for ICU admission etiology indicator: postsurgical)

• Description: 1 = subject’s ICU admission etiology is postsurgical (elective or urgent surgical recovery, not polytrauma); 0 = otherwise. One of three mutually-exclusive ICU admission etiology indicators (see ICU_ADM_POLYTRAUMA for the partition definition). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (other admission etiology – ICU_ADM_POLYTRAUMA = 1 or ICU_ADM_MEDICAL = 1).
• Source aliases:
  • Admission for postsurgical – printed row label in Georges 2009 Table 1 (count column); renamed to canonical ICU_ADM_POSTSURG when assembling input data. Used in Georges_2009_ceftazidime.R.
• Example models: Georges_2009_ceftazidime.R (selector effect on V2: TVV2 = theta7 = 25.7 L at ICU_ADM_POSTSURG = 1; multiplicative log-effect form encodes the shift relative to the ICU_ADM_MEDICAL reference stratum as e_icu_adm_postsurg_vp = log(25.7 / 13.6)).
• Notes: See ICU_ADM_POLYTRAUMA for shared scope rationale and partition rules. Specific scope because the postsurgical stratum definition is cohort-dependent and the partition into the three Georges 2009 categories is not universal across ICU popPK papers (some cohorts merge postsurgical with medical or split it into elective vs urgent). Ratified canonically on 2026-06-10 alongside the Georges 2009 ceftazidime extraction.

ICU_ADM_MEDICAL (canonical for ICU admission etiology indicator: medical)

• Description: 1 = subject’s ICU admission etiology is a medical reason (sepsis, pneumonia, cardiac, neurologic, or other non-surgical / non-polytrauma medical condition); 0 = otherwise. One of three mutually-exclusive ICU admission etiology indicators (see ICU_ADM_POLYTRAUMA for the partition definition). Time-fixed per subject. Commonly serves as the structural reference category in models that compare polytrauma / postsurgical strata against the medical baseline, because medical-reason patients in adult ICU cohorts tend to have distribution volumes closer to non-critically-ill individuals (per Georges 2009 Discussion).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (other admission etiology – ICU_ADM_POLYTRAUMA = 1 or ICU_ADM_POSTSURG = 1). When this stratum is chosen as the multiplicative-effect reference (as in Georges 2009), e_icu_adm_medical_<param> = 0 is implicit and only the two non-reference effects are estimated.
• Source aliases:
  • Admission for medical reason – printed row label in Georges 2009 Table 1 (count column); renamed to canonical ICU_ADM_MEDICAL when assembling input data. Used in Georges_2009_ceftazidime.R.
• Example models: Georges_2009_ceftazidime.R (selector effect on V2: TVV2 = theta8 = 13.6 L at ICU_ADM_MEDICAL = 1; this is the structural reference of the multiplicative parameterisation – exp(lvp) recovers 13.6 L when both other indicators are zero).
• Notes: See ICU_ADM_POLYTRAUMA for shared scope rationale. Specific scope. The Georges 2009 Discussion notes ‘Patients with a medical reason for admission presented a volume of distribution in the same order of magnitude as those of healthy subjects’ which motivates the choice of this stratum as the structural reference for V2 in the model file. Ratified canonically on 2026-06-10 alongside the Georges 2009 ceftazidime extraction.

HIE_POST (canonical for post hypoxic-ischemic event indicator)

• Description: 1 = subject has undergone a hypoxic-ischemic (HI) event and is in the post-insult state (sustained cerebral injury and / or multi-organ stress that alters drug disposition); 0 = no HI event. Time-varying within a subject as the indicator flips from 0 to 1 at the documented HI insult time and remains 1 thereafter for the modeled observation window. Common in neonatal-encephalopathy / perinatal-asphyxia popPK studies (and potentially in adult cardiac-arrest / traumatic-arrest popPK studies) where the HI event causes persistent reductions in hepatic and renal drug clearance via CYP450 / UGT pathway impairment, decreased cardiac output, and multi-organ dysfunction. The exact definition of “post-HI state” (e.g., calculated from a continuous severity score thresholded to binary, or simply set to 1 from the documented insult time onward) is paper-specific; document per-model in covariateData[[HIE_POST]]$notes. Used as a binary state indicator on PK parameters (most commonly CL).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no HI event; pre-insult or never-insulted).
• Source aliases:
  • AED – used in Ezzati_2014_dexmedetomidine_piglet.R (the paper defines AED as “acute energy depletion”, computed by the time integral of beta-nucleotide-triphosphate (NTP) peak-area changes during hypoxia-ischemia; in the final model the AED severity score is used as a binary 0 / 1 state flag for whether the subject experienced the HI insult, NOT as a continuous covariate – 9 piglets had AED = 1 throughout dosing and 1 control piglet had AED = 0). NOTE: the paper’s AED column-name encodes a calculated NTP-integral severity score that the model dichotomises to a 0 / 1 state flag; downstream users assembling event tables should set HIE_POST = 1 from the HI insult time onward for all HI-exposed subjects.
• Example models: Ezzati_2014_dexmedetomidine_piglet.R (multiplicative factor on dexmedetomidine CL post-insult: CL = CLstd * (Wt/70)^0.75 * (1 + Ftemp * (TEMP - 37)) * (1 + (FAED - 1) * HIE_POST) with FAED = 0.558 – clearance is multiplied by 0.558 in the post-HI state, i.e. CL is reduced TO 55.8% of the pre-insult value, equivalent to a 44.2% reduction relative to the pre-insult typical value).
• Notes: General scope because hypoxic-ischemic injury is a recurring exposure across neonatal-encephalopathy, perinatal-asphyxia, and potentially adult cardiac-arrest popPK studies. Distinct from BURN_RECENT (acute hypermetabolic phase indicator with different physiology and a time-windowed threshold) and from DIS_* disease-state indicators (which describe chronic conditions rather than acute insult states). Distinct from ORG_FAIL_COUNT (integer organ-failure count) and MORTRISK_HIGH (mortality risk score) – HIE_POST is a binary state flag for a specific insult exposure, not a downstream severity or outcome score. Future papers that report a continuous HI severity score (e.g., AED integral, Sarnat staging, MRI biomarker) could either (a) re-use HIE_POST as a binary derived from the continuous score, or (b) register a sibling continuous canonical (e.g., HIE_SEVERITY) – operator decides per paper. Ratified canonically on 2026-06-10 alongside the Ezzati 2014 dexmedetomidine piglet extraction.

Interferon / biomarker panels

BGENE21 (canonical for 21-gene type I interferon signature score)

• Description: Baseline 21-gene type I interferon signature score – a composite transcriptomic score summarising the expression of 21 interferon-regulated genes in whole blood relative to a healthy-donor reference, used as a biomarker of type I IFN pathway activation in SLE and related autoimmune conditions.
• Units: unitless fold-change score (relative to healthy-donor reference).
• Type: continuous
• Scope: specific
• Reference category: n/a – used with power scaling (BGENE21 / ref)^exponent. Reference values observed: 32 in Narwal 2013 (study-population median was 33), 12.04 in Zheng 2016 (median of the SLE phase IIb cohort, range 0.32-38.59).
• Source aliases: none.
• Example models: Narwal_2013_sifalimumab.R (reference 32, exponent 0.0558 on CL), Zheng_2016_sifalimumab.R (reference 12.04, power effect on CL with exponent 0.09).
• Notes: Specific to drugs whose mechanism targets the type I IFN pathway (e.g., anti-IFN-alpha antibodies like sifalimumab, anifrolumab). Higher BGENE21 indicates stronger target engagement / disease activity and is associated with increased drug clearance via target-mediated mechanisms. The 21-gene panel composition is tied to the MedImmune/AstraZeneca SLE development programme; a different IFN gene signature (e.g., a 4-gene or 5-gene panel) should be registered under its own canonical name (BGENE4, IFN_SIG, …) to avoid conflating panel definitions.

BGENE21_HIGH (canonical for binary high-vs-low IFN-21-gene indicator)

• Description: 1 = subject’s baseline 21-gene type I IFN signature score is at or above the paper-specified high/low cut-off, 0 = below cut-off.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (low).
• Source aliases: none.
• Example models: Almquist_2022_anifrolumab.R (binary high-IFN indicator on CL).
• Notes: Pair with continuous BGENE21 when the paper reports both. The high/low cut-off is paper-specific (commonly the population median) and must be documented in covariateData[[BGENE21_HIGH]]$notes for every model that uses this covariate. Operator decision (2026-04-28): use BGENE21_HIGH (not IFNGS_HIGH) so the link to the existing BGENE21 register entry is explicit while the binary nature stays visible in the column name.

Inflammation markers

EOS (canonical for blood eosinophil count)

• Description: Blood eosinophil count (baseline or time-varying).
• Units: cells/uL
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (EOS / ref)^exponent.
• Source aliases:
  • BEOS (baseline EOS) – used in Kotani_2022_astegolimab.R.
• Example models: Kotani_2022_astegolimab.R (reference 180 cells/uL, baseline).
• Notes: Used as a surrogate of inflammatory burden that correlates with protein turnover and therefore mAb clearance. Canonical name drops the B prefix to match the SCORE_EASI / AGE / WT / ALB pattern; baseline-vs-time-varying status is documented in covariateData[[EOS]]$notes.

FENO (canonical for fractional exhaled nitric oxide concentration)

• Description: Fractional concentration of nitric oxide in exhaled breath (FeNO), measured by chemiluminescence or electrochemical analyser at a 50 mL/s expiratory flow rate per the ATS/ERS standard. A non-invasive marker of eosinophilic (type-2) airway inflammation: elevated FeNO reflects IL-4 / IL-13-driven upregulation of inducible nitric oxide synthase in airway epithelium. Standard baseline biomarker covariate in asthma exposure-response models, typically alongside blood eosinophil count [[EOS]].
• Units: ppb (parts per billion)
• Type: continuous
• Scope: specific
• Reference category: n/a – used with power scaling (FENO / ref)^exponent. Reference values observed: 25 ppb (Zhang 2025, median of the pooled Phase 2b + Phase 3 uncontrolled moderate-to-severe asthma cohort; range 3-387 ppb). 25 ppb is also the conventional clinical cut-point separating low from intermediate type-2 inflammation in adults.
• Source aliases:
  • BFENO (baseline FeNO) – used in Zhang_2025_dupilumab_fev1.R.
• Example models: Zhang_2025_dupilumab_fev1.R (power effect (FENO / 25)^0.682 on the dupilumab maximum treatment effect Emax for pre-bronchodilator FEV1; patients with higher baseline FeNO gain substantially more lung function – +154% Emax at the 95th percentile of 98 ppb, -54% at the 5th percentile of 8 ppb).
• Notes: Canonical name drops the B prefix of the common BFENO source column to match the EOS / IGE / ALB pattern; baseline-vs-time-varying status is documented in covariateData[[FENO]]$notes per model. Record the expiratory flow rate in the model notes if the source paper used a non-standard one (values are strongly flow-dependent, so a FeNO measured at 100 or 200 mL/s is not interchangeable with the 50 mL/s standard). Scope: specific until a second model ratifies the semantics; promote to general at that point.

BLBCELL (canonical for baseline CD19+ B cell count)

• Description: Baseline CD19+ B cell count (cells/uL) measured by fluorescence-activated cell sorting (FACS) prior to first dose. Used as a covariate / scaling biomarker for B-cell-targeted antibody PK-PD models (e.g., anti-CD20 mAbs in multiple sclerosis or B cell malignancies).
• Units: cells/uL
• Type: continuous
• Scope: specific
• Reference category: n/a – used with power scaling (BLBCELL / ref)^exponent. Reference value observed: 200 cells/uL (Yu 2022, median of the pooled five-study cohort).
• Source aliases:
  • Bcell0 – used in Yu_2022_ofatumumab.R.
  • BBCC (NHL Phase I/Ib/II convention; values in 10^6 cells/L = cells/uL) – used in Lu_2019_polatuzumab.R.
• Example models: Yu_2022_ofatumumab.R (power effect on the maximum B-cell-lysis stimulatory effect Emax, exponent 0.275, reference 200 cells/uL), Lu_2019_polatuzumab.R (two distinct effects: power on CL_INF with input floored at 1 cell/uL, and a thresholded power on CL_T with the BLBCELL/121-cells/uL ratio floored at 1), Lu_2017_polatuzumab_neuropathy.R (carries forward the Lu 2019 acMMAE PK-side effects via the inlined acMMAE popPK layer; not used directly by the Lu 2017 TTE PD layer).
• Notes: Distinct from a time-varying B cell count, which is the PD response variable rather than a covariate. Scope: specific because the clinically relevant baseline depends on the surface marker (CD19, CD20, CD22) and whether the panel reports total B cells or memory/naive subsets – register a new canonical name if a future paper uses a different marker. Both Yu 2022 (anti-CD20 ofatumumab) and Lu 2019 (anti-CD79b polatuzumab vedotin) use CD19+ counts, so the canonical is reused; subtype-specific differences are documented in each model’s covariateData[[BLBCELL]]$notes.

BL_PARP_PBL (canonical for baseline poly(ADP-ribose) polymerase activity in peripheral blood lymphocytes)

• Description: Subject-specific baseline (pre-dose) poly(ADP-ribose) polymerase (PARP) activity in peripheral blood lymphocytes (PBL), measured as picomoles of PAR polymer formed per 10^6 PBL by an enzyme-activity assay. Used as a covariate / scaling biomarker on the maximal-inhibition residual activity (Emin) parameter in PARP-inhibitor PK/PD models.
• Units: pmol/10^6 PBL (document per-model via covariateData[[BL_PARP_PBL]]$units).
• Type: continuous
• Scope: specific
• Reference category: n/a – used with power scaling (BL_PARP_PBL / ref)^exponent. Reference value observed: 90.8 pmol/10^6 PBL (Wang 2015 rucaparib; population typical baseline E0 used as a stand-in for the unreported study-cohort median because the paper reports only the typical E0 and the exponent value, not the numeric BLB_median).
• Source aliases:
  • BLB (Wang 2015’s notation for “baseline level in blood / PBL”) – used in Wang_2015_rucaparib.R.
• Example models: Wang_2015_rucaparib.R (power effect on residual maximum-inhibition parameter Emin; exponent 0.620 with the form Emin = TV(Emin) * (BL_PARP_PBL / BLB_median)^alpha; PBL paired with a separate tumor-tissue PARP activity covariate that is not yet a registered canonical because the units differ – pmol/mg protein for tumor vs pmol/10^6 PBL for blood).
• Notes: Specific scope because the column is meaningful only for PARP-inhibitor PK/PD models (rucaparib, olaparib, niraparib, talazoparib, veliparib, etc.) and the units are tied to the PBL-specific assay format. A future tumor-tissue PARP activity covariate would need a separate canonical because the units differ (pmol/mg protein) and the biology of cellular PARP activity per mg of protein is not numerically interchangeable with PBL-normalized PARP activity. The Wang 2015 model uses BL_PARP_PBL only on Emin (residual maximum inhibition) and not on E0 or IC50; per-paper effects must be documented in each model’s covariateData[[BL_PARP_PBL]]$notes. The paper does not publish the numeric study-cohort median of BLB used to center the covariate; the model file uses 90.8 pmol/10^6 PBL (the population typical baseline E0 reported in Wang 2015 Table 2) as a defensible default reference and documents the assumption in the vignette’s Assumptions and deviations section.

BL_TGN_RBC (canonical for baseline erythrocyte 6-thioguanine-nucleotide concentration)

• Description: Subject-specific baseline (first observed) concentration of 6-thioguanine nucleotides (TGN), the active metabolites of 6-mercaptopurine, inside red blood cells. Reported clinically as “E-TGN”. Used as the initial condition of the rbc_tgn compartment in thiopurine PK/PD models whose observation record begins during ongoing maintenance therapy, when the red-cell pool has already accumulated and cannot be initialised at zero.
• Units: umol/L. Document per-model via covariateData[[BL_TGN_RBC]]$units. Papers frequently report E-TGN as nmol per mmol hemoglobin; convert to umol/L using the assay’s assumed hemoglobin molecular weight and erythrocyte hemoglobin concentration and record the conversion in $notes (Gebhard 2023 uses MW 64458 g/mol and 330 g Hb/L erythrocytes).
• Type: continuous
• Scope: specific
• Reference category: n/a – supplied as an ODE initial condition (rbc_tgn(0) = BL_TGN_RBC), not as a scaling covariate. Cohort median 0.83 umol/L, range 0-7.6 umol/L (Gebhard 2023 Table 1).
• Source aliases:
  • INITGN – used in Gebhard_2023_mercaptopurine.R and Gebhard_2023_mercaptopurine_anc.R (Gebhard 2023: “X_E^6MP(0) = INITGN … with INITGN being the first observation in the data set at time point 0”).
• Example models: Gebhard_2023_mercaptopurine.R, Gebhard_2023_mercaptopurine_anc.R.
• Notes: Ratified 2026-07-30 (sidecar oare_PMC10359452 request-001 q5, option A) as a member of the existing BL_<biomarker> family; the _RBC cell-compartment suffix mirrors the _PBL suffix of BL_PARP_PBL. Distinct from CONMED_MP, which is a concomitant-6-mercaptopurine-medication FLAG, not a measured red-cell concentration. Specific scope because the column is meaningful only for thiopurine models that carry an intracellular red-cell TGN state. A model that can start its estimation at a drug-free time point should initialise rbc_tgn(0) = 0 and not carry this covariate; Gebhard 2023’s own Discussion names that as the preferred remedy, but the published model as estimated requires the observed initial value.

BL_MTX_RBC (canonical for baseline erythrocyte methotrexate concentration)

• Description: Subject-specific baseline (first observed) concentration of methotrexate – predominantly as methotrexate polyglutamates – inside red blood cells. Reported clinically as “E-MTX”. Used as the initial condition of the rbc_mtx compartment in low-dose-methotrexate PK models whose observation record begins during ongoing maintenance therapy, when the red-cell pool has already accumulated and cannot be initialised at zero.
• Units: umol/L. Document per-model via covariateData[[BL_MTX_RBC]]$units. Papers frequently report E-MTX as nmol per mmol hemoglobin; convert to umol/L and record the conversion in $notes (Gebhard 2023 uses MW 64458 g/mol and 330 g Hb/L erythrocytes).
• Type: continuous
• Scope: specific
• Reference category: n/a – supplied as an ODE initial condition (rbc_mtx(0) = BL_MTX_RBC), not as a scaling covariate. Cohort median 0.026 umol/L, range 0-0.10 umol/L (Gebhard 2023 Table 1).
• Source aliases:
  • INIMTX – used in Gebhard_2023_methotrexate.R (Gebhard 2023: “X_E^MTX(0) = INIMTX with INIMTX being the first observation in the data set at time point 0”).
• Example models: Gebhard_2023_methotrexate.R.
• Notes: Ratified 2026-07-30 (sidecar oare_PMC10359452 request-001 q5, option A) alongside BL_TGN_RBC. Distinct from CONMED_MTX, which is a concomitant-methotrexate-medication FLAG, not a measured red-cell concentration. Note that the red-cell methotrexate half-life is long (30-40 days in the literature Gebhard 2023 cites), so this initial condition remains influential over a much longer horizon than a typical plasma baseline.

DOSE_MTX_MGM2 (canonical for administered methotrexate dose per body-surface area)

• Description: The methotrexate dose administered on the current dose record, normalised to body-surface area and expressed in mg/m^2. Per-dose-record covariate; constant within an inter-dose interval and updated when the prescriber alters the dose.
• Units: mg/m^2
• Type: continuous
• Scope: specific
• Reference category: n/a – enters a saturable dose-dependent bioavailability term F = 1 - Imax * DOSE_MTX_MGM2 / (D50 + DOSE_MTX_MGM2) (Gebhard 2023 Supplementary Table S1, Imax = 0.77, D50 = 15.01 mg/m^2, both fixed from the literature source Table S1 cites). Cohort median weekly dose 15.0 mg/m^2, range 1.3-45.0 mg/m^2 (Gebhard 2023 Table 1).
• Source aliases:
  • MTX – used in Gebhard_2023_methotrexate.R (the bare symbol Gebhard 2023 Supplementary Table S1 uses inside the bioavailability formula, annotated “MTX in mg/m2”).
• Example models: Gebhard_2023_methotrexate.R.
• Notes: Well-formed member of the auto-approved DOSE_<DRUG>_<UNITS> family (cf. DOSE_EMPA_MGD, DOSE_PHT_MGKGD). A dedicated column is required rather than the general DOSE canonical because Gebhard_2023_methotrexate.R supplies its amt in umol/m^2 (the unit the model’s concentration states demand) while the published bioavailability formula is calibrated in mg/m^2; carrying both in one DOSE column would silently mix units. Convert with the methotrexate molecular weight 454.44 g/mol: amt [umol/m^2] = DOSE_MTX_MGM2 [mg/m^2] * 1000 / 454.44. Ratified 2026-07-30 alongside the Gebhard 2023 extraction.

CSF1 (canonical for colony-stimulating factor 1 / macrophage-colony-stimulating factor concentration)

• Description: Plasma colony-stimulating factor 1 (CSF-1, also known as macrophage colony-stimulating factor, M-CSF) concentration (baseline or time-varying). The hematopoietic cytokine that signals through CSF-1R to drive monocyte / macrophage differentiation and survival; used as both a target-engagement biomarker (anti-CSF-1R mAbs increase circulating free CSF-1) and a baseline covariate on PK / PD parameters in CSF-1R-pathway PopPK/PD models.
• Units: pg/mL (= ng/L; the two labels are numerically equivalent). Document per-model via covariateData[[CSF1]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – used with power scaling (CSF1 / ref)^exponent. Reference values observed: 549 pg/mL (Yang 2024 axatilimab; pooled-cohort median).
• Source aliases: none formally; informal aliases include BLCSF1 (baseline CSF-1) and BL_CSF1 (model-parameter notation in Monolix / NONMEM control streams).
• Example models: Yang_2024_axatilimab.R (baseline-only covariate on linear clearance CL with power exponent 0.912 and on the model parameter BL_CSF1 with power exponent 0.656; reference 549 pg/mL).
• Notes: Specific scope because the column is meaningful only for CSF-1R-pathway-targeting drugs (axatilimab and future anti-CSF-1R molecules). Distinct from any CSF-1 model state representing time-course dynamics – covariate column is the pre-dose laboratory observation, typically measured by an ELISA assay (Yang 2024 used the R&D Systems Quantikine ELISA). Per-model covariateData[[CSF1]]$notes should document the assay used and any LOQ-related imputation for samples below the assay’s limit of detection.

CRP (canonical for C-reactive protein)

• Description: C-reactive protein concentration. Covers both standard and high-sensitivity (hs-CRP) assays and both baseline and time-varying usages. Each model’s covariateData[[CRP]]$description and notes must state the assay type (standard vs hs-CRP) and whether the column carries a baseline-only or time-varying value, including the paper-specific reference value used for power scaling.
• Units: mg/L (document per-model via covariateData[[CRP]]$units).
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (CRP / ref)^exponent or exponential effects exp(coef * (CRP - ref)). Reference values observed: 4.23 mg/L (Moein 2022, IBD standard assay), 4.31 mg/L (Moein 2022 Table 3 median), 5.21 mg/L (Thakre 2022, baseline hs-CRP), 7.41 mg/L (Chua 2025, baseline standard assay), 14.2 mg/L (Xu 2019, baseline standard assay), 15.7 mg/L (Ma 2020, baseline standard assay), 0.837 mg/dL = 8.37 mg/L (Wang 2020, IBD standard assay; the model carries the source unit mg/dL).
• Source aliases:
  • hsCRP – high-sensitivity CRP (mixed-case preserved from earlier register drafts).
  • HSCRP – all-caps variant.
  • CRPHS – used in Thakre_2022_risankizumab.R (baseline, high-sensitivity assay).
  • BLCRP – baseline CRP; used in Xu_2019_sarilumab.R and Ma_2020_sarilumab_das28crp.R.
• Example models: Thakre_2022_risankizumab.R, Xu_2019_sarilumab.R, Chua_2025_mirikizumab.R, Moein_2022_etrolizumab.R, Ma_2020_sarilumab_das28crp.R, Wang_2020_ontamalimab.R (mg/dL, reference 0.837).
• Notes: The prior separate hsCRP, BLCRP, and standard-assay CRP canonicals were merged on 2026-04-20 to a single general-scope CRP canonical. Assay type (standard vs hs-CRP), baseline-vs-time-varying status, and the paper-specific reference value all live in each model’s covariateData[[CRP]]$description / notes. Only aggregate values from hs-validated assays as CRP when the downstream analysis relies on low-range sensitivity; for most inflammatory-disease cohorts (IBD, RA/PsA), baseline CRP is well above the hs-sensitivity range and the distinction is moot.

AAG (canonical for alpha-1 acid glycoprotein concentration)

• Description: Serum alpha-1 acid glycoprotein (AAG; orosomucoid; ORM1) concentration, an acute-phase plasma glycoprotein that binds basic and lipophilic drugs (including taxanes such as docetaxel and paclitaxel). Elevated in cancer, inflammation, and infection; influences free-drug fraction and downstream PD effects in cytotoxic-chemotherapy myelosuppression models.
• Units: g/L (= mg/mL; 1 g/L is the conventional clinical-PK reporting unit). Document per-model via covariateData[[AAG]]$units if a different unit is used.
• Type: continuous
• Scope: general
• Reference category: n/a – used in piecewise-linear, power, or exponential effect forms (e.g., the Kloft 2006 cytotoxic-chemotherapy myelosuppression family fits a piecewise-linear effect with breakpoint at the cohort median 1.34 g/L: separate slopes apply for AAG <= 1.34 and AAG > 1.34). Reference values observed: 1.34 g/L (Kloft 2006 / Netterberg 2017, cohort median in mixed adult-cancer cohort).
• Source aliases:
  • AAG – used in Netterberg_2017_docetaxel.R (per the bundle’s NM-TRAN $INPUT block; matching Kloft 2006).
  • AAGI2 – LOCF-imputed time-varying AAG series; NONMEM control-stream column name used in Said_2025_imatinib.R (Said 2025 Data S1 $INPUT, consumed inside $DES by the closed-form binding solution).
  • AGP1 – used in Ozawa_2007_docetaxel.R (Appendix I $INPUT block); reported in mg/dL with conversion to canonical g/L via AAG_g_per_L = AGP_mg_per_dL / 100.
• Example models: Netterberg_2017_docetaxel.R (piecewise-linear effects on baseline ANC with separate low-AAG and high-AAG slopes around median 1.34 g/L; linear effect on the drug-effect slope SL via (1 + theta * (AAG - 1.34))), Ozawa_2007_docetaxel.R (multiplicative power-form effect on the linear drug-effect slope: SLOPE = theta_SLOPE * (AAG / 0.94)^e_aag_slope with e_aag_slope = -1.38; reference value 0.94 g/L from the published NONMEM control stream AGPm = 94 mg/dL), Said_2025_imatinib.R (STRUCTURAL rather than covariate-effect use: AAG has no reference value and no e_aag_* coefficient, and instead sets the binding capacity bmax = 11700 * AAG inside the closed-form saturable-binding solution that yields unbound imatinib from total imatinib, so the unbound fraction that drives elimination, metabolite formation, and distribution falls as AAG rises; time-varying, LOCF-imputed, median 4 measurements per patient).
• Notes: General scope because serum AAG is a routine clinical-laboratory measurement that recurs across cytotoxic-chemotherapy population-PK / PD analyses (Bruno 1996/1998 docetaxel popPK uses AAG as a CL covariate; Kloft 2006 and downstream Friberg-family myelosuppression models use it on baseline ANC and drug-effect slope; Ozawa 2007 uses a power-form effect on the drug-effect slope only). Time-fixed at baseline unless the source paper states otherwise – Said_2025_imatinib.R is the counter-example, carrying a time-varying LOCF-imputed series. Two structurally different uses of this column now coexist and must not be conflated: (a) a covariate effect on a typical value, which needs a reference / breakpoint value (Kloft 2006, Netterberg 2017, Ozawa 2007); and (b) a structural binding-capacity input with no reference value, where AAG multiplies a molar scale factor to give Bmax inside a saturable-binding equation (Said 2025). Papers of type (b) should record no reference value and no e_aag_* coefficient. The Kloft 2006 piecewise-linear breakpoint at 1.34 g/L corresponds to the population median in their pooled cancer cohort; the Ozawa 2007 normalisation reference 0.94 g/L is the cohort median used inside that paper’s NONMEM control stream. Future papers may use different breakpoints / reference values, so document the per-model reference in covariateData[[AAG]]$notes. Distinct from CRP (a different acute-phase reactant with different binding properties).

AAG_MISSING (canonical for alpha-1-acid-glycoprotein-result-missing indicator)

• Description: 1 = baseline alpha-1-acid glycoprotein was not measured for this subject and a population value was imputed; 0 = AAG measured. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (AAG measured). Pairs with AAG, which carries the measured-or-imputed value on the same record.
• Source aliases:
  • A1AGLP_MISS – used in Yin_2024_soticlestat.R (NONMEM $INPUT block).
• Example models: Yin_2024_soticlestat.R (does not shift the typical value; it inflates the between-subject SD of relative bioavailability by (1 + 0.42 * AAG_MISSING), so imputed subjects carry extra unexplained variability).
• Notes: Member of the established <COV>_MISSING missing-indicator family (ADA_MISSING, CYP2C9_MISSING, SNP_CYP2C19_RS3814637_MISSING, SNP_CYP3A4_RS2242480_MISSING): a binary flag that distinguishes “value imputed because unmeasured” from a measured value, so the imputed subjects are not silently treated as exchangeable with measured ones. General scope because whole-trial AAG imputation recurs whenever a pooled analysis spans trials that did not all collect the acute-phase protein panel (Yin 2024: four of eight trials). Note the two distinct modelling roles this family takes – a shift of the typical value (ADA_MISSING on clearance in Suri_2018_brentuximab.R) versus a shift of the between-subject VARIANCE only (AAG_MISSING here); document which applies in covariateData[[AAG_MISSING]]$notes.

IL6 (canonical for serum interleukin-6 concentration)

• Description: Serum (or plasma) interleukin-6 (IL-6) concentration. Pro-inflammatory cytokine; elevated in rheumatoid arthritis, Castleman’s disease, sepsis, COVID-19, and other inflammatory conditions. Both baseline and time-varying usages are covered; document per-model in covariateData[[IL6]]$notes whether the column is baseline-only or time-varying.
• Units: pg/mL (= ng/L; the two labels are numerically equivalent).
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (IL6 / ref)^exponent or with the log-transformed form (log(IL6 * 1000) / log(ref * 1000))^exponent that some legacy NONMEM analyses adopt. Reference values observed: 20 pg/mL (Frey 2013 baseline; the formula (log(IL-6 * 1000)/9.9)^exponent is the algebraic equivalent of (log(IL-6) / log(20))^exponent after the constant-factor rescaling that ties the reference log to 9.9 = log(20000)).
• Source aliases:
  • BLIL6, bIL6, IL6_BASE – baseline IL-6 (used in some NONMEM control streams; canonical drops the BL prefix per the EOS / SCORE_EASI / AGE convention with baseline-vs-time-varying status documented in per-model notes).
  • IL-6, IL_6 – punctuation variants seen in publication tables and figures.
• Example models: Frey_2013_tocilizumab.R (baseline IL-6, reference 20 pg/mL; log-transformed power effects on EC50, BASE, and the DMARD background-effect parameter).
• Notes: Anti-IL-6 / anti-IL-6R drugs (tocilizumab, sarilumab, siltuximab) often see large transient increases in measured IL-6 after dosing – when the column is time-varying for these drugs, the assay typically detects total (free + drug-bound) IL-6 because the antibody complex slows clearance of IL-6. Document the assay type (free vs. total IL-6) and whether the value is pre-dose (baseline-only) or post-dose / time-varying in each model’s covariateData[[IL6]]$notes. Frey 2013’s formula relies on the relative log-IL-6 ratio rather than the linear concentration, so the column units must be pg/mL exactly (not ng/mL) for the published exponents to apply unchanged.

IL22 (canonical for serum interleukin-22 concentration)

• Description: Serum (or plasma) interleukin-22 (IL-22) concentration. Effector cytokine produced downstream of IL-23 receptor signalling by Th17 / Th22 and innate lymphoid cells; elevated in Crohn’s disease and ulcerative colitis and correlated with disease activity. Both baseline and time-varying usages are covered; document per-model in covariateData[[IL22]]$notes whether the column is baseline-only or time-varying.
• Units: pg/mL (= ng/L; the two labels are numerically equivalent).
• Type: continuous
• Scope: general
• Reference category: n/a – used as the driver variable of a sigmoid Imax / Emax term rather than as a multiplicative covariate. Reference (half-maximal) values observed: 22.8 pg/mL (Zhang 2023 brazikumab IB50 on the CDAI production-rate inhibition).
• Source aliases:
  • BIL22, BLIL22, IL22_BASE – baseline IL-22 (used in NONMEM $INPUT columns; the canonical drops the B / BL prefix per the IL6 / CRP / SCORE_EASI convention, with baseline-vs-time-varying status documented in per-model notes).
  • IL-22, IL_22 – punctuation variants seen in publication tables and figures.
• Example models: Zhang_2023_brazikumab_il22.R (baseline serum IL-22 as the per-subject predictive-biomarker driver of the brazikumab drug effect on the CDAI input rate: idrug = imax * IL22^hill / (ec50^hill + IL22^hill) with imax = 0.297, ec50 (the paper’s IB50) = 22.8 pg/mL, hill (the paper’s gamma) fixed at 20; the effect is gated by ON_TREATMENT because the drug term is zero in the placebo arm).
• Notes: Ratified 2026-07-29 alongside the Zhang 2023 brazikumab extraction (sidecar request-001 / response-001, option A). Sibling of IL6, but a biologically distinct cytokine and NOT an alias of it: IL-22 sits downstream of IL-23 whereas IL-6 is an independent pro-inflammatory mediator, and the two are measured on separate assays. Anti-IL-23 antibodies (brazikumab, risankizumab, guselkumab, mirikizumab) block IL-23 signalling and thereby suppress downstream IL-22 production, so for those drugs a post-dose IL-22 column is pharmacodynamically driven rather than a stable baseline covariate – record the assay (Zhang 2023 used the R&D Systems Quantikine ELISA, quantifiable range 10-800 pg/mL in 100% serum) and the baseline-vs-time-varying status in each consuming model’s covariateData[[IL22]]$notes. Distinct from CRP, which Zhang 2023 fits as a parallel alternative predictive biomarker in a separate model file.

IFNG (canonical for total serum interferon-gamma concentration)

• Description: Total (free plus therapeutic-antibody-bound) serum or plasma interferon-gamma (IFN-gamma) concentration. Type II interferon produced by activated T cells and NK cells; the driver cytokine of the hyperinflammatory feedback loop in haemophagocytic lymphohistiocytosis (HLH) and macrophage activation syndrome (MAS), and the pharmacological target of anti-IFN-gamma antibodies. Both baseline and time-varying usages are covered; document per-model in covariateData[[IFNG]]$notes which applies, and whether the assay measures free or total (drug-bound plus free) IFN-gamma.
• Units: pg/mL (= ng/L; the two labels are numerically equivalent).
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (IFNG / ref)^exponent. Reference values observed: 1e6 pg/mL (Brossard 2025 / Brossard & Laveille 2024, the normalisation point at which the target-mediated clearance component CLNL is tabulated).
• Source aliases:
  • IFNg, IFN-gamma, IFNgamma, TIFNG – punctuation and spelling variants seen in publication tables; TIFN denotes total IFN-gamma explicitly in the Jacqmin 2022 primary-HLH control stream.
• Example models: Brossard_2025_emapalumab.R (time-varying total serum IFN-gamma as the driver of the non-linear, target-mediated clearance component: clnl = exp(lclnl) * (IFNG / 1e6)^0.542, normalised to 1e6 pg/mL; the branch is switched off entirely in MAS patients by DIS_MAS).
• Notes: Ratified 2026-08-14 alongside the Brossard 2025 emapalumab extraction (task oare_PMC11822261 sidecar request-001 question q2, answer A). Direct sibling of the existing IL6 and IL22 serum-cytokine canonicals and registered on the same terms, but a biologically distinct mediator and NOT an alias of either: IFN-gamma is the sole type II interferon, signals through IFNGR1/IFNGR2-JAK1/JAK2-STAT1, and is measured on its own assay. As with IL6 under anti-IL-6R therapy, an anti-IFN-gamma antibody (emapalumab) causes measured total IFN-gamma to rise sharply after the first dose because the antibody-cytokine complex clears more slowly than free cytokine – so a post-dose IFN-gamma column for these drugs is a drug-perturbed quantity, not a stable baseline covariate, and the free/total distinction is load-bearing. Above roughly 10,000 pg/mL, total IFN-gamma drives target-mediated disposition of emapalumab; below it, emapalumab clearance is effectively linear. Distinct from BGENE21 / BGENE21_HIGH, which are type I interferon transcriptomic signature scores rather than a measured type II cytokine concentration, and from CONMED_IFNALPHA / CONMED_IFNB1A, which flag coadministration of exogenous interferon products.

Cardiometabolic / target biomarkers

HDLC (canonical for high-density lipoprotein cholesterol)

• Description: Serum high-density lipoprotein cholesterol concentration (baseline or time-varying).
• Units: mg/dL or mmol/L – document the unit used in each model via covariateData[[HDLC]]$units (1 mmol/L ~= 38.67 mg/dL for cholesterol).
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (HDLC / ref)^exponent. Reference value observed: 54 mg/dL (Frey 2010 pooled-cohort median).
• Source aliases:
  • HDL-C – Frey 2010 spelling with hyphen.
  • HDL_C – common alternative spelling.
• Example models: Frey_2010_tocilizumab.R (mg/dL, reference 54; small negative exponent -0.2 on linear CL; the paper interprets the effect as a body-size surrogate rather than a mechanism).
• Notes: Cardiometabolic lipid-panel covariate. In Frey 2010 it correlates with body size (HDL-C is lower in larger patients) and the small CL effect (-14% to +15% across the observed 23-135 mg/dL range) was retained but not interpreted as mechanistic.

TCHOL (canonical for total serum cholesterol)

• Description: Serum (or plasma) total cholesterol concentration (baseline or time-varying). Sum of HDL, LDL, VLDL, and other lipoprotein-associated cholesterol fractions; distinct from HDLC which captures only the HDL fraction.
• Units: mmol/L or mg/dL – document the unit used in each model via covariateData[[TCHOL]]$units (1 mmol/L ~= 38.67 mg/dL for cholesterol).
• Type: continuous
• Scope: general
• Reference category: n/a – used with linear-deviation forms 1 + theta * (TCHOL - ref) or power scaling (TCHOL / ref)^exponent. Reference value observed: 3 mmol/L (Archary 2018, severely malnourished pediatric LPV cohort, baseline mean 2.7-2.9 mmol/L).
• Source aliases:
  • CHOL – Archary 2018 NONMEM column abbreviation; the universal short form.
  • TC – alternative abbreviation common in lipid-panel literature.
• Example models: Archary_2018_lopinavir.R (mmol/L, reference 3; linear effect on apparent CL/F: 1 + 0.207 * (TCHOL - 3); serves as a surrogate for nutritional / hepatic-function recovery rather than a mechanistic effect).
• Notes: In severe acute malnutrition, total cholesterol tracks lipid-pool repletion and hepatic recovery and may serve as a surrogate covariate when the actual driver of bioavailability or clearance variability is not directly measured. Lopinavir is highly protein-bound (to albumin and AAG) and lipophilic, so circulating cholesterol can correlate with binding capacity for lipophilic drugs. Distinct from HDLC (HDL fraction only) and from CRP / ALB / AAG (separate canonicals for related malnutrition / inflammation markers).

TRIG (canonical for serum triglyceride concentration)

• Description: Serum triglyceride concentration (baseline or time-varying).
• Units: mmol/L or mg/dL – document the unit used in each model via covariateData[[TRIG]]$units (1 mmol/L ~= 88.5 mg/dL for triglyceride).
• Type: continuous
• Scope: general
• Reference category: n/a – used with linear-deviation form (1 + theta * (TRIG - ref)) or power scaling (TRIG / ref)^exponent. Reference value observed: 5.3 mmol/L (Archary 2019 lamivudine equation centring – the equation centring in the source is reported as the cohort average; cohort median in Archary 2019 Table 1 is 2.2-2.3 mmol/L, see model-file Errata).
• Source aliases: none known.
• Example models: Archary_2019_lamivudine.R (mmol/L, reference 5.3; linear-deviation effect on Vc/F with coefficient -0.13 per mmol/L deviation from the reference; lower triglyceride implies higher apparent central volume).
• Notes: Cardiometabolic lipid-panel covariate. In Archary 2019 the inverse triglyceride–Vc relationship is interpreted as a nutritional-status / hydrophilic-drug-distribution surrogate: triglycerides rise during nutritional rehabilitation, and lamivudine (a hydrophilic drug) shows decreasing apparent volume as nutritional status improves. The linear-deviation form preserves the source’s published parameterization; per-model covariateData[[TRIG]]$units is load-bearing because the centring reference and slope are unit-specific.

LDLC (canonical for low-density lipoprotein cholesterol)

• Description: Serum low-density lipoprotein cholesterol concentration (baseline or time-varying). The pharmacologically meaningful endpoint for lipid-lowering therapies (statins, PCSK9 inhibitors, ANGPTL3 inhibitors); also serves as a baseline covariate or as the time-varying PD response in indirect-response exposure-response models.
• Units: mg/dL or mmol/L – document the unit used in each model via covariateData[[LDLC]]$units (1 mmol/L ~= 38.67 mg/dL for cholesterol).
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (LDLC / ref)^exponent for the baseline-LDLC covariate role, or with no reference (used directly as the PD response state) when it is the modelled output. Reference values observed: 211 mg/dL (Pu 2021 HoFH typical-patient definition).
• Source aliases:
  • LDL-C – common spelling with hyphen.
  • LDL_C – common alternative spelling.
  • LDLBL (baseline LDL-C) – used in Pu_2021_evinacumab.R (Pu 2021 NM-TRAN $INPUT column for centred baseline LDL-C as a covariate on IC50).
• Example models: Pu_2021_evinacumab.R (mg/dL, baseline reference 211 mg/dL; power exponent -1.17 on IC50, where higher baseline LDL-C predicts a smaller IC50 and therefore greater sensitivity to evinacumab; LDL-C is also the PD output state initialised at the baseline value).
• Notes: Cardiometabolic lipid-panel covariate. Distinct from HDLC (high-density lipoprotein cholesterol) and from any total-cholesterol or non-HDL-C derivation. When LDL-C is both the response variable AND a covariate (as in Pu 2021, where baseline LDLC drives IC50 and the time-varying state is the modelled PD), document the dual role in covariateData[[LDLC]]$notes.

ANGPTL3 (canonical for angiopoietin-like protein 3 concentration)

• Description: Total serum angiopoietin-like protein 3 (ANGPTL3) concentration. ANGPTL3 is the pharmacological target for anti-ANGPTL3 monoclonal antibodies (evinacumab) and antisense oligonucleotides (vupanorsen). Baseline ANGPTL3 acts as a soluble-target biomarker that contributes to target-mediated drug disposition; higher baseline target predicts a higher saturable Vmax.
• Units: mg/L (equivalent to ug/mL). Document per-model via covariateData[[ANGPTL3]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – used with power scaling (ANGPTL3 / ref)^exponent. Reference value observed: 0.08 mg/L (Pu 2021 typical-patient median).
• Source aliases:
  • ANGBL (baseline ANGPTL3) – used in Pu_2021_evinacumab.R (Pu 2021 NM-TRAN $INPUT column for centred baseline ANGPTL3).
  • ANGBL0 – alternative Pu 2021 raw column.
• Example models: Pu_2021_evinacumab.R (mg/L, baseline reference 0.08 mg/L; power exponent +0.405 on Vmax, where higher baseline target predicts a faster saturable elimination – biologically consistent with evinacumab being co-cleared along with bound ANGPTL3).
• Notes: Specific scope because the column is meaningful only for drugs whose mechanism involves ANGPTL3 (anti-ANGPTL3 mAbs / ASOs). Reusing the name for another anti-ANGPTL3 agent is acceptable (extend the example-models list). The assay in Pu 2021 detects both free and target-bound ANGPTL3 after acid pretreatment of serum; document the assay type (free vs. total) per model in covariateData[[ANGPTL3]]$notes.

FPCSK9 (canonical for free (unbound) proprotein convertase subtilisin/kexin type 9 concentration)

• Description: Free (unbound, non-drug-bound) serum proprotein convertase subtilisin/kexin type 9 (PCSK9) concentration. For anti-PCSK9 monoclonal antibodies (alirocumab, evolocumab, bococizumab) the free-PCSK9 pool is the pharmacologically active target fraction; drug-target binding reduces FPCSK9 relative to total PCSK9.
• Units: ng/mL (document per-model if a paper reports a different unit).
• Type: continuous
• Scope: specific
• Reference category: n/a – used with linear-deviation forms TVPARAM + theta * (FPCSK9 / ref) or power-form (FPCSK9 / ref)^theta. Reference values observed: 72.9 ng/mL (Martinez 2019 time-varying median).
• Source aliases: none known.
• Example models: Martinez_2019_alirocumab.R (time-varying; additive-linear effect on Km with slope -0.541 per (FPCSK9/72.9), reference 72.9 ng/mL).
• Notes: Specific scope because the column is meaningful only for drugs whose mechanism is PCSK9 inhibition; reusing the name for a different anti-PCSK9 agent is acceptable (add to Example models). For non-PCSK9 drugs that use a similar target-concentration biomarker, register a new canonical (e.g., FIL6R, FTNF) rather than overloading FPCSK9. Per-model covariateData[[FPCSK9]]$notes should state whether the value is baseline-only or time-varying and how missing values were imputed (Martinez 2019 used LOCF).

SBCMA (canonical for soluble B-cell maturation antigen concentration)

• Description: Baseline serum (or plasma) concentration of soluble B-cell maturation antigen (sBCMA), the shed extracellular domain of the BCMA receptor (TNFRSF17). Serves as a soluble-target biomarker for BCMA-directed therapeutics (anti-BCMA antibody-drug conjugates such as belantamab mafodotin, BCMA-targeted bispecifics, and BCMA CAR-T) – sBCMA is elevated in multiple-myeloma and reflects tumour burden, and contributes to target-mediated drug disposition by sequestering circulating drug.
• Units: ng/mL (equivalent to ug/L; 1 ng/mL = 1 ug/L). Document per-model via covariateData[[SBCMA]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – used with power scaling (SBCMA / ref)^exponent. Reference value observed: 50 ng/mL (Papathanasiou 2025 typical-patient definition).
• Source aliases:
  • SBCMABL (baseline soluble BCMA) – used in Papathanasiou_2025_belantamab.R.
• Example models: Papathanasiou_2025_belantamab.R (ng/mL, reference 50; power exponents on initial CL +0.113, on ADC Vc +0.0401, on Imax +0.160), Collins_2023_belantamab_mprotein.R (ng/mL, reference 100; power exponent -0.414 on the effect-compartment rate constant KEO of the serum M-protein tumour-growth-inhibition model).
• Notes: Specific scope because the column is meaningful only for drugs whose mechanism involves the BCMA receptor (and thus a circulating soluble-target pool). Reusing the name for another anti-BCMA agent is acceptable (extend the example-models list). For other oncology TMDD targets register a new canonical (e.g., HER2_ECD already exists for HER2; an analogous SCD20, SCD38 would follow the same pattern). Multiple myeloma populations show sBCMA spanning roughly 2 to 2,000 ng/mL, so the (SBCMA/50)^exponent form should be evaluated with care over the full clinical range.

HBA1C (canonical for glycated hemoglobin)

• Description: Glycated hemoglobin (HbA1c, %). Routine clinical laboratory measurement on whole blood reflecting average glycemia over the preceding ~2-3 months; reported in National Glycohemoglobin Standardization Program (NGSP) units.
• Units: % (NGSP). Document the unit standard (NGSP vs IFCC mmol/mol) per-model via covariateData[[HBA1C]]$units when a paper reports IFCC units (IFCC mmol/mol = 10.93 * NGSP% - 23.50).
• Type: continuous
• Scope: general
• Reference category: n/a – used with a power form (HBA1C / ref)^exponent or linear-deviation form. Reference value observed: 5.88% (Oniki 2018 NAFLD-risk dataset baseline mean).
• Source aliases:
  • HbA1c – used in Oniki_2018_nafld_risk.R (dataset column for glycated hemoglobin in %).
• Example models: Oniki_2018_nafld_risk.R (%, reference 5.88; power exponent -3.34 for (HBA1C / 5.88) on the (BMI50 - 17) half-saturation offset of the sigmoidal logit-of-NAFLD function, Oniki 2018 Eq. 4 / Figure 2c).
• Notes: General scope because HbA1c is a routine, paper-independent glycemic-control laboratory measurement. Companion glycemic covariate to FPG (baseline fasting plasma glucose), which is routinely reported alongside HbA1c in T2DM / metabolic-syndrome populations. Distinct from GLU (time-varying within-subject glucose regressor). Ratified canonically alongside the Oniki 2018 NAFLD-risk extraction.

Bone biomarkers

TRACP5B_BL (canonical for baseline serum tartrate-resistant acid phosphatase 5b concentration)

• Description: Pre-treatment serum tartrate-resistant acid phosphatase 5b (TRACP-5b) concentration, an osteoclast-derived bone-resorption biomarker. Time-fixed per subject. Used as the per-subject anchor for the steady-state marker pool in indirect-response bone-turnover models (Kin = TRACP5B_BL * Kout, marker(0) <- TRACP5B_BL) and as a continuous covariate on disease-progression and BMD-coupling parameters (power-model standardisation to the cohort median).
• Units: mU/dL (clinical reporting convention used in Japanese osteoporosis cohorts and the Osteolinks TRACP-5b kit; document per-model via covariateData[[TRACP5B_BL]]$units). Some clinical contexts report TRACP-5b in U/L; 1 U/L = 100 mU/dL only if the assays are calibrated equivalently – per-model unit documentation is load-bearing.
• Type: continuous
• Scope: specific
• Reference category: n/a – used with power scaling (TRACP5B_BL / ref)^exponent. Reference value observed: 400 mU/dL (Mori 2018 cohort – mean baseline 401.1 mU/dL across N = 306 Japanese primary-osteoporosis patients; the paper says continuous covariates were standardised to their cohort median but the numeric median is not published, so the rounded cohort mean serves as the documented reference).
• Source aliases:
  • TRACP-5b – text-form of the marker name used in Mori 2018; transliterated to TRACP5B_BL for the per-subject baseline canonical name.
• Example models: Mori_2018_zoledronicAcid.R (mU/dL, reference 400; power exponents -1.534 on EKD50, -1.350 on Slope, -1.319 on T50; and -1.112 on Scale in the active-treatment arm only – the Scale covariate is gated by ON_TREATMENT).
• Notes: Specific scope because the cohort-median reference (400 mU/dL) is tied to the Mori 2018 Japanese primary-osteoporosis cohort and the Osteolinks assay; future bone-resorption-marker extractions using a different assay (different antibodies, different normalisation) should ratify the same canonical and document the per-model assay / reference in covariateData[[TRACP5B_BL]]$notes. Ratified canonically alongside the Mori 2018 zoledronic-acid BMD extraction. Distinct from any time-varying serum TRACP-5b state output (which would be a model output rather than a covariate column).

BMD_BL (canonical for baseline lumbar-spine bone mineral density)

• Description: Pre-treatment lumbar-spine (L2-L4) bone mineral density measured by dual-energy X-ray absorptiometry (DXA). Time-fixed per subject. Used as the per-subject initial condition for a BMD state compartment (bmd(0) <- BMD_BL) and as the per-subject deviation reference inside the BMD effect-compartment ODE (d/dt(bmd) <- Ke0 * [Scale * (marker - Marker0) - (bmd - BMD_BL)]).
• Units: g/cm^2 (the standard DXA areal-BMD unit; document per-model via covariateData[[BMD_BL]]$units). Some clinical contexts report BMD as the dimensionless T-score; the canonical column stores the raw areal density.
• Type: continuous
• Scope: specific
• Reference category: n/a – used as a per-subject anchor; no power-form covariate effect in the example model.
• Source aliases:
  • BMD0 – in-text mathematical symbol used by Mori 2018 for the baseline BMD value entering the BMD ODE; the covariate column name is BMD_BL.
• Example models: Mori_2018_zoledronicAcid.R (g/cm^2, used as the initial condition bmd(0) <- BMD_BL and the deviation reference inside the BMD ODE; per-subject median 0.677 g/cm^2 across the Mori 2018 cohort).
• Notes: Specific scope because the anatomical site (lumbar spine L2-L4) and the modality (DXA Hologic instrument) are tied to the Mori 2018 ZONE-study protocol; future BMD extractions from other anatomical sites (femoral neck, total hip, distal radius) should ratify a sibling canonical (e.g., BMD_FN_BL, BMD_TH_BL) rather than overloading this column. Ratified canonically alongside the Mori 2018 zoledronic-acid BMD extraction. Companion bone biomarker to TRACP5B_BL.

Kallikrein-kinin system biomarkers and hereditary-angioedema disease-activity covariates

PKK (canonical for plasma prekallikrein concentration as a time-varying PD driver)

• Description: Plasma prekallikrein concentration used as a time-varying regressor / exposure driver in downstream exposure-response models for hereditary-angioedema (HAE) prophylactic therapies (e.g., prekallikrein-lowering antisense oligonucleotides such as donidalorsen). Averaging convention is per-model (e.g., per-4-week average PKKavg,4W in Singh 2025); the canonical column stores the appropriate exposure summary at each observation row and is consumed with LOCF (piecewise-constant) or linear() interpolation semantics as documented per model.
• Units: mg/L (document per-model via covariateData[[PKK]]$units).
• Type: continuous
• Scope: specific
• Reference category: n/a – enters the sigmoidal Emax attack-rate model as PKK^Hill / (EC50^Hill + PKK^Hill) (Singh 2025 page 3 equation). Set to PKK_BL at the pre-dose baseline row and to model-predicted values thereafter (either from an on-disk companion popPK/PD like Diep_2026_donidalorsen or from observed data for placebo arms).
• Source aliases:
  • PKKavg,4W – Singh 2025 Section 2.2 and Table 1 (per-4-week average plasma prekallikrein).
• Example models: Singh_2025_donidalorsen.R (per-4-week average PKK reads at each observation row; drives the sigmoidal Emax term).
• Notes: Specific scope because PKK is meaningful only for HAE / kallikrein-kinin-pathway PD models that consume prekallikrein as an exogenous exposure driver. Companion baseline canonical PKK_BL. The companion popPK/PD backbone for donidalorsen simulations is Diep_2026_donidalorsen (indirect-response model with donidalorsen-driven inhibition of PKK production). Ratified canonically alongside the Singh 2025 donidalorsen exposure-response extraction.

PKK_BL (canonical for per-subject baseline plasma prekallikrein concentration)

• Description: Per-subject baseline (pre-dose) plasma prekallikrein concentration, time-fixed per subject. For donidalorsen-treated cohorts the value is typically the initial condition of an upstream popPK/PD model (e.g., Diep_2026_donidalorsen’s baseline PKK = rbase, derived from lrbase + etalrbase with HAE-status adjustment); for placebo arms the observed baseline PKK is used directly. Enters downstream exposure-response models as a covariate on EC50 via a power form.
• Units: mg/L (document per-model via covariateData[[PKK_BL]]$units).
• Type: continuous
• Scope: specific
• Reference category: n/a – used with power scaling (PKK_BL / ref)^exponent. Reference value observed: 122 mg/L (Singh 2025 page 3 equation (BLPKK/122)^bPKK; matches the Section 2.3 simulation baseline PKK).
• Source aliases:
  • BLPKK – Singh 2025 page 3 equation and Table 1 abbreviation.
• Example models: Singh_2025_donidalorsen.R (per-subject baseline PKK reference 122 mg/L; power exponent e_pkk_bl_ec50 = 0.13 on the EC50^Hill denominator term of the sigmoidal Emax attack-rate model).
• Notes: Specific scope for the same reasons as PKK (HAE / kallikrein-kinin-pathway-bound). Sister to PKK (time-varying). Distinct from the general-purpose _BL baseline-biomarker family (TRACP5B_BL, BMD_BL, HGB_BL, INS_BL, FERRITIN_BL) whose members serve broader clinical contexts; PKK_BL is narrowly the prekallikrein-pathway baseline. Ratified canonically alongside the Singh 2025 donidalorsen exposure-response extraction.

HAERATE_BL (canonical for baseline per-4-week normalized hereditary-angioedema attack rate)

• Description: Per-subject baseline HAE attack rate normalised to a 4-week window, computed from the number of investigator-confirmed HAE attacks during a defined screening / run-in period divided by the contributed days and multiplied by 28 days. Time-fixed per subject. Used as a per-subject covariate on Emax in HAE exposure-response models (higher baseline attack burden -> proportionally higher on-treatment maximum attack rate).
• Units: attacks per 4 weeks (document per-model via covariateData[[HAERATE_BL]]$units).
• Type: continuous
• Scope: specific
• Reference category: n/a – used with power scaling (HAERATE_BL / ref)^exponent. Reference value observed: 3 attacks per 4 weeks (Singh 2025 page 3 equation (BLRATE/3)^bHAE; matches the Section 2.3 simulation baseline attack rate).
• Source aliases:
  • BLRATE – Singh 2025 page 3 equation and Table 1 abbreviation (“baseline per-4-week normalized HAE attack rate”).
• Example models: Singh_2025_donidalorsen.R (per-subject baseline attack rate reference 3 attacks/4W; power exponent e_haerate_bl_emax = 1.03 on Emax of the sigmoidal Emax attack-rate model).
• Notes: Specific scope because the variable is HAE-domain-bound. Time-fixed per subject (baseline-only). Conceptually a specific-domain analogue of ACUTE_MED_DAYS (baseline migraine acute-medication days per month, migraine E-R models); both encode a baseline symptom / event rate as a power-law scaling covariate on the on-treatment response. If future HAE E-R models use a different screening window or normalisation window (e.g., per-day, per-month), document the per-model window in covariateData[[HAERATE_BL]]$notes. Ratified canonically alongside the Singh 2025 donidalorsen exposure-response extraction.

Drug exposure metrics

CAV (canonical for average drug plasma concentration over a dosing interval)

• Description: Average plasma concentration of the modelled drug over a dosing interval (Cav = AUC_tau / tau). Used as the time-varying or per-period exposure metric in exposure-response models that feed individual empirical-Bayes PK predictions from a previously published population PK model into a downstream PD model.
• Units: ug/mL (document per-model via covariateData[[CAV]]$units).
• Type: continuous
• Scope: specific
• Reference category: n/a – used in Emax/EC50 (e.g., Emax * CAV / (EC50 + CAV)) or power (e.g., (CAV / CavMedian)^exponent) drug-effect terms. Set to 0 for placebo periods.
• Source aliases:
  • CAV, Cav, CAVG, Cav,W (Svensson 2017 weekly-average bedaquiline plasma concentration; same orientation as the canonical, in mg/L).
  • METRIC_TASPO_C – Li 2015 (per-arm taspoglutide average plasma concentration over weeks 2-4, in pmol/L; the model’s source_name is “Cavg.2-4w (Li 2015 Section 3.2)”). This descriptive column name maps onto CAV via this alias rather than being a separate canonical, consistent with the MBMA usage already documented in this entry’s Notes.
• Example models: FiedlerKelly_2020_fremanezumab_em.R, FiedlerKelly_2020_fremanezumab_cm.R, Schoemaker_2018_levetiracetam.R (DDMODEL00000239; LEV plasma concentration in mg/L), Svensson_2017_bedaquiline.R (weekly-average bedaquiline concentration in mg/L driving an Emax effect on the mycobacterial-load half-life; EC50 = 1.42 mg/L, Emax fixed at -100%; placebo subjects use CAV = 0), Li_2015_taspoglutide_mbma.R (MBMA study-arm-level Cavg of taspoglutide between weeks 2 and 4 of QW dosing, in pmol/L; 0 / 59.85 / 119.7 pmol/L for placebo / 10 mg / 20 mg arms; drives an additive Emax response on body-weight change), Lacy_2018_cabozantinib_tumor.R (individual predicted daily-average cabozantinib plasma concentration in ng/mL from the upstream Lacy 2018 popPK; drives a saturable Cavg/(EC50 + Cavg) Hill-1 term on a time-attenuating decay rate of tumor SOD; EC50 = 251 ng/mL; per-cohort steady-state Cavg 375 / 750 / 1125 ng/mL for 20 / 40 / 60 mg/day starting doses; dose-hold periods set CAV = 0), Lacy_2018_cabozantinib_dose_modification.R (same upstream-popPK-derived Cavg in ng/mL; drives a log-linear effect with coefficient theta_drug = 0.000807 per ng/mL on the active-dose log hazard for repeated dose modifications; dose-hold periods set CAV = 0 and switch to the dose-hold baseline log hazard), Sano_2023_fesoterodine_mcc.R (individual 5-HMT average steady-state concentration Cavg,ss in ng/mL from the companion Sano 2023 population PK model, computed as F * DOSE / (CL/F * tau) with tau = 24 h; drives an Emax model on maximum cystometric capacity with EC50 = 6.22 ng/mL; set to 0 on the baseline occasion per Sano 2023 Online Resource 8b, which collapses the Emax term so the baseline prediction is exactly BASE), Yin_2021_pexidartinib.R (running average pexidartinib plasma concentration up to the current tumor-measurement time, in mg/L, from the upstream Yin 2020 popPK modellib('Yin_2020_pexidartinib'); drives a saturable 1 - exp(-kdrug * CAV) drug-effect term in the longitudinal RECIST tumor-size PD model with kdrug typical value 0.196 (mg/L)^-1; placebo periods carry CAV = 0).
• Notes: Specific scope because the value is intrinsically tied to the modelled drug – there is no shared meaning across drugs or studies. Each model’s covariateData[[CAV]]$notes should state how the Cav values are derived (e.g., empirical-Bayes from a referenced population PK model) and that the column is set to 0 for placebo periods. The averaging window is also model-specific (per-dosing-interval Cav = AUC_tau / tau in Schoemaker 2018 / Fiedler-Kelly 2020, but weekly-rolling-mean Cav_W in Svensson 2017 – where the bedaquiline once-daily loading + thrice-weekly maintenance schedule makes “per dosing interval” ambiguous; weeks 2-4 Cavg in Li 2015 carried forward for the entire 8-52 week follow-up); document the averaging convention in each model’s covariateData[[CAV]]$notes. MBMA usage (Li 2015) treats CAV as a study-arm-level (not individual-level) exposure metric – the meaning is the same (period-averaged plasma concentration of the modelled drug) so a separate canonical is not warranted.

DOSE_EMPA_MGD (canonical for daily empagliflozin dose)

• Description: Patient’s own once-daily empagliflozin dose, in mg/day. Per-dose-record covariate; constant within an inter-dose interval and updated when the prescriber alters the daily dose. Set to 0 mg/day for placebo arms.
• Units: mg/day
• Type: continuous
• Scope: specific
• Reference category: n/a – enters into the steady-state AUC computation AUCss [nM*h] = DOSE_EMPA_MGD * 1e6 / 450.91 / cl that drives the Emax stimulation of FPG elimination in the indirect-response PD model.
• Source aliases:
  • DOSE – used in Baron_2016_empagliflozin.R (Baron 2016 Methods; studied doses 1, 5, 10, 25, 50 mg QD per Table S2 with 10 and 25 mg dominating; 1129 / 40.9% and 1269 / 46.0% of patients respectively).
• Example models: Baron_2016_empagliflozin.R (drives AUCss feeding Gmax * AUCss / (AUC50 + AUCss) – AUC50 = 703 nM*h).
• Notes: Follows the DOSE_<DRUG>_<UNITS> auto-approve family (e.g., DOSE_PHT_MGKGD for phenytoin). Distinct from DOSE_PHT_MGKGD (per-kg phenytoin daily-dose) – empagliflozin is dosed in flat mg/day (no per-kg adjustment in label). Future once-daily SGLT2-inhibitor extractions should register sibling canonicals (e.g., DOSE_DAPA_MGD for dapagliflozin, DOSE_CANA_MGD for canagliflozin) rather than reuse this name. Ratified canonically on 2026-06-24 alongside the Baron 2016 empagliflozin extraction.

DOSE_LOR_MGD (canonical for daily lorlatinib dose)

• Description: Patient’s own total daily lorlatinib dose, in mg/day. Per-dose-record covariate; constant within an inter-dose interval and updated when the prescriber alters the daily dose (e.g., following a dose reduction for tolerability). For q.d. regimens the value equals the single-dose amount (e.g., 100 mg q.d. -> DOSE_LOR_MGD = 100); for b.i.d. regimens the value is the sum across the day (e.g., 75 mg b.i.d. -> DOSE_LOR_MGD = 150). Set to 0 mg/day during off-treatment periods (e.g., planned drug holidays).
• Units: mg/day
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as a linear centered effect 1 + e_dose_lor_cl * (DOSE_LOR_MGD - 100) on total lorlatinib clearance (both the single-dose CLI and the steady-state induced CLMX arm), with reference 100 mg/day (the phase II / labelled recommended dose). Effect coefficient observed: e_dose_lor_cl = 0.001 per mg/day in Chen_2021_lorlatinib.R (Chen 2021 Table 4 theta_TDOSE_on_CL); 200 mg/day gives a 10% CL increase, 10 mg/day gives a 9% CL reduction relative to the 100 mg/day reference.
• Source aliases:
  • TDOSE – used in Chen_2021_lorlatinib.R (Chen 2021 Methods “Inclusion of covariates”; total daily lorlatinib dose across the 10-200 mg/day range spanning phase I/II B7461001 escalation cohorts and healthy-participant single-dose studies).
• Example models: Chen_2021_lorlatinib.R (drives dose-nonlinearity of CL via a linear centered term shared between CLI and CLMX; captures increased auto-induction potency at higher doses).
• Notes: Follows the DOSE_<DRUG>_<UNITS> auto-approve family (siblings: DOSE_EMPA_MGD for empagliflozin, DOSE_PHT_MGKGD for phenytoin, DOSE_CIPARGAMIN_MG for cipargamin). Lorlatinib is dosed in flat mg/day (no per-kg adjustment in label), matching the DOSE_EMPA_MGD pattern. Distinct from the rxode2/nlmixr2 event column amt (which carries the administered dose at each dose event); DOSE_LOR_MGD is a per-record covariate carrying the current daily-dose LEVEL used by the covariate model, updated at each dose-schedule change. When simulating typical q.d. regimens without dose changes it can be set as a per-subject constant equal to the assigned dose level. Ratified canonically on 2026-07-24 alongside the Chen 2021 lorlatinib extraction.

DOSE_TPM_MGD (canonical for daily topiramate dose)

• Description: Patient’s total daily topiramate dose, in mg/day. Per-record covariate; constant within a dosing interval and updated when the prescriber alters the daily dose. For a twice-daily regimen the value is the sum across the day (89.2 mg b.i.d. -> DOSE_TPM_MGD = 178.4), matching the [[DOSE_LOR_MGD]] convention. Set to 0 mg/day during off-treatment periods.
• Units: mg/day
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as a power effect (DOSE_TPM_MGD / 100)^e_dose_tpm_cl on apparent clearance, normalized at 100 mg/day. Effect coefficient observed: e_dose_tpm_cl = 0.193 in Lee_2024_topiramate.R (Lee 2024 Table S1 theta9); 200 mg/day raises CL/F by 14.3% and 50 mg/day lowers it by 12.5% relative to the 100 mg/day reference.
• Source aliases:
  • DOSE – used in Lee_2024_topiramate.R (Lee 2024 Results CL/F equation and Table S1; cohort mean 178.4 +/- 117.9 mg/day).
• Example models: Lee_2024_topiramate.R (mild positive dose-dependence of topiramate apparent clearance, multiplying the additive enzyme-inducing-comedication clearance sum alongside the [[CRCL]] power term).
• Notes: Follows the DOSE_<DRUG>_<UNITS> auto-approve family (siblings: [[DOSE_EMPA_MGD]] for empagliflozin, [[DOSE_LOR_MGD]] for lorlatinib, [[DOSE_PHT_MGKGD]] for phenytoin). Topiramate is dosed in flat mg/day for adults (no per-kg adjustment in the adult label), matching the DOSE_EMPA_MGD / DOSE_LOR_MGD pattern rather than the per-kg DOSE_PHT_MGKGD pattern; a future paediatric topiramate extraction dosing in mg/kg/day should register a sibling DOSE_TPM_MGKGD rather than reuse this name. Distinct from the rxode2 / nlmixr2 event column amt, which carries the amount of each individual administration; DOSE_TPM_MGD is a per-record covariate carrying the current daily-dose LEVEL used by the covariate model. Because the covariate is the daily dose while amt is the per-administration amount, the two must be kept consistent when simulating: for a b.i.d. regimen amt is half of DOSE_TPM_MGD. A dose-level covariate on clearance encodes a mild PK nonlinearity as a covariate rather than as a saturable elimination term, so simulations that vary the dose must update this column or the nonlinearity will be silently lost. Ratified canonically on 2026-08-03 alongside the Lee 2024 topiramate extraction.

DOSE_SEMAGLUTIDE_MG (canonical for per-subject maintenance semaglutide dose)

• Description: Per-subject maintenance dose of semaglutide, in mg per once-weekly SC injection (the target maintenance dose reached after the dose-escalation phase). Constant per subject in the phase III SUSTAIN maintenance analysis. Values used in the founding example (Carlsson Petri 2018): 0.5 or 1.0 mg once weekly. A binary indicator such as DOSE_LOW_MAINT = as.integer(DOSE_SEMAGLUTIDE_MG < 0.75) can be derived inline in model() when the source paper tests a binary low-vs-high maintenance-dose contrast.
• Units: mg (per once-weekly injection)
• Type: continuous
• Scope: specific
• Reference category: n/a – used with a binary indicator derived from the continuous value (e.g., theta^DOSE_LOW_MAINT in Carlsson Petri 2018 with 1.0 mg as the reference).
• Source aliases:
  • DOSE (per-subject maintenance dose column) – used in CarlssonPetri_2018_semaglutide.R (Carlsson Petri 2018 Methods ‘For semaglutide, there are two maintenance doses (0.5 and 1.0 mg), which were included as covariates to assess the dose dependency of semaglutide exposure’).
• Example models: CarlssonPetri_2018_semaglutide.R (binary 0.5-vs-1.0 mg maintenance-dose indicator derived inline in model(); CL/F ratio 1.00 per Table S3 confirming dose proportionality; the coefficient is fixed at 1.00 in the model file to preserve fidelity to the published estimate).
• Notes: Follows the DOSE_<DRUG>_<UNITS> auto-approve family. Distinct from the rxode2/nlmixr2 event column amt (which carries the administered dose at dose events; DOSE_SEMAGLUTIDE_MG is a per-subject fixed covariate carrying the assigned MAINTENANCE dose target, not the per-record actual dose which varies during the dose-escalation phase). Future once-weekly-semaglutide extractions with the same 0.5-vs-1.0 mg contrast (or an extension to 2.0 mg / oral 3-14 mg tablets) can reuse this canonical; other GLP-1 receptor agonists dosed in mg (dulaglutide, tirzepatide) should register sibling canonicals (e.g. DOSE_DULAGLUTIDE_MG, DOSE_TIRZEPATIDE_MG) rather than overload this name because the numeric coefficient value is drug-specific. Founded alongside the CarlssonPetri_2018_semaglutide extraction.

DOSE_BPN_SL_MG (canonical for administered sublingual buprenorphine dose)

• Description: Sublingual (SL) buprenorphine dose in mg, supplied as a data column so a dose-dependent SL bioavailability can be evaluated inside model(). Needed because rxode2 model code cannot read the amt of the dose record it is scaling, and because a multi-route buprenorphine model must scale only the SL depots by the dose-dependent term while leaving the intravenous and subcutaneous-depot routes at their own (dose-proportional) bioavailability. Route-qualified in the name (_SL_) for exactly that reason: the same model carries intravenous, sublingual, and two subcutaneous depot routes, so an unqualified dose column would be ambiguous.
• Units: mg (per sublingual administration)
• Type: continuous
• Scope: specific
• Reference category: n/a – used with power scaling normalised to a 16 mg reference dose, the typical daily SL maintenance dose: F_SL = 0.14 * (DOSE_BPN_SL_MG / 16)^-0.371.
• Source aliases:
  • Dose – Bjornsson 2023 Table 3 row “Dose dependency on FSL” and the Results 3.1.3 equation FSL = 0.14 x (Dose/16)^-0.371; the source NMTRAN column name is not separately reported.
• Example models: Bjornsson_2023_buprenorphine.R (power-form effect on the logit-transformed SL bioavailability F_SL, exponent -0.371, reference 16 mg; the paper verifies the equation against its own point values of 18.1, 14.0, and 12.0 percent at 8, 16, and 24 mg, the dose-dependence arising from the first-pass metabolism of the swallowed fraction of a sublingual tablet. Set to the SL dose on records in an SL treatment period; the value does not affect intravenous or CAM2038 depot simulations because it scales only the two SL depot compartments).
• Notes: Follows the DOSE_<DRUG>_<UNITS> auto-approve family, with a route qualifier between the drug and unit tokens on the precedent of DOSE_EFP_MAX_MG. BPN is the source paper’s own abbreviation for buprenorphine. Distinct from the rxode2/nlmixr2 event column amt (which carries the administered dose at dose events but is not readable from model()), from the generic DOSE canonical (which is unqualified and therefore ambiguous in a model carrying four routes of administration), and from a CAM2038 depot dose column (no such covariate is needed – Bjornsson 2023 Results 3.1.3 found buprenorphine PK dose-proportional for CAM2038 weekly 8-32 mg and monthly 64-192 mg). A future extraction of a different buprenorphine formulation whose own parameters depend on the administered amount should register a sibling (e.g. DOSE_BPN_SC_MG) rather than overload this name. Founded alongside the Bjornsson 2023 buprenorphine / CAM2038 extraction.

DOSE_EFP_MAX_MG (canonical for per-subject maximum administered efaproxiral dose)

• Description: Per-subject maximum administered single-dose efaproxiral dose, in mg. Time-fixed per subject; defined as the largest single-administration dose the subject received during the trial. Distinct from a per-administration DOSE column because the covariate enters the model as a static per-subject scalar (a power-model effect on the RBC:plasma proportionality SLPRBC) rather than a per-dose-record exposure regressor.
• Units: mg
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as a power-model effect (DOSE_EFP_MAX_MG / 6800)^theta_SLPRBC~MDOS on the RBC:plasma proportionality SLPRBC. Reference value 6800 mg per Equation 1 of Gastonguay 2005 (close to the median 100 mg/kg x ~72 kg derived from the cohort).
• Source aliases:
  • MDOS – used in Gastonguay_2005_efaproxiral.R (Gastonguay 2005 Data section; per-subject maximum administered dose value used as a power-model covariate on SLPRBC).
• Example models: Gastonguay_2005_efaproxiral.R (efaproxiral; theta_SLPRBC~MDOS = -0.125, 95% CI -0.220 to -0.0222, classified NCI = not clinically important).
• Notes: Specific scope because the absolute reference value (6800 mg) is tied to the efaproxiral cohort and the per-administration MDOS abstraction is uncommon outside hemoglobin-modifier infusion programs. Follows the DOSE_<DRUG>_<MODIFIER>_<UNITS> auto-approve family (e.g., DOSE_PHT_MGKGD for phenytoin, DOSE_EMPA_MGD for empagliflozin) – the _MAX_ token disambiguates from a per-administration DOSE_EFP_MG column that a future extraction may need. Ratified canonically on 2026-06-30 alongside the Gastonguay 2005 efaproxiral extraction.

DOSE_CIPARGAMIN_MG (canonical for administered cipargamin single-dose amount)

• Description: Administered single oral dose of the spiroindolone antimalarial cipargamin (formerly KAE609), in mg. Time-fixed per subject in the founding single-dose study (each patient receives one dose on day 1); a per-dose-record covariate in principle if the design ever ran a multi-dose regimen. Not a PK covariate – the amount already appears on the dose record via amt. Used inside model() as the regressor in the dose-dependent Emax equation Emax_i = TVEmax * (DOSE_CIPARGAMIN_MG / 10)^COVdose_Emax (Hien 2017 equation 7 in the supplemental text).
• Units: mg
• Type: continuous
• Scope: specific
• Reference category: n/a – enters via (DOSE_CIPARGAMIN_MG / 10)^e_dose_emax with reference dose 10 mg (the smallest study cohort dose in Hien 2017).
• Source aliases:
  • DOSE – used in Hien_2017_cipargamin.R (Hien 2017 Methods ‘Study design’; studied doses 10, 15, 20, 21 [one patient administered 21 mg in error], and 30 mg).
• Example models: Hien_2017_cipargamin.R (drives the dose-dependent typical Emax in the two-population parasite clearance PD model: emax = emax_typical * (DOSE_CIPARGAMIN_MG / 10)^e_dose_emax with e_dose_emax = 0.0463 per Hien 2017 Table 3).
• Notes: Follows the DOSE_<DRUG>_<UNITS> auto-approve family (siblings: DOSE_EMPA_MGD, DOSE_PHT_MGKGD). Distinct from those two because cipargamin is a single-dose administration (mg, not mg/day) in the founding study, so the units suffix is MG. Sibling canonicals may be registered for other single-dose antimalarial extractions using the same auto-approve pattern. Ratified canonically on 2026-07-08 alongside the Hien 2017 cipargamin extraction.

DOSE_CABAMIQUINE_MG (canonical for administered cabamiquine single-dose amount)

• Description: Administered single oral dose of the Plasmodium eEF2 inhibitor cabamiquine (formerly DDD107498 / M5717), in milligrams of free base. Time-fixed per subject in the founding studies (each participant receives one dose). Not a PK covariate in the usual sense – the amount already appears on the dose record via amt – but it is required as a data column because the model uses it as the regressor in an empirical power effect of dose on the apparent central volume, V2/F = 2363 * (WT/70)^1 * DOSE_CABAMIQUINE_MG^e_dose_vc with e_dose_vc = -0.50 (Courlet 2023 Supplementary Material 1). The authors introduced the term to describe greater-than-dose-proportional exposure that they could not explain mechanistically: “it was ultimately necessary to include an empirical covariate effect of dose on V2/F in the model”.
• Units: mg (free base)
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as an uncentred power term, DOSE_CABAMIQUINE_MG^e_dose_vc, i.e. an implicit reference dose of 1 mg. Courlet 2023 never reports a centering dose for the term; the uncentred reading is the only one compatible with the reported 146-193 h terminal half-life, because centering on a typical study dose would leave V2/F = 2363 L and a steady-state volume above the Vz implied by that half-life. Ratified by operator sidecar oare_PMC10720512 request 001 question 1 (answer A).
• Source aliases:
  • DOSE – used in Courlet_2023_cabamiquine_pk.R and Courlet_2023_cabamiquine.R (Courlet 2023 Results ‘PK/PD data’; 14 dose levels pooled across the two phase I studies).
• Example models: Courlet_2023_cabamiquine_pk.R, Courlet_2023_cabamiquine.R (drives the empirical dose effect on the apparent central volume of the three-compartment transit-absorption / enterohepatic-recycling PK model).
• Notes: Follows the DOSE_<DRUG>_<UNITS> auto-approve family (siblings: DOSE_CIPARGAMIN_MG, DOSE_EMPA_MGD, DOSE_TBPPI_MG). The free-base basis is load-bearing: Courlet 2023 study 1 (IBSM) administered cabamiquine as the succinate salt and study 2 (SpzCh) as free base, with 1 mg succinate salt = 0.797 mg free base (Methods ‘Data set’), so the 150 / 400 / 800 mg salt IBSM cohorts are 119.6 / 318.8 / 637.6 mg free base. Populate this column – and the dose-record amt – on the free-base scale. The vignette shows that the free-base reading reproduces the published per-dose recrudescence rates (all three predictions inside the observed Clopper-Pearson 95% CIs) whereas the salt reading does not. Distinct from DOSE_CIPARGAMIN_MG (a different antimalarial, and a covariate on Emax rather than on volume). Ratified canonically alongside the Courlet 2023 cabamiquine extraction.

DOSE_TBPPI_MG (canonical for administered tebipenem pivoxil hydrobromide dose level)

• Description: Administered dose of the oral carbapenem pro-drug tebipenem pivoxil hydrobromide (TBP-PI-HBr) carried on each record, in milligrams of pro-drug (not of the active moiety tebipenem). Time-fixed per subject in fixed-dose designs and time-varying in crossover designs where the same subject receives more than one dose level.
• Units: mg
• Type: continuous
• Scope: specific
• Reference category: n/a – used as a normalized linear term (DOSE_TBPPI_MG / 1200) inside a gated covariate effect, where 1200 mg is the high dose of the study SPR994-104 crossover.
• Source aliases:
  • DOSEMG – the column name printed in Ganesan 2023 Eq. 6.
• Example models: Ganesan_2023_tebipenem.R (dose effect on the absorption rate constant, gated to a single study: ka * (1 + e_dose_ka * (DOSE_TBPPI_MG / 1200) * STUDY_SPR994_104) with e_dose_ka = -0.478).
• Notes: A drug-specific member of the DOSE_<drug>_<units> family, preferred here over the general DOSE canonical for two reasons. First, the dose recorded is pro-drug mass, so every apparent PK parameter estimated against it is conditioned on the pro-drug-to-active-moiety mass ratio and on the (unmeasured, presumed near-complete) conversion fraction – a drug-specific caveat worth carrying in the column name. Second, rxode2’s event-table translator (etTrans()) consumes a column literally named DOSE and does not expose it to model() as a covariate, so a model that reads the dose level as a covariate must use a distinguishable name or supply it through params.

DOSE_TAK071_MG (canonical for administered TAK-071 dose level)

• Description: Administered dose of TAK-071 (a muscarinic M1 positive allosteric modulator) carried on each dose record, in milligrams. Time-fixed per subject in the fixed-dose Phase 2 cohorts and time-varying in the Phase 1 study, where the same participant received a single dose and then a multiple-dose regimen at a different level, and in the 3-way crossover relative-bioavailability arm.
• Units: mg
• Type: continuous
• Scope: specific
• Reference category: n/a – used as a normalized power term (DOSE_TAK071_MG / 5)^exponent, where 5 mg is the reference dose stated in Jia 2025 Methods (“dose to 5 mg”) and in the Table 1 footnote b (“Typical value for a 5 mg dose with formulation DIC”).
• Source aliases:
  • DOSE – the NONMEM $INPUT column name in the Jia 2025 Supplemental Code control stream.
• Example models: Jia_2025_tak_071.R (two simultaneous power effects on the same column: relative bioavailability Frel = (DOSE_TAK071_MG / 5)^-0.0965 applied as f(depot), and the slow first-order absorption rate ka_slow *= (DOSE_TAK071_MG / 5)^-0.885, which the parallel quick route inherits because the quick rate is parameterised as a ratio to the slow rate. Both exposures and absorption rate therefore fall as dose rises – less-than-dose-proportional exposure for a Biopharmaceutics Classification System Class II compound).
• Notes: A drug-specific member of the auto-approved DOSE_<drug>_<units> family, required here rather than the general DOSE canonical because rxode2’s event-table translator (etTrans()) consumes a column literally named DOSE on the rxUi / readModelDb() solve path and never exposes it to model(), producing “The following parameter(s) are required for solving: DOSE” at solve time. Same rationale as DOSE_TBPPI_MG.

DOSE_GHI_MLKG (canonical for administered guhong injection volume dose per kg body weight)

• Description: Volume dose of guhong injection (GHI), a Chinese herbal compound preparation of N-acetyl-L-glutamine plus an aqueous safflower (Carthamus tinctorius L.) extract, administered as a single intravenous injection and recorded in mL of preparation per kg body weight. The preparation is dosed by volume rather than by the mass of any one constituent, so the volume dose is the natural per-record quantity: the dose of each individual quantified constituent is this column multiplied by that constituent’s content in GHI (Chen 2024 Results 3.2: 30.0 mg/mL N-acetyl-L-glutamine, 1 mg/mL hydroxysafflor yellow A, 12.9 ug/mL chlorogenic acid, 78.3 ug/mL p-coumaric acid, 11.7 ug/mL rutin, 3.4 ug/mL hyperoside, 65.7 ug/mL kaempferol-3-O-rutinoside, 8.7 ug/mL kaempferol-3-O-glucoside).
• Units: mL/kg
• Type: continuous
• Scope: specific
• Reference category: n/a – used as a dose-group selector, not as a normalized continuous term. Chen 2024 fitted an independent two-compartment disposition model and an independent set of sigmoid-Emax parameters in each of its three dose groups (GHI-L 2.5 mL/kg, GHI-M 5 mL/kg, GHI-H 10 mL/kg) rather than estimating a dose-covariate function, so models that carry this column select a parameter set by dose level. The Chen_2024_*_rat.R family assigns GHI-L below 3.75 mL/kg, GHI-M from 3.75 to below 7.5 mL/kg, and GHI-H at or above 7.5 mL/kg – the midpoints of the studied levels. Values outside 2.5-10 mL/kg extrapolate the nearest fitted group and are not supported by the source data.
• Source aliases:
  • GHI dose (mL/kg) – the wording used in Chen 2024 Section 2.3 (“GHI-administered groups were respectively injected with GHI (2.5, 5, and 10 mL/kg, i.v.)”); the paper prints no data-column name.
• Example models: Chen_2024_chlorogenicAcid_rat.R, Chen_2024_hydroxysafflorYellowA_rat.R, Chen_2024_hyperoside_rat.R, Chen_2024_kaempferol3OGlucoside_rat.R, Chen_2024_kaempferol3ORutinoside_rat.R, Chen_2024_nAcetylglutamine_rat.R, Chen_2024_pCoumaricAcid_rat.R, Chen_2024_rutin_rat.R (all eight select V1, V2, CL1 and Q – and, where a PK/PD equation was fitted, Emax, ED50 and gamma – from this column).
• Notes: A drug-specific member of the DOSE_<drug>_<units> family. The general DOSE canonical is unusable here for the mechanical reason documented under DOSE_TBPPI_MG: rxode2’s etTrans() consumes a column literally named DOSE and never exposes it to model(). Because GHI is a multi-constituent botanical preparation with no single active moiety, the volume dose – not a mass dose – is what the study randomized and what indexes the fitted parameter sets; a per-constituent DOSE_<constituent>_UGKG column would have to be derived separately for each of the eight analytes and would lose the fact that they are dosed together in fixed proportion. Distinct from the general DOSE_HIGH high-dose-cohort indicator, which is binary; this column preserves the actual administered volume so a downstream user can recover each constituent’s dose.

DOSE_TV46000_ML (canonical for administered TV-46000 subcutaneous injection volume)

• Description: Volume of the TV-46000 risperidone long-acting subcutaneous antipsychotic (LASCA) suspension delivered at a given injection, in millilitres of suspension. TV-46000 is a fixed-strength copolymer-based risperidone suspension (approximately 360 mg risperidone per mL), so the injected volume and the milligram dose are proportional and the paper’s own extrinsic-factor list names the covariate “injection volume (dose)”. The volume is nonetheless the quantity the model reads, because the mechanism the covariate encodes is physical: a larger bolus of suspension deposited subcutaneously has a smaller surface-area-to-volume ratio and releases drug more slowly by the direct route. Per-dose-record (a subject who switches dose strength between injections carries a different value on each dose record).
• Units: mL
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as a median-normalized power term on the fast direct-release rate constant, ka1 * (DOSE_TV46000_ML / ref)^e_dose_ka1 (Perlstein 2025 Methods, continuous-covariate equation Pi = P * (COVi / COVmedian)^theta_i). Reference values observed: Perlstein 2025 reports the pooled cohort mean injection volume as 0.303 mL (Table 2, Overall column, 3287 injections) but never prints the median that the covariate equation actually centres on; Perlstein_2025_risperidone_tv46000.R therefore uses 0.303 mL and documents the substitution. Volumes observed in the source programme: 0.035 mL (12.5 mg), 0.070 mL (25 mg), 0.139 mL (50 mg) and 0.278 mL (100 mg) are printed in the paper; the remaining strengths follow the same ~360 mg/mL proportionality (75 mg ~ 0.208 mL, 125 mg ~ 0.347 mL, 150 mg ~ 0.417 mL, 200 mg ~ 0.556 mL, 250 mg ~ 0.695 mL). Must be strictly positive; a zero or NA value makes the power term undefined.
• Source aliases:
  • INJV – the parameter-name stem printed in Perlstein 2025 Table 3 (KA1INJV1, “injection volume effect on KA1”) and the row label “Injection volume, mL, mean (SD)” in Table 2. The paper does not disclose the NONMEM $INPUT column name.
• Example models: Perlstein_2025_risperidone_tv46000.R (power effect on the fast direct-release absorption rate constant: ka1 * (DOSE_TV46000_ML / 0.303)^(-0.384), so a larger injected volume slows the direct release; Perlstein 2025 Table 3, KA1INJV1 = -0.384).
• Notes: A drug-specific member of the DOSE_<drug>_<units> family, in the same vein as DOSE_GHI_MLKG (a volume rather than a mass dose) but recorded as an absolute volume rather than per kilogram, because the TV-46000 model normalizes against a cohort median volume and not against body size. The general DOSE canonical is unusable for the mechanical reason documented under DOSE_TBPPI_MG: rxode2’s etTrans() consumes a column literally named DOSE and never exposes it to model(). Registered as a volume rather than as DOSE_TV46000_MG because the paper’s covariate is explicitly the administration volume and its interpretation is physical (Perlstein 2025 Results: “The effect of injection volume was pronounced at subtherapeutic dose levels of 12.5 mg and 25 mg (administration volume of 0.035 mL and 0.07 mL, respectively), while at therapeutic doses of 50 mg or higher (>= 0.139 mL), the effect of administration volume was minimal”); a downstream user dosing in mg can derive the column as DOSE_TV46000_ML = dose_mg / 360. Distinct from INJSITE_ARM, which records where the same volume was injected, and from the binary DOSE_HIGH high-dose-cohort indicator.

DOSE_UFH_UH (canonical for concomitant continuous-infusion unfractionated heparin dose rate)

• Description: Patient’s concurrent continuous intravenous infusion rate of unfractionated heparin (UFH), in absolute units per hour. Absolute (not weight-normalized) infusion rate is preserved at the column level because the source model parameterizes the UFH-on-clearance effect against a cohort-median absolute rate rather than a per-kg rate. Time-varying per observation as the UFH infusion is titrated. Set to 0 units/h for patients not receiving concurrent UFH.
• Units: units/h (absolute infusion rate; document per-model via covariateData[[DOSE_UFH_UH]]$units if a different unit is reported).
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as an additive linear increment on clearance in the source paper’s final model: CL = CL_base * (WT / 70)^0.75 + e_ufh_cl * (DOSE_UFH_UH / ref). Reference values observed: 173 units/h (Moffett 2017 cohort median absolute UFH infusion rate). The additive form (rather than multiplicative) reflects the observed clinical mechanism – UFH binds to and potentiates antithrombin activity, so higher UFH infusion rates deplete circulating antithrombin at a constant additive rate on top of the baseline weight-scaled clearance.
• Source aliases:
  • UFH – used in Moffett_2017_antithrombin.R (Moffett 2017 Table 3 covariate; the paper’s absolute UFH infusion-rate column in units/h). The cohort mean per-kg UFH dose was 34.1 +/- 22.7 units/kg/h (Moffett 2017 Results and Table 5); the per-record absolute rate is the value that entered the (UFH/173) covariate ratio.
• Example models: Moffett_2017_antithrombin.R (linear-additive effect on AT clearance: CL_dL_h = 0.917 * (WT/70)^0.75 + 0.129 * (DOSE_UFH_UH / 173); reference 173 units/h from Moffett 2017 Table 3).
• Notes: Specific scope until a second antithrombin / anticoagulation-covariate model ratifies the canonical. UFH is a heterogeneous polymeric anticoagulant that acts by potentiating endogenous antithrombin; concurrent UFH therefore accelerates depletion of dosed exogenous antithrombin, motivating its inclusion as a covariate on AT clearance. Future models parameterizing the same covariate on a per-kg basis (units/kg/h) should either add a source alias here with the value transformation DOSE_UFH_UKGH = DOSE_UFH_UH / WT documented, or register a sibling canonical DOSE_UFH_UKGH if the effect coefficient’s meaning changes. Distinct from the AT-related coagulation biomarker AT_BL_UDL (baseline antithrombin activity, used as a covariate on VD in the same Moffett 2017 model). Set to 0 for patients not receiving concurrent UFH; the additive covariate term collapses to zero and the clearance reduces to the baseline weight-scaled arm. Ratified canonically on 2026-06-21 alongside the Moffett 2017 antithrombin extraction.

DOSE_PHT_MGKGD (canonical for daily phenytoin dose per kg body weight)

• Description: Patient’s own total daily dose of phenytoin (mg) divided by current body weight (kg), expressed as mg/kg/d. Per-dose-record covariate; constant within an inter-dose interval and updated when the prescriber alters the daily dose.
• Units: mg/kg/d
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as a self-dose-rate regressor in the dose-dependent powder bioavailability formula F_powder = 1 - exp(-theta / DOSE_PHT_MGKGD). Has no effect when paired with FORM_POWDER = 0 (tablet); a non-NA non-zero placeholder must still be supplied.
• Source aliases:
  • Dij – used in Yukawa_1990_phenytoin.R (paper’s per-record daily-dose-per-weight regressor, mg/kg/d, in the powder bioavailability equation 4 of Yukawa 1990).
• Example models: Yukawa_1990_phenytoin.R (powder bioavailability F_powder = 1 - exp(-9.92 / DOSE_PHT_MGKGD); F approaches 1 below ~2 mg/kg/d and decreases monotonically as the daily dose increases, reflecting the lower wettability of the Aleviatin brand phenytoin powder formulation).
• Notes: Specific scope because the value is intrinsically tied to phenytoin (PHT) and the Yukawa 1990 powder-vs-tablet bioavailability contrast. Drug-self-dose covariates for other drugs should register sibling canonicals (e.g., DOSE_<DRUG>_MGKGD) rather than reuse this name – the absolute coefficient (theta_BA2 = 9.92 in Yukawa 1990) is not transferable across drugs. Computed as the total daily dose summed across the 2-3 daily phenytoin doses (mg/d) divided by the patient’s body weight at the dose record (kg). Ratified canonically on 2026-05-10 alongside the Yukawa 1990 phenytoin extraction.

DOSE_VPA_MGKGD (canonical for daily valproic acid dose per kg body weight)

• Description: Patient’s own total daily dose of valproic acid (mg), summed across the 1-3 daily administrations, divided by the current body weight (kg). Per-dose-record covariate; constant within an inter-dose interval and updated when the prescriber alters the daily dose.
• Units: mg/kg/d
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as a self-dose-rate power regressor on apparent clearance, cl = CLp/F * (DOSE_VPA_MGKGD / 25)^k, with 25 mg/kg/d the rounded cohort mean daily dose. Must be strictly positive; a zero or NA value makes the power term undefined.
• Source aliases:
  • DD – used in Zhang_2023_valproic_acid_exponent.R (Zhang 2023 Eq. 7, the “simple exponent model”; the paper’s abbreviation list defines DD as daily dose in mg/day and DDW as daily dose in mg/kg/day, but Eq. 7’s reference value of 25 and the reported CLp/F of 0.331 L/h against a base-model CL/F of 0.311 L/h only reconcile when DD is read on the mg/kg/day scale – see that model’s vignette Errata).
• Example models: Zhang_2023_valproic_acid_exponent.R (power effect on CL/F with exponent 0.658 and reference 25 mg/kg/d; observed range 8.70-57.69 mg/kg/d in a paediatric epilepsy therapeutic-drug-monitoring cohort).
• Notes: Specific scope because the value is intrinsically tied to valproic acid and to the saturable-plasma-protein-binding non-linearity that makes valproate clearance rise with dose; the exponent is not transferable across drugs. Sibling canonical of DOSE_PHT_MGKGD (same drug-self-daily-dose-per-weight concept for phenytoin) – register a further DOSE_<DRUG>_MGKGD sibling rather than reusing either name. Note the modelling caveat the source authors themselves raise: because the daily dose is the quantity a therapeutic-drug-monitoring model is meant to predict, using it as a clearance covariate is circular, so models built on this covariate reproduce a published comparison rather than recommend a dosing strategy.

DOSE_VPA_MGD (canonical for total daily valproic acid dose)

• Description: Patient’s own total daily dose of valproic acid (mg), summed across the 1-3 daily administrations and NOT normalised by body weight. Per-dose-record covariate; constant within an inter-dose interval and updated when the prescriber alters the daily dose.
• Units: mg/d
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as the driver of a sigmoid-Emax term on apparent clearance, cl = CLp/F * (1 + Emax * DOSE_VPA_MGD^gamma / (DD50^gamma + DOSE_VPA_MGD^gamma)). Must be strictly positive.
• Source aliases:
  • DD – used in Zhang_2023_valproic_acid_ddemax.R (Zhang 2023 Eq. 5, the “dose-dependent maximum effect model”).
• Example models: Zhang_2023_valproic_acid_ddemax.R (sigmoid-Emax effect on CL/F with Emax 2.8, Hill 1.68 and DD50 37.4 mg/d, all fixed from Ding 2015; observed range 60-1250 mg/d, median 480, in a paediatric epilepsy therapeutic-drug-monitoring cohort).
• Notes: The mass-scale sibling of DOSE_VPA_MGKGD (same drug, same self-daily-dose concept, per-patient rather than per-kilogram). Both are needed because Zhang 2023 uses the per-kilogram scale in its Eq. 7 simple exponent model and the per-patient scale in its Eq. 5 dose-dependent maximum effect model – do not merge them or convert one into the other inside model(), since the reference values (25 mg/kg/d and 37.4 mg/d respectively) are not interconvertible without a weight. The scale for Eq. 5 is an operator-ratified reading (sidecar oare_PMC10587682 q2): Eq. 5’s text defines DD in mg/kg/day and Ding 2015 tabulates DD50 = 37.4 against a mg/kg/day daily dose, but only the mg/day reading reproduces Zhang’s own reported clearance (0.307 L/h against a base-model 0.311 L/h) and its near-zero MDPE; the mg/kg/day reading gives 0.153 L/h and a typical concentration roughly 2.7-fold the observed median. The conflicting evidence – Supplementary Figure S5A favours mg/kg/day – is recorded in that model’s vignette Errata. Never name a dose covariate column bare DOSE: rxode2::etTrans() consumes a column of that name (any casing) before model() sees it.
• Register a further DOSE_<DRUG>_MGD sibling rather than reusing this name for another drug.

DOSE_MPA_MGD (canonical for total daily mycophenolic acid dose)

• Description: Patient’s own total daily dose of mycophenolic acid (MPA), summed across the two daily administrations and NOT normalised by body weight. Expressed on a single MPA-equivalent mass scale so that enteric-coated mycophenolate sodium (EC-MPS) and mycophenolate mofetil (MMF) daily doses are directly comparable. Per-subject / per-occasion covariate; constant within a dosing interval and updated when the prescriber alters the daily dose.
• Units: mg/d
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as a power term on apparent clearance normalised to a cohort-mean daily dose, cl = CL/F * (DOSE_MPA_MGD / 1500)^e_dose_mpa_mgd_cl. Must be strictly positive.
• Source aliases:
  • TDD (total daily dose, mg) – used in Tsyplakova_2025_mycophenolateSodium.R and Tsyplakova_2025_mycophenolateMofetil.R (Tsyplakova 2025 Eq. 8 and Eq. 11; both models normalise to the same 1500 mg/day constant).
• Example models: Tsyplakova_2025_mycophenolateSodium.R (power effect on CL/F with exponent 0.77 against a 1500 mg/day reference; EC-MPS cohort), Tsyplakova_2025_mycophenolateMofetil.R (same covariate, exponent 1.27 against the same 1500 mg/day reference; MMF cohort).
• Notes: A DOSE_<DRUG>_MGD sibling of DOSE_VPA_MGD, registered rather than reusing that entry per its own instruction. The effect direction is POSITIVE (higher daily dose associates with higher apparent clearance), which in a stable, therapeutically-monitored transplant cohort is an indication-by-confounding artefact rather than a mechanistic dose-dependency: Tsyplakova 2025 Discussion states that “patients receiving higher total daily doses may have required these regimens to compensate for greater clearance”, and additionally notes the covariate may partly proxy for body weight, which was unavailable in that dataset. Treat the coefficient as descriptive of the source cohort, not as a causal dose-dependency to extrapolate outside the observed dose range. The MPA-equivalent mass scale matters: Tsyplakova 2025 converted MMF doses with a 0.72 factor (MMF 500-2000 mg/day maps onto EC-MPS 360-1440 mg/day) so that both formulations share one covariate scale, and both of its models normalise to the same 1500 mg/day constant – when populating this column for a new MPA model, state the formulation basis in covariateData[[DOSE_MPA_MGD]]$notes. Distinct from DOSE (the generic administered-dose-level column) and from the per-administration amount carried on the event table’s amt: this is the summed 24-hour dose used as a typical-value covariate. Never name a dose covariate column bare DOSE: rxode2::etTrans() consumes a column of that name (any casing) before model() sees it.

PRED_DOSE (canonical for concomitant oral prednisolone daily dose)

• Description: Concomitant oral prednisolone (or prednisolone-equivalent glucocorticoid) daily dose. Time-varying across the dosing period as the post-transplant conmed_steroid taper progresses.
• Units: mg/day. Document per-model via covariateData[[PRED_DOSE]]$units when a paper uses a different unit (mg/kg/day) or a different glucocorticoid (methylprednisolone, dexamethasone, hydrocortisone) – in the latter case convert to prednisolone-equivalent mg/day before populating the column and record the conversion factor in covariateData[[PRED_DOSE]]$notes.
• Type: continuous
• Scope: general
• Reference category: n/a – continuous, with 0 mg/day (no prednisolone) the natural reference value. Effect forms in the registered example models: sigmoid-Emax fractional reduction (1 - Pred_max * PRED_DOSE / (Pred_50 + PRED_DOSE)) on bioavailability (Storset 2014, Pred_max = 0.67, Pred_50 = 35 mg/day, Hill = 1); threshold-form binary multiplier (1 + e * (PRED_DOSE >= 20)) on intrinsic clearance (ter Heine 2018, e = 0.31 for the >= 20 mg/day high-dose contrast). Document the per-model functional form in covariateData[[PRED_DOSE]]$notes.
• Source aliases:
  • Prednisolone dose – used in Storset_2014_tacrolimus.R (mg/day).
  • Prednisolone dose (total daily dose, mg/day) – used in TerHeine_2018_everolimus.R (mg/day; collapsed to a binary high-dose indicator at the >= 20 mg/day threshold inside model()).
• Example models: Storset_2014_tacrolimus.R (Emax-style fractional reduction in tacrolimus oral bioavailability via prednisolone-driven induction of intestinal CYP3A / P-glycoprotein; Storset 2014 Methods Equations 4 + 6 with Hill = 1), TerHeine_2018_everolimus.R (threshold-form binary high-dose indicator at >= 20 mg/day driving a multiplicative +31% increase in apparent intrinsic clearance for everolimus via prednisolone-driven CYP3A4 induction; ter Heine 2018 Table 2 ‘Final model’).
• Notes: Distinct from PRED_CMAX_FREE (free prednisolone Cmax co-medication exposure) – PRED_CMAX_FREE is the modelled-from-data peak free concentration, whereas PRED_DOSE is the administered daily-dose level supplied directly from the dosing record. Both can coexist in a future model that simultaneously tests dose-driven and exposure-driven effects of prednisolone on a tacrolimus PK parameter. Distinct from CONMED_STEROID (binary baseline / concomitant corticosteroid use indicator) and PRICORT (binary prior corticosteroid use indicator) – PRED_DOSE carries the daily dose value, not just an on / off flag. Time-varying because tacrolimus / everolimus PK depend on the conmed_steroid dose at the time of each observation; the conmed_steroid taper schedule must be supplied as a per-time-row covariate column. The corresponding methylprednisolone single-dose induction-bolus indicator (Storset 2014 binary covariate, not retained in the final model) would warrant a separate canonical (e.g. MPRED_BOLUS) if a future model retains it. Threshold-form binary indicators (e.g. PRED_DOSE >= 20 mg/day in ter Heine 2018) are derived inside model() from the continuous PRED_DOSE column rather than as separate registered canonicals so the underlying continuous dose value remains available for sensitivity analyses. Scope promoted from specific to general on 2026-05-24 with the ter Heine 2018 everolimus extraction (second model ratifying the canonical, this time as a CYP3A4-induction covariate on intrinsic clearance rather than a CYP3A / P-gp-induction covariate on oral bioavailability).

PRED_CMAX_FREE (canonical for free prednisolone Cmax co-medication exposure)

• Description: Maximum free (unbound, ultrafiltrable) plasma prednisolone concentration over a co-medication dosing interval, used as a co-medication-exposure covariate on a primary modelled drug (e.g., tacrolimus) when concomitant prednisolone is suspected to alter the primary drug’s PK via membrane-permeability or fluid-balance effects. Time-fixed per subject in the source paper (one Cmax value per subject, derived from limited-sampling concentrations).
• Units: nmol/L
• Type: continuous
• Scope: specific
• Reference category: n/a – used in centred-deviation form (1 + e * (PRED_CMAX_FREE - ref)). Reference value observed: 155.5 nmol/L (Bergmann 2014 study median).
• Source aliases:
  • PredCmax,free / PREDCFR – used in Bergmann_2014_tacrolimus.R (Bergmann 2014 Table 2 footnote; 162 nmol/L median per Table 1, 155.5 nmol/L centring value per Table 2 equation).
• Example models: Bergmann_2014_tacrolimus.R (linear deviation effect on tacrolimus apparent central volume V1/F: every 1 nmol/L increase from 155.5 nmol/L decreases V1/F by 0.28%).
• Notes: Specific scope because the value is intrinsically tied to a particular co-medication (oral prednisolone) and a particular reference population (kidney transplant recipients on a tapering conmed_steroid regimen). Future popPK models that test free prednisolone Cmax as a covariate on a different primary drug should reuse this canonical; if a future model uses a different prednisolone exposure metric (e.g., PRED_AUC0_12_FREE for free AUC 0-12 h, or PRED_CMAX_TOTAL for total Cmax), register parallel canonicals rather than overload PRED_CMAX_FREE. The source-paper Cmax is derived from limited-sampling concentrations at 1 / 2 / 4 hours postdose per Bergmann 2014 Methods (validated against earlier full-profile data from the same cohort). Distinct from CAV (average concentration of the modelled drug) and CP_MGL (instantaneous concentration of the modelled drug as a time-varying PD driver) – PRED_CMAX_FREE is the maximum concentration of a co-medication, used as a per-subject covariate. Ratified canonically on 2026-05-08 alongside the Bergmann 2014 extraction.

CU_ASP8232 (canonical for time-varying unbound ASP8232 plasma concentration input to a PD-only exposure-response model)

• Description: Time-varying unbound (not bound to soluble or membrane-bound VAP-1) plasma concentration of the vascular adhesion protein-1 (VAP-1) inhibitor ASP8232 (Cu), used as the exposure driver for a PD-only exposure-response model whose PK layer is defined in a separate paper. Per-observation covariate; supplied by the user in the event table because the modelling paper (Hoefman 2021, PD/ER) does not embed a PK compartment. The exposure time-course is produced by the companion multi-target TMDD PK-PD paper (Snelder 2021, doi:10.1007/s10928-020-09717-w) and consumed by the PD-layer paper (Hoefman 2021, doi:10.1007/s10928-020-09716-x).
• Units: nM
• Type: continuous
• Scope: specific
• Reference category: n/a – 0 nM for placebo subjects at every record; the paper reports Cu at steady state of 125.58 nM for 40 mg qd oral ASP8232 in a typical DKD subject (Hoefman 2021 Methods Simulations).
• Source aliases:
  • Cu – Hoefman 2021 Methods Sampling-and-measurements subsection and Table 1 caption: Cu model-predicted unbound ASP8232 plasma concentration in nM; identical orientation, no value transformation.
• Example models: Hoefman_2021_asp8232.R (drives three drug-effect terms: (a) acute additive eGFR CysC decline theta_4 * Cu; (b) sigmoid Imax albuminuria-lowering on AER via logCu = log(1 + 1000 * Cu), theta_9 * logCu^theta_24 / (theta_23^theta_24 + logCu^theta_24) with theta_24 = Hill = 10 FIXED; (c) proportional Emax creatinine-transporter-inhibition on sCr theta_13 * Cu / (theta_31 + Cu) with theta_31 = 52.9 nM FIXED from a separate multi-trial PopPK model per Discussion / “data on file”).
• Notes: Specific scope because the value is intrinsically tied to a particular drug (ASP8232) and a particular upstream PK model; the concept “time-varying unbound driver-drug concentration supplied as external data because the modelling paper does not embed a PK layer” is not currently generalizable across drugs. Future PD-only exposure-response models for other drugs that inherit their PK from a companion paper should register sibling canonicals (e.g., CU_<DRUG>) rather than reuse this name. The log(1 + 1000 * Cu) linearization stated in Table 1’s caption is a nM-to-per-unit unit-shift plus a log(1 + x) transform for numerical stability at Cu = 0 – the 1 + ... is what allows the transform to remain finite in the placebo condition. Distinct from CAV (period-averaged concentration of the modelled drug from an in-file PK model) and PRED_CMAX_FREE (co-medication free concentration): CU_ASP8232 is the concentration of the modelled drug, but supplied externally rather than computed in the model file. Ratified canonically on 2026-07-08 alongside the Hoefman 2021 ASP8232 PD extraction.

AUC_CARBO (canonical for per-cycle average AUC of carboplatin)

• Description: Per-cycle average AUC of carboplatin used as the time-varying drug-exposure covariate driving cytotoxic tumour-death rates in tumour-size-dynamics models of platinum-based chemotherapy. The value is held step-wise constant over each chemotherapy cycle and resets at the start of the next cycle.
• Units: carboplatin AUC units (typically mg*min/mL); document per-model via covariateData[[AUC_CARBO]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – set to 0 in cycles where carboplatin is not administered (e.g., post-discontinuation or non-platinum arms).
• Source aliases:
  • CB (NONMEM $INPUT column in DDMODEL00000217 / DDMODEL00000218) – used in Zecchin_2016_tumorovarian.R and Zecchin_2016_survival.R. The DDMORE bundles ship the simulated datasets with the column re-labelled AUC0; downstream consumers should map AUC0 -> AUC_CARBO.
• Example models: Zecchin_2016_tumorovarian.R (Zecchin 2016 SLD model for advanced ovarian cancer, DDMODEL00000217), Zecchin_2016_survival.R (Zecchin 2016 OS model, DDMODEL00000218; the OS model integrates the same SLD ODE inline, with the prior IPP-fit subject-level KG/KD0/KD1/IBASE supplied via the dataset).
• Notes: Specific scope because the column meaning is tied to a particular cytotoxic agent (carboplatin) and a particular per-cycle averaging convention. Reusing the same column for another platinum analogue (cisplatin, oxaliplatin) is not appropriate – register a sibling canonical (AUC_CISPLATIN, AUC_OXALIPLATIN) when needed. The Zecchin 2016 SLD and OS models use the value directly in the death-rate term kd0 * AUC_CARBO * tumorSize, with an internal /1000 numerical scaling carried verbatim from the source $DES block.

AUC_BAST_FW (canonical for first-week AUC in the BAST PTTE 2017 teaching dataset)

• Description: Area under the plasma concentration-time curve over the first week of treatment for the unspecified hypothetical drug used in the BAST PTTE 2017 teaching guiding-document (DDMODEL00000243). Per-subject, time-fixed (a single early-exposure summary value carried as a baseline covariate). Used to drive the Event 2 Gompertz hazard via centred-deviation form exp(coef * (AUC_BAST_FW - 3065.5)).
• Units: ug*h/L (per BAST PTTE guiding document Section 2.2.2)
• Type: continuous
• Scope: specific
• Reference category: n/a – used in centred-deviation form. Reference value observed: 3065.5 ug*h/L (BAST PTTE 2017 simulated cohort median; runEV2_105 base hazard).
• Source aliases:
  • AUC – verbatim NM-TRAN $INPUT column name in DDMODEL00000243’s executable .mod files and in the bundle’s Simulated_event_data.csv. Renamed to AUC_BAST_FW in the canonical register so that future models using a generic AUC column with different semantics will not silently collide.
• Example models: NA_NA_tte_gompertz_ev2.R (BAST PTTE 2017 / DDMODEL00000243 Event 2 hazard model; centred at 3065.5 ug*h/L, coefficient 3.09e-4 on the NONMEM rescaled scale, equivalent to exp(3.09e-4 * (AUC_BAST_FW - 3065.5)) on the hazard).
• Notes: Specific scope because the value is intrinsically tied to an unspecified hypothetical drug and a fictional simulated population in the BAST PTTE 2017 guiding document – the column has no shared meaning across drugs or studies. Future models should register a sibling canonical (e.g., AUC_<DRUG>) with explicit drug semantics rather than overload this name. The BAST guiding document Section 2.2.2 defines this as “AUC of drug treatment given within the first week (ug*h/L).”

AUC_GEM (canonical for per-cycle average AUC of gemcitabine)

• Description: Per-cycle average AUC of gemcitabine (sum of parent and active metabolite exposure, per Zecchin 2016 Methods) used as the time-varying drug-exposure covariate driving cytotoxic tumour-death rates in tumour-size-dynamics models of gemcitabine-containing chemotherapy.
• Units: gemcitabine AUC units (typically mg*h/L or the paper’s mol*day / 10^6 cells scaling for the parent-plus-active-metabolite composite); document per-model via covariateData[[AUC_GEM]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – set to 0 in cycles where gemcitabine is not administered (e.g., carboplatin-monotherapy arms).
• Source aliases:
  • G (NONMEM $INPUT column in DDMODEL00000217 / DDMODEL00000218) – used in Zecchin_2016_tumorovarian.R and Zecchin_2016_survival.R. The DDMORE bundles ship the simulated datasets with the column re-labelled AUC1; downstream consumers should map AUC1 -> AUC_GEM.
• Example models: Zecchin_2016_tumorovarian.R (Zecchin 2016 SLD model for advanced ovarian cancer, DDMODEL00000217), Zecchin_2016_survival.R (Zecchin 2016 OS model, DDMODEL00000218).
• Notes: Specific scope. The Zecchin 2016 SLD and OS models use the value directly in the death-rate term kd1 * AUC_GEM * tumorSize, with an internal /100 numerical scaling carried verbatim from the source $DES block.

AUC_GCV (canonical for per-q12h-interval AUC of ganciclovir)

• Description: Time-varying ganciclovir AUC over a q12h dosing interval (AUC_0-12), used as the drug-exposure input to indirect-response viral-turnover PK/PD models of cytomegalovirus (CMV) viral load decline under (val)ganciclovir treatment. The Koloskoff 2025 source computes individual AUC_0-12 from an upstream popPK model (Franck 2021) and feeds it to the PD model as a Monolix “varying input”; the PD model itself does not integrate a PK ODE, so AUC_GCV is supplied to nlmixr2 as a time-varying data column.
• Units: mg*h/L (document per-model via covariateData[[AUC_GCV]]$units if a different exposure unit is reported).
• Type: continuous
• Scope: specific
• Reference category: n/a – set to 0 in pre-treatment / off-treatment records so the drug-stimulation term Emax * AUC_GCV / (EC50 + AUC_GCV) vanishes and the viral load returns to the kin / kout steady-state baseline.
• Source aliases:
  • AUC_0-12 – the printed variable name in Koloskoff 2025 (Methods Section 2.3, Eq. 1, and Table 1). Q24h dosing intervals are entered as AUC_0-24 / 2 so all data live in the q12h framework (Koloskoff 2025 Methods Section 2.1).
• Example models: Koloskoff_2025_ganciclovir.R (Koloskoff 2025 indirect viral turnover model for CMV viral load in pediatric SOT / HSCT recipients; AUC_GCV enters the ODE via kout * (1 + Emax * AUC_GCV / (EC50 + AUC_GCV)) * viralLoad).
• Notes: Specific scope – the column meaning is tied to ganciclovir as the drug and to a q12h interval-averaging convention. Sibling drug-specific AUC canonicals (AUC_CARBO, AUC_GEM, AUC_BAST_FW, AUC_PAZO) follow the same AUC_<DRUG> naming pattern; a future PK/PD model that uses a different exposure metric for ganciclovir (e.g., trough concentration, instantaneous concentration) should register a parallel canonical rather than overload AUC_GCV. Koloskoff 2025 Monte Carlo simulations are reported under AUC_0-24 (Tables 3 and 4) assuming AUC_0-24 = 2 x AUC_0-12 at steady state; nlmixr2 simulations should set AUC_GCV to the q12h-interval value (i.e., AUC_0-24 / 2).

AUC_LCM (canonical for daily AUC of lacosamide at steady state)

• Description: Daily area under the plasma concentration-time curve of lacosamide (LCM) at steady state (mg*h/L), used as the drug-exposure covariate on the seizure hazard in time-to-seizure models of LCM anti-epileptic therapy. Dose-step-varying (the value tracks the patient’s currently assigned LCM target dose level: 100 -> 200 -> 400 -> 600 mg/day). Set to 0 for subjects not on lacosamide so the centred-deviation covariate contribution is gated by a treatment-arm indicator (CONMED_LCM) inside the model rather than by the covariate value itself.
• Units: mg*h/L (document per-model via covariateData[[AUC_LCM]]$units if a different exposure unit is reported).
• Type: continuous
• Scope: specific
• Reference category: n/a – enters via centred-deviation form slope * (AUC_LCM - AUC_ref). Reference value observed: 104 mgh/L (Lindauer 2017 SP0993 typical daily AUC at the first LCM target dose 200 mg/day; Lindauer 2017 Table 3 note c). Approximate scaling at higher doses: ~208 mgh/L at 400 mg/day, ~312 mg*h/L at 600 mg/day.
• Source aliases:
  • AUC_LCM – printed name in Lindauer 2017 Table 3 (rows AUC_LCM * k1 and AUC_LCM * k2); the paper computes per-subject daily AUC from an empirical-Bayes clearance estimate of a previously published lacosamide popPK model (Lindauer 2017 Section 2.3).
• Example models: Lindauer_2017_lacosamide_seizure.R (centred-deviation form on both first-seizure and subsequent-seizure Weibull scale parameters: slopes -0.00917 (1st) and -0.00751 (2nd+); gated by CONMED_LCM = 1).
• Notes: Specific scope; lacosamide-specific. Follows the AUC_<DRUG> sibling family (AUC_CARBO, AUC_GEM, AUC_BAST_FW, AUC_PAZO, AUC_GCV, AUC_RTV). Downstream users should compute the per-subject daily AUC from an on-disk lacosamide popPK model (e.g., a Cawello 2013 / 2015 popPK) or from the actual LCM dose regimen if a compartmental model is being coupled to the TTE hazard. Ratified canonically on 2026-07-03 alongside the Lindauer 2017 lacosamide time-to-seizure extraction.

AUC_CBZ (canonical for daily AUC of carbamazepine at steady state)

• Description: Daily area under the plasma concentration-time curve of carbamazepine (CBZ; typically as controlled-release CBZ-CR) at steady state (mg*h/L), used as the drug-exposure covariate on the seizure hazard in time-to-seizure models of CBZ anti-epileptic therapy. Dose-step-varying (the value tracks the patient’s currently assigned CBZ-CR target dose level: 200 -> 400 -> 800 -> 1200 mg/day in the Lindauer 2017 SP0993 design). Set to 0 for subjects not on carbamazepine so the centred-deviation covariate contribution is gated by a treatment-arm indicator (CONMED_LCM in Lindauer 2017; equivalent to 1 - CONMED_CBZ for that design) inside the model rather than by the covariate value itself.
• Units: mg*h/L (document per-model via covariateData[[AUC_CBZ]]$units if a different exposure unit is reported).
• Type: continuous
• Scope: specific
• Reference category: n/a – enters via centred-deviation form slope * (AUC_CBZ - AUC_ref). Reference value observed: 132 mg*h/L (Lindauer 2017 SP0993 typical daily AUC at the first CBZ-CR target dose 400 mg/day for a 70-kg patient; Lindauer 2017 Section 3.4 first paragraph).
• Source aliases:
  • AUC_CBZ – printed name in Lindauer 2017 Table 3 (rows AUC_CBZ * k1 and AUC_CBZ * k2); the paper computes per-subject daily AUC from an empirical-Bayes clearance estimate of a previously published carbamazepine popPK model (Lindauer 2017 Section 2.3).
• Example models: Lindauer_2017_lacosamide_seizure.R (centred-deviation form on both first-seizure and subsequent-seizure Weibull scale parameters: slopes -0.00658 (1st) and -0.0153 (2nd+); gated by CONMED_LCM = 0 – i.e., the CBZ-CR arm).
• Notes: Specific scope; carbamazepine-specific. Distinct from CONMED_CBZ – CONMED_CBZ is the binary carbamazepine coadministration indicator (used in models where the effect of CBZ enters as a categorical on / off shift, e.g., Schoemaker 2017 brivaracetam), while AUC_CBZ carries the magnitude of CBZ exposure and is used when the effect scales with dose. Follows the AUC_<DRUG> sibling family. Ratified canonically on 2026-07-03 alongside the Lindauer 2017 lacosamide time-to-seizure extraction.

AUC_PAZO (canonical for per-period mean AUC of pazopanib)

• Description: Per-period (per-dose-group in preclinical xenograft studies; per-subject mean dose-adjusted in clinical studies) mean AUC of pazopanib used as the drug-exposure covariate driving the antiangiogenic and cytotoxic effect rates in semi-mechanistic tumour-growth / angiogenesis-inhibition (TGI) models of pazopanib in renal-cell carcinoma. Time-varying step-wise (held constant within a treatment period and resetting when dose level changes or treatment ends).
• Units: ug*h/mL (= mg*h/L). Document per-model via covariateData[[AUC_PAZO]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – enters via power form a = a0 * AUC_PAZO^e_auc_pazo_a and c = c0 * AUC_PAZO^e_auc_pazo_c. Set to 0 in periods where pazopanib is not administered; the model rate terms reduce to baseline drug-off (a, c -> 0 when AUC_PAZO -> 0) under that convention.
• Source aliases:
  • AUC – used in Ouerdani_2015_pazopanib_mouse.R and Ouerdani_2015_pazopanib.R (Ouerdani 2015 Methods Equations 2-3; preclinical values 220.2, 656.8, 1140.8 ugh/mL for the 10, 30, 100 mg/kg mouse dose groups; clinical mean 771.6 ugh/mL for 800 mg QD pazopanib in RCC patients, with per-subject values 629.4-802.4 ug*h/mL derived from an Emax fit to mean AUCs at the patient’s dose history).
• Example models: Ouerdani_2015_pazopanib_mouse.R (preclinical TGI in CAKI-2 xenograft mice; AUC enters as c = c0 * AUC_PAZO^0.332 only – the cytotoxic exponent e_auc_pazo_a was fixed to 0 because the in-mouse cytotoxic effect did not vary with exposure across the 10-100 mg/kg range), Ouerdani_2015_pazopanib.R (clinical TGI in RCC patients; AUC enters as both a = a0 * AUC_PAZO^0.125 and c = c0 * AUC_PAZO^0.142).
• Notes: Specific scope because the column meaning is tied to a particular drug (pazopanib) and the power-form parameterisation that the Ouerdani 2015 model uses. Reusing the name for a different tyrosine-kinase inhibitor is not appropriate – register a sibling canonical (AUC_SORAF for sorafenib, AUC_SUNI for sunitinib, etc.) when needed. The Ouerdani 2015 paper reports the preclinical AUCs in ug*h/mL from a separate preclinical PK study (cited as the FDA Pharmacology Review for pazopanib NDA 022465); the clinical AUCs come from an Emax fit (Equation derived from Methods) to pooled mean AUCs at varying daily doses (5 mg to 2000 mg) across five prior pazopanib trials. Ratified canonically on 2026-05-12 alongside the Ouerdani 2015 pazopanib mouse and clinical extractions.

AUC_RTV (canonical for ritonavir AUC over the 0-24 h dosing interval)

• Description: Per-subject (time-fixed within an evaluated regimen) ritonavir AUC over the 0-24 h once-daily dosing interval, used as a co-medication exposure covariate driving boosted-protease-inhibitor (atazanavir, lopinavir, darunavir, etc.) clearance in popPK models that account for ritonavir’s CYP3A4-inhibition effect. In Dickinson 2009 the value is computed by non-compartmental methods on the observed ritonavir concentration-time profile (WinNonlin 5.2) and feeds the atazanavir CL/F power form cl = exp(lcl) * (AUC_RTV / 7.52)^e_aucrtv_cl.
• Units: mg*h/L (document per-model via covariateData[[AUC_RTV]]$units if a different exposure unit is reported).
• Type: continuous
• Scope: specific
• Reference category: n/a – enters via centred power form. Reference value observed: 7.52 mg*h/L (Dickinson 2009 cohort median across HIV-infected and healthy volunteers; healthy 7.36, HIV 7.59 per Table 1).
• Source aliases:
  • RTVAUC – printed name in Dickinson 2009 Table 1 / Table 3 (the paper writes RTVAUC0-24 with the dosing-interval subscript; the column name is registered without the subscript to fit standard data-column character constraints).
• Example models: Dickinson_2009_atazanavir.R (atazanavir CL/F power-function dependence on RTVAUC0-24, centred at 7.52 mg*h/L with exponent -0.8).
• Notes: Specific scope because the column meaning is tied to ritonavir as the booster drug and to the 0-24 h once-daily dosing-interval AUC convention. Sibling drug-specific AUC canonicals (AUC_CARBO, AUC_GEM, AUC_BAST_FW, AUC_PAZO, AUC_GCV) follow the same AUC_<DRUG> naming pattern. A future PK model that uses a different ritonavir exposure metric (trough concentration, q12h-interval AUC for BID ritonavir regimens) should register a parallel canonical rather than overload AUC_RTV. For simulation users without observed ritonavir AUC, the Dickinson 2009 cohort median 7.52 mg*h/L reproduces typical-value behaviour (the centring point of the covariate effect).

AUC_VERUB (canonical for verubecestat AUC over the 24 h dosing interval at steady state)

• Description: Time-varying verubecestat plasma AUC over the once-daily 24 h dosing interval at steady state, used as the driver of the inhibitory Emax sigmoid on the amyloid plaque formation rate Kin in the van Maanen 2025 amyloid plaque turnover model (paper Eq 4: Inh_verub = Imax * AUC_VERUB / (AUC_VERUB + AUC50)). The van Maanen 2025 analysis derives individual AUC_VERUB from the upstream Dockendorf 2022 verubecestat population PK model (cited in Table S1).
• Units: uM*h (micromolar * hour). Must be in the same units as the model’s AUC50 parameter (uM*h) so the sigmoid AUC/(AUC + AUC50) is dimensionless. Document per-model via covariateData[[AUC_VERUB]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – enters via Emax-sigmoid form Imax * AUC / (AUC + AUC50). Set to 0 in periods where verubecestat is not administered so the inhibition term vanishes and Kin returns to its typical (undrugged) value. Typical AUC values reverse-computed from the paper’s Table S5 stable-plaque predictions and AUC50 = 0.392 uMh: 40 mg daily -> approximately 4.4 uMh (91.8% inhibition); 11.9 mg -> approximately 1.44 uMh (78.7%); 2.9 mg -> approximately 0.35 uMh (47.2%); 1.7 mg -> approximately 0.21 uM*h (34.5%).
• Source aliases:
  • AUC_verub – printed name in van Maanen 2025 Eq 4 and Figure 1.
• Example models: vanMaanen_2025_amyloid.R (verubecestat inhibition of plaque formation via Imax = 1 fixed and AUC50 = 0.392 uM*h).
• Notes: Specific scope because the value is intrinsically tied to verubecestat (BACE1 inhibitor) and the once-daily 24 h dosing-interval AUC convention. Sibling drug-specific AUC canonicals (AUC_CARBO, AUC_GEM, AUC_BAST_FW, AUC_PAZO, AUC_GCV, AUC_LCM, AUC_CBZ, AUC_RTV, AUC_EMPA, AUC_ADU, AUC_DON, AUC_GAN, AUC_LEC) follow the same AUC_<DRUG> naming pattern. Ratified canonically on 2026-07-24 alongside the van Maanen 2025 amyloid plaque turnover extraction.

AUC_ADU (canonical for aducanumab serum AUC over the 4-week dosing interval at steady state)

• Description: Time-varying aducanumab serum AUC over the 4-week Q4W dosing interval at steady state, used as the driver of the linear stimulation term on the amyloid plaque elimination rate Kout in the van Maanen 2025 amyloid plaque turnover model (paper Eq 6: Stim_ADU = slope_adu * (AUC_ADU / MW_ADU)). The van Maanen 2025 analysis derives aducanumab AUC from the upstream Sevigny 2016 phase 1b popPK study (Table S1) with linear dose scaling where the target dose differs from the source PK study dose.
• Units: mg*day/L (milligrams * day per litre). Must be in mgday/L (not mgh/mL, not ugh/mL) so that AUC_ADU / MW_ADU_g_per_mol evaluates in mmolday/L = mMday and the paper’s slope units (mM^-1 day^-1) multiply to a dimensionless stimulation term. Conversion from mg*h/L: divide by 24. Document per-model via covariateData[[AUC_ADU]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – enters via linear form slope_adu * AUC_ADU / MW_ADU. Set to 0 in periods where aducanumab is not administered so the stimulation term vanishes and Kout returns to its typical (undrugged) value. Reference regimen (Table S3): EMERGE/ENGAGE high-dose titration -> target 10 mg/kg IV Q4W, giving a typical steady-state 4-week AUC of approximately 1944 mg*day/L for a 70 kg subject at CL approximately 0.36 L/day.
• Source aliases:
  • AUC_mAb (for aducanumab) – printed name in van Maanen 2025 Eq 6 and Figure 1.
• Example models: vanMaanen_2025_amyloid.R (aducanumab stimulation of plaque elimination via slope_adu = 719 mM^-1 day^-1 and MW_ADU = 145912 g/mol).
• Notes: Specific scope. Sibling anti-A-beta mAb AUC canonicals AUC_DON (donanemab), AUC_GAN (gantenerumab), AUC_LEC (lecanemab) follow the same convention and are consumed by the same van Maanen 2025 model. Ratified canonically on 2026-07-24 alongside the van Maanen 2025 amyloid plaque turnover extraction.

AUC_DON (canonical for donanemab serum AUC over the 4-week dosing interval at steady state)

• Description: Time-varying donanemab serum AUC over the 4-week Q4W dosing interval at steady state, used as the driver of the linear stimulation term on the amyloid plaque elimination rate Kout in the van Maanen 2025 amyloid plaque turnover model (paper Eq 6: Stim_DON = slope_don * (AUC_DON / MW_DON)). The van Maanen 2025 analysis derives donanemab AUC from the Lowe 2021 phase 1b SAD/MAD popPK data (Table S1).
• Units: mg*day/L. Same unit convention as AUC_ADU (see notes there). Document per-model via covariateData[[AUC_DON]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – set to 0 in periods where donanemab is not administered. Reference regimen (Table S3): TRAILBLAZER-ALZ 1/2/4 titration to 1400 mg IV Q4W after 3 loading doses of 10 mg/kg IV Q4W.
• Source aliases:
  • AUC_mAb (for donanemab) – printed name in van Maanen 2025 Eq 6 and Figure 1.
• Example models: vanMaanen_2025_amyloid.R (donanemab stimulation of plaque elimination via slope_don = 1120 mM^-1 day^-1 and MW_DON = 145087 g/mol).
• Notes: Specific scope. Sibling anti-A-beta mAb AUC canonicals AUC_ADU, AUC_GAN, AUC_LEC follow the same convention. Ratified canonically on 2026-07-24 alongside the van Maanen 2025 extraction.

AUC_GAN (canonical for gantenerumab serum AUC over the 4-week dosing interval at steady state)

• Description: Time-varying gantenerumab serum AUC over the 4-week dosing interval at steady state (delivered as SC Q4W or IV titration in the source trials, expressed here as a 4-week interval-AUC to match the paper’s Kout stimulation term), used as the driver of the linear stimulation term on the amyloid plaque elimination rate Kout in the van Maanen 2025 amyloid plaque turnover model (paper Eq 6: Stim_GAN = slope_gan * (AUC_GAN / MW_GAN)). The van Maanen 2025 analysis derives gantenerumab AUC from the Portron 2020 phase 1 single active dose PK data (Table S1).
• Units: mg*day/L. Same unit convention as AUC_ADU. Document per-model via covariateData[[AUC_GAN]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – set to 0 in periods where gantenerumab is not administered. Reference regimen (Table S3): GRADUATE 1/2 titration to 1020 mg every 4 weeks (delivered as 510 mg SC Q2W) after a 9-month step-up sequence of 120 -> 255 -> 510 -> 1020 mg per 4 weeks.
• Source aliases:
  • AUC_mAb (for gantenerumab) – printed name in van Maanen 2025 Eq 6 and Figure 1.
• Example models: vanMaanen_2025_amyloid.R (gantenerumab stimulation of plaque elimination via slope_gan = 397 mM^-1 day^-1 and MW_GAN = 146300 g/mol).
• Notes: Specific scope. Sibling anti-A-beta mAb AUC canonicals AUC_ADU, AUC_DON, AUC_LEC follow the same convention. Ratified canonically on 2026-07-24 alongside the van Maanen 2025 extraction.

AUC_LEC (canonical for lecanemab serum AUC over the 4-week dosing interval at steady state)

• Description: Time-varying lecanemab serum AUC over the 4-week dosing interval at steady state (delivered as IV Q2W in Clarity AD, expressed here as the equivalent 4-week interval-AUC = 2 * Q2W-interval AUC), used as the driver of the linear stimulation term on the amyloid plaque elimination rate Kout in the van Maanen 2025 amyloid plaque turnover model (paper Eq 6: Stim_LEC = slope_lec * (AUC_LEC / MW_LEC)). The van Maanen 2025 analysis derives lecanemab AUC from the Logovinsky 2016 phase 1 SAD/MAD PK data (Table S1).
• Units: mg*day/L. Same unit convention as AUC_ADU. Document per-model via covariateData[[AUC_LEC]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – set to 0 in periods where lecanemab is not administered. Reference regimen (Table S3): Clarity AD 10 mg/kg IV Q2W (equivalent to approximately 20 mg/kg per 4 weeks).
• Source aliases:
  • AUC_mAb (for lecanemab) – printed name in van Maanen 2025 Eq 6 and Figure 1.
• Example models: vanMaanen_2025_amyloid.R (lecanemab stimulation of plaque elimination via slope_lec = 606 mM^-1 day^-1 and MW_LEC = 150000 g/mol).
• Notes: Specific scope. Sibling anti-A-beta mAb AUC canonicals AUC_ADU, AUC_DON, AUC_GAN follow the same convention. Ratified canonically on 2026-07-24 alongside the van Maanen 2025 extraction.

AUC_IBRU (canonical for daily AUC(0-24) of ibrutinib)

• Description: Daily 0-24 h area under the plasma concentration-time curve of ibrutinib, used as the systemic-exposure driver in ibrutinib exposure-response models. Dose-step-varying: the value tracks the patient’s currently assigned daily ibrutinib dose level (420, 280 or 140 mg/day in the de-escalation schedules simulated by Ibrahim 2023; 420 and 840 mg/day in the PCYC-1102 study itself) and drops to 0 during treatment interruptions. Ibrahim 2023 selected AUC(0-24) rather than a concentration because ibrutinib’s half-life is short (4-13 h) and daily AUC has been proposed as the surrogate exposure metric for its therapeutic drug monitoring.
• Units: h*ng/mL (document per-model via covariateData[[AUC_IBRU]]$units if a different exposure unit is reported).
• Type: continuous
• Scope: specific
• Reference category: n/a – enters saturable Emax / Imax forms of the shape AUC_IBRU / (AUC50 + AUC_IBRU) rather than a centred-deviation or power form. Reference (half-maximal) values observed: 34.1 hng/mL for inhibition of phosphorylated-Btk production (Ibrahim 2023 Table 1, IAUC50,pBTK); 91.7 hng/mL for stimulation of systolic blood pressure and 63.1 h*ng/mL for diastolic blood pressure (Ibrahim 2023 Table 2, AUC50,sBP / AUC50,dBP).
• Source aliases:
  • DAILYAUC – printed name in the Ibrahim 2023 nlmixr control streams (Supporting Information S4, auc <- DAILYAUC in run100 / run200 / run300).
• Example models: Ibrahim_2023_ibrutinib_leukocyte_spd.R (Imax inhibition of pBtk production, IAUC50 = 34.1), Ibrahim_2023_ibrutinib_sbp.R (Emax stimulation of sBP production, AUC50 = 91.7), Ibrahim_2023_ibrutinib_dbp.R (Emax stimulation of dBP production, AUC50 = 63.1), Ibrahim_2025_ibrutinib_cll.R (Imax inhibition of pBtk production, IAUC50 = 28.4), Ibrahim_2025_ibrutinib_bp.R (Emax stimulation of sBP and dBP production through a single shared AUC50,BP = 62.3), Ibrahim_2025_ibrutinib_venetoclax.R (same Imax term as the 2025 efficacy model, alongside the venetoclax concentration covariate [[CONC_VEN_MGL]]).
• Notes: Specific scope; ibrutinib-specific. Follows the AUC_<DRUG> sibling family (AUC_CARBO, AUC_GEM, AUC_LCM, AUC_CBZ, AUC_PAZO, AUC_GCV, AUC_RTV, AUC_EMPA). As with AUC_LCM, downstream users must compute the per-subject daily AUC from an ibrutinib popPK model rather than from this register – Ibrahim 2023 integrated individual post hoc profiles of the two-compartment ibrutinib popPK model of Marostica et al. (Cancer Chemother Pharmacol. 2015;75(1):111-121), which is not currently part of nlmixr2lib. Because all three Ibrahim 2023 models use the same exposure column with different half-maximal constants, the ratio of those constants is itself interpretable: AUC50,sBP and AUC50,dBP are about three and two times IAUC50,pBTK, which is the quantitative basis for the paper’s finding that dose de-escalation sheds hypertension risk faster than it sheds efficacy. Ratified canonically on 2026-07-30 alongside the Ibrahim 2023 ibrutinib extraction.

AUC_AMPH (canonical for per-24-h-interval AUC of amphenmulin in an in-vitro dynamic model)

• Description: Amphenmulin area under the concentration-time curve over the current 24 h dosing interval (AUC_0-24h), measured in the medium of an in-vitro dynamic (dilution) apparatus rather than in plasma, used as the drug-exposure input to antibacterial PK/PD-index models. Wang 2024 samples the reaction chamber of a peristaltic-pump-driven three-necked-flask model by HPLC-MS/MS and derives the per-interval value non-compartmentally in Phoenix; because that source publishes no structural PK model for the apparatus, the PD model consumes AUC_AMPH directly as a piecewise-constant time-varying covariate (one value per 24 h interval) rather than integrating a PK ODE. The model forms the PK/PD index AUC24h/MIC by dividing this covariate by the challenge strain’s MIC.
• Units: h*ug/mL (equivalently mg*h/L). Document per-model via covariateData[[AUC_AMPH]]$units if a different exposure unit is reported.
• Type: continuous
• Scope: specific
• Reference category: n/a – set to 0 for untreated control records so the sigmoid term (E0 - Emax) * (AUC_AMPH/MIC)^N / (EC50^N + (AUC_AMPH/MIC)^N) vanishes and the predicted 24 h change in mycoplasma count reduces to the control value E0.
• Source aliases:
  • AUC24h / AUC 24 h – printed names in Wang 2024 Materials and Methods (Integration and modeling of pharmacokinetics/pharmacodynamics, defining the AUC24h/MIC index) and Table 3. Wang 2024 Results (In vitro pharmacokinetics and the effects on M. gallisepticum) reports the per-interval ranges across the six regimens: 0.34-6.19 hug/mL over 0-24 h, 0.57-7.69 hug/mL over 24-48 h and 0.62-8.23 h*ug/mL over 48-72 h.
• Example models: Wang_2024_amphenmulin_pkpd_index.R (Wang 2024 inhibitory sigmoid Emax PK/PD integration against Mycoplasma gallisepticum strain S6 in an in-vitro dynamic model; AUC_AMPH / mic drives the per-interval signed change in log10 CFU/mL).
• Notes: Specific scope – amphenmulin-specific, and tied to a 24 h interval-AUC convention. Member of the AUC_<DRUG> family; AUC_TILM is the closest structural analogue, being likewise a per-24-h-interval antibacterial exposure handed to a sigmoid PK/PD-index model in place of a PK ODE, and originating from the same laboratory (Ding H, South China Agricultural University). Distinct from the CONC_<DRUG>_<UNITS> family, which carries an instantaneous in-vitro concentration driving a mechanism-based kill-rate model: the sibling model Wang_2024_amphenmulin_killrate.R needs no such covariate because it integrates the apparatus PK as an ODE state. A future amphenmulin model using the Cmax/MIC metric, which Wang 2024 Table 3 also reports, should register a parallel canonical rather than overload AUC_AMPH. Ratified canonically alongside the Wang 2024 amphenmulin extraction.

AUC_CEFQ (canonical for per-24-h-interval serum AUC of cefquinome)

• Description: Cefquinome area under the serum concentration-time curve over the current 24 h dosing interval (AUC_0-24h), used as the drug-exposure input to ex vivo antibacterial PK/PD-index models. Gao 2025 dosed foals 1 mg/kg intramuscularly, sampled serum by HPLC, derived AUC non-compartmentally in WinNonlin 5.2.1, and then incubated E. coli in those same serum samples to build ex vivo time-kill curves; because that source publishes no structural compartmental PK model, the PD model consumes AUC_CEFQ directly as a piecewise-constant time-varying covariate (one value per 24 h interval) rather than integrating a PK ODE. Unlike the other members of this family, the index Gao 2025 forms is AUC_CEFQ / MIC / 24 h – the AUC/MIC ratio divided by 24 h, which the paper adopts deliberately so the index is dimensionless and can be substituted straight into its dose equation. Divide by the MIC and by 24 when reproducing that paper’s targets.
• Units: h*ug/mL (equivalently mg*h/L). Document per-model via covariateData[[AUC_CEFQ]]$units if a different exposure unit is reported.
• Type: continuous
• Scope: specific
• Reference category: n/a – set to 0 for drug-free control records so the sigmoid term vanishes and the predicted 24 h change in bacterial count reduces to the no-drug value.
• Source aliases:
  • AUC0-24h – printed name in Gao 2025 Section 2.9 (PK/PD Integration and Modeling, defining the AUC0-24h/MIC divided by 24 h index), Section 2.10 (Monte Carlo Simulation, where it is the log-normally distributed PK input) and Section 2.11 (Estimation of Dosage Regimen). Gao 2025 Table 1 reports the closely related truncated statistic AUC0-last as 12.33 +/- 0.69 hug/mL intravenously and 5.41 +/- 0.81 hug/mL intramuscularly after 1 mg/kg; because the last quantifiable sample is at 12 h (Figure 2) and the terminal half-life is 2.35-4.16 h, AUC0-last and AUC0-24h are numerically interchangeable in that study.
• Example models: Gao_2025_cefquinome_pkpd_index.R (Gao 2025 inhibitory sigmoid Emax PK/PD integration against Escherichia coli in foal serum; AUC_CEFQ / mic / 24 drives the per-interval signed change in log10 CFU/mL).
• Notes: Specific scope – cefquinome-specific, and tied to a 24 h interval-AUC convention. Member of the AUC_<DRUG> family; AUC_AMPH and AUC_TILM are the closest structural analogues, being likewise per-24-h-interval antibacterial exposures handed to a sigmoid PK/PD-index model in place of a PK ODE. It differs from both in carrying a serum (ex vivo) rather than an in-vitro-apparatus or tissue-cage exposure, and in that the companion model Gao_2025_cefquinome_foal.R does supply a compartmental PK model, so a user who wants a closed PK-PD loop can simulate the AUC rather than supply it; the covariate is retained because Gao 2025’s own PD analysis is defined per 24 h interval on non-compartmental AUC. Distinct from the CONC_<DRUG>_<UNITS> family, which carries an instantaneous concentration driving a mechanism-based kill-rate model. Ratified canonically alongside the Gao 2025 cefquinome extraction.

AUC_LEN (canonical for lenvatinib steady-state daily AUC)

• Description: Lenvatinib area under the plasma concentration-time curve over the once-daily 24 h dosing interval at steady state, used as the drug-exposure driver of PK/PD models in which lenvatinib exposure acts on serum biomarker turnover or on tumor growth inhibition. Time-varying step-wise: held constant between assessments and updated when the dose level changes (or when treatment is interrupted or stopped). Majid 2024 derives it inside the upstream population PK run as AUC = 1000 * F1 * DGRP / CL – 1000 x relative bioavailability x current daily dose (mg) divided by the individual apparent clearance (L/h) – and carries it into the two downstream PK/PD runs as a data column. Two closely related conventions occur in that paper and are pooled onto this single canonical: the value at the time of the biomarker assessment (LENAUC, biomarker model) and the average over the interval between two tumor assessments computed from the average dose in that interval (LEVAVAUC, tumor model); record which one applies via covariateData[[AUC_LEN]]$notes.
• Units: ng*h/mL (equivalently ug*h/L). Must be in the same units as the model’s EC50 so the Emax term is dimensionless. Note that the Majid 2024 tumor control stream (Text S3) rescales the column to ug*h/mL with AUC = LEVAVAUC/1000 and divides its carried EC50 values by 1000 to match; the register keeps the untransformed ng*h/mL scale so the column is interchangeable across the biomarker and tumor models. Document per-model via covariateData[[AUC_LEN]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – enters via Emax / sigmoid-Emax forms Emax * AUC^gamma / (EC50^gamma + AUC^gamma) (biomarker turnover) and Emax * AUC / (AUC + EC50 * exp(lambda*t)) (tumor growth inhibition). Set to 0 for placebo subjects and for off-treatment periods; both forms then vanish exactly, leaving the disease-progression / pure-growth dynamics. Reference values observed: the common biomarker EC50 is 930 ngh/mL and the tumor EC50 is 1420 ngh/mL (Majid 2024 Tables 2 and 3); records above 10 000 ng*h/mL were excluded from the biomarker fit (Text S2 $DATA IGNORE).
• Source aliases:
  • LENAUC (Majid 2024 Text S2 biomarker control stream; lenvatinib AUC at the time of the biomarker assessment).
  • LEVAVAUC (Majid 2024 Text S3 tumor control stream; lenvatinib average AUC based on the average dose between two tumor assessments).
  • AUC / DNAUC (Majid 2024 Text S1 population PK $TABLE output columns; DNAUC is the dose-normalised form AUC * 24 / DGRP and is NOT this canonical).
• Example models: Majid_2024_lenvatinib_biomarkers.R (drives the sigmoid Emax effect on Kout for VEGF and FGF-23 and on Kin for Tie-2 and Ang-2, with a common EC50 of 930 ngh/mL), Majid_2024_lenvatinib_tumor.R (drives both the tumor-shrinkage Emax term with EC50 1420 ngh/mL and the re-integrated Tie-2 / Ang-2 turnover sub-models), Majid_2024_lenvatinib_tumor_asdeposited.R (same, but rescales the column internally to ug*h/mL as the deposited control stream does, which is what exposes the stream’s unscaled Tie-2 EC50).
• Notes: Specific scope because the column meaning is tied to lenvatinib and to the once-daily steady-state 24 h AUC convention. Member of the AUC_<DRUG> family (AUC_CARBO, AUC_GEM, AUC_GCV, AUC_PAZO, AUC_RTV, AUC_VERUB, AUC_ADU, AUC_DON, AUC_GAN, AUC_LEC, AUC_IBRU, AUC_LCM, AUC_CBZ, AUC_AMPH); AUC_PAZO is the closest structural analogue, being likewise a per-period mean AUC of a tyrosine-kinase inhibitor driving a tumour-growth / angiogenesis model in place of a PK ODE, and the AUC_PAZO entry explicitly directs that a different TKI be given its own sibling canonical rather than reusing that name. A future lenvatinib model using a different exposure metric (Cmax, Ctrough, or a cumulative rather than per-interval AUC) should register a parallel canonical rather than overload AUC_LEN. Ratified canonically alongside the Majid 2024 lenvatinib PK/PD extraction.

AUC_MPAG (canonical for dose-normalised AUC of mycophenolic acid 7-O-glucuronide (MPAG))

• Description: Per-subject, dose-normalised area under the plasma concentration-time curve of mycophenolic acid 7-O-glucuronide (MPAG), the inactive phenolic glucuronide metabolite of mycophenolic acid (MPA) formed by UGT1A9. Used as a metabolite-exposure covariate on the apparent clearance of the parent MPA. MPAG accumulates in renal impairment and displaces MPA from albumin, so a subject’s MPAG burden is a marker of both renal function and free-MPA fraction.
• Units: mg*h/L per g MMF (dose-normalised to the administered mycophenolate mofetil dose). Document per-model via covariateData[[AUC_MPAG]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as the numerator of the dose-normalised MPAG:MPA AUC ratio, which is raised to a power exponent on CL/F. Reference value observed: 588.8 mg*h/L/g (Rong 2019 as transcribed in the Maizaud 2025 S1 File mrgsolve @covariates defaults), which paired with AUC_MPA = 53 gives a ratio of 11.11.
• Source aliases:
  • AUCMPAG – Maizaud 2025 S1 File mrgsolve [PARAM] @annotated @covariates column name for the Rong 2019 model.
• Example models: Rong_2019_mycophenolic_acid.R (numerator of the (AUC_MPAG / AUC_MPA)^0.68 power term on MPA apparent clearance).
• Notes: Rong 2019 prints a single covariate – the MPAG:MPA AUC ratio – rather than the two component AUCs. The ratio is deliberately NOT registered as its own canonical: the register has no *_RATIO covariate anywhere, and founding a one-off ratio pattern for a single model was rejected in favour of joining the existing 17-member AUC_<DRUG> family (AUC_CARBO, AUC_GEM, AUC_GCV, AUC_LCM, AUC_CBZ, AUC_PAZO, AUC_RTV, AUC_ADU, …). Consuming models must form the ratio inside model() from the two members. Because both AUCs are dose-normalised by the same MMF dose, the normalisation cancels in the ratio and the internally-computed value reproduces the paper’s printed covariate exactly. The two-column form is also the more informative data contract: MPAG and MPA AUCs are separately measurable, and a future MPA/MPAG parent-metabolite model can reuse either alone. Ratified canonically on 2026-08-22 alongside the Maizaud 2025 missed-mycophenolate-dose extraction (sidecar request 001, operator answer B). Companion canonicals AUC_MPA and ACMPAG_CC registered in the same pass.

AUC_MPA (canonical for dose-normalised AUC of mycophenolic acid (MPA))

• Description: Per-subject, dose-normalised area under the plasma concentration-time curve of mycophenolic acid (MPA), the active moiety of mycophenolate mofetil (MMF). Supplied as a measured per-subject historical-exposure covariate, not as a model output. Serves as the denominator of the dose-normalised MPAG:MPA AUC ratio used as a covariate on MPA apparent clearance.
• Units: mg*h/L per g MMF (dose-normalised to the administered mycophenolate mofetil dose). Document per-model via covariateData[[AUC_MPA]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as the denominator of the dose-normalised MPAG:MPA AUC ratio, which is raised to a power exponent on CL/F. Reference value observed: 53 mg*h/L/g (Rong 2019 as transcribed in the Maizaud 2025 S1 File mrgsolve @covariates defaults).
• Source aliases:
  • AUCMPA – Maizaud 2025 S1 File mrgsolve [PARAM] @annotated @covariates column name for the Rong 2019 model.
• Example models: Rong_2019_mycophenolic_acid.R (denominator of the (AUC_MPAG / AUC_MPA)^0.68 power term on MPA apparent clearance).
• Notes: See the AUC_MPAG entry for the rationale behind registering the two component AUCs rather than a single ratio canonical, and for the requirement that consuming models form the ratio inside model(). Note the unusual role: AUC_MPA is the exposure of the very analyte the model predicts, entering as a covariate on that analyte’s own clearance. This is legitimate only because it is a measured historical per-subject quantity in the Rong 2019 dataset, not a quantity the model computes – a model that tried to self-consistently derive it would be circular. Ratified canonically on 2026-08-22 alongside the Maizaud 2025 missed-mycophenolate-dose extraction (sidecar request 001, operator answer B).

ACMPAG_CC (canonical for acyl mycophenolic acid glucuronide (AcMPAG) plasma concentration)

• Description: Plasma concentration of acyl mycophenolic acid glucuronide (AcMPAG), the acyl-linked glucuronide conjugate of mycophenolic acid (MPA) formed by UGT2B7. Distinct from MPAG, the inactive 7-O-phenolic glucuronide formed by UGT1A9: AcMPAG is pharmacologically active (it inhibits inosine monophosphate dehydrogenase and has been implicated in the gastrointestinal toxicity of mycophenolate mofetil). Used as a metabolite-exposure covariate on the apparent clearance of the parent MPA.
• Units: mg/L. Document per-model via covariateData[[ACMPAG_CC]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as an uncentered power term ACMPAG_CC^exponent on CL/F. Reference value observed: 0.54 mg/L (Rong 2019 as transcribed in the Maizaud 2025 S1 File mrgsolve @covariates defaults).
• Source aliases:
  • AcMPAG – Maizaud 2025 S1 File mrgsolve [PARAM] @annotated @covariates column name for the Rong 2019 model. The S1 annotation text for that column reads “Mycophenolic acid glucuronide C0” (i.e. MPAG) while both the parameter name and Maizaud 2025 Table 1 identify the acyl glucuronide; the parameter name and Table 1 are authoritative.
• Example models: Rong_2019_mycophenolic_acid.R (uncentered power term ACMPAG_CC^(-0.09) on MPA apparent clearance; because this term is uncentered, the model’s lcl is an intercept rather than a typical clearance – with the source covariate defaults the realised CL/F is 15.60 L/h, not 2.87 L/h).
• Notes: The _CC suffix follows the concentration-column convention already used by CONMED_SILDENAFIL_NMETAB_CC. Unlike that canonical, which carries the metabolite of a co-medication, ACMPAG_CC carries the study drug’s own metabolite acting on the parent’s clearance, so it is not a member of the CONMED_* family. Consuming models must state whether the value is a trough (C0) or a time-averaged concentration; Rong 2019 as transcribed does not disambiguate, and the S1 annotation’s “C0” wording is the only hint. Ratified canonically on 2026-08-22 alongside the Maizaud 2025 missed-mycophenolate-dose extraction (sidecar request 001, operator answer B). Companion canonicals AUC_MPAG and AUC_MPA registered in the same pass.

PLAQUE_BL (canonical for per-subject baseline amyloid plaque burden (initial-condition use))

• Description: Per-subject pre-treatment amyloid plaque burden on the Centiloid scale, used as the initial condition for a plaque state variable in indirect-response amyloid plaque turnover models. Parallel to HGB_BL (per-subject baseline haemoglobin as an initial condition for haemoglobin turnover models): PLAQUE_BL is a static per-subject baseline that supplies the plaque state’s initial value at t = 0 so the trajectory is anchored to the observed baseline before any drug effect starts perturbing it. van Maanen 2025 Supplement Section ‘Further details on the exposure-response model’ specifies this pattern: ‘Individual participant “observed” baseline amyloid plaque burden (before the first dose) was an initial condition at time 0. No additional interindividual variability was included.’
• Units: CL (Centiloid; 0 = amyloid-negative, 100 = typical AD plaque burden per Klunk 2015 Centiloid consensus). Document per-model via covariateData[[PLAQUE_BL]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – subject-level baseline supplied as a covariate column. Reference values observed: 70-104 CL across the van Maanen 2025 modelling cohorts (Table S2); 71.2 CL median in the APECS individual analysis dataset. Amyloid-positivity threshold used in the van Maanen 2025 ADNI external validation was > 24 CL.
• Source aliases:
  • Baseline_Plaque – printed name in van Maanen 2025 Eq 5 (initial condition line Plaque_{t=0} = Baseline_Plaque).
• Example models: vanMaanen_2025_amyloid.R (van Maanen 2025 indirect-response plaque turnover model; sets plaque(0) <- PLAQUE_BL per subject).
• Notes: Specific scope because the initial-condition idiom is paper-defined (van Maanen 2025 specifies per-subject baseline rather than estimating a typical plaque baseline). Promote to general if a second paper ratifies the same baseline-plaque-as-initial-condition pattern (e.g., an amyloid-plaque or biomarker turnover model in an independent Alzheimer’s disease programme). Ratified canonically on 2026-07-24 alongside the van Maanen 2025 amyloid plaque turnover extraction.

CLI (canonical for individual posthoc clearance from an upstream popPK fit)

• Description: Subject-specific empirical-Bayes (posthoc) total plasma clearance from a separately published population PK model that the current PD model treats as a fixed input. Used as a per-subject (time-fixed) covariate in PD-only models that derive a per-cycle exposure metric (e.g., AUC = DOSE / CLI) without instantiating a PK ODE.
• Units: L/h (document per-model via covariateData[[CLI]]$units).
• Type: continuous
• Scope: specific
• Reference category: n/a – appears directly in derived-exposure expressions; not a covariate effect coefficient.
• Source aliases:
  • CL – used in the Hansson 2013 sunitinib biomarker / TGI / fatigue / OS PD-model family (DDMODEL00000197 and siblings, including DDMODEL00000222 and the paper-derived Hansson_2013_sunitinib_svegfr3_os.R) and in Schindler_2016_sunitinib.R (DDMODEL00000221) as the posthoc CL column from the paper’s upstream 2-compartment popPK fit.
• Example models: Hansson_2013a_sunitinib.R (DDMODEL00000197; typical-value reference 32.819 L/h, drawn from the bundle’s simulated dataset for subject 1 – broadly consistent with Houk et al. 2010 typical sunitinib CL), Hansson_2013b_sunitinib.R (DDMODEL00000198; tumor growth inhibition variant; same per-subject CL column as Hansson 2013a/c), Hansson_2013c_sunitinib.R (DDMODEL00000222; uses a per-record CL column with subject-specific values 30-43 L/h in the bundle’s three-subject simulated dataset), Hansson_2013_sunitinib_svegfr3_os.R (parametric overall-survival Weibull TTE from Hansson 2013 e84 paper text; same per-subject CL covariate fed in alongside DOSE for the per-cycle exposure summary auc = DOSE / CLI), Schindler_2016_sunitinib.R (DDMODEL00000221; per-subject post-hoc CL fed in as the CL column, vignette uses 50 L/h literature-typical sunitinib CL/F per Houk 2010), Hansson_2013_sunitinib_myelosuppression.R (Hansson 2013 e85 paper-direct extraction; uses the same upstream Houk 2009 sunitinib popPK as the source of CL / CLI per Methods), Hansson_2013_sunitinib_dbp.R (Hansson 2013 e85; indirect-response dBP sub-model), Hansson_2013_sunitinib_hfs.R (Hansson 2013 e85; hand-foot syndrome Markov + PO sub-model paralleling Hansson_2013c fatigue), Schindler_2017_sunitinib_fatigue.R (Schindler 2017 mCTMM fatigue model; consumes the same per-subject upstream sunitinib CL as the Hansson 2013 family), Schindler_2017_sunitinib_hfs.R (Schindler 2017 mCTMM HFS model; consumes the same per-subject upstream sunitinib CL as the Hansson 2013 family), Centanni_2025_sunitinib_thrombocytopenia.R (Centanni 2025 absolute-thrombocyte-count Friberg model newly developed as an addition to the Hansson 2013 sunitinib framework; consumes the same per-subject upstream sunitinib CL and forms the identical auc = DOSE / CLI daily-exposure driver).
• Notes: Specific scope because the values are intrinsically tied to a specific upstream popPK fit (sunitinib in this case; another model adopting CLI would carry its own upstream-PK lineage). Renamed from the source’s CL column because cl is the canonical nlmixr2 PK parameter name (a parameter, not a data column). Each model’s covariateData[[CLI]]$notes should cite the upstream popPK source (paper or DDMORE ID) and explain how to populate the column for new simulations (typically: simulate the upstream popPK first to obtain individual CL, or set every subject to the typical-value CL for typical-trajectory simulations). Distinct from DOSE (current administered dose level) – the two columns jointly carry a per-cycle drug-exposure summary (AUC = DOSE / CLI) for PD-only models that consume posthoc PK from an upstream popPK fit instead of instantiating their own PK ODE.

CL_INDIV (canonical for per-subject empirical-Bayes drug clearance)

• Description: Individual point estimate of the modelled drug’s clearance, supplied per-subject as a fixed data column. Used in sequential PK->PD models where the PK structure has been fixed from a previously-published population PK analysis and the per-subject empirical-Bayes (POSTHOC) clearances are passed through to drive the PD layer rather than being re-estimated alongside the PD parameters.
• Units: L/h (document per-model via covariateData[[CL_INDIV]]$units).
• Type: continuous
• Scope: specific
• Reference category: n/a – used directly inside model() as CL_INDIV in place of an estimated cl <- exp(lcl + etalcl).
• Source aliases: CLI – used in Friberg_2002_paclitaxel.R (NM-TRAN data column for per-subject paclitaxel CL).
• Example models: Friberg_2002_paclitaxel.R, Wahlby_2004_paclitaxel_myelosuppression.R (per-subject paclitaxel CL EBE supplied as a covariate column following the Friberg 2002 convention; the Wahlby 2004 PD model layers BIL and DBIL covariate effects on top of the Friberg-Karlsson chain).
• Notes: Specific scope because the value is intrinsically tied to the modelled drug – there is no shared meaning across drugs. Each model’s covariateData[[CL_INDIV]]$notes should state which upstream popPK source the EBE values come from (e.g., Henningsson 2001 paclitaxel popPK, fixed in the DDMORE encoding) and whether placebo periods are present. Companion volumes are registered as VC_INDIV and VP_INDIV.

CMAX_M1 (canonical for maximum drug plasma concentration during month 1)

• Description: Empirical-Bayes maximum plasma concentration (Cmax) reached during the first month (or the first cycle) of dosing for the modelled drug, used as a per-subject early-exposure covariate in PD / safety models that condition later-time outcomes on early exposure. Continuous, time-invariant per subject (set once from the month-1 / cycle-1 PK simulation).
• Units: ng/mL (document per-model via covariateData[[CMAX_M1]]$units if a different concentration unit is reported).
• Type: continuous
• Scope: specific
• Reference category: n/a – used as an additive logit shift (CMAX_M1 - CMAX_M1_REF) * theta (Girard 2012) or as a power scaling depending on the source. Reference value observed: 0 ng/mL (Girard 2012 sets MED17 = 0 as the centering reference).
• Source aliases:
  • CMAXM1 – used in Girard_2012_pimasertib.R.
• Example models: Girard_2012_pimasertib.R (additive logit shift on the cumulative-logit AE-score model: theta_cmaxm1 * CMAXM1).
• Notes: Specific scope because the value depends on the upstream drug-specific population-PK model used to derive the empirical-Bayes Cmax. Sibling of the existing CAV (average dosing-interval concentration); both are derived exposure metrics fed into downstream PD / safety models. Document the upstream PK model in covariateData[[CMAX_M1]]$notes for any future user.

VC_INDIV (canonical for per-subject empirical-Bayes central volume of distribution)

• Description: Individual point estimate of the modelled drug’s central volume of distribution, supplied per-subject as a fixed data column. Companion to CL_INDIV in sequential PK->PD encodings.
• Units: L (document per-model via covariateData[[VC_INDIV]]$units).
• Type: continuous
• Scope: specific
• Reference category: n/a – used directly inside model() as VC_INDIV in place of an estimated vc <- exp(lvc + etalvc).
• Source aliases: V1I – used in Friberg_2002_paclitaxel.R (NM-TRAN data column for per-subject paclitaxel V1).
• Example models: Friberg_2002_paclitaxel.R, Wahlby_2004_paclitaxel_myelosuppression.R (per-subject paclitaxel V1 EBE following the Friberg 2002 convention).
• Notes: See CL_INDIV notes for the broader convention.

VP_INDIV (canonical for per-subject empirical-Bayes peripheral volume of distribution)

• Description: Individual point estimate of the modelled drug’s first peripheral volume of distribution, supplied per-subject as a fixed data column. Companion to CL_INDIV and VC_INDIV in sequential PK->PD encodings.
• Units: L (document per-model via covariateData[[VP_INDIV]]$units).
• Type: continuous
• Scope: specific
• Reference category: n/a – used directly inside model() as VP_INDIV in place of an estimated vp <- exp(lvp + etalvp).
• Source aliases: V2I – used in Friberg_2002_paclitaxel.R (NM-TRAN data column for per-subject paclitaxel V2).
• Example models: Friberg_2002_paclitaxel.R, Wahlby_2004_paclitaxel_myelosuppression.R (per-subject paclitaxel V2 EBE following the Friberg 2002 convention).
• Notes: See CL_INDIV notes for the broader convention. For models requiring a second peripheral compartment, register VP2_INDIV (and add a follow-on entry to this register) when a second model legitimately needs it.

BAS_SVEGFR3 (canonical for individual posthoc baseline soluble VEGFR-3 concentration from an upstream PD fit)

• Description: Subject-specific empirical-Bayes (posthoc) baseline plasma sVEGFR-3 (soluble vascular endothelial growth factor receptor 3) concentration from a separately published indirect-response biomarker model that the current downstream PD model treats as a fixed input. Used as a per-subject (time-fixed) covariate in fatigue / adverse-event PD models that consume the upstream biomarker dynamics as data covariates without instantiating the biomarker ODE.
• Units: pg/mL (document per-model via covariateData[[BAS_SVEGFR3]]$units).
• Type: continuous
• Scope: specific
• Reference category: n/a – appears directly inside model() as the initial condition svegfr3(0) <- BAS_SVEGFR3 and inside the relative-change driver bm = (svegfr3 - BAS_SVEGFR3) / BAS_SVEGFR3.
• Source aliases:
  • BAS3 – used in Hansson_2013b_sunitinib.R (DDMODEL00000198), Hansson_2013c_sunitinib.R (DDMODEL00000222), and Hansson_2013_sunitinib_svegfr3_os.R as the posthoc sVEGFR-3 baseline column from the paper’s upstream Hansson 2013a biomarker indirect-response fit (DDMODEL00000197).
• Example models: Hansson_2013b_sunitinib.R (DDMODEL00000198; tumor growth inhibition with sVEGFR-3 driven shrinkage), Hansson_2013c_sunitinib.R (DDMODEL00000222; bundle’s three-subject simulated dataset reports BAS_SVEGFR3 values 42554-57365 pg/mL), Hansson_2013_sunitinib_svegfr3_os.R (parametric overall-survival Weibull TTE from Hansson 2013 e84 paper text; sVEGFR-3 dynamics simulated inline using the upstream biomarker-PD per-subject parameters), Hansson_2013_sunitinib_myelosuppression.R (Hansson 2013 e85 paper-direct extraction; simulates sVEGFR-3 turnover in-model to drive the Emax effect on neutrophil proliferation), Hansson_2013_sunitinib_hfs.R (Hansson 2013 e85; hand-foot syndrome Markov + PO sub-model driven by sVEGFR-3 relative change through an effect compartment), Schindler_2017_sunitinib_fatigue.R (Schindler 2017 mCTMM fatigue model; sVEGFR-3 baseline consumed as the upstream Hansson 2013a per-subject BAS_SVEGFR3 posthoc), Schindler_2017_sunitinib_hfs.R (Schindler 2017 mCTMM HFS model; sVEGFR-3 baseline consumed as the upstream Hansson 2013a per-subject BAS_SVEGFR3 posthoc).
• Notes: Specific scope because the value is intrinsically tied to a specific upstream biomarker model (sVEGFR-3 indirect response under sunitinib in this case). The downstream fatigue model only consumes individual posthoc baseline / MRT / EC50 of the upstream biomarker; it does not re-fit them. Each model’s covariateData[[BAS_SVEGFR3]]$notes should cite the upstream biomarker-PD source (paper or DDMORE ID) and explain how to populate the column for new simulations (typically: simulate from the upstream biomarker model to obtain individual posthoc baselines, or set every subject to the typical-value baseline for typical-trajectory simulations).

MRT_SVEGFR3 (canonical for individual posthoc mean residence time of soluble VEGFR-3 from an upstream PD fit)

• Description: Subject-specific empirical-Bayes (posthoc) mean residence time of plasma sVEGFR-3 from a separately published indirect-response biomarker model that the current downstream PD model treats as a fixed input. Used as a per-subject (time-fixed) covariate in fatigue / adverse-event PD models that consume the upstream biomarker dynamics as data covariates without instantiating the biomarker ODE; appears as kout3 = 1 / MRT_SVEGFR3 inside model().
• Units: h (hours) – document per-model via covariateData[[MRT_SVEGFR3]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – appears directly in derived rate-constant expressions; not a covariate effect coefficient.
• Source aliases:
  • MRT3 – used in Hansson_2013b_sunitinib.R (DDMODEL00000198), Hansson_2013c_sunitinib.R (DDMODEL00000222), and Hansson_2013_sunitinib_svegfr3_os.R as the posthoc sVEGFR-3 MRT column from the paper’s upstream Hansson 2013a biomarker indirect-response fit (DDMODEL00000197).
• Example models: Hansson_2013b_sunitinib.R (DDMODEL00000198), Hansson_2013c_sunitinib.R (DDMODEL00000222; bundle’s three-subject simulated dataset reports MRT_SVEGFR3 values 313-408 h, broadly consistent with the Hansson 2013a typical sVEGFR-3 MRT of 401 h), Hansson_2013_sunitinib_svegfr3_os.R (parametric overall-survival Weibull TTE; sVEGFR-3 dynamics simulated inline using the upstream biomarker-PD per-subject MRT), Hansson_2013_sunitinib_myelosuppression.R (Hansson 2013 e85 paper-direct extraction), Hansson_2013_sunitinib_hfs.R (Hansson 2013 e85 paper-direct extraction), Schindler_2017_sunitinib_fatigue.R (Schindler 2017 mCTMM fatigue; sVEGFR-3 MRT consumed as the upstream Hansson 2013a per-subject MRT_SVEGFR3 posthoc), Schindler_2017_sunitinib_hfs.R (Schindler 2017 mCTMM HFS; sVEGFR-3 MRT consumed as the upstream Hansson 2013a per-subject MRT_SVEGFR3 posthoc).
• Notes: Specific scope; same upstream-biomarker dependency rationale as BAS_SVEGFR3. The downstream fatigue model consumes the upstream MRT directly without re-fitting it.

EC50_SVEGFR3 (canonical for individual posthoc drug-effect EC50 on soluble VEGFR-3 from an upstream PD fit)

• Description: Subject-specific empirical-Bayes (posthoc) half-maximum-effect concentration of the modelled drug on the production of sVEGFR-3 (or, depending on the upstream model parameterization, on the analogous Imax inhibition pathway) from a separately published indirect-response biomarker model that the current downstream PD model treats as a fixed input. Used as a per-subject (time-fixed) covariate in fatigue / adverse-event PD models that consume the upstream biomarker dynamics as data covariates without instantiating the biomarker ODE.
• Units: mgh/L (when the per-cycle drug-exposure summary is auc = DOSE / CLI in mgh/L, EC50_SVEGFR3 carries the same units; document per-model via covariateData[[EC50_SVEGFR3]]$units).
• Type: continuous
• Scope: specific
• Reference category: n/a – appears directly inside model() in the simple-Imax drug-effect term eff3 = auc / (EC50_SVEGFR3 + auc).
• Source aliases:
  • EC53 – used in Hansson_2013b_sunitinib.R (DDMODEL00000198), Hansson_2013c_sunitinib.R (DDMODEL00000222), and Hansson_2013_sunitinib_svegfr3_os.R as the posthoc sVEGFR-3 EC50 column from the paper’s upstream Hansson 2013a biomarker indirect-response fit (DDMODEL00000197).
• Example models: Hansson_2013b_sunitinib.R (DDMODEL00000198), Hansson_2013c_sunitinib.R (DDMODEL00000222; bundle’s three-subject simulated dataset reports EC50_SVEGFR3 values 1.0-2.8 mgh/L, consistent with the Hansson 2013a typical sVEGFR-3 IC50 typical value of 1.0 mgh/L), Hansson_2013_sunitinib_svegfr3_os.R (parametric overall-survival Weibull TTE; sVEGFR-3 dynamics simulated inline using the upstream biomarker-PD per-subject EC50), Hansson_2013_sunitinib_myelosuppression.R (Hansson 2013 e85 paper-direct extraction), Hansson_2013_sunitinib_hfs.R (Hansson 2013 e85 paper-direct extraction), Schindler_2017_sunitinib_fatigue.R (Schindler 2017 mCTMM fatigue; sVEGFR-3 EC50 consumed as the upstream Hansson 2013a per-subject EC50_SVEGFR3 posthoc), Schindler_2017_sunitinib_hfs.R (Schindler 2017 mCTMM HFS; sVEGFR-3 EC50 consumed as the upstream Hansson 2013a per-subject EC50_SVEGFR3 posthoc).
• Notes: Specific scope; same upstream-biomarker dependency rationale as BAS_SVEGFR3. The downstream fatigue model consumes the upstream EC50 directly without re-fitting it.

BAS_SKIT (canonical for individual posthoc baseline soluble KIT concentration from an upstream PD fit)

• Description: Subject-specific empirical-Bayes (posthoc) baseline plasma sKIT (soluble stem cell factor receptor) concentration from a separately published indirect-response biomarker model that the current downstream PD model treats as a fixed input. Used as a per-subject (time-fixed) covariate in tumor-growth-inhibition / fatigue / adverse-event PD models that consume the upstream sKIT dynamics as data covariates without instantiating the biomarker ODE for sKIT in isolation.
• Units: pg/mL (document per-model via covariateData[[BAS_SKIT]]$units).
• Type: continuous
• Scope: specific
• Reference category: n/a – appears directly inside model() as the initial condition for treated and placebo sKIT compartments and inside the relative-change driver (skit_pla - skit_drug) / skit_pla (or the analogous BAS_SKIT-denominated form when only one sKIT compartment is simulated).
• Source aliases:
  • SBAS – used in Hansson_2013b_sunitinib.R (DDMODEL00000198) as the posthoc sKIT baseline column from the paper’s upstream Hansson 2013a biomarker indirect-response fit (DDMODEL00000197).
• Example models: Hansson_2013b_sunitinib.R (DDMODEL00000198; the Hansson 2013 e84 paper Table 2 reports a typical sKIT baseline of 39200 pg/mL with ~50% CV, matching Hansson_2013a_sunitinib’s typical value).
• Notes: Specific scope because the value is intrinsically tied to a specific upstream biomarker model (sKIT indirect response under sunitinib in this case). The downstream tumor-growth-inhibition model only consumes individual posthoc baseline / MRT / EC50 / DP-slope of the upstream biomarker; it does not re-fit them. Each model’s covariateData[[BAS_SKIT]]$notes should cite the upstream biomarker-PD source (paper or DDMORE ID) and explain how to populate the column for new simulations (typically: simulate from the upstream biomarker model to obtain individual posthoc baselines, or set every subject to the typical-value baseline for typical-trajectory simulations). Sister covariates: MRT_SKIT, EC50_SKIT, SLOPE_SKIT (companions for the same upstream biomarker fit).

MRT_SKIT (canonical for individual posthoc mean residence time of soluble KIT from an upstream PD fit)

• Description: Subject-specific empirical-Bayes (posthoc) mean residence time of plasma sKIT from a separately published indirect-response biomarker model that the current downstream PD model treats as a fixed input. Used as a per-subject (time-fixed) covariate in tumor-growth-inhibition / fatigue / adverse-event PD models that consume the upstream sKIT dynamics as data covariates without instantiating the biomarker ODE for sKIT in isolation; appears as kout_skit = 1 / MRT_SKIT inside model().
• Units: h (hours) – document per-model via covariateData[[MRT_SKIT]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – appears directly in derived rate-constant expressions; not a covariate effect coefficient.
• Source aliases:
  • SMRT – used in Hansson_2013b_sunitinib.R (DDMODEL00000198) as the posthoc sKIT MRT column from the paper’s upstream Hansson 2013a biomarker indirect-response fit (DDMODEL00000197).
• Example models: Hansson_2013b_sunitinib.R (DDMODEL00000198; the Hansson 2013 e84 paper Table 2 reports a typical sKIT MRT of 101 days = 2424 h, matching Hansson_2013a_sunitinib’s typical value of 2430 h).
• Notes: Specific scope; same upstream-biomarker dependency rationale as BAS_SKIT. The downstream tumor-growth-inhibition model consumes the upstream MRT directly without re-fitting it.

EC50_SKIT (canonical for individual posthoc drug-effect EC50 on soluble KIT from an upstream PD fit)

• Description: Subject-specific empirical-Bayes (posthoc) half-maximum-effect concentration of the modelled drug on the production of sKIT (or, depending on the upstream model parameterization, on the analogous Imax inhibition pathway) from a separately published indirect-response biomarker model that the current downstream PD model treats as a fixed input. Used as a per-subject (time-fixed) covariate in tumor-growth-inhibition / fatigue / adverse-event PD models that consume the upstream sKIT dynamics as data covariates without instantiating the biomarker ODE for sKIT in isolation.
• Units: mgh/L (when the per-cycle drug-exposure summary is auc = DOSE / CLI in mgh/L, EC50_SKIT carries the same units; document per-model via covariateData[[EC50_SKIT]]$units).
• Type: continuous
• Scope: specific
• Reference category: n/a – appears directly inside model() in the simple-Imax drug-effect term eff_skit = auc / (EC50_SKIT + auc).
• Source aliases:
  • SEC5 – used in Hansson_2013b_sunitinib.R (DDMODEL00000198) as the posthoc sKIT EC50 column from the paper’s upstream Hansson 2013a biomarker indirect-response fit (DDMODEL00000197).
• Example models: Hansson_2013b_sunitinib.R (DDMODEL00000198; the Hansson 2013 e84 paper Table 2 reports a typical (common across all four biomarkers) IC50 of 1.0 mg*h/L, matching Hansson_2013a_sunitinib’s shared typical value).
• Notes: Specific scope; same upstream-biomarker dependency rationale as BAS_SKIT. The downstream tumor-growth-inhibition model consumes the upstream EC50 directly without re-fitting it.

SLOPE_SKIT (canonical for individual posthoc linear disease-progression slope on soluble KIT from an upstream PD fit)

• Description: Subject-specific empirical-Bayes (posthoc) linear disease-progression slope on the placebo / untreated sKIT compartment from a separately published indirect-response biomarker model that the current downstream PD model treats as a fixed input. Used as a per-subject (time-fixed) covariate in tumor-growth-inhibition models that need to simulate the placebo-arm sKIT trajectory as a comparator for the drug-arm sKIT compartment; appears as dps_skit = BAS_SKIT * (1 + SLOPE_SKIT * t) and kin_skit = dps_skit * kout_skit inside model().
• Units: 1/h (document per-model via covariateData[[SLOPE_SKIT]]$units).
• Type: continuous
• Scope: specific
• Reference category: n/a – appears directly in the placebo-arm Kin expression for sKIT; not a covariate effect coefficient on a structural rate.
• Source aliases:
  • SLO – used in Hansson_2013b_sunitinib.R (DDMODEL00000198) as the posthoc sKIT linear disease-progression slope column from the paper’s upstream Hansson 2013a biomarker indirect-response fit (DDMODEL00000197).
• Example models: Hansson_2013b_sunitinib.R (DDMODEL00000198; the Hansson 2013 e84 paper Table 2 reports a typical disease-progression slope of 0.0261/month shared between VEGF and sKIT, which equals approximately 3.5e-5/h, matching Hansson_2013a_sunitinib’s typical value).
• Notes: Specific scope; same upstream-biomarker dependency rationale as BAS_SKIT. Sign convention follows the upstream biomarker fit – positive slope means the placebo / natural-history sKIT trajectory drifts upward over time (capturing disease progression).

CSS_RBV (canonical for individual posthoc ribavirin steady-state plasma concentration from an upstream PK fit)

• Description: Subject-specific empirical-Bayes (posthoc) modal estimate of the ribavirin (RBV) steady-state plasma trough concentration from a separately fitted population PK model (e.g., the Laouenan 2015 upstream RBV popPK fit) that the current downstream PD model treats as a fixed input. Used together with K_RBV inside model() to reconstruct the individual ribavirin concentration time-course analytically as riba(t) = CSS_RBV * (1 - exp(-K_RBV * t)).
• Units: ng/mL (document per-model via covariateData[[CSS_RBV]]$units).
• Type: continuous
• Scope: specific
• Reference category: n/a – appears directly inside model() in the analytical RBV-concentration expression riba = CSS_RBV * (1 - exp(-K_RBV * t)) that drives the inhibition term riba / (riba + ec50).
• Source aliases:
  • css_mode (modal posterior estimate of individual ribavirin Css from the upstream popPK fit, in ng/mL) – used in Laouenan_2015_ribavirin.R (DDMODEL00000285). Rename css_mode -> CSS_RBV before passing the dataset to rxSolve.
• Example models: Laouenan_2015_ribavirin.R (DDMODEL00000285; the bundle’s Simulated_Laouenant_2015_CPTPSP_hb_RBV.txt carries css_mode values 2,400-4,000 ng/mL in the 15-subject ANRS-CO20-CUPIC cohort).
• Notes: Specific scope because the value is intrinsically tied to a specific upstream popPK fit (ribavirin in HCV-cirrhotic patients on triple therapy in this case) and to the analytical Css*(1-exp(-k*t)) parameterization; another drug or another popPK parameterization would carry its own canonical. Each model’s covariateData[[CSS_RBV]]$notes should cite the upstream popPK source (paper or DDMORE ID) and explain how to populate the column for new simulations (typically: simulate the upstream popPK first to obtain individual Css and approach-rate, or set every subject to the typical Css for typical-trajectory simulations). Companion column: K_RBV. Distinct from CAV (dosing-interval-averaged concentration used in Emax / EC50 PD models with a single per-period exposure number) – CSS_RBV carries the asymptotic steady-state value paired with the approach-rate constant, supporting the full time-course reconstruction.

K_RBV (canonical for individual posthoc ribavirin approach-to-steady-state rate constant from an upstream PK fit)

• Description: Subject-specific empirical-Bayes (posthoc) modal estimate of the first-order rate constant governing the exponential approach of ribavirin plasma trough concentrations to steady state, from the same upstream popPK fit that supplies CSS_RBV. Used together with CSS_RBV inside model() to reconstruct the individual ribavirin concentration time-course analytically as riba(t) = CSS_RBV * (1 - exp(-K_RBV * t)).
• Units: 1/day (document per-model via covariateData[[K_RBV]]$units).
• Type: continuous
• Scope: specific
• Reference category: n/a – appears directly inside the analytical RBV-concentration expression alongside CSS_RBV.
• Source aliases:
  • k_mode (modal posterior estimate of individual ribavirin approach-to-Css rate constant from the upstream popPK fit, in 1/day) – used in Laouenan_2015_ribavirin.R (DDMODEL00000285). Rename k_mode -> K_RBV before passing the dataset to rxSolve.
• Example models: Laouenan_2015_ribavirin.R (DDMODEL00000285; the bundle’s Simulated_Laouenant_2015_CPTPSP_hb_RBV.txt carries k_mode values 0.013-0.47 day^-1 across the 15-subject cohort, corresponding to approach-to-Css half-lives of 1.5-55 days).
• Notes: Specific scope; same upstream-PK-dependency rationale as CSS_RBV. Distinct from any structural kel PK parameter – K_RBV is an apparent approach-to-Css rate from a lumped exponential parameterization of the trough time-course, not the elimination-rate constant of a one-compartment IV model (the lumped form absorbs absorption, distribution, and elimination into a single first-order rate). Companion column: CSS_RBV.

CSS_DFO (canonical for deferoxamine average steady-state plasma concentration from an upstream PK fit)

• Description: Subject-specific (or time-varying) average steady-state plasma concentration of deferoxamine (DFO; iron chelator). In Bellanti 2015 it is generated externally by a literature-derived two-compartment, zero-order-absorption (8-h SC infusion), first-order-elimination PK model (CL/F 19.3 L/h, Q/F 17.6 L/h, V/F 77.4 L, Vp/F 238 L at adult 70-kg reference; allometric exponents 0.75 on clearances and 1.00 on volumes) and fed into the downstream ferritin disease model as the drug-exposure driver in the linear concentration-effect term DFO = SLP * CSS_DFO on the ferritin degradation rate. Treated as time-varying so that drug holidays / partial compliance are captured by setting CSS_DFO = 0 (or a reduced value) over the affected interval – the paper’s compliance-corrected effective concentration TCss_AV = SCss_AV * (1 - CMPL) collapses to a CSS_DFO scaling in this implementation.
• Units: ug/mL (equivalently mg/L; the reporting convention used in Bellanti 2015 Fig 1 and Fig 3). Document per-model via covariateData[[CSS_DFO]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – enters directly into the drug-effect expression DFO = SLP * CSS_DFO, where SLP has units of 1/(ug/mL). Set to 0 to disable the chelation effect (drug holidays, untreated baseline disease-progression simulations). Reference values observed: ~3.5 ug/mL for 30 mg/kg/day, ~5.5 ug/mL for 45 mg/kg/day, ~7.5 ug/mL for 60 mg/kg/day at 45 kg body weight on the 5-days-per-week 8-h SC infusion schedule (Bellanti 2015 Fig 3).
• Source aliases:
  • SCssAV (Bellanti 2015 paper symbol; “simulated steady-state concentration, average”) – the per-subject population-PRED value before compliance correction.
  • TCssAV (Bellanti 2015 paper symbol; “true steady-state concentration, average”) – the post-compliance value SCssAV * (1 - CMPL); collapse into a single time-varying CSS_DFO column for nlmixr2lib by precomputing the (1 - CMPL) reduction in the input data.
• Example models: Bellanti_2015_deferoxamine.R (ug/mL; time-varying input on the linear DFO = SLP * CSS_DFO ferritin-degradation effect; 27 transfusion-dependent beta-thalassaemia major paediatric / adolescent patients on 20-60 mg/kg/day DFO 5 days per week).
• Notes: Specific scope because the column is intrinsically tied to deferoxamine PK and to the linear slope * CssAV effect form used in this paper. Sibling drug-specific Css canonical: CSS_RBV (ribavirin). The companion K_RBV approach-to-Css rate constant is not needed here because Bellanti 2015 treats CssAV as a population-typical steady-state value rather than reconstructing the rise-to-Css trajectory. For new simulations: a user supplies CSS_DFO directly (either a constant typical-Css value, or a time-varying column that switches to 0 during drug holidays); the vignette walks through computing CssAV analytically from a desired dose schedule via CssAV = (dose_per_week * F) / (CL_i * 168 h) with CL_i = 19.3 * (WT/70)^0.75 L/h for allometric scaling to paediatric / adolescent body weights. Ratified canonically on 2026-05-22 alongside the Bellanti 2015 extraction.

CP0_MAT_NGML (canonical for maternal plasma concentration at delivery used to initialise a neonate’s central compartment)

• Description: Observed maternal plasma concentration of the modelled drug in a sample drawn at (or within minutes of) delivery, supplied as a static per-subject covariate on the neonate and used to set the neonate’s central-compartment initial condition at t = 0. This is the standard way a mother-infant dyad analysis carries in-utero (transplacental) exposure into a neonatal PK model when the umbilical cord is not modelled as an intermediate compartment: the neonate is born with drug already on board, and the maternal sample is the only measurement of it. Distinct from the CP_<DRUG>_<UNITS> family, which is an instantaneous, time-varying concentration of an upstream drug fed per event record into a PD or perpetrator model – CP0_MAT_NGML is time-fixed, one value per subject, of the same drug the model itself describes, and drives a state initial condition rather than an equation. Parallel in role to PLAQUE_BL and HGB_BL (per-subject observed baseline supplying a state’s t = 0 value), differing in that the baseline was measured in a different individual (the mother) than the one being modelled.
• Units: ng/mL. Document per-model via covariateData[[CP0_MAT_NGML]]$units; register a sibling only if a paper reports maternal concentrations in units that cannot be converted (the _NGML suffix is part of the canonical name, so a mg/L analysis should convert rather than add a name).
• Type: continuous
• Scope: general
• Reference category: n/a – static per-subject concentration supplied as a covariate column. Set to 0 for a neonate with no detectable transplacental transfer (the value is an amount-defining input, so 0 correctly means “born with no drug on board”). Reference values observed: mean (SD) 28.2 (23.5) ng/mL for ondansetron across 29 maternal samples in Lam 2025 (Table 1).
• Source aliases:
  • C0 – printed column name in the Lam 2025 Appendix S1 NONMEM control stream ($INPUT ... DV C0 MDV CMT; $PK line A_0(2) = C0 * S2) – used in Lam_2025_ondansetron.R.
• Example models: Lam_2025_ondansetron.R (Lam 2025 neonatal ondansetron popPK; sets central(0) <- CP0_MAT_NGML * vc / 1000 so that Cc(0) equals the maternal concentration).
• Notes: Drug-agnostic (general scope) by operator ratification on 2026-08-14: maternal concentration at delivery is a general transplacental-exposure concept and the next drug to use it should not need a second canonical, so the drug is implicitly “the drug this model describes” (the same convention CP_MGL uses). Note the unit bookkeeping this covariate implies: the model must convert the concentration to an amount using the central volume before assigning the initial condition, and the conversion factor depends on the model’s dose and volume units (in Lam 2025, central in mg and vc in L give CP0_MAT_NGML * vc / 1000). Founding example: Lam_2025_ondansetron.R.

CP_MGL (canonical for instantaneous drug plasma concentration as a time-varying PD driver)

• Description: Instantaneous (per-event-record) plasma concentration of the modeled drug, supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in PD-only myelosuppression / toxicity / response models that consume an upstream PK trajectory as input – typically because the source analysis was done as a sequential PK-then-PD fit, with the PK model fixed from a previously published popPK analysis (e.g., a Bruno-style docetaxel popPK feeding a Friberg / Kloft myelosuppression PD model).
• Units: mg/L (= ug/mL; the two labels are numerically equivalent and the canonical reporting convention for cytotoxic-chemotherapy concentrations in Kloft 2006 / Friberg 2002 family analyses). Document per-model via covariateData[[CP_MGL]]$units if a different unit is used.
• Type: continuous
• Scope: specific
• Reference category: n/a – typically enters as a linear term in the drug-effect expression (e.g., drug = SL * CP_MGL, where SL has units of 1/(mg/L)). Set to 0 for placebo periods or any time outside the drug-exposure window. Reference values observed: docetaxel typical Cmax ~3 mg/L after a 100 mg/m^2 1-hour IV infusion (Kloft 2006 / Netterberg 2017 simulated dataset).
• Source aliases:
  • CP (Kloft 2006 / Netterberg 2017 NM-TRAN $INPUT convention for “predicted drug concentration”; values in mg/L) – used in Netterberg_2017_docetaxel.R.
• Example models: Netterberg_2017_docetaxel.R (linear drug effect on the proliferation rate of the Friberg myelosuppression chain: (1 - SL * CP_MGL) with SL = 19.27 (mg/L)^-1 after Kloft 2006’s THETA(3)/808*1000 MW-808 conversion; CP_MGL supplied per event row from an upstream docetaxel popPK simulation), Puisset_2007_docetaxel.R (linear drug effect Slope * CP_MGL on the proliferating compartment of the Friberg myelosuppression chain; the upstream docetaxel PK in Puisset 2007 was Baille 1997 / Bruno 1996 individual Bayesian posthoc profiles supplied externally and not encoded in the model file).
• Notes: Specific scope because the covariate’s mechanistic meaning is bound to the modeled drug and the source paper’s chosen PK input (e.g., a Bruno 1996/1998 docetaxel popPK trajectory for Netterberg 2017). Distinct from CAV (dosing-interval-averaged exposure used in Emax / EC50 PD models) – CP_MGL is the instantaneous concentration, sampled at every PD event time. When a future paper requires the same time-varying-PK-as-PD-input pattern for a different drug, register a drug-specific canonical (e.g., CP_PACL_MGL for paclitaxel) rather than overloading this name; CP_MGL retains the implicit “drug = the modeled drug under the PD analysis” semantics. When a paper supplies the time-varying PK as separate per-subject empirical-Bayes PK parameters (e.g., CL_INDIV, VC_INDIV, VP_INDIV), use those columns in a coupled PK-PD ODE model (see Friberg_2002_paclitaxel.R) rather than reducing to CP_MGL. The choice between PK-as-covariate (this canonical) and PK-as-EBE-parameters depends on whether the source paper’s NM-TRAN dataset shipped Cp directly or shipped the upstream individual PK parameters.

CP_OXA_MGL (canonical for instantaneous oxaliplatin plasma concentration as a time-varying PD driver)

• Description: Instantaneous plasma concentration of oxaliplatin supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in PD-only platelet-dynamics / myelosuppression models that consume an upstream oxaliplatin popPK trajectory as input.
• Units: mg/L (= ug/mL; oxaliplatin MW = 397.29 g/mol, so 1 mg/L = 2.52 umol/L).
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as a power-function term in the platelet-dynamics drug-effect expression E_drug = alpha * CP_OXA_MGL^beta. Set to 0 outside the drug-exposure window (e.g., pre-HIO or CRS-alone patients). Reference values observed: hyperthermic intraperitoneal oxaliplatin (HIO) administered at 200 mg/L initial peritoneal concentration for 30 min gives typical plasma Cmax ~1 mg/L with half-life ~14 h (Perez-Ruixo 2013 Cancer Chemother Pharmacol 71:693-704).
• Source aliases:
  • Cp (Perez-Ruixo 2015 The AAPS Journal Methods and Eq. 8 symbol for the oxaliplatin plasma concentration driving E_drug) – used in PerezRuixo_2015_oxaliplatin_platelet_dynamics.R.
• Example models: PerezRuixo_2015_oxaliplatin_platelet_dynamics.R (drug effect E_drug = alpha * CP_OXA_MGL^beta on the megakaryocyte-progenitor proliferation rate ktr, with alpha = 0.881 L/mg and beta = 2.63 per Perez-Ruixo 2015 Table I; CP_OXA_MGL supplied per event row from an upstream oxaliplatin popPK simulation not packaged in nlmixr2lib at extraction time – the vignette shows a placeholder monoexponential Cp trajectory tuned to typical HIO Cmax ~1 mg/L and half-life ~14 h).
• Notes: Specific scope; oxaliplatin-specific. The drug-specific naming follows the CP_<drug>_<units> precedent from CP_MGL (general docetaxel-family form), CP_OXY_NGML, CP_FBX_NGML, CP_LSN_NGML, CP_MORPH_NGML, CP_RIF_UM, CP_GDC_UM, CP_REM_NGML, and CP_GLASDEGIB_NGML. The paper uses Cp as the paper-symbol; renamed to CP_OXA_MGL in the register per the naming pattern. The natural input source is a coupled oxaliplatin popPK simulation (Perez-Ruixo 2013 Cancer Chemother Pharmacol; not on disk at extraction time); users can either digitise Cp trajectories from Figure 5 of Perez-Ruixo 2015 or simulate a placeholder monoexponential decline with typical HIO parameters. Ratified canonically on 2026-07-10 alongside the Perez-Ruixo 2015 platelet-dynamics extraction.

CP_OXY_NGML (canonical for instantaneous oxypurinol plasma concentration as a time-varying PD driver)

• Description: Instantaneous plasma concentration of oxypurinol (the active metabolite of allopurinol; xanthine oxidase inhibitor) supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in semi-mechanistic uric-acid disposition models that take XOI exposure as input to the production-inhibition equation.
• Units: ng/mL
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as a Hill term in the production-inhibition expression 1 - Rmax * CP_OXY_NGML / (CP_OXY_NGML + p50). Set to 0 outside the drug-exposure window or for non-XOI scenarios. Reference values observed: mean daily concentration on 300 mg/day allopurinol is approximately 10,000 ng/mL (Aksenov 2018, Eq. 13).
• Source aliases:
  • [P]_PIN (oxypurinol) – the symbol used in Aksenov 2018 Eq. 9 for the production-inhibitor concentration when the inhibitor is oxypurinol.
• Example models: Aksenov_2018_uricAcid.R (Hill-type production inhibition with rmax_oxy = 0.84 and p50_oxy = 14000 ng/mL per Aksenov 2018 Table 1).
• Notes: Specific scope because the canonical name is bound to oxypurinol; allopurinol’s PK is conventionally summarized via the active metabolite oxypurinol (Day et al. 2007). Distinct from CP_FBX_NGML (febuxostat) and CP_LSN_NGML (lesinurad). When the source paper supplies an upstream popPK for oxypurinol (e.g., Wright et al. 2013, Anzai & Endou 2012), the user simulates that PK to populate this column; otherwise a steady-state value can be used. Ratified canonically on 2026-05-08 alongside the Aksenov 2018 extraction.

CP_FBX_NGML (canonical for instantaneous febuxostat plasma concentration as a time-varying PD driver)

• Description: Instantaneous plasma concentration of febuxostat (xanthine oxidase inhibitor) supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in semi-mechanistic uric-acid disposition models that take XOI exposure as input to the production-inhibition equation.
• Units: ng/mL
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as a Hill term in the production-inhibition expression 1 - Rmax * CP_FBX_NGML / (CP_FBX_NGML + p50). Set to 0 outside the drug-exposure window or for non-XOI scenarios. Reference values observed: mean daily concentration on 40 mg/day febuxostat is approximately 1000-2000 ng/mL (Aksenov 2018, Bhattaram & Gobburu 2017 regulatory review).
• Source aliases:
  • [P]_PIN (febuxostat) – the symbol used in Aksenov 2018 Eq. 9 for the production-inhibitor concentration when the inhibitor is febuxostat.
• Example models: Aksenov_2018_uricAcid.R (Hill-type production inhibition with rmax_fbx = 1 (fixed) and p50_fbx = 120 ng/mL for hyperuricemic subjects (or 87 ng/mL for normouricemic subjects) per Aksenov 2018 Table 1).
• Notes: Specific scope; febuxostat-specific. The p50 parameter differs between hyperuricemic and normouricemic populations in Aksenov 2018; Rmax is fixed at 1 per Bhattaram & Gobburu 2017. Distinct from CP_OXY_NGML (oxypurinol) and CP_LSN_NGML (lesinurad). Ratified canonically on 2026-05-08 alongside the Aksenov 2018 extraction.

CP_LSN_NGML (canonical for instantaneous lesinurad plasma concentration as a time-varying PD driver)

• Description: Instantaneous plasma concentration of lesinurad (uricosuric URAT1 inhibitor) supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in semi-mechanistic uric-acid disposition models that take uricosuric exposure as input to the fractional-excretion-increase equation.
• Units: ng/mL
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as a Hill term in the fractional-excretion expression FE = FE0 + Fmax * CP_LSN_NGML / (CP_LSN_NGML + p50). Set to 0 outside the drug-exposure window. Reference values observed: peak plasma concentration after single dose 200 mg lesinurad is approximately 6,000-9,000 ng/mL (Fleischmann et al. 2014; Shen et al. 2015).
• Source aliases:
  • [P]_RIN (lesinurad) – the symbol used in Aksenov 2018 Eq. 10 for the reabsorption-inhibitor concentration when the inhibitor is lesinurad.
• Example models: Aksenov_2018_uricAcid.R (Hill-type increase in fractional excretion with fmax_lsn = 0.56 (fixed) and p50_lsn = 23000 ng/mL for hyperuricemic subjects (or 11000 ng/mL for normouricemic subjects) per Aksenov 2018 Table 1).
• Notes: Specific scope; lesinurad-specific. The p50 parameter differs between hyperuricemic and normouricemic populations in Aksenov 2018; Fmax was fixed during estimation. Distinct from CP_OXY_NGML (oxypurinol) and CP_FBX_NGML (febuxostat). Ratified canonically on 2026-05-08 alongside the Aksenov 2018 extraction.

CP_MORPH_NGML (canonical for instantaneous morphine plasma concentration as a time-varying PD driver)

• Description: Instantaneous plasma concentration of morphine supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in PD-only IRT / latent-pain models that take morphine exposure as an external input to a concentration-effect equation.
• Units: ng/mL
• Type: continuous
• Scope: specific
• Reference category: n/a – enters linearly into the latent-pain equation pain = pain_state - e_morph_pain * CP_MORPH_NGML + e_time_pain * time (Valitalo 2017). Reference values observed: most individual predicted concentrations in Valitalo 2017 were within 0-60 ng/mL (Figure 2a); the IRT linear morphine-effect slope is 0.0091 (ng/mL)^-1, so a 20 ng/mL morphine exposure reduces the latent pain by ~0.18 latent-variable units.
• Source aliases:
  • CP (Valitalo 2017 NM-TRAN $INPUT convention for “morphine plasma concentration”; values in ng/mL) – used in Valitalo_2017_morphine.R.
• Example models: Valitalo_2017_morphine.R (linear morphine concentration-effect on the IRT latent pain variable; CP_MORPH_NGML supplied per event row from an upstream morphine popPK simulation, typically Knibbe_2009_morphine.R).
• Notes: Specific scope; morphine-specific. The drug-specific naming follows the existing CP_OXY_NGML / CP_FBX_NGML / CP_LSN_NGML precedent established with Aksenov 2018. Distinct from the broader CP_MGL (mg/L PD-driver convention used in Netterberg 2017 docetaxel myelosuppression and similar) because the IRT PD models in this family use ng/mL natively. When a future morphine PD analysis uses mg/L, the conversion is CP_MORPH_NGML = CP_MORPH_MGL * 1000. Ratified canonically alongside the Valitalo 2017 morphine extraction (DDMODEL00000247).

CP_BPN_NGML (canonical for instantaneous buprenorphine plasma concentration as a time-varying PD driver)

• Description: Instantaneous plasma concentration of buprenorphine (BPN) supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in PD-only opioid-blockade models that take buprenorphine exposure as an external input to an Imax concentration-effect equation for abuse-liability and withdrawal endpoints.
• Units: ng/mL
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as the Hill term of an Imax inhibition expression, e.g. 1 - imax * CP_BPN_NGML^hill / (ic50^hill + CP_BPN_NGML^hill) (Walsh 2024). Reference values observed: the observed BPN plasma concentration range across the Walsh 2024 phase 2 study was 0.636-12.3 ng/mL, and the paper’s own COWS application simulation swept 0-10 ng/mL. Set to 0 for the pre-treatment (unblocked) condition, which returns the model to its baseline.
• Source aliases:
  • Cp (Walsh 2024 notation, “the time-matched BPN concentration”; values in ng/mL) – used in Walsh_2024_buprenorphine_drugLiking.R, Walsh_2024_buprenorphine_desireToUse.R and Walsh_2024_buprenorphine_cows.R.
• Example models: Walsh_2024_buprenorphine_drugLiking.R (Imax blockade of period-corrected drug liking Emax VAS after an intramuscular hydromorphone 18 mg challenge; IC50 0.075 ng/mL), Walsh_2024_buprenorphine_desireToUse.R (Imax suppression of desire to use VAS with an exponential onset delay; IC50 0.0129 ng/mL), Walsh_2024_buprenorphine_cows.R (bounded-integer Imax suppression of the COWS total score; IC90 0.109 ng/mL).
• Notes: Specific scope; buprenorphine-specific. The drug-specific naming follows the established CP_<drug>_<units> precedent (CP_OXY_NGML, CP_FBX_NGML, CP_LSN_NGML, CP_MORPH_NGML, CP_RIF_UM, CP_REM_NGML, CP_GLASDEGIB_NGML). Walsh 2024 sampled one BPN plasma concentration per challenge day, approximately 60 min before each hydromorphone administration, and time-matched it to the PD score; the analysis is therefore driven by daily concentrations rather than a dense profile. The upstream CAM2038 (subcutaneous long-acting buprenorphine depot) popPK model is NOT reported in Walsh 2024 and no buprenorphine popPK model exists in the nlmixr2lib registry, so downstream users must supply the concentration trajectory externally – either from their own popPK source or by digitising Supplementary Figure S2 of Walsh 2024. Ratified canonically on 2026-08-05 alongside the Walsh 2024 CAM2038 extraction.

CP_EIDD_NGML (canonical for instantaneous EIDD-1931 plasma concentration as a time-varying antiviral PD driver)

• Description: Instantaneous plasma concentration of EIDD-1931, the pharmacologically active nucleoside-analogue metabolite of the antiviral prodrug molnupiravir, supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in within-host viral-dynamics / QSP models that consume an external molnupiravir exposure profile as the driver of an Imax inhibition of viable-virus production.
• Units: ng/mL
• Type: continuous
• Scope: specific
• Reference category: n/a – enters the Imax inhibition function 1 - Imax * CP_EIDD_NGML^nhill / (IC50_av^nhill + CP_EIDD_NGML^nhill); set to 0 for placebo arms, for neutralizing-antibody-only arms, and for any time outside the drug-exposure window. Reference values observed: Rao 2023 Supplementary Figure 16 (digitized from Painter et al. 2021, doi:10.1128/AAC.02428-20) shows single-dose peaks of roughly 300 ng/mL (50 mg) to 3000 ng/mL (800 mg) within 1-2 h, falling below ~100 ng/mL by 12 h; the fitted antiviral IC50 is 1086 ng/mL.
• Source aliases:
  • Cav / Cantiviral (Rao 2023 Supplementary Methods and src/covid19_dxdt.m, where the concentration enters as an interp1() lookup on the digitized profile) – used in Rao_2023_covid19_qsp.R.
• Example models: Rao_2023_covid19_qsp.R (Imax inhibition of viable-virus production from infected cells in the COVID-19 QSP model; the digitized 0-12 h profiles for the 50, 100, 200, 300, 400, 600 and 800 mg single doses ship with the authors’ code release as data/molnupiravir_PK.csv).
• Notes: Specific scope; bound to EIDD-1931 and to the plasma (rather than intracellular) compartment. Rao 2023 states explicitly that the intracellular active triphosphate was not modelled and that the IC50 was optimized against the reported plasma EIDD-1931 concentration, so this canonical must not be reused for an intracellular-metabolite driver. Named for the active metabolite rather than the prodrug because that is the moiety whose concentration drives the effect; a future model that drives an effect from molnupiravir parent concentrations should register a separate CP_MOLNUPIRAVIR_NGML. Follows the drug-specific CP_<DRUG>_NGML precedent established with Aksenov 2018 (CP_OXY_NGML, CP_FBX_NGML, CP_LSN_NGML) and Valitalo 2017 (CP_MORPH_NGML).

CP_RIF_UM (canonical for instantaneous rifampicin plasma concentration as a time-varying OATP1B-perpetrator covariate)

• Description: Instantaneous plasma concentration of rifampicin supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used as the perpetrator-drug input to a competitive OATP1B inhibition term in DDI popPK models for OATP1B substrates and endogenous OATP1B biomarkers (e.g., coproporphyrin I, rosuvastatin). Set to 0 outside the rifampicin co-administration window so the inhibition term collapses to the baseline form.
• Units: umol/L (= uM; rifampicin MW = 822.94 g/mol so 1 umol/L = 0.823 mg/L = 823 ng/mL).
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as the denominator of a Michaelis-Menten-style competitive-inhibition term cl_b_eff = cl_b / (1 + CP_RIF_UM / ki) where ki is the OATP1B inhibition constant in umol/L. Reference peak observed: a single 600 mg oral rifampicin dose in the Barnett 2018 cohort produces a typical Cmax of approximately 29 umol/L in the modellib(‘Barnett_2018_rifampicin’) typical-value simulation.
• Source aliases:
  • CRIF (Barnett 2018 Eq. 4 and the analogous RSV inhibition equation; values reported in umol/L).
• Example models: Barnett_2018_coproporphyrin_I.R (drives competitive OATP1B inhibition of biliary CPI clearance: cl_b_eff = cl_b / (1 + CP_RIF_UM / ki) with ki = 1.15 umol/L total / 0.13 umol/L unbound), Barnett_2018_rosuvastatin.R (analogous form with ki = 2.23 umol/L total / 0.25 umol/L unbound), Yoshida_2018_coproporphyrin_I_rifampin.R (drives competitive OATP1B inhibition of the hepatic component of CPI clearance in Yoshida 2018’s one-compartment fNH-parameterised model: kdeg_eff = kdeg * (fnh + (1 - fnh) / (1 + CP_RIF_UM / kiu)) with kiu = 0.0203 umol/L unbound).
• Notes: Specific scope; rifampicin-specific. The drug-specific naming follows the established CP_<drug>_<units> precedent (CP_OXY_NGML, CP_FBX_NGML, CP_LSN_NGML, CP_MORPH_NGML). The natural input source is a coupled rifampicin popPK simulation; in the Barnett 2018 extraction package, users typically simulate modellib('Barnett_2018_rifampicin') first (which returns rifampicin Cc in umol/L after the in-model MW conversion) and feed its central-compartment output as the CP_RIF_UM column on the CPI or RSV event table. Distinct from the binary indicator CONMED_RIF (which captures period-level effects like the V1 / V2 / Q binary covariate shifts and does not carry magnitude information). Ratified canonically on 2026-05-26 alongside the Barnett 2018 CPI / RSV extractions. The Yoshida 2018 rifampin-CPI extraction (2026-05-30) used the Simcyp v16r1 default single-dose rifampin model output for portal-vein unbound concentration; that PBPK profile is not reproducible from on-disk sources, and the paper itself documents a ~5x sensitivity of the estimated Ki,u to the choice of perpetrator-PK model, so downstream users must supply their own CP_RIF_UM profile and treat the resulting CPI excursion as conditional on that choice.

CP_GDC_UM (canonical for instantaneous GDC-0810 portal-vein unbound concentration as a time-varying OATP1B-perpetrator covariate)

• Description: Instantaneous unbound portal-vein concentration of GDC-0810 (an orally bioavailable selective estrogen receptor downregulator and weak OATP1B inhibitor) supplied directly as a time-varying covariate column rather than computed from a coupled PBPK model. Used as the perpetrator-drug input to a competitive OATP1B inhibition term in DDI models for OATP1B substrates and endogenous OATP1B biomarkers (coproporphyrin I). Set to 0 outside the GDC-0810 co-administration window so the inhibition term collapses to the baseline (no-inhibition) form.
• Units: umol/L (= uM)
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as the denominator of a Michaelis-Menten-style competitive-inhibition term kdeg_eff = kdeg * (fnh + (1 - fnh) / (1 + CP_GDC_UM / kiu)) where kiu is the OATP1B unbound inhibition constant in umol/L (Yoshida 2018 estimated kiu = 0.00174 umol/L for GDC-0810).
• Source aliases:
  • CGDC (Yoshida 2018 NM-TRAN convention for the per-record GDC-0810 portal-vein unbound concentration; values in umol/L). The Yoshida 2018 paper itself reports values from the in-house Y. Chen et al. PBPK model (referenced as personal communication; not on disk).
• Example models: Yoshida_2018_coproporphyrin_I_GDC0810.R (drives competitive OATP1B inhibition of the hepatic component of CPI clearance in Yoshida 2018’s one-compartment fNH-parameterised model).
• Notes: Specific scope; GDC-0810-specific. The drug-specific naming follows the established CP_<drug>_<units> precedent (CP_OXY_NGML, CP_FBX_NGML, CP_LSN_NGML, CP_MORPH_NGML, CP_RIF_UM). The natural input source in Yoshida 2018 was an in-house PBPK model for GDC-0810 (Y. Chen et al., personal communication); that source is not on disk and no GDC-0810 PK model exists in the nlmixr2lib registry, so downstream users must supply CP_GDC_UM externally. The paper notes (Discussion) that observed GDC-0810 plasma AUC IIV was approximately 20%, so the IIV reported on Ki,u (30.1% CV) partially includes per-subject variability in portal-vein exposure rather than purely the intrinsic inhibition-constant variability. Ratified canonically on 2026-05-30 alongside the Yoshida 2018 GDC-0810-CPI extraction.

STIM_QUININE_MM (canonical for applied quinine HCl dihydrate stimulus concentration in a brief-access taste aversion experiment)

• Description: Applied sipper-tube concentration of quinine HCl dihydrate (mM) directly presented to the rat during an 8-second brief-access taste aversion (BATA) trial. This is the experimental stimulus level that contacts the taste receptors in solution – not a systemic plasma concentration of an absorbed drug. Drives the sigmoid-Emax effect on the logistic mixing probability in Sheng 2016’s two-generalized-Poisson mixture model for bimodal lick-count data.
• Units: mM
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as a Hill term in the logistic-Emax expression E = E0 + Emax * STIM_QUININE_MM^c / (RIC50^c + STIM_QUININE_MM^c) (Sheng 2016, Methods, “Model for the drug effect”). Set to 0 for the water (control) presentation. Reference values observed: Sheng 2016 presented seven concentrations 0 / 0.01 / 0.03 / 0.1 / 0.3 / 1 / 3 mM (Table 1); the final-model RIC50 is 0.0423 mM.
• Source aliases:
  • QUININE (Sheng 2016 NM-TRAN $INPUT column name for the per-record applied stimulus concentration; values in mM).
• Example models: Sheng_2016_quinine_rat.R (sigmoid Emax on the logistic mixing probability between a low-count generalized-Poisson distribution and a right-truncated high-count generalized-Poisson distribution; STIM_QUININE_MM is the only model covariate).
• Notes: Specific scope because the canonical name is bound to quinine HCl dihydrate as the bitter stimulus. Distinct from CAV (systemic average drug plasma concentration over a dosing interval) and the CP_* family (instantaneous plasma concentration as time-varying PD driver) – STIM_QUININE_MM is the applied stimulus concentration in solution that contacts the taste receptors directly, with no PK absorption / distribution involved. Future BATA-style extractions with a different bitter compound (caffeine, denatonium, etc.) should register a parallel canonical (e.g. STIM_CAFFEINE_MM) rather than overload this name; the STIM_<drug>_<units> pattern mirrors the established CP_<drug>_<units> precedent. Ratified canonically alongside the Sheng 2016 quinine BATA extraction.

STIM_ARTESUNATE_NM (canonical for applied in-vitro artesunate concentration driving a P. falciparum hypoxanthine-uptake-inhibition model)

• Description: Applied artesunate concentration in the in vitro hypoxanthine-uptake-inhibition assay well (nM). Per-record covariate (the applied well concentration that contacts the parasite culture directly; the model has no PK and no time evolution). Drives the sigmoid Emax inhibition of normalised hypoxanthine uptake by clinical Plasmodium falciparum isolates.
• Units: nM
• Type: continuous
• Scope: specific
• Reference category: n/a – enters the sigmoid Emax inhibition expression as the concentration argument; set to 0 for the drug-free control well. Reference values observed: Simpson 2013 used a doubling-dilution series 0.044 to 87.0 nM plus a drug-free control well; the WT-reference (Genotype 1) population EC50 is 2.3 nM (Table 3).
• Source aliases:
  • C – used in Simpson_2013_artesunate.R (per-record applied well concentration in nM).
• Example models: Simpson_2013_artesunate.R (the only model covariate that varies per record; drives the sigmoid Emax inhibition with the pfmdr1 genotype effects entering on EC50).
• Notes: Specific scope because the canonical name is bound to artesunate as the applied antimalarial stimulus. Member of the in-vitro applied-drug-concentration STIM_<drug>_<units> family (siblings STIM_CHLOROQUINE_NM, STIM_LUMEFANTRINE_NM, STIM_MEFLOQUINE_NM, and the bitter-stimulus STIM_QUININE_MM) – the applied stimulus / well concentration that contacts the target directly, distinct from CAV (systemic average plasma concentration), the CP_<drug> plasma-PD-driver family, and the CONC_<drug>_MGL in-vitro antibacterial family (which is reported in mg/L). Ratified canonically alongside the Simpson 2013 antimalarial in-vitro extractions.

STIM_CHLOROQUINE_NM (canonical for applied in-vitro chloroquine concentration driving a P. falciparum hypoxanthine-uptake-inhibition model)

• Description: Applied chloroquine concentration in the in vitro hypoxanthine-uptake-inhibition assay well (nM). Per-record covariate (the applied well concentration that contacts the parasite culture directly; the model has no PK and no time evolution). Drives the sigmoid Emax inhibition of normalised hypoxanthine uptake by clinical Plasmodium falciparum isolates.
• Units: nM
• Type: continuous
• Scope: specific
• Reference category: n/a – enters the sigmoid Emax inhibition expression as the concentration argument; set to 0 for the drug-free control well. Reference values observed: Simpson 2013 used a doubling-dilution series 10.02 to 10255.9 nM plus a drug-free control well; the WT-reference (Genotype 1) population EC50 is 242 nM (Table 3).
• Source aliases:
  • C – used in Simpson_2013_chloroquine.R (per-record applied well concentration in nM).
• Example models: Simpson_2013_chloroquine.R (the only model covariate that varies per record; drives the sigmoid Emax inhibition with the pfmdr1 genotype effects entering on EC50).
• Notes: Specific scope because the canonical name is bound to chloroquine as the applied antimalarial stimulus. Member of the in-vitro applied-drug-concentration STIM_<drug>_<units> family (siblings STIM_ARTESUNATE_NM, STIM_LUMEFANTRINE_NM, STIM_MEFLOQUINE_NM, and the bitter-stimulus STIM_QUININE_MM) – the applied stimulus / well concentration that contacts the target directly, distinct from CAV, the CP_<drug> family, and the CONC_<drug>_MGL in-vitro antibacterial family. Ratified canonically alongside the Simpson 2013 antimalarial in-vitro extractions.

STIM_LUMEFANTRINE_NM (canonical for applied in-vitro lumefantrine concentration driving a P. falciparum hypoxanthine-uptake-inhibition model)

• Description: Applied lumefantrine concentration in the in vitro hypoxanthine-uptake-inhibition assay well (nM). Per-record covariate (the applied well concentration that contacts the parasite culture directly; the model has no PK and no time evolution). Drives the sigmoid Emax inhibition of normalised hypoxanthine uptake by clinical Plasmodium falciparum isolates.
• Units: nM
• Type: continuous
• Scope: specific
• Reference category: n/a – enters the sigmoid Emax inhibition expression as the concentration argument; set to 0 for the drug-free control well. Reference values observed: Simpson 2013 used a doubling-dilution series 2.40 to 235.8 nM plus a drug-free control well; the WT-reference (Genotype 1) population EC50 is 35.7 nM (Table 3).
• Source aliases:
  • C – used in Simpson_2013_lumefantrine.R (per-record applied well concentration in nM).
• Example models: Simpson_2013_lumefantrine.R (the only model covariate that varies per record; drives the sigmoid Emax inhibition with the pfmdr1 genotype effects entering on EC50).
• Notes: Specific scope because the canonical name is bound to lumefantrine as the applied antimalarial stimulus. Member of the in-vitro applied-drug-concentration STIM_<drug>_<units> family (siblings STIM_ARTESUNATE_NM, STIM_CHLOROQUINE_NM, STIM_MEFLOQUINE_NM, and the bitter-stimulus STIM_QUININE_MM) – the applied stimulus / well concentration that contacts the target directly, distinct from CAV, the CP_<drug> family, and the CONC_<drug>_MGL in-vitro antibacterial family. Ratified canonically alongside the Simpson 2013 antimalarial in-vitro extractions.

STIM_MEFLOQUINE_NM (canonical for applied in-vitro mefloquine concentration driving a P. falciparum hypoxanthine-uptake-inhibition model)

• Description: Applied mefloquine concentration in the in vitro hypoxanthine-uptake-inhibition assay well (nM). Per-record covariate (the applied well concentration that contacts the parasite culture directly; the model has no PK and no time evolution). Drives the sigmoid Emax inhibition of normalised hypoxanthine uptake by clinical Plasmodium falciparum isolates.
• Units: nM
• Type: continuous
• Scope: specific
• Reference category: n/a – enters the sigmoid Emax inhibition expression as the concentration argument; set to 0 for the drug-free control well. Reference values observed: Simpson 2013 used a doubling-dilution series 1.62 to 1646.6 nM plus a drug-free control well; the WT-reference (Genotype 1) population EC50 is 53.0 nM (Table 3).
• Source aliases:
  • C – used in Simpson_2013_mefloquine.R (per-record applied well concentration in nM).
• Example models: Simpson_2013_mefloquine.R (the only model covariate that varies per record; drives the sigmoid Emax inhibition with the pfmdr1 genotype effects entering on EC50).
• Notes: Specific scope because the canonical name is bound to mefloquine as the applied antimalarial stimulus. Member of the in-vitro applied-drug-concentration STIM_<drug>_<units> family (siblings STIM_ARTESUNATE_NM, STIM_CHLOROQUINE_NM, STIM_LUMEFANTRINE_NM, and the bitter-stimulus STIM_QUININE_MM) – the applied stimulus / well concentration that contacts the target directly, distinct from CAV, the CP_<drug> family, and the CONC_<drug>_MGL in-vitro antibacterial family. Ratified canonically alongside the Simpson 2013 antimalarial in-vitro extractions.

ETSEVO (canonical for end-tidal sevoflurane concentration in the breathing circuit)

• Description: End-tidal sevoflurane concentration (vol %) recorded continuously by an anesthesia gas monitor during sevoflurane general anesthesia. Distinct from a systemic plasma concentration – ETSEVO is the alveolar / breathing-circuit concentration in vol %, used directly as the PD driver in sigmoid-Emax models of consciousness / recovery endpoints. Per-record covariate (per observation epoch); decreases during emergence from approximately the maintenance MAC value (e.g. 1 MAC, 1.5-1.7 vol % in school-aged children with 50% N2O) to 0.
• Units: vol % (volume percent in the breathing circuit; equivalent to fractional inspired/expired sevoflurane x 100)
• Type: continuous
• Scope: specific
• Reference category: n/a – enters directly as the concentration argument C in a sigmoid Emax probability expression P(ROC) = C50^gamma / (C50^gamma + C^gamma) (Shin 2014 Methods). Reference values observed: typical C50 for return of consciousness 0.37 vol % (mentally intact) and 0.19 vol % (mentally disabled) in Shin 2014 Table 2; literature MAC-awake for sevoflurane is 0.6-0.78 vol % in healthy adults / children.
• Source aliases:
  • DOSE – used in Shin_2014_sevoflurane.R (Shin 2014 Appendix 1 $INPUT column name; the NONMEM column is labelled DOSE but holds the per-record end-tidal concentration, not an administered dose – the model uses it directly in PROB = 1 - DOSE**GAM/(CE50**GAM + DOSE**GAM)).
• Example models: Shin_2014_sevoflurane.R (drives the sigmoid-Emax probability of return of consciousness in pediatric dental-surgery patients during emergence from sevoflurane / N2O general anesthesia).
• Notes: Specific scope because the canonical name is bound to sevoflurane as the volatile anesthetic. Future emergence-from-anesthesia PD extractions for a different volatile (isoflurane, desflurane) should register a parallel canonical (e.g. ETISO, ETDES) rather than overload this name; the ET<agent> pattern parallels the CP_<drug>_<units> precedent for IV PD drivers. Distinct from CP_* (systemic plasma concentration), CAV (steady-state average plasma exposure), and STIM_* (applied non-systemic stimulus concentration): ETSEVO is the alveolar / circuit concentration that equilibrates with brain tissue during inhalation anesthesia, sampled at the exhalation peak.

CEFFECT (canonical for time-varying effect-site (biophase) concentration of an IV anesthetic / sedative supplied as a PD driver)

• Description: Instantaneous (per-event-record) effect-site (biophase) concentration of an IV anesthetic, sedative, or other agent whose pharmacodynamics are commonly modelled against the effect-site Ce rather than plasma Cp. Supplied directly as a time-varying covariate column rather than computed from a coupled PK + effect-site model inside the consuming PD model file. Used in sigmoid Emax / probability-of-event PD-only analyses (e.g., probability of recovery of consciousness, probability of LOC, BIS-response models) where the source paper fixed the upstream PK + effect-site from an external target-controlled-infusion (TCI) controller (Schnider 1998 / 1999 for propofol; Minto 1997 for remifentanil; etc.) and fit only the downstream PD parameters. The PD model consumes CEFFECT directly in model() (typically conc <- CEFFECT followed by a sigmoid Emax of conc); the user is responsible for generating the CEFFECT trajectory upstream (TCI-controller simulation, a registered propofol PK model chained with an external Keo effect-site link, or observed plasma concentrations equilibrated with an effect-site rate constant).
• Units: drug-specific; document the actual unit per-model via covariateData[[CEFFECT]]$units. Reference values observed: propofol effect-site Ce in Koo_2012_propofol.R is reported in ug/mL.
• Type: continuous
• Scope: general (the abstract concept “effect-site PD driver” applies to any drug whose PD is fit on Ce; the drug-specific identity is conveyed by the surrounding model file, not by an embedded drug suffix on the column name). Drug-specific override aliases are documented per-model in covariateData[[CEFFECT]]$notes when the source paper uses a different column name.
• Reference category: n/a – enters directly as the concentration argument C in a sigmoid Emax expression such as P(ROC) = C50^lambda / (C50^lambda + C^lambda). Set to 0 for the drug-free reference (e.g., before infusion start or after full washout). Reference values observed: propofol effect-site Ce range approximately 4.4 +/- 1.1 ug/mL at LOC, 3.2 +/- 1.0 ug/mL at end of surgery, and 1.1 +/- 0.3 ug/mL at ROC in Koo 2012’s adult population (Results).
• Source aliases:
  • Ce (Koo 2012 Methods; the per-record propofol effect-site concentration predicted by the Schnider TCI controller, ug/mL). Not stored as a single named NONMEM column in the source $TABLE – the TCI controller computes it and feeds the NONMEM dataset directly.
  • Cv(t) (Crass 2025 Data S1; the per-record individual-predicted pegcetacoplan concentration in the vitreous humour of an intravitreally dosed eye, ug/mL, generated by an external population PK model and supplied to the PD model as data).
• Example models: Koo_2012_propofol.R (drives the sigmoid Emax probability of return of consciousness in adult eye / ENT patients during emergence from propofol-remifentanil TCI anesthesia; CEFFECT carries propofol effect-site Ce in ug/mL), Crass_2025_pegcetacoplan_ga_exposureresponse.R (drives the linear-in-log-concentration reduction of the geographic-atrophy lesion growth rate, (1 + e_ceffect_slope_study * log(CEFFECT + 1)); CEFFECT carries vitreous-humour pegcetacoplan concentration in ug/mL, computed upstream from the intravitreal dosing history and an assumed 4 mL vitreous volume).
• Notes: General-scope canonical for the abstract “effect-site PD driver” concept; the per-model units field tells the user which drug and which scale the trajectory must use. Distinct from CP_* (systemic plasma concentration, mass-balance from the modeled-drug central compartment), CAV (dosing-interval-averaged steady-state plasma exposure), STIM_* (applied in-vitro / sipper-tube stimulus concentration), ETSEVO / ETISO / ETDES (alveolar end-tidal volatile-anesthetic concentration in vol %), and L_OPIOID_pM / L_ANTAGONIST_pM (Mann 2022 multi-ligand competitive-binding effect-site slots in pM, where the SLOT identity is the abstraction rather than the drug identity). The CEFFECT canonical is the operator-ratified auto-approve name for the “effect-site PD driver” family per the SKILL.md policy precheck (effect-site PD driver -> CEFFECT). Future PD extractions that consume an effect-site Ce as a PD driver should reuse this canonical with the per-model units / notes recording the drug and the scale; if a future extraction requires simultaneous occupation of TWO separate effect-site slots (a competitive / interaction model akin to Mann 2022’s two ligand slots), register sibling canonicals (e.g., CEFFECT_AGONIST, CEFFECT_ANTAGONIST) rather than overloading CEFFECT with both. Ratified canonically on 2026-06-27 alongside the Koo 2012 propofol-ROC extraction (task frompeople-670).

FPG (canonical for baseline fasting plasma glucose)

• Description: Fasting plasma glucose concentration at baseline (or time-varying baseline-style observation; document per-model). Distinct from GLU (time-varying plasma glucose regressor input for mechanistic glucose-kinetics models).
• Units: mmol/L (or mg/dL – 1 mmol/L glucose is approximately 18.02 mg/dL). Document per-model via covariateData[[FPG]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – used with linear-deviation form 1 + theta * (FPG - ref). Reference values observed: 8.90 mmol/L (Retlich 2015 popPK/PD linagliptin median fasting glucose at baseline).
• Source aliases: none known.
• Example models: Retlich_2015_linagliptin.R (mmol/L, reference 8.90; linear-deviation effect on baseline DPP-4 activity BSL with coefficient 1.46 % per mmol/L deviation).
• Notes: Glycemic-control covariate (baseline FPG); routinely reported alongside HbA1c in T2DM populations. Distinct from GLU which is a time-varying within-subject glucose regressor for mechanistic glucose-kinetics models (Bizzotto 2016). Ratified canonically alongside the Retlich 2015 linagliptin extraction.

DPP4_BL_RFU (canonical for baseline plasma dipeptidyl peptidase-4 activity in relative fluorescence units)

• Description: Baseline plasma dipeptidyl peptidase-4 (DPP-4) enzymatic activity, measured by relative fluorescence units (RFU). DPP-4 is the pharmacological target of the gliptin (DPP-4-inhibitor) drug class; baseline activity correlates with circulating DPP-4 protein concentration in the central compartment and serves as a covariate on the central-compartment binding-site concentration in TMDD models for gliptins.
• Units: RFU (assay-specific; document the assay in covariateData[[DPP4_BL_RFU]]$notes since RFU values are not directly comparable across assays).
• Type: continuous
• Scope: specific
• Reference category: n/a – used with linear-deviation form 1 + theta * (DPP4_BL_RFU - ref). Reference values observed: 12,497 RFU (Retlich 2015 popPK linagliptin median baseline), 11,600 RFU (Retlich 2015 popPK/PD linagliptin median baseline, applied to the individual-predicted BSL_i parameter on EC50).
• Source aliases: none known.
• Example models: Retlich_2015_linagliptin.R (RFU, reference 12,497; linear-deviation effect on the central-compartment binding-site concentration Bmax,C with coefficient 0.00332 % per RFU deviation – captures the inter-individual correlation between baseline DPP-4 protein concentration and the apparent saturable-binding amplitude).
• Notes: Specific scope because DPP-4-activity values in RFU are assay-specific and not directly transferable between studies / instruments. Future gliptin extractions reporting DPP-4 activity in the same assay can reuse this canonical; extractions in absolute enzymatic-rate units (pmol AMC per minute) or normalised units should register a sibling canonical (e.g., DPP4_BL_PMOL_MIN). Ratified canonically alongside the Retlich 2015 linagliptin extraction.

GLU (canonical for plasma glucose time-course regressor)

• Description: Plasma glucose concentration as a time-varying regressor input that drives a mechanistic glucose-kinetics model. Not a covariate that modifies a parameter; the model integrates GLU directly through a smoothing filter into a site-of-action glucose variable.
• Units: mmol/L
• Type: continuous
• Scope: specific
• Reference category: n/a – used as a time-varying regressor. The model declares linear(GLU) so rxode2 linearly interpolates GLU between dataset rows.
• Source aliases:
  • iglu (glucose at the current row time) – used in the DDMORE bundle’s Simulated_glucoseKinetics.csv for DDMODEL00000227. Rename iglu -> GLU before passing to rxSolve.
• Example models: Bizzotto_2016_glucose.R (driving regressor for the glucose-at-site-of-action delay), Lu_2014_sglt_qsp.R (drives the rate of glucose entry into PCT1 by glomerular filtration in the SGLT renal-glucose-reabsorption QSP model: filtered glucose load = GFR * GLU mmol/h), Bosch_2025_glp1ra_hba1c.R (drives the IGRH HbA1c sub-model: time-varying average daily glucose -> glycation flux KG*GLU_mgdl and glucose-dependent RBC life span (GLU_mgdl/149)^gamma_ls; internal conversion mmol/L -> mg/dL via factor 18.016).
• Notes: Specific scope because GLU is meaningful only for glucose-kinetics or glucose-PD models that take plasma glucose as an exogenous regressor. The DDMORE bundle’s hand-rolled piecewise-linear interpolation (GL = (t-T1)/(TOBS-T1)*(GLU-GLU1)+GLU1 with bracketing columns iglu / glun / td / tn) is replaced in nlmixr2 by linear(GLU) declared in model(); the bracketing columns are not required.

INS (canonical for plasma insulin time-course regressor)

• Description: Plasma insulin concentration as a time-varying regressor input that drives a mechanistic glucose-kinetics model. Not a covariate that modifies a parameter; the model integrates INS directly through a smoothing filter into a site-of-action insulin variable.
• Units: pmol/L
• Type: continuous
• Scope: specific
• Reference category: n/a – used as a time-varying regressor. The model declares linear(INS) so rxode2 linearly interpolates INS between dataset rows.
• Source aliases:
  • iins (insulin at the current row time) – used in the DDMORE bundle’s Simulated_glucoseKinetics.csv for DDMODEL00000227. Rename iins -> INS before passing to rxSolve.
  • INSU – used in the DDMORE bundle’s Simulated_ddmoremockdata2.txt for DDMODEL00000228. Rename INSU -> INS before passing to rxSolve.
• Example models: Bizzotto_2016_glucose.R (driving regressor for the insulin-at-site-of-action delay), NA_NA_paracetamol.R (DDMODEL00000228 OGTT model: drives the insulin-on-glucose-elimination first-order effect compartment via kie * (INS / 6.945 - effect_ins)), Denti_2010_glucoseMinimal.R (Bergman minimal-model insulin forcing function entering the insulin-action ODE as p2 * si * (INS - INS_BL); units pmol/L; supplied via linear(INS)).
• Notes: Specific scope because INS is meaningful only for glucose-kinetics or insulin-PD models that take plasma insulin as an exogenous regressor. For drugs that modify circulating insulin as a downstream effect, use a different mechanism-specific name. The DDMORE bundle’s hand-rolled piecewise-linear interpolation (I = (t-T1)/(TOBS-T1)*(INS-INS1)+INS1 with bracketing columns iins / insn / td / tn) is replaced in nlmixr2 by linear(INS) declared in model(); the bracketing columns are not required.

INS_BL (canonical for baseline (fasting) plasma insulin concentration)

• Description: Baseline (fasting) plasma insulin concentration, time-fixed per subject. Used as the per-subject anchor for steady-state insulin-driven processes (e.g., initial condition of an insulin-on-elimination effect compartment) and as the per-subject baseline-state insulin input to a baseline-glucose-production rate calculation.
• Units: pmol/L (or uU/mL; document per-model via covariateData[[INS_BL]]$units). The example model rescales via INS_BL / 6.945 to convert pmol/L to uU/mL.
• Type: continuous
• Scope: specific
• Reference category: n/a.
• Source aliases:
  • BASI (baseline insulin) – used in the DDMORE bundle’s Simulated_ddmoremockdata2.txt for DDMODEL00000228. Rename BASI -> INS_BL before passing to rxSolve.
• Example models: NA_NA_paracetamol.R (DDMODEL00000228 OGTT model: initialises the insulin-on-elimination effect compartment effect_ins(0) = INS_BL / 6.945 and feeds the steady-state baseline-glucose-production rate gpro = gss * (kg + kgi * INS_BL / 6.945) * vg * 180 / 1000), Hong_2013_glucose_insulin_HGC.R (anchors the dynamic-state insulin(0) = ICss * VI initial condition in mU plus the baseline insulin secretion ICss * CLI, no rescaling), Hong_2013_glucose_insulin_MTT.R (same anchor as the HGC companion), Denti_2010_glucoseMinimal.R (Bergman minimal-model basal-insulin anchor; covariate effect on lsi (-0.0282 per pmol/L) and lp2 (-0.0150 per pmol/L), and Ib in the insulin-action ODE p2 * si * (INS - INS_BL); units pmol/L despite the source paper’s apparent ‘pmol/ml’ table label, which is documented as an apparent typo).
• Notes: Distinct from INS (time-varying regressor); INS_BL is a per-subject baseline-state anchor used in initial conditions and steady-state derived quantities, not the dynamic regressor itself. Specific scope because the conversion factor (1/6.945) and the rescaled-units interpretation are paper-specific; future extractions that report baseline insulin in mIU/L or pmol/L directly without rescaling can ratify the same canonical and document the per-model units / conversion in covariateData[[INS_BL]]$units / notes. Companion concept to FPG (baseline fasting plasma glucose).

CINH (canonical for plasma SGLT-inhibitor concentration time-course regressor)

• Description: Plasma SGLT-inhibitor (e.g., dapagliflozin, canagliflozin) concentration as a time-varying regressor input that drives the rate of unbound-drug entry into the proximal tubule via glomerular filtration in renal-glucose-reabsorption QSP models. Not a covariate that modifies a parameter; the model integrates CINH directly through linear(CINH) and multiplies by the unbound fraction fup inside model().
• Units: nmol/L (matching the units of the inhibitor’s affinity constants Ki1 and Ki2 in the SGLT MM kinetics).
• Type: continuous
• Scope: specific
• Reference category: n/a – used as a time-varying regressor. The model declares linear(CINH) so rxode2 linearly interpolates CINH between dataset rows.
• Source aliases: none known.
• Example models: Lu_2014_sglt_qsp.R (drives the rate of unbound inhibitor entering PCT1 by glomerular filtration: filtered drug load = GFR * fup * CINH nmol/h; set CINH = 0 for baseline / no-inhibitor simulations).
• Notes: Specific scope because CINH is meaningful only for renal-glucose-reabsorption or other SGLT-mediated models that take plasma SGLT-inhibitor exposure as an exogenous regressor. The clinical reporting unit is ng/mL; convert by 1 ng/mL = (1000 / MW_drug) nmol/L (dapagliflozin MW = 409 g/mol so 1 ng/mL = 2.44 nmol/L; canagliflozin MW = 454 g/mol so 1 ng/mL = 2.20 nmol/L). The Lu 2014 paper feeds the model an interpolated dapagliflozin observed mean profile (DeFronzo et al. 2013) or a fitted two-compartment canagliflozin PK profile (Devineni et al. 2013) – the SGLT-mechanism model itself does not include an internal PK sub-model for the inhibitor. Companion concept to GLU (the plasma-glucose regressor that drives the same filtration arm).

IGE (canonical for serum total immunoglobulin E concentration)

• Description: Baseline serum total immunoglobulin E concentration (free IgE plus, in patients on anti-IgE therapy, omalizumab-IgE complex). For anti-IgE monoclonal antibodies (omalizumab, ligelizumab) IgE is the pharmacologic target; baseline IgE sets the magnitude of the target sink and modifies free-IgE clearance and the rate of IgE production in mechanism-based binding/turnover models.
• Units: ng/mL (typical clinical-PK convention). Pretreatment values reported in IU/mL are converted via 1 IU/mL = 2.42 ng/mL (Hayashi 2007 Methods). Document per-model via covariateData[[IGE]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (IGE / ref)^exponent. Reference value observed: 482.4 ng/mL (Hayashi 2007 Japanese atopic-asthma cohort).
• Source aliases:
  • IgE0 (baseline IgE concentration) – used in Hayashi_2007_omalizumab.R.
• Example models: Hayashi_2007_omalizumab.R (ng/mL, reference 482.4; power exponents -0.281 on apparent CL of free IgE and +0.657 on apparent IgE production rate; also used as the initial value for the total-IgE state at t = 0).
• Notes: General scope because baseline serum total IgE is a routine clinical-laboratory measurement, not a target tied to one drug. In mechanism-based anti-IgE binding/turnover models the in-model IgE state is a separate dynamic variable (X_TE, in nmol or nmol/L) – IGE is the per-subject baseline column used for covariate scaling and (when applicable) state initialization, not the dynamic state itself. For models that use the alternative reporting unit IU/mL, multiply by 2.42 before applying the canonical-units (ng/mL) reference value, or document the per-model unit choice in covariateData[[IGE]]$units so downstream tooling can interpret the values correctly. Distinct from IGE_FREE (the time-varying serum free IgE concentration used as the PD-driver input in PD-only IgE-FEV1 models).

IGE_FREE (canonical for time-varying serum free immunoglobulin E concentration as PD-driver input)

• Description: Time-varying serum free immunoglobulin E concentration (the unbound fraction not in drug-IgE complex) used as the exogenous PD-driver input in PD-only IgE-FEV1 (and other anti-IgE biomarker) models that consume free IgE as an externally-supplied trajectory rather than computing it from a coupled PK / binding ODE. Per-event-record (one value per PD observation time). Cross-reference: distinct from IGE (per-subject baseline total IgE used for power scaling in mechanism-based binding / turnover popPK models like Hayashi 2007). For sequential PK-PD use, this column is populated from an upstream omalizumab popPK/IgE-binding model (e.g. modellib("Hayashi_2007_omalizumab") emits a freeIgE observable in ng/mL); for standalone fits the column is interpolated from observed free-IgE assays.
• Units: ng/mL (matches the Zhu 2023 paper’s reported values and the Hayashi 2007 freeIgE output convention). Document per-model via covariateData[[IGE_FREE]]$units.
• Type: continuous
• Scope: general (anti-IgE PD modeling – omalizumab today, plausibly ligelizumab or other anti-IgE agents in future).
• Reference category: n/a – enters directly into the inhibition Hill term imax * IGE_FREE^hill / (ec50^hill + IGE_FREE^hill). Set to the subject’s baseline total IgE at pretreatment / placebo records (per Zhu 2023 Methods: free IgE equals total IgE before omalizumab treatment) so the inhibition term evaluates at the subject’s natural free-IgE level. Reference values observed: 16-960 ng/mL range across placebo + omalizumab observations in pediatric Study IA05 (Zhu 2023 Results paragraph).
• Source aliases:
  • C_IgE,i(t) (Zhu 2023 paper symbol in the structural-model equation, PDF page 2). The NM-TRAN dataset column name is not disclosed in the paper text.
• Example models: Zhu_2023_omalizumab_pediatric.R (founding example; ng/mL, per-event-record free IgE driving the IDR-Type-IV FEV1 percent predicted response with imax = 0.0717, ec50 = 39.4 ng/mL, hill = 9 fixed).
• Notes: Distinct from IGE (baseline total IgE for power-scaling). The free-IgE-vs-total-IgE distinction is load-bearing for anti-IgE drugs: free IgE is the pharmacologically active fraction and the relevant PD driver, while total IgE is the assay-reported sum of free + drug-bound IgE that does NOT decline under omalizumab treatment (Zhu 2023 Methods ‘Data Used in the Population Pediatric IgE-FEV1 Model’). Sequential PK-PD compatibility: Hayashi_2007_omalizumab.R emits a freeIgE observable in ng/mL; downstream users simulate that model and populate the IGE_FREE column in the PD dataset from the simulated trajectory. For placebo subjects (no anti-IgE drug), the paper used the subject’s average total IgE level across the steroid-stable period as the IGE_FREE input, because free IgE equals total IgE in the absence of anti-IgE drug (Zhu 2023 Methods).

ESAD (canonical for prior erythropoiesis-stimulating-agent (ESA) dose at baseline)

• Description: Subject’s recorded prior erythropoiesis-stimulating-agent (ESA) dose at study baseline, in epoetin-equivalent activity units per week. Used by ESA-switch population PK/PD models for hemoglobin response to a new ESA when prior endogenous-erythropoietin levels were not measured – the prior-ESA dose acts as a surrogate for the residual hematopoietic stimulation present at randomization. Time-fixed per subject (records the patient’s stable maintenance dose immediately before the new-ESA start).
• Units: units/week (epoetin-equivalent activity units per week). Document per-model via covariateData[[ESAD]]$units when the source paper uses a different per-time unit (units/month, units/day) or converts darbepoetin / methoxy-polyethylene-glycol epoetin beta doses to epoetin equivalents.
• Type: continuous
• Scope: specific
• Reference category: n/a – used with the paper-specific log-scale slope on baseline hemoglobin (HgbBL). Reference values observed: 7996 units/week in Naik_2013_peginesatide.R (Naik 2013 eq 16; paper-reported population median). The covariate effect is gated by an ESADF indicator (1 if ESAD > 0, else 0) so that subjects with missing or unrecorded prior ESA dose (encoded as ESAD = 0) carry no covariate adjustment to HgbBL, matching Naik 2013’s “no effect of ESAD was incorporated for subjects whose ESAD dose information was not available.”
• Source aliases:
  • ESAD – used in Naik_2013_peginesatide.R (Naik 2013 paper notation; prior epoetin alfa / darbepoetin alfa weekly dose in units/week for CKD hemodialysis subjects enrolling on peginesatide).
• Example models: Naik_2013_peginesatide.R (Naik 2013 eq 16; exponential covariate on the baseline-hemoglobin parameter: hgbbl = exp(lhgbbl + etalhgbbl + e_esad_lhgbbl * (ESAD - 7996) * ESADF) with e_esad_lhgbbl = -4.49e-7 1/(units/week); effect is small in magnitude but retained as the only PD-side statistically significant covariate per backward-elimination at P < 0.005).
• Notes: Specific scope because the values are intrinsically tied to ESA-switch popPK/PD studies and the units/week reporting convention is paper-specific (different ESAs have different specific activities; epoetin-equivalent conversion factors must be documented per-model). The ESADF indicator (built inline in the model() block as (ESAD > 0)) handles the “no prior ESA dose available” data-quality case used by Naik 2013; future papers that distinguish prior-ESA-naive from prior-ESA-treated-with-unrecorded-dose may register a parallel canonical (e.g., ESA_NAIVE). Ratified canonically on 2026-05-22 alongside the Naik 2013 peginesatide extraction.

DOSE_IND (canonical for per-arm once-daily inhaled indacaterol dose)

• Description: Per-study-arm once-daily inhaled indacaterol dose (ug/day; 0 for placebo arms). Study-arm-level (not individual-level) drug-exposure covariate used as the dose regressor in a model-based meta-analysis (MBMA) of bronchodilator dose-response.
• Units: ug/day
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as the dose regressor in the MBMA (Emax-type) bronchodilator dose-response; set to 0 for placebo arms.
• Source aliases:
  • DOSE_IND – used in Renard_2011_indacaterol.R (per-arm indacaterol dose, ug/day; Renard 2011 Table 1).
• Example models: Renard_2011_indacaterol.R (per-arm once-daily inhaled indacaterol dose driving the MBMA dose-response; dose range 18.75-600 ug/day across 11 trials, with the six discrete reported doses 18.75, 37.5, 75, 150, 300, and 600 ug).
• Notes: Specific scope because the value is intrinsically tied to indacaterol and the Renard 2011 MBMA dose-response. Drug-specific dose canonical paralleling DOSE_PHT_MGKGD (phenytoin); future MBMA / dose-response models for other drugs should register a sibling DOSE_<DRUG> canonical rather than overloading this name. Ratified canonically on 2026-05-27 alongside the Renard 2011 indacaterol extraction.

CELLS_INTACT (canonical for the intact-versus-lysed cell-preparation flag in an in-vitro target-binding assay)

• Description: Binary flag recording which cell preparation an in-vitro target-binding observation came from: 1 = intact (whole) bacterial cells, in which the outer membrane is present and drug must penetrate it to reach a periplasmic or intracellular target; 0 = lysed cells, i.e. isolated target-containing membrane fractions, in which the outer-membrane barrier has been removed and drug is applied in vast excess. The flag is a property of the sample preparation, not of the bioanalytical measurement method – it is therefore distinct from the ASSAY_<METHOD> bioanalytical-method family.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 = lysed cells (isolated membranes). Structural, not a coefficient multiplier: the flag gates both the outer-membrane influx term (rate_influx = CELLS_INTACT * rate_influx_scaled * CONC_<DRUG>_MGL) and the periplasmic initial condition (periplasm(0) = (1 - CELLS_INTACT) * n_peri_lysed).
• Source aliases:
  • INTACT – used in the Lopez-Arguello 2023 S-ADAPT-TRAN estimation code (Fig. S8 line 66, IF (INTACT.EQ.1) THEN).
• Example models: LopezArguello_2023_<drug>_qsp.R.
• Notes: General scope because the intact-versus-lysed contrast is a standard design in Gram-negative target-site-penetration work and is not specific to beta-lactams or to PBPs; a new model using the same design should reuse this name rather than register a sibling. Because the flag changes model structure rather than scaling a parameter, it has no associated e_<cov>_<param> covariate-effect coefficient. Ratified canonically on 2026-07-29 (operator sidecar oare_PMC10269149 request-001 q1, answer A) alongside the Lopez-Arguello 2023 PBP-binding extraction.

CONC_DOR_MGL (canonical for static in-vitro doripenem concentration driving a receptor-binding or antibacterial PD model)

• Description: Static (time-invariant) doripenem concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (Cc) and from the CP_<DRUG> plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
• Units: mg/L
• Type: continuous
• Scope: specific
• Reference category: n/a – scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_DOR_MGL). The lysed-cell arm of that assay sets CELLS_INTACT = 0, which zeroes the influx term, so the covariate has no effect there.
• Source aliases: none standardized (Lopez-Arguello 2023 writes C_drug in Eq 1 and CDRUG in the Fig. S8 estimation code).
• Example models: LopezArguello_2023_doripenem_qsp.R (static 2 mg/L, 2x the MIC of 1 mg/L; Lopez-Arguello 2023 Table 1).
• Notes: Specific scope because the value is bound to doripenem and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration CONC_<DRUG>_MGL family (siblings CONC_RIF_MGL, CONC_INH_MGL, CONC_EMB_MGL, CONC_IPM_MGL, CONC_TOB_MGL). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

CONC_MEM_MGL (canonical for static in-vitro meropenem concentration driving a receptor-binding or antibacterial PD model)

• Description: Static (time-invariant) meropenem concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (Cc) and from the CP_<DRUG> plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
• Units: mg/L
• Type: continuous
• Scope: specific
• Reference category: n/a – scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_MEM_MGL). The lysed-cell arm of that assay sets CELLS_INTACT = 0, which zeroes the influx term, so the covariate has no effect there.
• Source aliases: none standardized (Lopez-Arguello 2023 writes C_drug in Eq 1 and CDRUG in the Fig. S8 estimation code).
• Example models: LopezArguello_2023_meropenem_qsp.R (static 1 mg/L, 2x the MIC of 0.5 mg/L; Lopez-Arguello 2023 Table 1).
• Notes: Specific scope because the value is bound to meropenem and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration CONC_<DRUG>_MGL family (siblings CONC_RIF_MGL, CONC_INH_MGL, CONC_EMB_MGL, CONC_IPM_MGL, CONC_TOB_MGL). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

CONC_ETP_MGL (canonical for static in-vitro ertapenem concentration driving a receptor-binding or antibacterial PD model)

• Description: Static (time-invariant) ertapenem concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (Cc) and from the CP_<DRUG> plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
• Units: mg/L
• Type: continuous
• Scope: specific
• Reference category: n/a – scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_ETP_MGL). The lysed-cell arm of that assay sets CELLS_INTACT = 0, which zeroes the influx term, so the covariate has no effect there.
• Source aliases: none standardized (Lopez-Arguello 2023 writes C_drug in Eq 1 and CDRUG in the Fig. S8 estimation code).
• Example models: LopezArguello_2023_ertapenem_qsp.R (static 8 mg/L, 2x the MIC of 4 mg/L; Lopez-Arguello 2023 Table 1).
• Notes: Specific scope because the value is bound to ertapenem and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration CONC_<DRUG>_MGL family (siblings CONC_RIF_MGL, CONC_INH_MGL, CONC_EMB_MGL, CONC_IPM_MGL, CONC_TOB_MGL). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

CONC_CAZ_MGL (canonical for static in-vitro ceftazidime concentration driving a receptor-binding or antibacterial PD model)

• Description: Static (time-invariant) ceftazidime concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (Cc) and from the CP_<DRUG> plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
• Units: mg/L
• Type: continuous
• Scope: specific
• Reference category: n/a – scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_CAZ_MGL). The lysed-cell arm of that assay sets CELLS_INTACT = 0, which zeroes the influx term, so the covariate has no effect there.
• Source aliases: none standardized (Lopez-Arguello 2023 writes C_drug in Eq 1 and CDRUG in the Fig. S8 estimation code).
• Example models: LopezArguello_2023_ceftazidime_qsp.R (static 2 mg/L, 2x the MIC of 1 mg/L; Lopez-Arguello 2023 Table 1), Kroemer_2024_ceftazidime_avibactam_fosfomycin_tkc.R (static time-kill concentrations 0.002-128 mg/L against a clinical MDR Escherichia coli; Kroemer 2024 Fig. 1).
• Notes: Specific scope because the value is bound to ceftazidime and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration CONC_<DRUG>_MGL family (siblings CONC_RIF_MGL, CONC_INH_MGL, CONC_EMB_MGL, CONC_IPM_MGL, CONC_TOB_MGL). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

CONC_FEP_MGL (canonical for static in-vitro cefepime concentration driving a receptor-binding or antibacterial PD model)

• Description: Static (time-invariant) cefepime concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (Cc) and from the CP_<DRUG> plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
• Units: mg/L
• Type: continuous
• Scope: specific
• Reference category: n/a – scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_FEP_MGL). The lysed-cell arm of that assay sets CELLS_INTACT = 0, which zeroes the influx term, so the covariate has no effect there.
• Source aliases: none standardized (Lopez-Arguello 2023 writes C_drug in Eq 1 and CDRUG in the Fig. S8 estimation code).
• Example models: LopezArguello_2023_cefepime_qsp.R (static 2 mg/L, 2x the MIC of 1 mg/L; Lopez-Arguello 2023 Table 1).
• Notes: Specific scope because the value is bound to cefepime and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration CONC_<DRUG>_MGL family (siblings CONC_RIF_MGL, CONC_INH_MGL, CONC_EMB_MGL, CONC_IPM_MGL, CONC_TOB_MGL). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

CONC_FOF_MGL (canonical for static in-vitro fosfomycin concentration driving a receptor-binding or antibacterial PD model)

• Description: Static (time-invariant) fosfomycin concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (Cc) and from the CP_<DRUG> plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
• Units: mg/L
• Type: continuous
• Scope: specific
• Reference category: n/a – 0 mg/L is the drug-free growth control.
• Source aliases: FOF (Kroemer 2024).
• Example models: Kroemer_2024_ceftazidime_avibactam_fosfomycin_tkc.R (static time-kill concentrations 2-16 mg/L against a clinical MDR Escherichia coli; Kroemer 2024 Fig. 1).
• Notes: Specific scope because the value is bound to fosfomycin and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration CONC_<DRUG>_MGL family (siblings CONC_CAZ_MGL, CONC_AVI_MGL, CONC_RIF_MGL, CONC_INH_MGL, CONC_EMB_MGL, CONC_IPM_MGL, CONC_TOB_MGL). Fosfomycin susceptibility testing and time-kill media are supplemented with 25 mg/L glucose-6-phosphate per EUCAST recommendations; the covariate carries the fosfomycin concentration only, not the glucose-6-phosphate. The dynamic sibling model Kroemer_2024_ceftazidime_avibactam_fosfomycin_hfim.R carries fosfomycin as a dosable ODE state (conc_fof) instead, because the hollow fiber concentrations are time-varying.

CONC_FOX_MGL (canonical for static in-vitro cefoxitin concentration driving a receptor-binding or antibacterial PD model)

• Description: Static (time-invariant) cefoxitin concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (Cc) and from the CP_<DRUG> plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
• Units: mg/L
• Type: continuous
• Scope: specific
• Reference category: n/a – scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_FOX_MGL). The lysed-cell arm of that assay sets CELLS_INTACT = 0, which zeroes the influx term, so the covariate has no effect there.
• Source aliases: none standardized (Lopez-Arguello 2023 writes C_drug in Eq 1 and CDRUG in the Fig. S8 estimation code).
• Example models: LopezArguello_2023_cefoxitin_qsp.R (static 2,048 mg/L, 2x the MIC of 1,024 mg/L; Lopez-Arguello 2023 Table 1).
• Notes: Specific scope because the value is bound to cefoxitin and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration CONC_<DRUG>_MGL family (siblings CONC_RIF_MGL, CONC_INH_MGL, CONC_EMB_MGL, CONC_IPM_MGL, CONC_TOB_MGL). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

CONC_ATM_MGL (canonical for static in-vitro aztreonam concentration driving a receptor-binding or antibacterial PD model)

• Description: Static (time-invariant) aztreonam concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (Cc) and from the CP_<DRUG> plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
• Units: mg/L
• Type: continuous
• Scope: specific
• Reference category: n/a – scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_ATM_MGL). The lysed-cell arm of that assay sets CELLS_INTACT = 0, which zeroes the influx term, so the covariate has no effect there.
• Source aliases: none standardized (Lopez-Arguello 2023 writes C_drug in Eq 1 and CDRUG in the Fig. S8 estimation code).
• Example models: LopezArguello_2023_aztreonam_qsp.R (static 8 mg/L, 2x the MIC of 4 mg/L; Lopez-Arguello 2023 Table 1).
• Notes: Specific scope because the value is bound to aztreonam and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration CONC_<DRUG>_MGL family (siblings CONC_RIF_MGL, CONC_INH_MGL, CONC_EMB_MGL, CONC_IPM_MGL, CONC_TOB_MGL). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

CONC_PIP_MGL (canonical for static in-vitro piperacillin concentration driving a receptor-binding or antibacterial PD model)

• Description: Static (time-invariant) piperacillin concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (Cc) and from the CP_<DRUG> plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
• Units: mg/L
• Type: continuous
• Scope: specific
• Reference category: n/a – scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_PIP_MGL). The lysed-cell arm of that assay sets CELLS_INTACT = 0, which zeroes the influx term, so the covariate has no effect there.
• Source aliases: none standardized (Lopez-Arguello 2023 writes C_drug in Eq 1 and CDRUG in the Fig. S8 estimation code).
• Example models: LopezArguello_2023_piperacillin_qsp.R (static 8 mg/L, 2x the MIC of 4 mg/L; Lopez-Arguello 2023 Table 1).
• Notes: Specific scope because the value is bound to piperacillin and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration CONC_<DRUG>_MGL family (siblings CONC_RIF_MGL, CONC_INH_MGL, CONC_EMB_MGL, CONC_IPM_MGL, CONC_TOB_MGL). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

CONC_CAR_MGL (canonical for static in-vitro carbenicillin concentration driving a receptor-binding or antibacterial PD model)

• Description: Static (time-invariant) carbenicillin concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (Cc) and from the CP_<DRUG> plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
• Units: mg/L
• Type: continuous
• Scope: specific
• Reference category: n/a – scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_CAR_MGL). The lysed-cell arm of that assay sets CELLS_INTACT = 0, which zeroes the influx term, so the covariate has no effect there.
• Source aliases: none standardized (Lopez-Arguello 2023 writes C_drug in Eq 1 and CDRUG in the Fig. S8 estimation code).
• Example models: LopezArguello_2023_carbenicillin_qsp.R (static 96 mg/L, 2x the MIC of 48 mg/L; Lopez-Arguello 2023 Table 1).
• Notes: Specific scope because the value is bound to carbenicillin and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration CONC_<DRUG>_MGL family (siblings CONC_RIF_MGL, CONC_INH_MGL, CONC_EMB_MGL, CONC_IPM_MGL, CONC_TOB_MGL). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

CONC_TIC_MGL (canonical for static in-vitro ticarcillin concentration driving a receptor-binding or antibacterial PD model)

• Description: Static (time-invariant) ticarcillin concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (Cc) and from the CP_<DRUG> plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
• Units: mg/L
• Type: continuous
• Scope: specific
• Reference category: n/a – scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_TIC_MGL). The lysed-cell arm of that assay sets CELLS_INTACT = 0, which zeroes the influx term, so the covariate has no effect there.
• Source aliases: none standardized (Lopez-Arguello 2023 writes C_drug in Eq 1 and CDRUG in the Fig. S8 estimation code).
• Example models: LopezArguello_2023_ticarcillin_qsp.R (static 48 mg/L, 2x the MIC of 24 mg/L; Lopez-Arguello 2023 Table 1).
• Notes: Specific scope because the value is bound to ticarcillin and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration CONC_<DRUG>_MGL family (siblings CONC_RIF_MGL, CONC_INH_MGL, CONC_EMB_MGL, CONC_IPM_MGL, CONC_TOB_MGL). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

CONC_AVI_MGL (canonical for static in-vitro avibactam concentration driving a receptor-binding or antibacterial PD model)

• Description: Static (time-invariant) avibactam concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (Cc) and from the CP_<DRUG> plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
• Units: mg/L
• Type: continuous
• Scope: specific
• Reference category: n/a – scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_AVI_MGL). The lysed-cell arm of that assay sets CELLS_INTACT = 0, which zeroes the influx term, so the covariate has no effect there.
• Source aliases: none standardized (Lopez-Arguello 2023 writes C_drug in Eq 1 and CDRUG in the Fig. S8 estimation code).
• Example models: LopezArguello_2023_avibactam_qsp.R (static 4 mg/L, a fixed concentration within the clinically relevant range; MIC not determined for beta-lactamase inhibitors; Lopez-Arguello 2023 Table 1), Kroemer_2024_ceftazidime_avibactam_fosfomycin_tkc.R (static time-kill concentrations up to ~64 mg/L against a clinical MDR Escherichia coli; Kroemer 2024 Fig. 1).
• Notes: Specific scope because the value is bound to avibactam and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration CONC_<DRUG>_MGL family (siblings CONC_RIF_MGL, CONC_INH_MGL, CONC_EMB_MGL, CONC_IPM_MGL, CONC_TOB_MGL). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

CONC_REL_MGL (canonical for static in-vitro relebactam concentration driving a receptor-binding or antibacterial PD model)

• Description: Static (time-invariant) relebactam concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (Cc) and from the CP_<DRUG> plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
• Units: mg/L
• Type: continuous
• Scope: specific
• Reference category: n/a – scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_REL_MGL). The lysed-cell arm of that assay sets CELLS_INTACT = 0, which zeroes the influx term, so the covariate has no effect there.
• Source aliases: none standardized (Lopez-Arguello 2023 writes C_drug in Eq 1 and CDRUG in the Fig. S8 estimation code).
• Example models: LopezArguello_2023_relebactam_qsp.R (static 4 mg/L, a fixed concentration within the clinically relevant range; MIC not determined for beta-lactamase inhibitors; Lopez-Arguello 2023 Table 1).
• Notes: Specific scope because the value is bound to relebactam and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration CONC_<DRUG>_MGL family (siblings CONC_RIF_MGL, CONC_INH_MGL, CONC_EMB_MGL, CONC_IPM_MGL, CONC_TOB_MGL). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

CONC_SUL_MGL (canonical for static in-vitro sulbactam concentration driving a receptor-binding or antibacterial PD model)

• Description: Static (time-invariant) sulbactam concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (Cc) and from the CP_<DRUG> plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
• Units: mg/L
• Type: continuous
• Scope: specific
• Reference category: n/a – scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_SUL_MGL). The lysed-cell arm of that assay sets CELLS_INTACT = 0, which zeroes the influx term, so the covariate has no effect there.
• Source aliases: none standardized (Lopez-Arguello 2023 writes C_drug in Eq 1 and CDRUG in the Fig. S8 estimation code).
• Example models: LopezArguello_2023_sulbactam_qsp.R (static 4 mg/L, a fixed concentration within the clinically relevant range; MIC not determined for beta-lactamase inhibitors; Lopez-Arguello 2023 Table 1).
• Notes: Specific scope because the value is bound to sulbactam and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration CONC_<DRUG>_MGL family (siblings CONC_RIF_MGL, CONC_INH_MGL, CONC_EMB_MGL, CONC_IPM_MGL, CONC_TOB_MGL). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

CONC_TZB_MGL (canonical for static in-vitro tazobactam concentration driving a receptor-binding or antibacterial PD model)

• Description: Static (time-invariant) tazobactam concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives target-receptor binding or bacterial kill. Distinct from a state-derived plasma concentration (Cc) and from the CP_<DRUG> plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
• Units: mg/L
• Type: continuous
• Scope: specific
• Reference category: n/a – scales the rate of net influx and PBP access in the whole-cell penicillin-binding-protein (PBP) binding QSP model (Eq 1: Rate_Influx/access = Rate_Influx/access,scaled x CONC_TZB_MGL). The lysed-cell arm of that assay sets CELLS_INTACT = 0, which zeroes the influx term, so the covariate has no effect there.
• Source aliases: none standardized (Lopez-Arguello 2023 writes C_drug in Eq 1 and CDRUG in the Fig. S8 estimation code).
• Example models: LopezArguello_2023_tazobactam_qsp.R (static 4 mg/L, a fixed concentration within the clinically relevant range; MIC not determined for beta-lactamase inhibitors; Lopez-Arguello 2023 Table 1).
• Notes: Specific scope because the value is bound to tazobactam and to the in-vitro assay design. Member of the in-vitro applied-drug-concentration CONC_<DRUG>_MGL family (siblings CONC_RIF_MGL, CONC_INH_MGL, CONC_EMB_MGL, CONC_IPM_MGL, CONC_TOB_MGL). Ratified canonically on 2026-07-29 alongside the Lopez-Arguello 2023 PBP-binding extraction.

CONC_RIF_MGL (canonical for static in-vitro rifampicin concentration driving an antibacterial PD model)

• Description: Static (time-invariant) rifampicin concentration in the growth medium of an in-vitro antibacterial time-kill experiment, supplied as an exogenous covariate that drives the bacterial-kill PD effect. Distinct from a state-derived plasma concentration (Cc) and from the CP_<DRUG> plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix.
• Units: mg/L
• Type: continuous
• Scope: specific
• Reference category: n/a – enters the sigmoidal kill function; set to 0 for regimens without rifampicin.
• Source aliases:
  • CRIF – used in Clewe_2018_TB_MTP_GPDI_invitro.R (Clewe 2018 Materials and methods; static rifampicin concentration).
• Example models: Clewe_2018_TB_MTP_GPDI_invitro.R (static rifampicin concentration driving the multistate-TB kill effects; tested concentrations 0.002, 0.008, 0.03, 0.125, 0.5, 8 mg/L per Figure 1).
• Notes: Specific scope because the value is bound to rifampicin and the in-vitro experimental design. Member of the in-vitro applied-drug-concentration CONC_<DRUG>_MGL family (siblings CONC_INH_MGL, CONC_EMB_MGL, CONC_IPM_MGL, CONC_TOB_MGL) – use this family for exogenous static or time-varying drug concentrations in in-vitro time-kill / hollow-fiber PD models, as distinct from the CP_<DRUG> plasma-concentration PD-driver family. Ratified canonically on 2026-05-27 alongside the Clewe 2018 extraction.

CONC_INH_MGL (canonical for static in-vitro isoniazid concentration driving an antibacterial PD model)

• Description: Static (time-invariant) isoniazid concentration in the growth medium of an in-vitro antibacterial time-kill experiment, supplied as an exogenous covariate that drives the bacterial-kill PD effect and the adaptive-resistance transition. Applied experimental concentration in the in-vitro matrix; distinct from Cc and the CP_<DRUG> plasma-PD-driver family.
• Units: mg/L
• Type: continuous
• Scope: specific
• Reference category: n/a – drives the kill effects on the fast- and slow-growing sub-states and the adaptive-resistance AR_on/AR_off transition rate; set to 0 for regimens without isoniazid.
• Source aliases:
  • CINH – used in Clewe_2018_TB_MTP_GPDI_invitro.R (Clewe 2018 Materials and methods; static isoniazid concentration).
• Example models: Clewe_2018_TB_MTP_GPDI_invitro.R (static isoniazid concentration driving the kill effects on the F and S sub-states and the adaptive-resistance transition kon * CONC_INH_MGL; tested concentrations 0.01, 0.039, 0.156, 0.625, 2.5, 10, 40 mg/L per Figure 1).
• Notes: Specific scope because the value is bound to isoniazid and the in-vitro experimental design. Member of the in-vitro applied-drug-concentration CONC_<DRUG>_MGL family (siblings CONC_RIF_MGL, CONC_EMB_MGL, CONC_IPM_MGL, CONC_TOB_MGL). Ratified canonically on 2026-05-27 alongside the Clewe 2018 extraction.

CONC_EMB_MGL (canonical for static in-vitro ethambutol concentration driving an antibacterial PD model)

• Description: Static (time-invariant) ethambutol concentration in the growth medium of an in-vitro antibacterial time-kill experiment, supplied as an exogenous covariate that drives the bacterial-kill PD effect. Applied experimental concentration in the in-vitro matrix; distinct from Cc and the CP_<DRUG> plasma-PD-driver family.
• Units: mg/L
• Type: continuous
• Scope: specific
• Reference category: n/a – enters the kill function; set to 0 for regimens without ethambutol.
• Source aliases:
  • CEMB – used in Clewe_2018_TB_MTP_GPDI_invitro.R (Clewe 2018 Materials and methods; static ethambutol concentration).
• Example models: Clewe_2018_TB_MTP_GPDI_invitro.R (static ethambutol concentration driving the multistate-TB kill effects; tested concentrations 0.0078, 0.031, 0.125, 0.5, 2, 8, 32 mg/L per Figure 1).
• Notes: Specific scope because the value is bound to ethambutol and the in-vitro experimental design. Member of the in-vitro applied-drug-concentration CONC_<DRUG>_MGL family (siblings CONC_RIF_MGL, CONC_INH_MGL, CONC_IPM_MGL, CONC_TOB_MGL). Ratified canonically on 2026-05-27 alongside the Clewe 2018 extraction.

CONC_BAI_UM (canonical for static in-vitro baicalein concentration driving an inflammatory-mediator PD model)

• Description: Static (time-invariant) baicalein concentration in the cell-culture medium of an in-vitro LPS-stimulated-macrophage experiment, supplied as an exogenous covariate that drives the anti-inflammatory PD effect. Applied experimental concentration in the in-vitro matrix; distinct from a state-derived plasma concentration (Cc) and from the CP_<drug> plasma-PD-driver family.
• Units: uM
• Type: continuous
• Scope: specific
• Reference category: n/a – enters the log-linear inhibition term f(Bai) = alpha * log(CONC_BAI_UM + 1) (the +1 shift encodes the control with CONC_BAI_UM = 0 -> f = 0 without an undefined ln(0)); set to 0 for the control well. Reference values observed: Xiang 2018 tested 0 (control), 10, 20, and 40 uM (Materials and Methods; Figures 3-4).
• Source aliases: none – the source uses C_Bai (Xiang 2018 Eq 2) in prose; the model column is the canonical CONC_BAI_UM.
• Example models: Xiang_2018_baicalein.R (time-invariant baicalein concentration driving the log-linear inhibition of LPS-stimulated TNF-alpha production in RAW264.7 macrophages, propagating downstream to IL-6, iNOS, and NO).
• Notes: Specific scope because the value is bound to baicalein and the in-vitro experimental design. Member of the in-vitro applied-drug-concentration CONC_<drug>_<units> family; here the unit is uM (sibling concentration covariates such as CONC_RIF_MGL are reported in mg/L, so the <units> suffix is load-bearing). Distinct from the STIM_<drug>_<units> antimalarial-well family and the CP_<drug> plasma-PD-driver family. Ratified canonically alongside the Xiang 2018 baicalein extraction.

CONC_OXA_UM (canonical for static in-vitro oxaliplatin concentration driving a tumour-organoid cytotoxicity PD model)

• Description: Static (time-invariant) oxaliplatin concentration applied to the culture medium of a patient-derived tumour-organoid (PDTO) drug-sensitivity assay, supplied as an exogenous covariate that drives the sigmoidal Emax killing term. Applied experimental concentration in the in-vitro matrix; distinct from a state-derived plasma concentration (Cc) and from the CP_<drug> plasma-PD-driver family.
• Units: uM
• Type: continuous
• Scope: specific
• Reference category: n/a – enters the Hill killing term Emax * C^hill / (EC50^hill + C^hill); set to 0 for the vehicle-control organoid. Zhu 2023 does not tabulate the tested concentration grid; Figure 2A spans roughly 1-1000 umol/L, bracketing the estimated EC50 range of 246-622 umol/L (Table 2).
• Source aliases: none – the source writes C in Equation (2); the model column is the canonical CONC_OXA_UM.
• Example models: Zhu_2023_oxaliplatin_organoid.R (96 h static oxaliplatin exposure of colorectal-cancer PDTOs; cell viability read as the treated-to-vehicle-control organoid volume ratio).
• Notes: Specific scope because the value is bound to oxaliplatin and to the in-vitro organoid assay design. Member of the in-vitro applied-drug-concentration CONC_<drug>_<units> family; the unit suffix is load-bearing because sibling entries such as CONC_RIF_MGL are reported in mg/L while the organoid assays are reported in umol/L. Ratified canonically alongside the Zhu 2023 extraction.

CONC_SN38_UM (canonical for static in-vitro SN-38 concentration driving a tumour-organoid cytotoxicity PD model)

• Description: Static (time-invariant) SN-38 concentration applied to the culture medium of a patient-derived tumour-organoid (PDTO) drug-sensitivity assay, supplied as an exogenous covariate that drives the sigmoidal Emax killing term. SN-38 is the active metabolite of irinotecan. Applied experimental concentration in the in-vitro matrix; distinct from a state-derived plasma concentration (Cc_sn38) and from the CP_<drug> plasma-PD-driver family.
• Units: uM
• Type: continuous
• Scope: specific
• Reference category: n/a – enters the Hill killing term Emax * C^hill / (EC50^hill + C^hill); set to 0 for the vehicle-control organoid. Zhu 2023 does not tabulate the tested concentration grid; Figure 2B spans roughly 0.01-100 umol/L, bracketing the estimated EC50 range of 4.17-15.5 umol/L (Table 2).
• Source aliases: none – the source writes C in Equation (2); the model column is the canonical CONC_SN38_UM.
• Example models: Zhu_2023_sn38_organoid.R (96 h static SN-38 exposure of colorectal-cancer PDTOs; cell viability read as the treated-to-vehicle-control organoid volume ratio).
• Notes: Specific scope because the value is bound to SN-38 and to the in-vitro organoid assay design. Member of the in-vitro applied-drug-concentration CONC_<drug>_<units> family; SN38 matches the registered metabolite suffix sn38 used for the paired compartment and parameter names. Ratified canonically alongside the Zhu 2023 extraction.

CONC_IPM_MGL (canonical for in-vitro imipenem concentration driving an antibacterial PD or receptor-binding model)

• Description: Unbound imipenem concentration applied to an in-vitro bacterial system, supplied externally as an exogenous covariate. Applied experimental concentration in the in-vitro matrix; distinct from Cc and the CP_<DRUG> plasma-PD-driver family. Used both time-varying (hollow-fiber infection model growth medium, driving the bacterial-kill PD effect) and static (60-min whole-cell penicillin-binding-protein binding assay, scaling the rate of net influx and PBP access).
• Units: mg/L
• Type: continuous
• Scope: specific
• Reference category: n/a – enters the sigmoidal (Hill) imipenem kill function; set to 0 for tobramycin-monotherapy or control arms.
• Source aliases: none standardized (the model column uses the canonical name directly; Landersdorfer 2018 labels it “imipenem concentration” in the Methods and Fig. 1 legend).
• Example models: Landersdorfer_2018_imipenem_tobramycin.R (externally-supplied time-varying unbound imipenem concentration driving the Hill kill function; the HFIM used continuous infusion targeting the 5th-percentile 7.6, median 13.4, and 95th-percentile 23.3 mg/L unbound concentrations from imipenem 4 g/day continuous infusion in critically ill patients), LopezArguello_2023_imipenem_qsp.R (static 2 mg/L = 2x the MIC of 1 mg/L, scaling the rate of net influx and PBP access in the whole-cell PBP-binding QSP model; Lopez-Arguello 2023 Table 1).
• Notes: Specific scope because the value is bound to imipenem and to an in-vitro assay design. Member of the in-vitro applied-drug-concentration CONC_<DRUG>_MGL family (siblings CONC_RIF_MGL, CONC_INH_MGL, CONC_EMB_MGL, CONC_TOB_MGL, and the fourteen CONC_<DRUG>_MGL entries ratified with the Lopez-Arguello 2023 extraction). Renamed from the model’s earlier bare Cipm column on 2026-05-27 for consistency with the CONC_<DRUG>_MGL family. Ratified canonically on 2026-05-27 alongside the Landersdorfer 2018 extraction; scope broadened on 2026-07-29 to cover the static in-vitro receptor-binding use.

CONC_TOB_MGL (canonical for time-varying in-vitro tobramycin concentration driving an antibacterial PD model)

• Description: Time-varying unbound tobramycin concentration in the hollow-fiber infection model (HFIM) growth medium, supplied externally as an exogenous covariate that drives the bacterial-kill PD effect and the mechanistic-synergy switch on the imipenem KC50. Applied experimental concentration in the in-vitro matrix; distinct from Cc and the CP_<DRUG> plasma-PD-driver family.
• Units: mg/L
• Type: continuous
• Scope: specific
• Reference category: n/a – enters the Emax tobramycin kill function and gates the discrete imipenem-KC50 synergy reduction at the tob_cut = 1.15 mg/L threshold; set to 0 for imipenem-monotherapy or control arms.
• Source aliases: none standardized (the model column uses the canonical name directly; Landersdorfer 2018 labels it “tobramycin concentration” in the Methods and Fig. S1 reference).
• Example models: Landersdorfer_2018_imipenem_tobramycin.R (externally-supplied time-varying unbound tobramycin concentration driving the Emax kill function and the 70-fold imipenem-KC50 synergy reduction against population 3 when CONC_TOB_MGL >= 1.15 mg/L; HFIM simulated the two-compartment unbound profile of 7 mg/kg q24h 0.5-h infusions).
• Notes: Specific scope because the value is bound to tobramycin and the in-vitro HFIM design. Member of the in-vitro applied-drug-concentration CONC_<DRUG>_MGL family (siblings CONC_RIF_MGL, CONC_INH_MGL, CONC_EMB_MGL, CONC_IPM_MGL). Renamed from the model’s earlier bare Ctob column on 2026-05-27 for consistency with the CONC_<DRUG>_MGL family. Ratified canonically on 2026-05-27 alongside the Landersdorfer 2018 extraction.

CONC_VORI_NGML (canonical for in-vivo voriconazole whole-blood concentration driving a CYP3A drug-drug-interaction term)

• Description: Voriconazole whole-blood concentration supplied externally as a (time-varying) covariate that drives a reversible CYP3A-inhibition term inside a victim-drug PBPK model. Distinct from the victim drug’s own Cc: this is the perpetrator’s concentration, provided as data rather than simulated, for models whose perpetrator sub-model is not reproducible from the source. Set to 0 for a victim-drug-alone arm, which collapses the inhibition ratio to 1 (no inhibition).
• Units: ng/mL
• Type: continuous
• Scope: specific
• Reference category: n/a – enters the reversible-inhibition ratio DDIRE = 1 / (1 + CONC_VORI_NGML * fub_vori / KI); 0 gives DDIRE = 1.
• Source aliases:
  • Cb,vor – symbol used in Pei 2023 supplement Eq 1 (“Cb,vor = whole blood concentration of voriconazole”).
• Example models: Pei_2023_tacrolimus_pbpk.R (reversible CYP3A inhibition applied to the CYP3A-mediated fraction of hepatic tacrolimus clearance, with fub_vori = 0.42 and KI = 8.70 ng/mL from Pei 2023 Table S6; Pei 2023 supplement Eqs 1-2).
• Notes: Member of the applied-drug-concentration CONC_<DRUG>_<UNITS> family (siblings CONC_RIF_MGL, CONC_INH_MGL, CONC_TOB_MGL, …), extended here from the in-vitro applied-concentration case to an in-vivo perpetrator concentration; the unit token is NGML rather than MGL because the founding source reports voriconazole in ng/mL and the inhibition constant KI is tabulated on the same scale. The column exists because Pei 2023 generated Cb,vor from a companion voriconazole PBPK model whose tissue-to-plasma partition coefficients, absorption rate and absorbed fraction are not tabulated anywhere in the paper or supplement (Table S6 gives only MW, pKa, LogP, fup, BPR, KI and CLint); exposing the perpetrator concentration as data encodes the published inhibition equation faithfully without substituting distribution parameters from another paper, which the PBPK / QSP sourcing rule forbids. Distinct from the binary [[CONMED_VORICONAZOLE]], which is the right column when a popPK model estimates an on/off coefficient rather than a concentration-driven inhibition term. Ratified 2026-08-05 alongside the Pei 2023 tacrolimus PBPK extraction.

CONC_VEN_MGL (canonical for in-vivo venetoclax plasma concentration driving a BCL-2-inhibitor killing effect on leukemic cells)

• Description: Venetoclax plasma concentration supplied externally as a time-varying covariate that drives saturable Emax killing terms on chronic lymphocytic leukemia (CLL) cell populations. Venetoclax has no PK compartment in the consuming model: the concentration is provided as data rather than simulated, exactly as the companion ibrutinib exposure is provided through [[AUC_IBRU]]. Set to 0 for an ibrutinib-monotherapy arm, which collapses every venetoclax term to zero (and the peripheral-blood term to the untreated death rate constant), so the combination model reduces exactly to its monotherapy sibling.
• Units: ug/mL (equivalently mg/L)
• Type: continuous
• Scope: specific
• Reference category: n/a – enters saturable Emax forms of the shape CONC_VEN_MGL / (EC50 + CONC_VEN_MGL) rather than a centred-deviation or power form. Two half-maximal values are used simultaneously in the founding model, and their ratio is the point of the parameterisation: EC50,blood = 0.04 ug/mL versus EC50,tissue = 2.24 ug/mL, a 56-fold potency difference encoding the clinical observation that venetoclax clears circulating CLL cells far more readily than lymph-node disease.
• Source aliases:
  • Ct,venetoclax – symbol used in Ibrahim 2025 Table S2 footnote a.
• Example models: Ibrahim_2025_ibrutinib_venetoclax.R (additive death rate kd,bld * Emax,i * C/(2.24 + C) on the three lymphoid-tissue CLL subpopulations with Emax,1&2 = 0.63 and Emax,3 = 3.15, and multiplicative enhancement kd,bld * (1 + 3465 * C/(0.04 + C)) of the peripheral-blood CLL death rate; Ibrahim 2025 Table S2).
• Notes: Member of the applied-drug-concentration CONC_<DRUG>_<UNITS> family (siblings CONC_RIF_MGL, CONC_INH_MGL, CONC_TOB_MGL, CONC_VORI_NGML, …); like CONC_VORI_NGML this is an in-vivo rather than an in-vitro applied concentration. The unit token is MGL because the founding source tabulates both EC50 values in ug/mL, which is numerically identical to mg/L. The column exists because Ibrahim 2025 generated venetoclax concentration-time profiles from the two-compartment population PK model of Jones et al. (AAPS J. 2016;18(5):1192-1202), which is not open access, is not reproduced anywhere in the paper or its supplement, and is not part of nlmixr2lib; exposing the venetoclax concentration as data encodes the published killing equations faithfully without substituting distribution parameters from another source, which the QSP sourcing rule forbids. Downstream users must therefore supply the concentrations from that model or from observed data. Ratified 2026-08-19 alongside the Ibrahim 2025 ibrutinib extraction.

CONMED_RTV_AUC (canonical for ritonavir AUC over the 0-24 h dosing interval)

• Description: Per-subject (time-fixed within an evaluated regimen) ritonavir AUC over the 0-24 h once-daily dosing interval, used as a co-medication exposure covariate driving boosted-protease-inhibitor (atazanavir, lopinavir, darunavir, etc.) clearance in popPK models that account for ritonavir’s CYP3A4-inhibition effect. In Dickinson 2009 the value is computed by non-compartmental methods on the observed ritonavir concentration-time profile (WinNonlin 5.2) and feeds the atazanavir CL/F power form cl = exp(lcl) * (CONMED_RTV_AUC / 7.52)^e_aucrtv_cl.
• Units: mg*h/L (document per-model via covariateData[[CONMED_RTV_AUC]]$units if a different exposure unit is reported).
• Type: continuous
• Scope: specific
• Reference category: n/a – enters via centred power form. Reference value observed: 7.52 mg*h/L (Dickinson 2009 cohort median across HIV-infected and healthy volunteers; healthy 7.36, HIV 7.59 per Table 1).
• Source aliases:
  • RTVAUC – printed name in Dickinson 2009 Table 1 / Table 3 (the paper writes RTVAUC0-24 with the dosing-interval subscript; the column name is registered without the subscript to fit standard data-column character constraints).
• Example models: Dickinson_2009_atazanavir.R (atazanavir CL/F power-function dependence on RTVAUC0-24, centred at 7.52 mg*h/L with exponent -0.8).
• Notes: Specific scope because the column meaning is tied to ritonavir as the booster drug and to the 0-24 h once-daily dosing-interval AUC convention. A future PK model that uses a different ritonavir exposure metric (trough concentration, q12h-interval AUC for BID ritonavir regimens) should register a parallel canonical rather than overload CONMED_RTV_AUC. For simulation users without observed ritonavir AUC, the Dickinson 2009 cohort median 7.52 mg*h/L reproduces typical-value behaviour (the centring point of the covariate effect). Distinct from CONMED_RTV (binary boost-status flag used in Colombo 2006); the two canonicals coexist for the binary-indicator and continuous-exposure parameterisations of the same co-medication.

DOSE_RTV_MGKG (canonical for concomitant ritonavir per-administration dose per kg body weight)

• Description: Concomitant ritonavir dose given per administration in mg per kg of body weight (mg/kg). Per-dose-record covariate carried on the lopinavir (or other RTV-boosted antiretroviral) dose row so the parent drug’s bioavailability or clearance equation can read the co-administered ritonavir dose at the moment of dosing. Time-varying across cohort / regimen changes; constant within an inter-dose interval at steady state. Distinct from the per-administration mg dose of ritonavir on the RTV dose-event row – this covariate is the value mg-divided-by-kg, available on every record so the bioavailability formula can be evaluated without back-computing dose from the event table.
• Units: mg/kg
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as a linear-shift regressor in the LPV bioavailability formula F_LPV = 1 - RIF * CONMED_RIF + SLP * (DOSE_RTV_MGKG - DOSE_RTV_STD) of Zhang 2012 Equation 4, with DOSE_RTV_STD = 3 mg/kg (the median ritonavir dose in the no-rifampicin standard-LPV/r arm). The standard 4:1 LPV/r without rifampicin reference uses DOSE_RTV_MGKG = 3 so the linear-shift term vanishes; super-boosted 1:1 LPV/r at median LPV 14 mg/kg gives DOSE_RTV_MGKG = 14; double-dose 4:1 LPV/r at median LPV 23 mg/kg gives DOSE_RTV_MGKG = 5.75.
• Source aliases:
  • DoseRTV – used in Zhang_2012_lopinavir_ritonavir.R (paper Equation 4 regressor; in the paper’s NONMEM dataset this is the per-dose individual ritonavir mg/kg carried as a per-dose covariate column rather than a derived state).
• Example models: Zhang_2012_lopinavir_ritonavir.R (linear-shift on LPV bioavailability: F_LPV = 1 - 0.832 * CONMED_RIF + 0.021 * (DOSE_RTV_MGKG - 3); the linear approximation is valid only inside the cohort-tested ritonavir-dose range of 2.9-14 mg/kg per Zhang 2012 Discussion paragraph 3).
• Notes: Specific scope because the absolute slope coefficient (0.021 / mg/kg in Zhang 2012) and the reference dose (3 mg/kg) are intrinsically tied to the lopinavir-ritonavir-rifampicin pediatric integrated model. Drug-self-dose covariates for other drugs should register sibling canonicals (e.g., DOSE_<DRUG>_MGKG for per-administration or DOSE_<DRUG>_MGKGD for daily) rather than reuse this name. Carrying ritonavir dose as an explicit covariate rather than reading it from the RTV dose-event amount avoids tightly coupling the LPV bioavailability formula to the rxode2 event-table structure and lets a downstream simulation user supply a synthetic per-administration ritonavir dose without constructing matching event records. Ratified canonically on 2026-06-10 alongside the Zhang 2012 lopinavir-ritonavir extraction.

DOSE_RIV_MGKG (canonical for per-administration rivaroxaban dose per kg body weight)

• Description: Per-administration rivaroxaban dose in mg per kg of body weight (mg/kg). Per-dose-record covariate carried on the rivaroxaban dose row so the dose-dependent relative oral bioavailability function can read the per-event mg/kg value at the moment of dosing. Time-varying across study phase / regimen changes (single dose, b.i.d., t.i.d.) and weight-band re-titrations; constant within an inter-dose interval at steady state.
• Units: mg/kg
• Type: continuous
• Scope: specific
• Reference category: n/a – enters non-linearly as the input to the exponential decay function tf1 = f1min + (f1max - f1min) * exp(-log(2)/d50 * DOSE_RIV_MGKG) of Willmann 2021 supplement S2 NONMEM $PK block, with f1max = 1.25, f1min = 0.59, d50 = 14.4/82.48 = 0.1746 mg/kg, anchored to F1 = 1.0 at DOSE_RIV_MGKG = 10/82.48 = 0.1213 mg/kg (the median adult dose per the integrated adult popPK analysis in Willmann 2021 reference 19). Reference values observed: 0.1213 mg/kg (adult-anchor F1 = 1.0); 0.30 mg/kg (F1 = 0.791); 0.50 mg/kg (F1 = 0.681) – Willmann 2021 Results p. 1199. Bodyweight-normalised single rivaroxaban doses ranged 0.1-0.5 mg/kg across the EINSTEIN-Jr pediatric program (Willmann 2021 Results p. 1199 and Figure 1).
• Source aliases:
  • DW (= DOSE/WGHT in supplement S2 $PK block) – used in Willmann_2021_rivaroxaban.R (supplement S2 NONMEM $PK block DW = DOSE/WGHT ; TF1 = F1MIN + (F1MAX - F1MIN) * EXP(-LOG(2)/D50 * DW)).
• Example models: Willmann_2021_rivaroxaban.R (input to the exponential-decay relative-bioavailability function for the pediatric popPK model of rivaroxaban; tf1 = 0.59 + 0.66 * exp(-log(2)/0.1746 * DOSE_RIV_MGKG) with f1 = tf1 * exp(etalfdepot) and f(depot) <- f1).
• Notes: Specific scope because the absolute constants f1max, f1min, and d50 are intrinsically tied to the dose-dependency function carried over from the integrated adult popPK analysis in Willmann 2021 reference 19 and re-anchored to F1 = 1.0 at 10 mg/82.48 kg for the pediatric extrapolation. Carrying rivaroxaban dose per kg as an explicit covariate column (rather than back-computing from the rxode2 event-table amt and a body-weight column) lets the bioavailability formula be evaluated on every record including non-dose observation records. Mirrors the sibling drug-self-dose canonicals DOSE_PHT_MGKGD (Yukawa 1990 daily phenytoin dose per kg) and DOSE_RTV_MGKG (Zhang 2012 per-administration ritonavir dose per kg); pattern DOSE_<DRUG>_MGKG (per-administration) vs DOSE_<DRUG>_MGKGD (daily). Ratified canonically alongside the Willmann 2021 rivaroxaban pediatric popPK extraction.

DOSE_BZN_MGKG (canonical for per-administration benznidazole dose per kg body weight)

• Description: Per-administration oral benznidazole dose in mg per kg of body weight (mg/kg). Carried on every record (not only dose rows) so the power covariate function on the absorption rate constant can be evaluated at observation times as well. Time-varying across dose levels; constant within a dose level. In a mouse study the physical dose amount is individually adjusted to each animal’s weight, so the mg/kg dose level – not the mg amount – is the quantity the covariate model is written against.
• Units: mg/kg
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as a power term normalised to the median dose of the satellite PK study, (DOSE_BZN_MGKG / 30)^e_dose_ka. Assmus 2025 studied 10, 30 and 100 mg/kg in the satellite PK cohort (reference 30 mg/kg) and 10, 20, 30, 50 and 100 mg/kg per administration across the ten efficacy regimens.
• Source aliases:
  • DOSE_PER_KG – the S1 Code $PK regressor in Assmus_2025_benznidazole_mouse.R (COV1 = (DOSE_PER_KG/30)**THETA(5)).
• Example models: Assmus_2025_benznidazole_mouse.R (power effect on the absorption rate constant: ka = 2.18 * (DOSE_BZN_MGKG/30)^-0.775, Assmus 2025 Table 2 theta_Dose = -0.775, giving KA = 5.11, 2.18 and 0.86 1/h at 10, 30 and 100 mg/kg).
• Notes: Specific scope until a second benznidazole model ratifies the canonical. Member of the DOSE_<DRUG>_<UNITS> drug-self-dose family alongside DOSE_RIV_MGKG, DOSE_RTV_MGKG, DOSE_GLN_GKG, DOSE_PHT_MGKGD and DOSE_VPA_MGKGD; pattern DOSE_<DRUG>_MGKG (per-administration) vs DOSE_<DRUG>_MGKGD (daily). Must NOT be named the bare DOSE: rxode2’s etTrans() consumes an event-table column literally named DOSE (any casing) and never exposes it to model(), so the solve fails with “The following parameter(s) are required for solving: DOSE”. Ratified canonically alongside the Assmus 2025 benznidazole mouse PK/PD extraction.

DOSE_GLN_GKG (canonical for per-administration L-glutamine dose per kg body weight)

• Description: Per-administration oral L-glutamine dose in grams per kg of body weight (g/kg). Carried on every record (not only dose rows) so a dose-dependent covariate term can be evaluated at observation times as well. Time-varying across dose-escalation periods; constant within a dose level.
• Units: g/kg
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as a power term normalised to the lowest studied dose level, (DOSE_GLN_GKG / 0.1)^theta. Sadaf 2024 studied 0.1, 0.3, and 0.6 g/kg, with 0.1 g/kg as the normalisation reference.
• Source aliases:
  • DOSE per kg – used in Sadaf_2024_glutamine.R (Sadaf 2024 Table 2 Vi = theta3 * (WT/70) * (DOSE per kg/0.1)^theta4).
• Example models: Sadaf_2024_glutamine.R (power effect on apparent central volume: vc = 52.3 * (WT/70)^1 * (DOSE_GLN_GKG/0.1)^0.27, Sadaf 2024 Table 2 theta4 = 0.27).
• Notes: Specific scope until a second L-glutamine model ratifies the canonical. Member of the DOSE_<DRUG>_<UNITS> drug-self-dose family alongside DOSE_RIV_MGKG, DOSE_RTV_MGKG, DOSE_PHT_MGKGD, and DOSE_VPA_MGKGD; the GKG unit token is used rather than MGKG because oral amino-acid supplementation is dosed in grams per kg, and silently rescaling to mg/kg would make the published normalisation reference (0.1) read as 100. Distinct from GLN_BL, the endogenous baseline glutamine concentration, which is the other Sadaf 2024 covariate. Ratified canonically alongside the Sadaf 2024 L-glutamine sickle-cell-disease extraction.

CP_PRB_MGL (canonical for instantaneous probenecid plasma concentration as a time-varying renal-tubular-perpetrator covariate)

• Description: Instantaneous plasma concentration of probenecid supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used as the perpetrator-drug input to a competitive inhibition term on saturable (Michaelis-Menten) renal tubular secretion of a co-administered substrate in DDI popPK models. Set to 0 outside the probenecid co-administration window so the apparent-Km inhibition term collapses to the baseline form km_apparent = km.
• Units: mg/L (probenecid MW = 285.34 g/mol; 1 mg/L = 3.505 umol/L).
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as the multiplier on the saturable-secretion apparent-Km term km_apparent = km * (1 + CP_PRB_MGL / ki) where ki is the probenecid competitive inhibition constant at the renal tubular transporter in mg/L. Reference peak observed: the Landersdorfer 2009 8-dose probenecid regimen (total 4.5 g over 70 h) produces typical plasma probenecid concentrations in the range ~30 to 60 mg/L during the gemifloxacin sampling window (Landersdorfer 2009 Fig. 1C).
• Source aliases:
  • [P] (Landersdorfer 2009 Methods / Table 1, “plasma probenecid concentration” symbol entering the competitive-inhibition formula km_apparent = km * (1 + [P] / kic)).
• Example models: Landersdorfer_2009_gemifloxacin.R (drives competitive inhibition of the renal-tubular-secretion arm of gemifloxacin clearance: cl_tubular_secretion = vmax * Cc / (km * (1 + CP_PRB_MGL / ki) + Cc) with ki = 69.3 mg/L; gemifloxacin filtration clearance (fu * GFR ~ 2 L/h, fixed) and non-renal clearance are unaffected by this term but the static-treatment-arm effect on non-renal clearance is encoded separately via CONMED_PROBENECID).
• Notes: Specific scope; probenecid-specific. The drug-specific naming follows the established CP_<drug>_<units> precedent (CP_OXY_NGML, CP_FBX_NGML, CP_LSN_NGML, CP_MORPH_NGML, CP_RIF_UM). Distinct from the binary indicator CONMED_PROBENECID (which captures treatment-arm-level static effects – e.g., on absorption or non-renal clearance – and does not carry plasma-concentration magnitude information). The Landersdorfer 2009 paper does NOT report a probenecid PK sub-model parameter table on disk (the Methods describe the structural form – 1-compartment + lag + parallel first-order + mixed-order elimination – but no parameter table), so users simulating the with-probenecid arm must supply CP_PRB_MGL from an external probenecid popPK source (e.g., literature digitisation of Landersdorfer 2009 Fig. 1C, or an unrelated published probenecid popPK model). Ratified canonically on 2026-06-07 alongside the Landersdorfer 2009 gemifloxacin extraction.

CONC_AGONIST_M (canonical for applied ex-vivo agonist concentration in the Grzesk 2016 vascular-reactivity sigmoidal Emax CRC model)

• Description: Applied concentration (mol/L) of the vasoactive agonist whose identity is selected by the companion AGONIST_CODE covariate, supplied as a per-record exogenous covariate to the static sigmoidal Emax concentration-response model of perfusion pressure in the isolated, perfused male Wistar rat tail artery. Polymorphic in agonist identity: a single CONC_AGONIST_M column carries phenylephrine concentrations on phenylephrine records, arg-vasopressin concentrations on arg-vasopressin records, mastoparan-7 concentrations on mastoparan-7 records, and Bay K8644 concentrations on Bay K8644 records, with the per-agonist EC50 / Emax pair selected inside model() by AGONIST_CODE. The model has no PK and no time dynamics; CONC_AGONIST_M is the dose-response x-axis.
• Units: M (mol/L)
• Type: continuous
• Scope: specific
• Reference category: n/a – enters the sigmoidal Emax expression PP = emax * CONC_AGONIST_M^hill / (ec50^hill + CONC_AGONIST_M^hill); CONC_AGONIST_M = 0 yields PP = 0 (no agonist contraction).
• Source aliases: none – Grzesk 2016 Table I reports per-agonist EC50 in M/L directly; the model column is the canonical CONC_AGONIST_M.
• Example models: Grzesk_2016_m3M3FBS.R (Grzesk 2016 Table I CRC data: per-agonist EC50 values 7.50e-8 M (PHE), 1.84e-8 M (AVP), 4.48e-8 M (mastoparan-7), 1.96e-6 M (Bay K8644); m-3M3FBS-shifted EC50 values 6.45e-8, 1.42e-8, 2.55e-8, 2.05e-6 M respectively).
• Notes: Specific scope because the polymorphic agonist-identity-selected-by-companion-column semantics are tied to the Grzesk 2016 four-agonist CRC design. Distinct from the STIM_<drug>_<units> and CONC_<DRUG>_<UNITS> in-vitro-drug-concentration families (where the drug identity is fixed in the canonical name and the unit suffix is mandatory) because Grzesk 2016 deliberately tests four mechanistically distinct vasoactive agonists in a shared experimental scaffold with a shared concentration axis – baking the agonist identity into the canonical name would force four parallel columns rather than the paper’s one-column-plus-selector encoding. M (mol/L) chosen as the unit because Grzesk 2016 Table I reports EC50 values in mol/L and the four agonists span 1e-8 to 1e-6 M (a uniform molar axis is the only practical choice); the _M suffix preserves the CONC_<...>_<UNITS> family’s unit-in-name discipline. Future ex-vivo / in-vitro CRC papers from the Grzesk-group series (Biomed Rep 2 / 2014; Mol Med Report 5 / 2012; Exp Ther Med 4 / 2012; etc.) that test multiple agonists on the same vascular-reactivity scaffold should reuse this canonical with an extended AGONIST_CODE mapping. Ratified canonically alongside the Grzesk 2016 extraction.

CONC_HCN_PPM (canonical for inhaled hydrogen cyanide gas concentration in ambient air)

• Description: Hydrogen cyanide (HCN) concentration in the ambient air a subject is breathing, supplied as an exogenous covariate that is the sole exposure driver of an inhalation toxicokinetic PBPK model. Enters the pulmonary mass balance as the air-side driving force of alveolar exchange, q_alv * (ca - plasma / ppa), where ca = CONC_HCN_PPM / 24 converts the gas-phase ppm reading to umol/L and ppa is the plasma:air partition coefficient. There is no dosing event and no administered amount: the entire exposure is specified by this column, so a constant value represents a steady atmosphere from t = 0 and a time-varying column represents a fluctuating fire-scene profile. In the founding example the value is not measured but estimated per decedent by grid search (0-18,000 ppm in 12 ppm steps) against paired post-mortem left and right cardiac blood cyanide concentrations.
• Units: ppm
• Type: continuous
• Scope: specific
• Reference category: n/a – CONC_HCN_PPM = 0 is clean air and yields no cyanide uptake, so zero is the natural unexposed reference.
• Source aliases:
  • Ca – the variable name used in the Harada 2025 Supplemental Material 1 Python script, where it carries the same quantity already converted to umol/L. A source data set expressed in umol/L must be multiplied by 24 to reach the canonical ppm column, or the model’s ppm_per_umol conversion factor overridden; the canonical is ppm because ppm is the unit both the paper’s text and its Table 2 report.
• Example models: Harada_2025_cyanide_pbpk.R (inhaled HCN air concentration driving alveolar cyanide uptake in the Stamyr 2015 six-state cyanide PBPK model; the paper’s 13 reconstructed exposures span 84-16,632 ppm).
• Notes: Specific scope because the value is bound to hydrogen cyanide and to an inhalation-exposure design. Member of the applied-concentration CONC_<drug>_<units> family, which was already not restricted to in-vitro media – CONC_VORI_NGML carries an in-vivo whole-blood concentration – and which this entry extends to the inhaled gas phase. The distinguishing feature against every sibling is the medium: CONC_HCN_PPM is a concentration in ambient air outside the body, whereas the CONC_<drug>_MGL / _UM siblings are concentrations in an in-vitro aqueous matrix and CONC_VORI_NGML is in whole blood. Because the medium is air, the covariate cannot be compared to a blood or plasma concentration without the partition coefficient, and the unit suffix is a gas-phase mixing ratio rather than a mass-per-volume. Distinct from ETSEVO (end-tidal sevoflurane), which is an exhaled alveolar concentration measured at the subject’s airway and therefore already equilibrated with tissue, whereas CONC_HCN_PPM is the inspired atmosphere and is an input rather than a readout. Distinct from CARDIAC_OUTPUT, the other canonical pre-registered for inhalation-model work. Future inhalation toxicokinetic extractions for a different gas should register a sibling (CONC_CO_PPM, CONC_HCL_PPM, …) following this pattern rather than overloading this name; combustion-toxicology papers commonly report carbon monoxide alongside HCN, and Harada 2025 measures carboxyhaemoglobin but does not model carbon monoxide kinetics, so no CONC_CO_PPM is registered yet.

CP_REM_NGML (canonical for instantaneous remikiren plasma concentration as a time-varying PD driver)

• Description: Instantaneous plasma concentration of remikiren (Ro 42-5892; orally active renin inhibitor) supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in PD-only Hill / Emax models that take remikiren exposure as an external input to a renin-inhibition concentration-effect equation.
• Units: ng/mL
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as a Hill / Emax term in the inhibition-of-angiotensin-I-production-rate expression APR = Imax * CP_REM_NGML^hill / (IC50^hill + CP_REM_NGML^hill) (Weber 1993). Set to 0 outside the drug-exposure window (the inhibition term then collapses to 0). Reference values observed: mean Cmax was 4-6 ng/mL after 200 mg oral, 23-27 ng/mL after 300 mg oral, 47-83 ng/mL after 600-800 mg oral, with substantial intersubject variability (Weber 1993 Table 1).
• Source aliases:
  • Cp (Weber 1993 Methods ‘Concentration-effect modelling’ Hill equation; values in ng/mL) – the observed plasma remikiren concentration measured by HPLC with fluorescence or coulometric electrochemical detection or by a protein-binding assay (Weber 1993 Methods, ‘Remikiren concentrations’ section).
• Example models: Weber_1993_remikiren.R (PD-only Hill / Emax model for inhibition of the angiotensin I production rate in hypertensive patients; CP_REM_NGML is the observed or simulated plasma concentration of remikiren supplied per event row).
• Notes: Specific scope; remikiren-specific. The drug-specific naming follows the established CP_<drug>_<units> precedent (CP_OXY_NGML, CP_FBX_NGML, CP_LSN_NGML, CP_MORPH_NGML, CP_RIF_UM). Weber 1993 fit the PD model directly to observed plasma concentrations because no structural population PK model was estimated – the paper characterised remikiren PK with model-independent NCA only. Users wishing to drive the PD model from a simulated PK source must supply their own concentration trajectory (the published single-dose Weber-group papers from the same series report compartmental PK in healthy volunteers but are not on disk in nlmixr2lib). Ratified canonically on 2026-06-10 alongside the Weber 1993 remikiren PD extraction.

CP_RITUXIMAB_UGML (canonical for instantaneous rituximab plasma concentration as a time-varying PD driver)

• Description: Instantaneous plasma concentration of rituximab (chimeric anti-CD20 monoclonal antibody), or of one of its biosimilars, supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in PD-only exposure-response models that take rituximab exposure as an external input to a concentration-effect equation (e.g., Emax on DAS28 change from baseline).
• Units: ug/mL
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as an Emax-style concentration-effect term fC = Emax * CP_RITUXIMAB_UGML / (CP_RITUXIMAB_UGML + ec50). Set to 0 outside the drug-exposure window (the concentration-effect term then collapses to 0). Reference values observed: typical steady-state Cmax after the two-dose loading course (1000 mg IV on days 1 and 15) reaches the low hundreds of ug/mL and washes out to <1 ug/mL by week 24 (Williams 2016 Supplemental Figure S1 VPC).
• Source aliases:
  • CONC – Williams 2016 popPK/PD analysis; the paper’s popPK model produced individual predicted concentrations Cij that were passed to the DAS28cfb PD model as an input variable.
• Example models: Williams_2016_rituximab_das28cfb.R (PD-only Emax-style exposure-response model for the DAS28 change from baseline; the source rituximab popPK model was two-compartment with baseline-BSA and sex covariates on CL and Vc, but Williams 2016 does not tabulate the popPK parameter estimates, so users must supply their own concentration trajectory).
• Notes: Specific scope; rituximab-specific. Same covariate is used for the biosimilar candidate PF-05280586 in the Williams 2016 trial (PF-05280586 was shown to be PK-similar to reference rituximab). Drug-product distinction is captured by the separate TRT covariate rather than by separate concentration columns. Ratified canonically on 2026-07-09 alongside the Williams 2016 biosimilar-rituximab DAS28cfb extraction.

CP_GLASDEGIB_NGML (canonical for instantaneous glasdegib plasma concentration as a time-varying PD driver)

• Description: Instantaneous plasma concentration of glasdegib (PF-04449913; orally active Hedgehog pathway / Smoothened inhibitor) supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in PD-only linear exposure-response models that take glasdegib exposure as an external input to a concentration-effect equation (e.g., concentration-QTc).
• Units: ng/mL
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as a linear-slope term in the concentration-QTc expression QTcF = e0 + slope * (CP_GLASDEGIB_NGML / 1000) (Fostvedt 2021; the in-model / 1000 converts the canonical ng/mL covariate to the paper’s reported microgram-per-mL slope scaling). Set to 0 outside the drug-exposure window (the concentration-slope term then collapses to 0). Reference values observed: geometric-mean steady-state Cmax was 1137 ng/mL at 100 mg QD (therapeutic dose) and 2445 ng/mL at 200 mg QD (a 2-fold supratherapeutic exposure used in Fostvedt 2021’s bootstrap predictions; see Fostvedt 2021 Methods 2.3 and Table 4).
• Source aliases:
  • CONC (Fostvedt 2021 Methods 2.2 and 2.3 ‘concentration-QTc modelling’ equations; values in ng/mL but model fit with the slope reported per microgram-per-mL after a CONC / 1000 rescaling internal to the estimation – “The estimates are reported on the microgram scale as the scaling helped the estimation procedure”, Fostvedt 2021 Table 3 footnote).
• Example models: Fostvedt_2021_glasdegib_QTcF.R, Fostvedt_2021_glasdegib_QTcS.R (PD-only linear mixed-effects E-R model for QTcF / QTcS prolongation in patients with cancer; CP_GLASDEGIB_NGML is the observed or simulated plasma concentration supplied per event row).
• Notes: Specific scope; glasdegib-specific. The drug-specific naming follows the established CP_<drug>_<units> precedent (CP_OXY_NGML, CP_FBX_NGML, CP_LSN_NGML, CP_MORPH_NGML, CP_RIF_UM, CP_REM_NGML). Fostvedt 2021 fit the PD model directly to observed plasma concentrations because no structural population PK model is published in the source paper – the geometric-mean Cmax values used for the parametric-bootstrap QTcF predictions (Fostvedt 2021 Section 3.4; cited to reference [15] of the paper) come from a separate Pfizer phase 2 study in patients with hematologic malignancies, not from a parameterised popPK model included in this paper. Users wishing to drive the PD model from a simulated PK source must supply their own concentration trajectory; no glasdegib popPK model exists in the nlmixr2lib registry. Ratified canonically on 2026-06-24 alongside the Fostvedt 2021 glasdegib QTc extraction.

CP_ORISMILAST_NGML (canonical for instantaneous orismilast plasma concentration as a time-varying PD driver)

• Description: Instantaneous plasma concentration of orismilast (UNION Therapeutics oral selective phosphodiesterase 4B/4D inhibitor) supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in PD-only exposure-response models that take orismilast exposure as an external input to a concentration-effect equation (e.g., sigmoidal Imax inhibition of whole-blood IL-13 production).
• Units: ng/mL
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as the driving concentration of the sigmoidal Imax expression il13Inhibition = imax * CP_ORISMILAST_NGML^hill / (ec50^hill + CP_ORISMILAST_NGML^hill) (Warren 2025). Set to 0 outside the drug-exposure window (the inhibition term then collapses to 0). Reference values observed: model-predicted steady-state exposures in atopic dermatitis patients read from Warren 2025 Fig. 1A span Caverage-ss ~38-63 ng/mL and Cmax-ss ~90-189 ng/mL across 20-40 mg twice daily, against a whole-blood IL-13 IC50 of 4.08 ng/mL and IC90 of 25.0 ng/mL.
• Source aliases: none known; Warren 2025 refers to it only as the predicted orismilast plasma concentration (Fig. 1A, 1B) and supplies no column name.
• Example models: Warren_2025_orismilast.R (PD-only sigmoidal Imax model of orismilast inhibition of LPS-stimulated whole-blood IL-13 production; CP_ORISMILAST_NGML is the observed or simulated plasma concentration supplied per event row).
• Notes: Specific scope; orismilast-specific. The drug-specific naming follows the established CP_<drug>_<units> precedent (CP_OXY_NGML, CP_FBX_NGML, CP_LSN_NGML, CP_MORPH_NGML, CP_REM_NGML, CP_GLASDEGIB_NGML). Warren 2025 measured the IL-13 potency in whole blood, so the paper maps plasma concentrations onto the potency curve directly with no protein-binding or cellular-penetration correction (“As the in vitro IL-13 potency was assessed in whole blood, adjustments for protein binding and cellular penetrations were not considered”, Warren 2025 Methods). No orismilast population PK model exists in the nlmixr2lib registry and none can be built from Warren 2025: the paper’s orismilast popPK model is a company-internal two-compartment-with-lag analysis of 366 subjects whose parameter values are not published in the paper, its reference list, or any supplement. Users wishing to drive this PD model must therefore supply their own concentration trajectory. Ratified canonically on 2026-08-15 alongside the Warren 2025 orismilast extraction.

CP_RACSOTALOL_UGML (canonical for instantaneous rac-sotalol plasma concentration as a time-varying PD driver)

• Description: Instantaneous plasma concentration of rac-sotalol (racemic 4-[1-hydroxy-2-(propan-2-ylamino)ethyl]-N-methyl-benzene-sulfonamide; class III antiarrhythmic with non-selective beta-adrenoceptor blocking activity) supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in PD-only linear exposure-response models that take rac-sotalol exposure as an external input to a concentration-DeltaQTc equation.
• Units: ug/mL
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as a linear-slope term in the concentration-DeltaQTc expression DeltaQTcI = e0 + slope * CP_RACSOTALOL_UGML + ... (Darpo 2014 Table 1 and Figure 6 captions; the slope is reported in ms per ug/mL, so no in-model unit rescaling is needed). Set to 0 outside the drug-exposure window (the concentration-slope term then collapses to 0). Reference values observed: Tmax 2.8 h (men) / 2.9 h (women); Cmax 1.4 ug/mL (men, range 0.9-1.9) / 1.8 ug/mL (women, range 1.1-2.8); AUC_inf 14.6 (men) / 17.1 (women) ug/mL*h after a single 160 mg oral dose (Darpo 2014 Results ‘Rac-sotalol pharmacokinetic profile’ and Figure 1).
• Source aliases:
  • rac-sotalol plasma concentration (Darpo 2014 Methods ‘Study outline’ and Table 1; values in ug/mL throughout, as quantitated by a validated HPLC assay with LLOQ 10 ng/mL and inter-day CV < 8.4%).
• Example models: Darpo_2014_racSotalol_QTcI.R, Darpo_2014_racSotalol_QTcF.R (PD-only linear mixed-effects E-R model for DeltaQTcI / DeltaQTcF in healthy adults after single 160 mg oral rac-sotalol; CP_RACSOTALOL_UGML is the observed or simulated plasma concentration supplied per event row).
• Notes: Specific scope; rac-sotalol-specific. The drug-specific naming follows the established CP_<drug>_<units> precedent (CP_OXY_NGML, CP_FBX_NGML, CP_LSN_NGML, CP_MORPH_NGML, CP_RIF_UM, CP_REM_NGML, CP_GLASDEGIB_NGML). The “rac” prefix is preserved from the source paper because Darpo 2014 consistently uses “rac-sotalol” to distinguish the dosed racemate (Betapace) from d-sotalol (the dextro-isomer studied separately in the SWORD trial cited in the Darpo Discussion). Darpo 2014 fit the PD model directly to observed plasma concentrations because no structural population PK model was estimated – the paper characterised rac-sotalol PK with NCA only (descriptive Cmax / Tmax / AUC). Users wishing to drive the PD model from a simulated PK source must supply their own concentration trajectory; no rac-sotalol popPK model exists in the nlmixr2lib registry. Ratified canonically on 2026-06-30 alongside the Darpo 2014 rac-sotalol concentration-QTc extraction.

CP_ABE_NGML (canonical for instantaneous abemaciclib plasma concentration as a time-varying target-engagement driver)

• Description: Instantaneous total (not free) plasma concentration of abemaciclib (ABE), the CDK4/6 inhibitor, supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in QSP target-engagement models that consume an external abemaciclib exposure profile as the driver of reversible drug-CDK4 / drug-CDK6 complex formation. The free concentration that actually drives binding is computed inside model() as CP_ABE_NGML * fu_abe, where fu_abe is the albumin-scaled fraction unbound.
• Units: ng/mL
• Type: continuous
• Scope: specific
• Reference category: n/a – after conversion to nmol/L it enters the association limb of the target-engagement ODE d/dt(complex) = koff/Kd * CDK_free * C_free - koff * complex. Set to 0 for untreated / pre-dose periods and for placebo arms, which returns every occupancy to 0 and the biomarker chain to its untreated baseline. Reference values observed (Zhang 2025 Table 2, predicted column, Patnaik 2016 regimens, steady state): Cmin 144 ng/mL and Cmax 198 ng/mL at 100 mg BID; Cmin 174 / Cmax 250 at 150 mg BID; Cmin 231 / Cmax 333 at 200 mg BID.
• Source aliases:
  • Cdrug (Zhang 2025 Eq 1, defined as “the free drug concentration in plasma or the CSF”; this register entry carries the TOTAL concentration and applies the fraction unbound in-model, because a total concentration is what a PK model returns) – used in Zhang_2025_abemaciclib_qsp.R.
• Example models: Zhang_2025_abemaciclib_qsp.R (drives the shared-pool CDK4 and CDK6 occupancy limbs in plasma and in CSF; the CSF free concentration is KCSF,p * fu * CP_ABE_NGML with KCSF,p = 0.68).
• Notes: Specific scope; abemaciclib-specific, and bound to the plasma (rather than CSF or brain-interstitial) matrix – the CSF concentration is derived in-model from the published CSF-to-plasma free-concentration ratio, so it must not be supplied through this column. Follows the established CP_<drug>_<units> precedent (CP_OXY_NGML, CP_FBX_NGML, CP_LSN_NGML, CP_MORPH_NGML, CP_EIDD_NGML, CP_REM_NGML, CP_GLASDEGIB_NGML). In the source study this profile is produced by a whole-body PK-Sim 9.1 PBPK model whose system-specific parameters came from the built-in PK-Sim database with no organ ODEs, volumes or blood flows published and no .pksim5 project deposited; that layer is not reproducible from the on-disk sources and is deliberately not extracted, following the Liang_2024_osimertinib_qsp precedent. Downstream users must therefore supply their own abemaciclib exposure trajectory and treat the resulting occupancy as conditional on it. Registered together with CP_M2_NGML, CP_M18_NGML and CP_M20_NGML: Zhang 2025 models all four active analytes as competitors for the same CDK pools, so a simulation that supplies only the parent understates total occupancy. Ratified canonically on 2026-08-21 alongside the Zhang 2025 abemaciclib CDK4/6-occupancy extraction.

CP_M2_NGML (canonical for instantaneous abemaciclib metabolite M2 plasma concentration as a time-varying target-engagement driver)

• Description: Instantaneous total plasma concentration of M2 (LSN3106726), one of the three pharmacologically active abemaciclib metabolites, supplied directly as a time-varying covariate column. Used alongside CP_ABE_NGML, CP_M18_NGML and CP_M20_NGML in QSP target-engagement models where parent and metabolites compete for a shared CDK4 pool and a shared CDK6 pool. The free driving concentration is CP_M2_NGML * fu_m2 in-model.
• Units: ng/mL
• Type: continuous
• Scope: specific
• Reference category: n/a – enters the same association limb as CP_ABE_NGML, with M2’s own Kd (1.2 nM for CDK4, 1.3 nM for CDK6), fraction unbound (0.093) and CSF-to-plasma ratio (0.14). Set to 0 to study the parent alone. Reference value observed: Zhang 2025 Introduction (citing the FDA multidiscipline review, ref 14) reports M2 as contributing 13% of the total plasma mass in vivo.
• Source aliases:
  • M2 (Zhang 2025 Table 1 column header and throughout the Methods) – used in Zhang_2025_abemaciclib_qsp.R.
• Example models: Zhang_2025_abemaciclib_qsp.R.
• Notes: Specific scope; plasma matrix only (see CP_ABE_NGML). M2 is the metabolite with the highest fraction unbound (0.093, roughly 2.4x the parent’s 0.039) but the lowest CSF-to-plasma ratio (0.14), so it contributes proportionally much more to plasma occupancy than to CSF occupancy. Named for the metabolite code the source paper uses rather than an INN because abemaciclib’s active metabolites are identified only by the M-numbering in both the paper and the FDA review. Registered together with CP_ABE_NGML, CP_M18_NGML and CP_M20_NGML. Ratified canonically on 2026-08-21 alongside the Zhang 2025 abemaciclib CDK4/6-occupancy extraction.

CP_M18_NGML (canonical for instantaneous abemaciclib metabolite M18 plasma concentration as a time-varying target-engagement driver)

• Description: Instantaneous total plasma concentration of M18 (LSN3106728), one of the three pharmacologically active abemaciclib metabolites, supplied directly as a time-varying covariate column. Free driving concentration is CP_M18_NGML * fu_m18 in-model.
• Units: ng/mL
• Type: continuous
• Scope: specific
• Reference category: n/a – enters the same association limb as CP_ABE_NGML, with M18’s own Kd (1.2 nM for CDK4, 2.7 nM for CDK6), fraction unbound (0.046) and CSF-to-plasma ratio (assumed 1.0). Set to 0 to study the parent alone. Reference value observed: Zhang 2025 Introduction (ref 14) reports M18 as contributing 5% of the total plasma mass in vivo, the smallest of the three metabolites.
• Source aliases:
  • M18 (Zhang 2025 Table 1 column header and throughout the Methods) – used in Zhang_2025_abemaciclib_qsp.R.
• Example models: Zhang_2025_abemaciclib_qsp.R.
• Notes: Specific scope; plasma matrix only (see CP_ABE_NGML). M18 is the analyte for which no CSF data existed: Zhang 2025 Methods 2.1 states that “as no data were available for M18, KCSF,p was assumed to be 1.0 for this metabolite”, which makes it the only analyte whose CSF free concentration equals its plasma free concentration and therefore the one whose CSF contribution is most uncertain. Registered together with CP_ABE_NGML, CP_M2_NGML and CP_M20_NGML. Ratified canonically on 2026-08-21 alongside the Zhang 2025 abemaciclib CDK4/6-occupancy extraction.

CP_M20_NGML (canonical for instantaneous abemaciclib metabolite M20 plasma concentration as a time-varying target-engagement driver)

• Description: Instantaneous total plasma concentration of M20 (LSN3106729), the largest-contributing pharmacologically active abemaciclib metabolite, supplied directly as a time-varying covariate column. Free driving concentration is CP_M20_NGML * fu_m20 in-model.
• Units: ng/mL
• Type: continuous
• Scope: specific
• Reference category: n/a – enters the same association limb as CP_ABE_NGML, with M20’s own Kd (1.5 nM for CDK4, 1.9 nM for CDK6), fraction unbound (0.030) and CSF-to-plasma ratio (0.91). Set to 0 to study the parent alone. Reference value observed: Zhang 2025 Introduction (ref 14) reports M20 as contributing 26% of the total plasma mass in vivo, twice M2’s share and five times M18’s.
• Source aliases:
  • M20 (Zhang 2025 Table 1 column header and throughout the Methods) – used in Zhang_2025_abemaciclib_qsp.R.
• Example models: Zhang_2025_abemaciclib_qsp.R.
• Notes: Specific scope; plasma matrix only (see CP_ABE_NGML). M20 combines the largest plasma mass share (26%) with a high CSF-to-plasma ratio (0.91) and a CDK6 affinity roughly 4x the parent’s (Kd 1.9 vs 8.2 nM), so it is the dominant contributor to CSF CDK6 occupancy in the founding model – a simulation that omits it will substantially understate the intracranial engagement the paper reports. Registered together with CP_ABE_NGML, CP_M2_NGML and CP_M18_NGML. Ratified canonically on 2026-08-21 alongside the Zhang 2025 abemaciclib CDK4/6-occupancy extraction.

DOSE_OBILTOXAXIMAB_MGKG (canonical for administered obiltoxaximab dose level in mg/kg)

• Description: Per-subject obiltoxaximab dose level, in mg/kg of body weight, carried as a covariate so a dose-response model can consume it directly. Constant per subject in single-dose trigger-to-treat designs. Set to 0 for placebo subjects, which reduces a dose-response term to its untreated baseline.
• Units: mg/kg
• Type: continuous
• Scope: specific
• Reference category: n/a – drives the Emax dose-response on the logit cure fraction, Emax * DOSE_OBILTOXAXIMAB_MGKG / (ED50 + DOSE_OBILTOXAXIMAB_MGKG), with ED50 = 1.64 mg/kg in the founding example.
• Source aliases:
  • DOSE – used in Nagy_2017_obiltoxaximab_survival.R (Nagy 2017 Results, “Animal survival modeling”; dose levels 0 / 1 / 4 / 8 / 16 / 32 mg/kg i.v. across rabbit Study 2 and macaque Studies 2-5 per Table 1).
• Example models: Nagy_2017_obiltoxaximab_survival.R (Weibull cure-rate survival model; the paper reports ED50 = 1.64 mg/kg (95% CI 0.515-5.22) and ED90 = 14.8 mg/kg (95% CI 4.6-47.0) from this term, and selects 16 mg/kg as the efficacious dose because it exceeds the ED90).
• Notes: Follows the DOSE_<DRUG>_<UNITS> auto-approve family (siblings: DOSE_EMPA_MGD, DOSE_LOR_MGD, DOSE_SEMAGLUTIDE_MG, DOSE_PHT_MGKGD). The _MGKG token records that obiltoxaximab is dosed per kilogram of body weight rather than as a flat dose, which is load-bearing here: the whole animal-to-human translation argument rests on the same 16 mg/kg per-kg dose producing comparable Cmax and superior AUC in humans versus animals. Distinct from the rxode2/nlmixr2 event column amt – this is a per-subject covariate carrying the assigned dose LEVEL for a model that has no PK compartment, whereas amt carries an administered amount at a dose event. The companion population PK model Nagy_2017_obiltoxaximab.R does not use this covariate; it takes the dose through the ordinary event table in mg. Ratified canonically alongside the Nagy 2017 obiltoxaximab extraction.

DOSE_APRAMYCIN_MGKG (canonical for administered apramycin dose level in mg/kg)

• Description: Administered apramycin dose level, in mg/kg of body weight, carried as a covariate because the subcutaneous absorption rate constant in the preclinical apramycin population PK model is a power function of the dose LEVEL rather than of the administered amount. Constant within a dosing occasion; a study that escalates the dose between occasions must carry it as a per-record (time-varying) column.
• Units: mg/kg
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as the power term ka = ka30 * (DOSE_APRAMYCIN_MGKG / 30)^pow with ka30 = 2.17 /h and pow = -0.160 in the founding example, so the reference dose level is 30 mg/kg and absorption slows as the dose rises.
• Source aliases:
  • DOSE – used in HernandezLozano_2025_apramycin_mouse.R; the symbol is Dose in Sou 2021 Eq. 3, where the upstream mouse PK model was estimated.
• Example models: HernandezLozano_2025_apramycin_mouse.R (mouse complicated-urinary-tract-infection PKPD; dose levels 0.03-51.2 mg/kg subcutaneously twice daily across three studies, with the dose-dependent absorption inherited from Sou T et al., Clin Pharmacol Ther 2021;109:1063-73, Table 1 mouse column and Eq. 3).
• Notes: Follows the DOSE_<DRUG>_<UNITS> auto-approve family (siblings: DOSE_OBILTOXAXIMAB_MGKG, DOSE_RTV_MGKG, DOSE_RIV_MGKG). Distinct from the rxode2/nlmixr2 event column amt, which carries the administered amount in mg (= dose level x body weight); both are needed in the founding example, because the amount drives the mass balance while the level drives the absorption rate constant. The dose-dependency was specific to the mouse in Sou 2021 (dOFV = -49.6); the rat and guinea-pig fits used a plain first-order ka, so a future apramycin model in another species may carry this column without using it, in which case it belongs in covariatesDataExcluded rather than covariateData. Ratified canonically alongside the Hernandez-Lozano 2025 apramycin extraction.

AUCMIC_TYLO (canonical for the tylosin AUC24h/MIC PK/PD index, carried as a ratio)

• Description: Tylosin PK/PD index formed as the plasma area under the concentration-time curve over 24 h divided by the MIC of the challenge isolate (AUC24h/MIC), supplied to an antibacterial sigmoid Emax PK/PD-integration model as an already-formed RATIO. Founding member of the AUCMIC_<DRUG> family, which is explicitly distinct from the AUC_<DRUG> family (AUC_CARBO, AUC_GEM, AUC_GCV, AUC_LCM, AUC_CBZ, AUC_PAZO, AUC_RTV, AUC_VERUB, AUC_IBRU, AUC_AMPH, AUC_EMPA, …): an AUC_<DRUG> column carries an ABSOLUTE interval-integrated exposure in concentration-time units, which the model then divides by a separate mic parameter, whereas an AUCMIC_<DRUG> column carries the DIMENSIONLESS-numerator ratio itself, in units of time. Use the AUCMIC_<DRUG> form when the source paper defines and reports the index only as a ratio and does not report an MIC that would let the ratio be split into numerator and denominator; use AUC_<DRUG> plus a model mic parameter whenever the paper does report the challenge strain’s MIC, because that form lets a user re-target the model to an isolate of different susceptibility.
• Units: h (equivalently h*ug/mL divided by ug/mL). Document per-model via covariateData[[AUCMIC_TYLO]]$units if a different exposure or susceptibility unit is reported.
• Type: continuous
• Scope: specific
• Reference category: n/a – set to 0 for drug-free control records so the sigmoid term Emax * AUCMIC_TYLO^gamma / (AUCMIC_TYLO^gamma + EC50^gamma) vanishes and the predicted 24 h change in log10 CFU/mL reduces to the control value E0.
• Source aliases:
  • AUC24 h/MIC / AUC/MIC – printed names in Lee 2023 Section 2.8 (defining the sigmoid Emax driver C), Section 2.10 (the dose equation Dose = (AUC24h/MIC x MIC x Cl) / (F x fu)) and Table 3 (rows AUC 24 h /MIC for bacteriostatic activity and AUC 24 h /MIC for bactericidal activity). Lee 2023 reports index values of 0.98-1.12 h for bacteriostatic activity (E = 0) and 1.97-2.54 h for bactericidal activity (E = -3) across the four pathogen x health-state fits, with EC50 estimates of 1.06-1.33 h.
• Example models: Lee_2023_tylosin_Apleuropneumoniae_healthy.R, Lee_2023_tylosin_Apleuropneumoniae_infected.R, Lee_2023_tylosin_Pmultocida_healthy.R, Lee_2023_tylosin_Pmultocida_infected.R (Lee 2023 ex vivo inhibitory sigmoid Emax PK/PD integration for intramuscular tylosin in healthy and co-infected pigs; AUCMIC_TYLO drives the signed 24 h change in log10 CFU/mL).
• Notes: Specific scope – tylosin-specific, and tied to a 24 h interval-AUC convention. The ratio-carrying form was chosen because Lee 2023 never reports the MIC of either challenge isolate (Sections 2.4 and 3.1 state only that strains “with MIC values similar to the MIC90” were selected: BA2000013 for A. pleuropneumoniae, BA1700127 for P. multocida), so there is no sourceable MIC with which to reuse the AUC_<DRUG> plus mic shape. Substituting the collection MIC90s (16 ug/mL for A. pleuropneumoniae, 32 ug/mL for P. multocida) would bake in a value the paper’s own Table 3 contradicts three ways: T > MIC is reported as 13.08 +/- 6.15 h for A. pleuropneumoniae in healthy pigs although Cmax (5.79 ug/mL, Table 2) never reaches 16 ug/mL, so T > MIC must be 0; the AUC/MIC and Cmax/MIC rows are identical for both pathogens although their MIC90s differ; and those two rows imply mutually inconsistent MICs of 10.3 and 19.3 ug/mL. Carrying the ratio introduces no inferred value and matches the paper’s printed equation literally. The closest structural analogues in the register are AUC_AMPH and the unregistered AUC_TILM (Chen_2023_tilmicosin.R), both of which are veterinary per-24-h-interval antibacterial exposures handed to a sigmoid PK/PD-index model in place of a PK ODE, but both of which take the absolute-AUC form because their source papers do report the challenge strain MIC. A future tylosin model that does report its isolate MIC should register AUC_TYLO rather than overload this name; a future model using the Cmax/MIC index should register a parallel canonical. Family and name ratified by the operator in the oare_PMC10556534 sidecar (2026-08-04) alongside the Lee 2023 tylosin extraction.

CP_MILTEFOSINE_UGML (canonical for instantaneous miltefosine plasma concentration as a time-varying PD driver)

• Description: Instantaneous plasma concentration of miltefosine (hexadecylphosphocholine; oral alkylphosphocholine antileishmanial) supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Used in PD-only antiparasitic kill-rate models that consume an upstream miltefosine population PK trajectory as input because the source analysis was a sequential PK-then-PD fit with the PK model fixed from a previously published popPK analysis.
• Units: ug/mL (= mg/L). NOTE the unit is NOT the one Table 2 prints. Verrest 2024 Table 2 gives lambda_MF in ug^-1 * L * h^-1, which would put this column in ug/L; that reading is falsified by the paper’s own Table 4 (see Notes), and the self-consistent unit is ug/mL, matching the UGML token already used by CP_RITUXIMAB_UGML and CP_RACSOTALOL_UGML.
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as a linear term in the drug-induced parasite-elimination rate, kdrug = kdrug + slope_miltefosine * CP_MILTEFOSINE_UGML, where slope_miltefosine (the paper’s lambda_MF, 0.0010) has units of mL/(ug*h). Set to 0 for study arms that do not receive miltefosine and outside the drug-exposure window. Reference values observed: miltefosine accumulates slowly over a 28-day 2.5 mg/kg/day course and, because of its long half-life, maintains above-target exposure for roughly 50 days from start of treatment (Verrest 2024 Fig 4 and Results 3.2.1).
• Source aliases:
  • individual predicted miltefosine concentration (Verrest 2024 Methods 2.4.2 and Fig 2 C drug; derived from the Palic 2020 non-linear paediatric-VL population PK model, J Antimicrob Chemother 75:3260-3268, and supplied to NONMEM as a data column).
• Example models: Verrest_2024_leishmania.R (linear parasite-kill term lambda_MF * CP_MILTEFOSINE_UGML in the Leishmania blood-parasite-dynamics PK-PD model; the driver covers the AmB+MF10D, MFC28D, and MFA28D arms).
• Notes: Specific scope; miltefosine-specific. The drug-specific naming follows the established CP_<drug>_<units> precedent (CP_OXY_NGML, CP_FBX_NGML, CP_MORPH_NGML, CP_GLASDEGIB_NGML, CP_RACSOTALOL_UGML) and the standing instruction on CP_MGL to register a drug-specific canonical rather than overload the generic name. Unlike most members of the family, an nlmixr2lib miltefosine popPK model DOES exist – modellib('Dorlo_2017_miltefosine'), fit to the same LEAP0208 trial (NCT01067443) that supplies three of Verrest 2024’s five arms – so users can generate this trajectory from the registry instead of supplying observed data; note that Verrest 2024 itself used the Palic 2020 model, which is a different (non-linear, paediatric) miltefosine popPK analysis. Unit provenance. Table 2 prints lambda_MF in ug^-1 * L * h^-1, implying a ug/L driver. Applying that literally makes kdrug about 5.2 /h on the 2.5 mg/kg/day miltefosine regimen (mean plasma concentration over Days 0-10 is roughly 5 ug/mL = 5180 ug/L), which drives the parasite load onto its 1 parasite/mL floor within hours. Verrest 2024 Table 4 instead reports the MFC28D arm declining only from 4583 parasites/mL on Day 1 to 2027 on Day 10, an implied kdrug of 0.0075 /h; the ug/mL reading gives 0.0010 * 5.18 = 0.0052 /h and reproduces it. The printed unit is therefore off by a factor of 1000 and this register entry records the corrected one. Distinct from CP_FEXINIDAZOLE_M1M2_UGML, the sibling driver registered alongside it. Ratified canonically alongside the Verrest 2024 Leishmania parasite-dynamics extraction.

CP_FEXINIDAZOLE_M1M2_UGML (canonical for instantaneous summed fexinidazole sulfoxide + sulfone whole-blood concentration as a time-varying PD driver)

• Description: Instantaneous whole-blood concentration of the SUM of the two active fexinidazole metabolites – fexinidazole sulfoxide (M1) and fexinidazole sulfone (M2) – supplied directly as a time-varying covariate column rather than computed from a coupled PK model. The parent nitroimidazole fexinidazole is NOT part of the driver: Verrest 2024 Fig 2 defines the drug concentration for the fexinidazole arm as “the sum of M1 and M2”, the two metabolites that carry the antiparasitic activity. Used in PD-only antiparasitic kill-rate models that consume an upstream fexinidazole/M1/M2 population PK trajectory as input.
• Units: ug/mL (= mg/L). As for CP_MILTEFOSINE_UGML, this is NOT the unit Table 2 prints for lambda_fexi; see Notes.
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as a linear term in the drug-induced parasite-elimination rate, kdrug = kdrug + slope_fexinidazole * CP_FEXINIDAZOLE_M1M2_UGML, where slope_fexinidazole (the paper’s lambda_fexi, 0.0011) has units of mL/(ug*h). Set to 0 for study arms that do not receive fexinidazole. Reference values observed: the Fexi10D regimen (1800 mg/day for 4 days then 1200 mg/day for 6 days) produced persistent drug exposure of about 11 days from start of treatment (Verrest 2024 Fig 4A and Results 3.2.1).
• Source aliases:
  • sum of M1 and M2 (Verrest 2024 Fig 2 legend and Methods 2.4.2; measured in dried blood spots by the assay of Tarral 2014 Clin Pharmacokinet 53:565-580 and predicted per subject by a fexinidazole/M1/M2 population PK model held in an unpublished internal report).
• Example models: Verrest_2024_leishmania.R (linear parasite-kill term lambda_fexi * CP_FEXINIDAZOLE_M1M2_UGML in the Leishmania blood-parasite-dynamics PK-PD model; the driver covers the Fexi10D arm).
• Notes: Specific scope; bound both to fexinidazole and to the summed-active-metabolite definition of the driver. The metabolite-sum semantics are load-bearing – a future analysis that drives a PD model from parent fexinidazole, or from M1 and M2 as two separate slots, must register a parallel canonical rather than overload this name. The upstream PK model is an unpublished internal report with no parameter values reported anywhere in Verrest 2024 or its S1 File, so no fexinidazole popPK model exists in the nlmixr2lib registry and the user must supply the trajectory. Whole blood rather than plasma is the source matrix (dried blood spots), an exception to the CP_ family’s usual plasma semantics that is recorded here rather than by a distinct prefix, since the covariate’s role – an externally supplied instantaneous systemic concentration driving a PD model – is identical. Unit provenance. The same 1000-fold discrepancy as CP_MILTEFOSINE_UGML. Inverting the Fexi10D arm’s published decline (Table 4: 3732 parasites/mL on Day 1 to 105 on Day 10, with kGR = 0.0037 /h) gives an implied kdrug of 0.020 /h and hence a driver of 0.020 / 0.0011 = 18 in this column’s units. Whole-blood M1 + M2 after 1800 mg/day fexinidazole is in the tens of ug/mL, not the tens of ug/L, so the column is in ug/mL. Distinct from CP_MILTEFOSINE_UGML, the sibling driver registered alongside it. Ratified canonically alongside the Verrest 2024 Leishmania parasite-dynamics extraction.

AUCR (canonical for the drug-drug-interaction AUC ratio of the modelled drug)

• Description: Dimensionless fold change in the modelled (victim) drug’s own area under the plasma concentration-time curve caused by a co-administered interacting drug (perpetrator), relative to the same drug given alone: AUCR = AUC(victim, with precipitant) / AUC(victim, without precipitant). Carries the magnitude of a pharmacokinetic drug-drug interaction as a continuous covariate rather than as a binary co-medication flag, so a model can be simulated across a graded range of interaction strengths (and can be inverted to answer “how strong an interaction is still acceptable at this dose?”). Typically enters apparent clearance as a divisor when the dose is fixed, since at constant dose and constant bioavailability AUC is inversely proportional to CL/F: cl = exp(lcl) * <other covariate terms> / AUCR. Values are usually taken from a drug-interaction compilation (e.g. the University of Washington Drug Interaction Database) rather than measured in the modelling dataset.
• Units: (unitless ratio)
• Type: continuous
• Scope: general
• Reference category: n/a – reference value is 1.0, meaning no interacting drug is co-administered. Values > 1 denote inhibition of the victim’s clearance; values < 1 would denote induction.
• Source aliases:
  • AUCR – printed name in Okada 2024 Table 1, Table 3 and Methods (“area under the blood concentration-time curve increase ratio”).
• Example models: Okada_2024_triazolam.R (oral triazolam PK/PD; Table 3 clearance model CL = theta3 * (age/30)^theta5 * 1/AUCR, driven across the 15 literature AUCR values of Table 1 from ranitidine 1.31 to ritonavir 40.7).
• Notes: General scope: the quantity is defined relative to whichever drug the model describes, so any popPK/PK-PD model that parameterises interaction magnitude continuously can reuse the column without renaming it. Deliberately distinct from three neighbouring families. (1) The AUC_<DRUG> family (AUC_RTV, AUC_IBRU, AUC_VERUB, AUC_CARBO, AUC_GEM, AUC_GCV, AUC_LCM, AUC_CBZ, AUC_PAZO, AUC_AMPH, AUC_ADU/_DON/_GAN/_LEC, AUC_BAST_FW) carries an absolute interval-integrated exposure of a named drug in concentration-time units; AUCR is a dimensionless ratio of the victim’s exposure to itself. (2) The binary CONMED_* DDI indicators (CONMED_CYP3A4_INH, CONMED_CYP3A4_INH_STRONG/_MOD/_HI/_LO, CONMED_CYP3A4_IND, CONMED_ITRACONAZOLE, CONMED_AZOLE, CONMED_RTV, CONMED_COBICISTAT, …) record only whether an interacting drug is present, not how strong the interaction is; use AUCR when the model’s conclusions depend on the graded magnitude and a CONMED_* flag when the paper fits a categorical shift. (3) The CP_<drug>_<units> family (CP_RIF_UM, CP_GDC_UM, CP_PRB_MGL) carries an instantaneous perpetrator plasma concentration feeding a mechanistic (time-varying) inhibition term; AUCR is a static, already-integrated, victim-side summary of the whole interaction. Note also that AUCR is unrelated to the similarly-spelled UACR (urine albumin-to-creatinine ratio). A model that needs the interaction split by perpetrator identity should carry AUCR alongside a CONMED_<INN> flag rather than encoding the perpetrator into this column’s name. Name and general scope ratified by the operator in the oare_PMC11370030 sidecar (2026-08-05) alongside the Okada 2024 triazolam extraction.

AUC_PAROX (canonical for cumulative paroxetine AUC over the first week of treatment)

• Description: Area under the plasma paroxetine concentration-time curve accumulated from treatment initiation to the end of the first week of treatment (AUC 0-1 week). Per-subject, time-fixed: a single early-exposure summary carried as a baseline covariate into a downstream PK/PD model, not a per-dosing-interval AUC. Not measured directly – predicted for each patient from a companion population PK model applied to that patient’s week-1 dosing history. Shigetome 2025 screened eight paroxetine exposure indices (measured trough; population-PK-predicted trough, Cmax and single-dose AUC; and cumulative AUC to weeks 1, 2, 4 and 6) and retained only the first-week cumulative AUC, which is what makes the cumulative-to-a-fixed-early-timepoint convention the defining property of this column rather than an incidental one.
• Units: ng*h/mL (equivalently ug*h/L). Document per-model via covariateData[[AUC_PAROX]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as a ratio-power term normalised to a cohort reference value. Reference value observed: 2610.43 ngh/mL (Shigetome 2025 Results 3.3 printed equation and NONMEM Text S3). Observed distribution in the 50-patient PK/PD cohort: 2764.9 +/- 703.1 ngh/mL in the remission group and 2472.2 +/- 385.6 in the non-remission group, overall range 1642.4-5813.6 (Table S5).
• Source aliases:
  • AUC_W1 – NONMEM $INPUT column name in Shigetome 2025 Text S3; identical orientation, no value transformation.
  • AUC 0-1week – the paper’s prose and figure-legend form.
• Example models: Shigetome_2025_paroxetine_madrs.R (enters the maximum enhancement rate in depression severity as the subtracted power term emax - (AUC_PAROX / 2610.43)^-8.56, so that higher first-week exposure raises the attainable MADRS improvement).
• Notes: Specific scope because the column meaning is tied to paroxetine and to the cumulative-first-week convention. Member of the AUC_<DRUG> family (AUC_CARBO, AUC_GEM, AUC_GCV, AUC_PAZO, AUC_RTV, AUC_VERUB, AUC_ADU, AUC_DON, AUC_GAN, AUC_LEC, AUC_IBRU, AUC_LCM, AUC_CBZ, AUC_AMPH, AUC_LEN), registered as the drug-specific sibling that the AUC_BAST_FW entry directs future first-week-AUC models to create rather than overloading that teaching-dataset name. Distinct from the other AUC_<DRUG> members in that all of those are per-interval or per-cycle exposures that are updated as treatment proceeds, whereas AUC_PAROX is cumulative from time zero and then frozen. A future paroxetine model using a per-interval AUC, or a cumulative AUC to a different horizon (weeks 2, 4 or 6, all of which Shigetome 2025 computed but did not retain), should register a parallel canonical rather than overload this name. The -8.56 exponent makes the term extremely steep: emax reaches zero near AUC_PAROX = 1541 ng*h/mL and the term is essentially saturated above the reference value, so the covariate must not be extrapolated outside the observed range.

AUC_BTP (canonical for bitopertin steady-state AUC over the 24 h dosing interval)

• Description: Individual bitopertin (RG1678, a glycine transporter 1 inhibitor) area under the plasma concentration-time curve over the once-daily 24 h dosing interval at steady state, used as the exposure driver of the Emax inhibition of hemoglobin synthesis in semi-mechanistic erythropoiesis PKPD models. Time-varying step-wise: constant while the subject is on a given dose level and 0 otherwise. Rognas 2025 derives it inside the control stream as DRUG = ADOS_BTP / ICL_BTP * TREAT_BTP – the actual last dose amount (mg) divided by the individual empirical-Bayes apparent clearance (L/h) from an upstream bitopertin population PK model that the paper does not report (“data not shown”) – multiplied by a treatment indicator. TREAT_BTP is 1 from the time of a dose until two days after the last dose and 0 thereafter, so the drug effect carries over for two days past treatment end; downstream users encode that carry-over in the AUC_BTP time course rather than in the model body.
• Units: mg*h/L (equivalently ug*h/mL). Must be in the same units as the model’s AUC50 so the Emax term is dimensionless. Document per-model via covariateData[[AUC_BTP]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – enters via the Emax form Imax * AUC_BTP^gamma / (AUC50^gamma + AUC_BTP^gamma) with gamma fixed to 1. Set to 0 for placebo subjects and for off-treatment periods, whereupon the inhibition term vanishes exactly and the erythropoiesis system rests at its homeostatic baseline. Reference value observed: AUC50 = 16.50 mgh/L (Rognas 2025 Table 2). The paper’s own simulations specify exposure as multiples of AUC50 rather than as doses: 0.5 x AUC50 = 8.25 mgh/L gives 20% inhibition and 2 x AUC50 = 33 mg*h/L gives 40% inhibition (Methods “Simulations”; reproduced exactly by the packaged model).
• Source aliases:
  • DRUG – Rognas 2025 Supplementary Appendix section 4 NONMEM control stream $PK (the computed exposure metric actually used in $DES).
  • ICL_BTP – the $INPUT column carrying the upstream empirical-Bayes apparent clearance from which DRUG is derived; this is NOT the canonical (it is a clearance, and belongs to the CL_INDIV / CLI family).
  • AUCss – the name used in the paper’s Methods narrative (“Individual AUCss values were used as the exposure metric driving the bitopertin drug effect”).
• Example models: Rognas_2025_bitopertin.R (founding example – drives the Emax inhibition of the corpuscular-hemoglobin input rate kinMch, and, when the mechanism switches are overridden, of the precursor recruitment rate and precursor differentiation rate for the paper’s hypothetical Mechanisms B and C).
• Notes: Specific scope; bitopertin-specific and tied to the once-daily steady-state 24 h AUC convention. Member of the AUC_<DRUG> family (AUC_CARBO, AUC_GEM, AUC_GCV, AUC_PAZO, AUC_RTV, AUC_VERUB, AUC_ADU, AUC_DON, AUC_GAN, AUC_LEC, AUC_IBRU, AUC_LCM, AUC_CBZ, AUC_AMPH, AUC_LEN); AUC_VERUB is the closest structural analogue, being likewise a steady-state dosing-interval AUC standing in for a PK ODE and driving an Emax inhibition of a biosynthetic input rate. Because the upstream bitopertin population PK model is unpublished, a downstream user must either supply per-subject AUC values directly or express the exposure as a multiple of AUC50 as the paper’s own simulations do. Ratified canonically alongside the Rognas 2025 bitopertin erythropoiesis extraction.

DOSE_LIDOCAINE_MG (canonical for total administered lidocaine dose over the treatment episode)

• Description: Total mass of lidocaine administered to a subject over the whole treatment episode, in milligrams – the loading infusion plus the entire continuous infusion, summed. Time-fixed per subject. Distinct from a per-administration or per-record dose level: this is a cumulative episode total, so it is only meaningful once the treatment episode is fully specified.
• Units: mg
• Type: continuous
• Scope: specific
• Reference category: n/a – used as a power term (DOSE_LIDOCAINE_MG / 312)^exponent, where 312 mg is the He 2025 study-typical episode total derived from Table 1 (mean loading dose 86.07 mg plus median continuous rate 57.97 mg/h times mean infusion duration 3.90 h).
• Source aliases:
  • DOSE – the column name printed in the He 2025 Supplementary Table S2 abbreviation list (“DOSE, Total doses of lidocaine”).
• Example models: He_2025_lidocaine.R (power effects on three parameters simultaneously: the lidocaine-to-MEGX fraction metabolised, exponent 0.669; the peripheral volume, exponent 1.27; and GX clearance, exponent 1).
• Notes: A drug-specific member of the DOSE_<drug>_<units> family, required here rather than the bare DOSE canonical because rxode2’s event-table translator (etTrans()) consumes a column literally named DOSE and never exposes it to model(); a model that reads it from an event table fails at solve time with The following parameter(s) are required for solving: DOSE. Confirmed for He_2025_lidocaine.R on the rxUi solve path. Because the covariate is an episode total rather than a dose level, it extrapolates aggressively: it spans roughly 200-500 mg across the He 2025 trial but reaches 6480 mg in that paper’s own 72 h postoperative-infusion simulation, where the exponent of 1.27 on the peripheral volume inflates V2 more than fortyfold. Treat predictions at episode totals outside the fitted range as illustrative.

Count / Markov-feedback PD covariates

These columns are specific to count / Markov / time-to-event PD models that fit per-record event counts (e.g., daily or monthly seizure counts) with optional dependence on the previous-period count. Register names retain their source-paper conventions where those names are unambiguous and readable.

TRT_PHASE (canonical for double-blind active-treatment-phase indicator)

• Description: 1 = the record falls within the active double-blind treatment phase (drug + placebo effects are switched on); 0 = baseline / run-in / off-treatment (drug + placebo effects are zeroed).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (baseline / off-treatment).
• Source aliases:
  • Q2 – used in the Schoemaker 2018 LEV / BRV pediatric extrapolation (DDMODEL00000239) as the treatment-phase gating multiplier on the combined placebo + drug-effect log-rate term (LE = LS0 + Q2 * LTRTE).
• Example models: Schoemaker_2018_levetiracetam.R (DDMODEL00000239).
• Notes: New canonical because the source name Q2 collides with the canonical PK parameter q2 (inter-compartmental clearance to peripheral2) – q2 could not be used as a covariate column without confusing source-trace lookups. Useful for any phase-gated PD model where placebo and drug effects are constrained to a specific study period; analogous to a “treatment-on” indicator.

PDV (canonical for previous-period observed count (Markov-feedback state))

• Description: Previous-period observed count, supplied per-record as a covariate input to capture Markov dependence on the previous count. For daily-count datasets PDV is the observation on the immediately preceding day; for monthly (or other aggregate) records PDV is the observed count from the immediately preceding interval. The biological identity of the count is paper-specific (seizures in Ahn 2010 / Schoemaker 2018, contrast-enhancing MS lesions in Velez de Mendizabal 2013); the canonical concept is “the immediately preceding observed count, used as a Markov-state covariate”.
• Units: (count, non-negative integer)
• Type: count
• Scope: specific
• Reference category: n/a – typically used as <param> * PDV / (ES50 + PDV) (Markov-Hill) or as a linear coefficient <param> * PDV in additive-mean Markov models (Velez de Mendizabal 2013 equation 5).
• Source aliases: PDV – used in the Schoemaker 2018 LEV/BRV pediatric extrapolation (DDMODEL00000239), in the Ahn 2010 pregabalin Markov seizure-count model that the Schoemaker 2018 publication cites as the precursor structure, and in the Velez de Mendizabal 2013 MS CEL-count NB nested MAK2 model.
• Example models: Schoemaker_2018_levetiracetam.R (DDMODEL00000239), VelezdeMendizabal_2013_multipleSclerosis.R (additive-mean Markov form, equation 5).
• Notes: Specific scope because the column’s interpretation (a Markov feedback state) is intrinsic to count-likelihood Markov-feedback PD models. nlmixr2 / rxode2 cannot natively express a Markov dependence of an observation on the immediately preceding observation as a model state – supplying PDV as a per-record data column is the operator-approved (sidecar response-001 Q2) way to preserve the published structure. For records where the Markov term should not contribute (e.g., monthly counts in a mixed daily / monthly cohort, or the first monthly observation per subject) two conventions exist: (a) the sentinel value -99, used in Schoemaker_2018_levetiracetam.R where the model gates the Markov contribution on CHILD = 1 so the sentinel is multiplied by zero and is harmless; (b) the natural zero value PDV = 0, used in VelezdeMendizabal_2013_multipleSclerosis.R for the first per-subject observation, which makes the Markov contribution exactly zero without any indicator gating. Pick whichever convention matches the source paper’s encoding for the model at hand. The companion second-order Markov-state covariate is [[PPDV]] (the observed count two periods prior); see the PPDV entry for the second-order form.

PPDV (canonical for second-prior-period observed count (second-order Markov-feedback state))

• Description: Observed count from two periods prior, supplied per-record as a covariate input to capture a second-order Markov dependence on the count two intervals before the current one. For monthly-count datasets PPDV is the observed count from two monthly MRIs ago; for daily-count datasets it would be the observation from two days prior if a second-order term were used. Companion to the first-order [[PDV]] state.
• Units: (count, non-negative integer)
• Type: count
• Scope: specific
• Reference category: n/a – typically used as a linear coefficient <param> * PPDV in additive-mean Markov-count models (Velez de Mendizabal 2013 equation 5).
• Source aliases: PPDV – used in the Velez de Mendizabal 2013 MS CEL-count NB nested MAK2 model (paper notation “PPDV” = “previous-previous DV”, paralleling PDV = “previous DV”).
• Example models: VelezdeMendizabal_2013_multipleSclerosis.R (equation 5 second-order Markov term; theta_PPDV = 0.150, materially smaller than theta_PDV = 0.447 per the source paper’s decreasing-pattern observation across Markov orders).
• Notes: Specific scope because the column’s interpretation (a second-order Markov feedback state) is intrinsic to count-likelihood Markov-feedback PD models. Set PPDV = 0 at the first two per-subject observations (no second-prior interval) so the second-order Markov contribution is exactly zero; this matches the convention adopted in VelezdeMendizabal_2013_multipleSclerosis.R. The Velez de Mendizabal 2013 paper also explored a third-order Markov term [[PPPDV]] – “previous-previous-previous DV”, coefficient theta_PPPDV – but the third-order fit improvement was not statistically significant, and the third-order column is not registered until / unless a future model retains it.

NDAYS (canonical for number of days in the count-record interval)

• Description: Number of days in the interval over which the observed seizure count was tabulated. Multiplies the per-day rate to give the expected count for the record (e.g., LAMB = exp(LE) * NDAYS).
• Units: days
• Type: count
• Scope: general
• Reference category: n/a – appears as a multiplier on a per-day rate.
• Source aliases: NDAYS – used in the Schoemaker 2018 LEV/BRV pediatric extrapolation (DDMODEL00000239).
• Example models: Schoemaker_2018_levetiracetam.R (DDMODEL00000239).
• Notes: General scope because the count-interval-length concept is shared across any count or rate-based PD model that mixes record granularity (e.g., daily and monthly counts in the same dataset). For pure-daily or pure-monthly cohorts the column is constant; including it as a covariate keeps the model usable in mixed-granularity simulations.

MOMENT (canonical for endotracheal suctioning procedural state)

• Description: Procedural state at the time of the pain / distress assessment in invasive-ventilation studies, used to select between separate pre-procedure / intra-procedure / post-procedure baseline parameters. Coded 1 = before suctioning, 2 = during suctioning, 3 = after suctioning.
• Units: (categorical, 3 levels)
• Type: categorical
• Scope: specific
• Reference category: 1 (before suctioning).
• Source aliases: MOMENT – used in the Valitalo 2017 IRT morphine PD model (DDMODEL00000247).
• Example models: Valitalo_2017_morphine.R (DDMODEL00000247; selects between the three baseline pain typical values presuct / suct / aftsuct and the matching 3x3 correlated etas).
• Notes: Specific scope because the column’s meaning is tied to a particular study procedure (endotracheal suctioning during mechanical ventilation in neonates). The Simons 2003 cohort that Valitalo 2017 re-analysed scheduled pain assessments around suctioning events, so MOMENT changes within-subject at each scheduled assessment. Ratified canonically alongside the Valitalo 2017 morphine extraction.

ITEM (canonical for pain-assessment item identifier in IRT graded-response models)

• Description: Identifier of the specific pain-assessment item being scored at each observation row, used to dispatch between the IRT discrimination / difficulty parameter sets in a graded-response model. Valitalo 2017 coding: 1 = COMFORT-B alertness; 2 = COMFORT-B calmness/agitation; 3 = COMFORT-B respiratory response; 5 = COMFORT-B body movement; 7 = COMFORT-B facial tension; 12 = VAS (cm, range 0-10); 25 = PIPP brow bulge; 26 = PIPP eye squeeze; 27 = PIPP nasolabial furrow; 28 = NIPS total.
• Units: (categorical, 9-10 levels depending on cohort)
• Type: categorical
• Scope: specific
• Reference category: n/a – selects per-item parameter sets rather than acting as a reference contrast.
• Source aliases: ITEM – used in the Valitalo 2017 IRT morphine PD model (DDMODEL00000247).
• Example models: Valitalo_2017_morphine.R (DDMODEL00000247; switches the IRT graded-response discrimination / difficulty parameters per row).
• Notes: Specific scope because the integer-to-item mapping is tied to the Valitalo 2017 NM-TRAN dataset’s coding. Other IRT graded-response models in the library (when they are added) may use different integer codings and should register their own canonical (e.g., ITEM_<study>) when the codings collide. The COMFORT-B “muscle tension” item is omitted from the Valitalo 2017 coding because it could not be assessed from video recordings.

OBSTYPE (canonical for VAS observer type)

• Description: Observer type for visual-analogue-scale pain assessments: 1 = investigator (video-based), 2 = bedside nurse. Used to select between observer-specific VAS difficulty / discrimination THETAs and between observer-specific residual-error SDs.
• Units: (categorical, 2 levels)
• Type: categorical
• Scope: specific
• Reference category: 1 (video investigator).
• Source aliases: OBSTYPE – used in the Valitalo 2017 IRT morphine PD model (DDMODEL00000247).
• Example models: Valitalo_2017_morphine.R (DDMODEL00000247; selects between diff_vas_video vs diff_vas_bedside, discr_vas_video vs discr_vas_bedside, and addSd_vas_video vs addSd_vas_bedside).
• Notes: Specific scope because the binary observer-coding is tied to the Valitalo 2017 NM-TRAN dataset. Future IRT models with a different observer split (e.g., parent / nurse / physician) should register their own canonical rather than overloading this 2-level coding.

Race / ethnicity

Canonical pattern: RACE_<GROUP>. Use one indicator per race/ethnicity group the source models. Reference category is the implicit 0 = all other groups; document explicitly which groups are in the reference. When the source uses composite groups (e.g., “Black or Other”), name them accordingly (RACE_BLACK_OTHER) and list the components in notes. The base RACE_<GROUP> indicators are scope: general; composite groupings are scope: specific because the grouping is tied to the study’s analysis plan.

RACE_BLACK (canonical for Black / African American race indicator)

• Description: 1 = Black / African American, 0 = other.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (document the actual reference groups used).
• Source aliases:
  • BLACK – used in Hu_2026_clesrovimab.R, Robbie_2012_palivizumab.R.
• Example models: Zhu_2017_lebrikizumab.R (canonical form), Robbie_2012_palivizumab.R, Wada_2023_sparsentan.R (log-additive effect on the apparent central volume: exp(0.309 * RACE_BLACK), i.e. 36% higher Vc/F than the White reference; paired with RACE_ASIAN so both = 0 selects White).

RACE_WHITE (canonical for White race indicator)

• Description: 1 = White, 0 = non-White.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (non-White; complement composition depends on the source paper, typically pooling Black/African American, Asian, American Indian/Alaska Native, Native Hawaiian/Pacific Islander, Other, Not reported, Unknown). Some papers (e.g., Hu 2014) instead use the Caucasian (RACE_WHITE = 1) subgroup as the typical-value reference; the column encoding is unchanged but the model implements the effect on (1 - RACE_WHITE).
• Source aliases:
  • RACE (with values 1 = White / 0 = non-White) – used in Lin_2024_casirivimab.R. Source column name RACE is generic; the canonical name is intentionally explicit because some other models use RACE for a different dichotomy.
  • RACE (Caucasian-vs-non-Caucasian dichotomy as named in Hu 2014 Table 2) – used in Hu_2014_bapineuzumab.R. Same canonical column name and 1 = White / 0 = non-White encoding; the typical-value reference is the Caucasian subgroup, so the model implements the 15% non-Caucasian effect on (1 - RACE_WHITE).
• Example models: Lin_2024_casirivimab.R (multiplicative fractional change on CL relative to non-White reference), Hu_2014_bapineuzumab.R (multiplicative 15% increase in CL for non-Caucasian relative to Caucasian reference).
• Notes: Used by papers that dichotomize race as White vs. non-White rather than decomposing into separate group indicators. Sign and reference-category interpretation are inverted relative to RACE_BLACK / RACE_ASIAN / etc.; do NOT combine RACE_WHITE with the decomposed indicators in the same model. The model’s typical-value reference category (which subgroup gets the unmodified lcl / lvc) varies between papers – Lin 2024 uses non-White as the reference, Hu 2014 uses Caucasian (White) as the reference; both share the same canonical column encoding.

RACE_BLACK_OTH (canonical for composite Black/Other group)

• Description: 1 = Black/African American or Other race, 0 = other groups.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = White or Native Hawaiian/Pacific Islander (Clegg 2024 grouping).
• Source aliases: BLACK_OTH – used in Clegg_2024_nirsevimab.R.
• Example models: Clegg_2024_nirsevimab.R.
• Notes: Kept distinct from RACE_BLACK because the composite is not interchangeable.

RACE_BLACK_HISPANIC (canonical for composite Black / Hispanic American group)

• Description: 1 = subject self-identifies as Black / African American or as Hispanic American; 0 = White or Asian. Paper-specific composite developed by Thoueille 2023, which first fitted a separate tenofovir clearance per ethnic group (rich model) and then pooled the levels into this two-way split, retained because the reduced grouping cost only dOFV = +2 relative to the full ethnicity model (P > 0.05).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = White or Asian (Thoueille 2023 grouping; the paper’s model-building cohort contained only White 71%, Black 23%, Hispanic American 4% and Asian 2%, with no “Other” level, so the indicator and its reference exactly partition the cohort).
• Source aliases:
  • Paper’s ethnicity levels “Black” + “Hispanic American” – decompose into RACE_BLACK_HISPANIC = as.integer(ETHNICITY %in% c("Black", "Hispanic American")). Thoueille 2023 Table 2 and Table S3 label the coefficient row “theta_Black or Hispano American” (the papers’s spelling of “Hispanic”).
• Example models: Thoueille_2023_tenofovir_full.R, Thoueille_2023_tenofovir_alafenamide.R (multiplicative fractional effect on apparent tenofovir clearance: (1 + e_race_black_hispanic_cl * RACE_BLACK_HISPANIC) with e_race_black_hispanic_cl = 0.119 and 0.122 respectively, i.e. ~12% higher CL/F than the White / Asian reference).
• Notes: Operator-ratified sidecar 2026-07-29 (oare_PMC10232258 request-001 Q2, option A). Mutually exclusive with the decomposed constituents [[RACE_BLACK]] and [[RACE_HISPANIC]] – a model uses either this composite or the two separate indicators, never both, because the composite carries a single pooled coefficient that the constituents would double-count. Distinct from [[RACE_BLACK_OTH]] (Black or Other, reference White / Native Hawaiian-Pacific Islander) and, importantly, from [[RACE_NONBLACK_NONWHITE]], which places Black in the reference group and therefore carries the opposite sign convention. Specific scope: the grouping is paper-specific and reflects the empirical clustering of Thoueille 2023’s ethnicity covariate step rather than any biological homogeneity across the two constituent populations; future papers that split Black from Hispanic, or that add an “Other” level, should register their own composite rather than overload this one.

RACE_ASIAN (canonical for Asian race indicator)

• Description: 1 = Asian, 0 = other.
• Units: (binary)
• Type: binary
• Scope: general
• Source aliases: ASIAN – used in Hu_2026_clesrovimab.R, Robbie_2012_palivizumab.R, Fau_2020_isatuximab.R. RAAS (race-Asian-vs-other indicator as named in Bajaj 2017 Table 1) – used in Bajaj_2017_nivolumab.R. RACEN (race-numeric indicator as named in Lu 2019 / Shi 2020 NONMEM control stream; ASIAN = 1 if RACEN == 1) – used in Lu_2019_polatuzumab.R.
• Example models: Zhu_2017_lebrikizumab.R (canonical form), Robbie_2012_palivizumab.R, Bajaj_2017_nivolumab.R, Fau_2020_isatuximab.R, Lu_2019_polatuzumab.R (multiplicative factor e_asian_vc = 0.929 on acMMAE Vc, i.e., 7.1% lower V1 in Asian patients; verbatim Shi 2020 (PMID 32770353) ethnicity-sensitivity re-quote of the Lu 2019 popPK Asian-race covariate), Wada_2023_sparsentan.R (log-additive effect on the apparent central volume: exp(0.265 * RACE_ASIAN), i.e. 30% higher Vc/F than the White reference; paired with RACE_BLACK so both = 0 selects White).

RACE_ASIAN_AMIND_OTH (canonical for composite Asian / American Indian / Other group)

• Description: 1 = Asian, American Indian / Alaska Native, or Other race; 0 = White or Black. Composite indicator that pools the smaller-N race groups in a population dominated by White and Black subjects, with White + Black serving as the reference category. Distinct from RACE_ASIAN_AMIND_MULTI (Clegg 2024 grouping that includes Multiracial; pooled against a different reference), from RACE_ASIAN_OTH (within-Asian-population sub-indicator), and from RACE_BLACK_OTH (different composite).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = White or Black (the larger-N pooled group used as the reference in the source paper).
• Source aliases: none formally; Frey 2013’s NONMEM control stream uses the inline race classification rather than a separate named column.
• Example models: Frey_2013_tocilizumab.R (multiplicative fractional effect on the DAS28 first-order loss rate Kout: Kout * (1 - 0.25 * RACE_ASIAN_AMIND_OTH) – Kout is 25% lower in the Asian/AmInd/Other composite group relative to the White+Black reference).
• Notes: Specific scope because the composite grouping is defined by the source paper’s analysis plan rather than by a uniform external standard. Do not combine with the decomposed RACE_ASIAN, RACE_OTHER, etc. indicators in the same model; the composite indicator is mutually exclusive with the decomposition. Ratified canonically on 2026-04-29 in support of the Frey 2013 tocilizumab DAS28 PKPD model. Frey 2013 uses TWO distinct race covariates: this RACE_ASIAN_AMIND_OTH indicator on Kout (DAS28-PD-side; pools Asian + AmInd + Other vs White+Black) AND the within-Asian RACE_ASIAN_OTH indicator on CL (PK-side; isolates the “Other Asian” subgroup within the Asian-only cohort).

RACE_ASIAN_AMIND_MULTI (canonical for composite group)

• Description: 1 = Asian, American Indian / Alaskan Native, or Multiple races, 0 = other.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = White, Black / African American, Native Hawaiian / Pacific Islander, or Other (Clegg 2024 grouping).
• Source aliases: ASIAN_AMIND_MULTI – used in Clegg_2024_nirsevimab.R.
• Example models: Clegg_2024_nirsevimab.R.
• Notes: Clegg 2024 applies this covariate to both CL and V2 with different coefficients.

RACE_ASIAN_OTH (canonical for Asian-other composite race indicator)

• Description: 1 = subject self-identifies as Asian-other (Asian heritage outside the locally-dominant Asian subgroup, e.g. non-Chinese in a Chinese-dominant cohort, or “Other Asian” as a study-defined catch-all category). 0 = otherwise. Reference category is the cohort’s dominant race grouping (typically Chinese or White, depending on the study).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0. Document the dominant subgroup explicitly in covariateData[[RACE_ASIAN_OTH]]$notes for every model that uses this covariate.
• Source aliases: none.
• Example models: Brown_2017_osimertinib.R (paper’s “Asian (not Japanese or Chinese)” composite indicator with linear effect on apparent clearance of the AZ5104 metabolite; reference category Caucasian).
• Notes: Distinct from RACE_ASIAN_AMIND_MULTI (a 4-way composite of Asian + American Indian + Multiple Races), RACE_ASIAN_AMIND_OTH (a 3-way Asian + AmInd + Other composite against a White+Black reference, used in Frey 2013), and RACE_BLACK_OTH (different composite). RACE_ASIAN_OTH is a within-Asian-population sub-indicator, not a multi-race composite. Operator decision (2026-04-28): kept separate from RACE_ASIAN because the paper’s “Other Asian” category is its own grouping, not an alias of “Asian (any)”. Brown 2017 uses Caucasian (not Chinese) as the dominant reference.

RACE_ASIAN_NORTHEAST (canonical for North East Asian composite race indicator)

• Description: 1 = North East Asian heritage (worldwide Chinese, Japanese, or Korean), 0 = non-North East Asian. Composite indicator analogous to RACE_ASIAN but specifically restricted to the East Asian subgroup most-relevant to ICH E5 ethnic-sensitivity / Asian-region bridging analyses.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (any non-North East Asian race, including South / Southeast Asian, White, Black, etc.).
• Source aliases:
  • RACE_NEAS – prior canonical name (pre-2026-06-19 readability standardization).
  • RAC4 – used in Zhou_2021_belimumab.R (Zhou 2021 Table 2 footnote d).
• Example models: Zhou_2021_belimumab.R (multiplicative factor 1.07 on V1).
• Notes: Distinct from the broader RACE_ASIAN (which can include South / Southeast Asian populations) because Zhou 2021 specifically tested whether Chinese/Japanese/Korean patients had different PK from the rest of the dataset; the analysis explicitly compared RAC4 (North East Asian) against alternative race definitions and chose RAC4 by AIC. Renamed from RACE_NEAS to RACE_ASIAN_NORTHEAST on 2026-06-19 per the canonical-register standardization audit (operator decision: spell out “Northeast” rather than the opaque NEAS abbreviation; consistent with RACE_<region> rather than RACE_<abbreviation>).

RACE_MULTI (canonical for multiracial indicator)

• Description: 1 = multiracial, 0 = other.
• Units: (binary)
• Type: binary
• Scope: general
• Source aliases: MULTIRACIAL – used in Hu_2026_clesrovimab.R.
• Example models: Hu_2026_clesrovimab.R.

RACE_OTHER (canonical for race-category ‘Other’ indicator)

• Description: 1 = race category “Other,” 0 = not.
• Units: (binary)
• Type: binary
• Scope: general
• Source aliases:
  • OTHER – used in Robbie_2012_palivizumab.R.
• Example models: Zhu_2017_lebrikizumab.R, Robbie_2012_palivizumab.R.

RACE_NONBLACK_NONWHITE (canonical for the non-Black non-White composite race indicator)

• Description: 1 = race is anything other than White or Black (composite that pools Asian + Hispanic + American Indian + Other or analogous smaller-N groups from a multi-level race classification), 0 = White or Black. Composite indicator used by papers that collapse a multi-level race column into a three-level effective structure with White as the reference, Black as a separate indicator, and “non-Black ethnic origin” as the composite.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = White or Black (the larger-N pooled reference group in the source paper). Pair with RACE_BLACK on the same model so the two indicators are mutually exclusive and the implicit White reference category is reproduced cleanly.
• Source aliases: none formally; Fisher 2008’s NONMEM control stream uses the 6-level inline race classification rather than a separate composite column.
• Example models: Fisher_2008_fosamprenavir.R (multiplicative CL/F factor 1.06 vs the White reference; the composite pools Race = 3 (Asian), 4 (Hispanic), 5 (American Indian), 6 (Other) into a single indicator alongside the separate RACE_BLACK indicator).
• Notes: Specific scope because the composite grouping is paper-defined and is mutually exclusive with the decomposed RACE_ASIAN, RACE_HISPANIC, RACE_AMIND, RACE_OTHER indicators in the same model (do not combine the composite indicator with its constituent decompositions). Distinct from RACE_ASIAN_AMIND_OTH (Frey 2013 grouping; explicitly excludes Hispanic) and from RACE_BLACK_OTH (Clegg 2024 grouping; includes Black in the composite). Ratified canonically alongside the Fisher 2008 fosamprenavir extraction.

RACE_HISPANIC (canonical for Hispanic / Latino ethnicity indicator)

• Description: 1 = Hispanic / Latino, 0 = non-Hispanic. Used by papers that report Hispanic as a separate category alongside Black, Asian, and Other rather than as a distinct ethnicity dimension.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (non-Hispanic; document the paper-specific reference race composition per-model).
• Source aliases:
  • HISPANIC – used in Robbie_2012_palivizumab.R.
  • Hispanic ethnicity indicator – used in Overgaard_2016_liraglutide.R (Overgaard 2016 Fig. 2 / Table S1 “Cov. Hispanic”; the paper reports race and ethnicity as separate dimensions but the column encoding, 1 = Hispanic vs 0 = non-Hispanic, is identical to the canonical form – per-model notes field records the ethnicity-vs-race interpretation).
• Example models: Robbie_2012_palivizumab.R (fractional effect on CL; additional effect on Vc), Overgaard_2016_liraglutide.R (log-scale multiplicative effect on CL/F, coefficient +0.08 per Table S1).
• Notes: In the US Office-of-Management-and-Budget (OMB) classification Hispanic is an ethnicity rather than a race, but clinical PK analyses frequently treat it as one of the race indicators. When a paper treats Hispanic as a race, use this column; otherwise encode ethnicity separately. Register-wise, this follows the RACE_<GROUP> indicator-decomposition pattern. Overgaard 2016 explicitly reports race and ethnicity as separate covariate dimensions but the numerical Hispanic-vs-non-Hispanic encoding matches this canonical – the per-model covariateData[[RACE_HISPANIC]]$notes records the “reported as ethnicity” interpretation.

RACE_JAPANESE (canonical for Japanese-heritage race indicator)

• Description: 1 = Japanese heritage, 0 = non-Japanese. Used when Japanese subjects form a distinct subgroup in the study design (e.g., ICH E5 bridging analyses or studies with a dedicated Japanese healthy-volunteer cohort).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (non-Japanese).
• Source aliases:
  • JAPANESE_HV – used in Wang_2017_benralizumab.R (Japanese healthy-volunteer cohort indicator; the healthy-volunteer vs. asthma-patient distinction is captured separately, not in this covariate).
• Example models: Wade_2015_certolizumab.R (multiplicative fractional effect on V/F; Wade 2015 breaks Japanese [RACE.EQ.8] out separately from RACE_ASIAN), Wang_2017_benralizumab.R (multiplicative factor 1.34 on Vc).
• Notes: Distinct from RACE_NEAS (North East Asian composite, includes Chinese, Japanese, and Korean) and from RACE_ASIAN. Use RACE_JAPANESE only when the source paper breaks out Japanese heritage as its own indicator; do not aggregate with other Asian groups when the paper keeps them separate. Ratified canonically on 2026-04-26.

RACE_CHINESE (canonical for Chinese-heritage race indicator)

• Description: 1 = Chinese heritage, 0 = non-Chinese. Used when Chinese subjects form a distinct subgroup alongside Japanese, Asian-other, and other race categories (e.g., multiregional oncology trials enrolling Chinese, Japanese, and other Asian cohorts as separate strata).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (non-Chinese; document the paper-specific reference race composition per-model).
• Source aliases: none formally; source NONMEM control streams typically use ad-hoc names (e.g., CHINESE, RACE.EQ.X).
• Example models: Brown_2017_osimertinib.R (linear additive effect (1 + 0.17 * RACE_CHINESE) on apparent clearance of the AZ5104 metabolite; reference category Caucasian).
• Notes: Distinct from RACE_NEAS (North East Asian composite, includes Chinese, Japanese, and Korean) and from RACE_ASIAN. Use RACE_CHINESE only when the source paper breaks out Chinese heritage as its own indicator alongside RACE_JAPANESE and RACE_ASIAN_OTH; do not aggregate with other Asian groups when the paper keeps them separate. Parallels the established RACE_JAPANESE entry. Ratified canonically on 2026-05-09.

RACE_PAPUAN (canonical for Papuan / indigenous Melanesian Indonesian heritage race indicator)

• Description: 1 = Papuan (indigenous Melanesian heritage native to the Indonesian provinces of Papua / West Papua, the western half of the island of New Guinea), 0 = non-Papuan (typically Indonesian residents of mainland-Indonesian Austronesian origin, e.g., Javanese / Sumatran transmigrants enrolled at Papua-based clinical sites; possibly also any non-Indonesian subjects in mixed cohorts). Used as a binary race / ethnicity indicator in clinical pharmacology studies conducted at sites in Indonesian Papua where the local population is a mixture of indigenous Melanesian Papuans and mainland-Indonesian transmigrants, and where PK differences across the two groups are clinically relevant.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (non-Papuan).
• Source aliases:
  • Paper’s binary Papuan indicator paired with the f multiplicative covariate term in Brussee 2016 Table 2 – used in Brussee_2016_arginine.R (values 1 = Papuan, 0 = non-Papuan; same orientation as the canonical).
• Example models: Brussee_2016_arginine.R (multiplicative factor f = 1.9 on L-arginine CL: CL_i = CL * (WT/60)^k1 * f^RACE_PAPUAN – Papuan patients have ~1.9x higher arginine clearance than the non-Papuan reference).
• Notes: Distinct from all existing RACE_<GROUP> indicators. Papuans are an indigenous Melanesian / Oceanic population genetically and culturally distinct from RACE_ASIAN (mainland-Indonesian transmigrants are typically Austronesian Asian by international population-genetics classification). The Brussee 2016 cohort was enrolled exclusively at the Mitra Masyarakat Hospital in Timika, Papua, so a paired REGION_INDONESIA indicator is not needed for that model – future models that mix Papuan and non-Papuan Indonesian sites may need such a region indicator separately. Ratified canonically on 2026-06-09 (general scope; the Brussee 2016 L-arginine PKPD model is the second example following Yeo 2008 arginine, which also used the same indigenous Papuan vs non-Papuan stratification).

RACE_IND_CHI_TWN (canonical for Indian/Chinese/Taiwanese composite race indicator (Schmid 2017 nintedanib))

• Description: 1 = subject self-identifies as Indian (South Asian), Chinese, or Taiwanese; 0 = otherwise (Caucasian, Black, Korean, or other Asian). Paper-specific composite developed by Schmid 2017 in pooling South Asian Indian and East Asian Chinese/Taiwanese subgroups together as a single nintedanib-bioavailability (F1) effect category against a Caucasian/Black/other-Asian reference. The grouping reflects the empirical clustering of the F1 covariate analysis (Schmid 2017 Table 3 ethnic-origin row); it does not imply biological homogeneity across the three constituent populations.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = Caucasian, Black, Korean, or other Asian (paired with RACE_KOREAN to capture the Korean subgroup separately).
• Source aliases:
  • Paper’s ethnic-origin categorical levels “Indian / Chinese / Taiwanese origin” – decompose into RACE_IND_CHI_TWN = as.integer(ETHNIC %in% c("Indian", "Chinese", "Taiwanese")).
• Example models: Schmid_2017_nintedanib.R (multiplicative effect 1.33 on nintedanib relative bioavailability F1; reference category Caucasian / Black / other Asian).
• Notes: Operator-ratified sidecar 2026-06-21 (request-002 Q1, option B). Specific scope: the composite grouping is paper-specific to Schmid 2017 and is unlikely to recur unchanged in other popPK extractions; future papers that distinguish South Asian from East Asian (or that lump Korean with Chinese) should register their own composite rather than overload this one. The constituent subgroups Chinese / Indian / Taiwanese remain available as individual indicators (RACE_CHINESE is canonical; RACE_INDIAN was added alongside this entry; RACE_TAIWANESE is not yet canonical but can be added when a future paper requires it).

RACE_KOREAN (canonical for Korean-heritage race indicator)

• Description: 1 = Korean heritage (subject self-identifies as Korean or is enrolled at a Korean-population stratum of a multiregional trial), 0 = non-Korean. Used when Korean subjects form a distinct subgroup whose PK differs from the Caucasian / Black / non-Korean Asian reference (e.g., Schmid 2017 nintedanib showed Korean patients have ~22% lower F1 than the Caucasian reference).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (non-Korean; document the paper-specific reference race composition per-model in the covariate’s notes).
• Source aliases: none formally; source NONMEM control streams typically use ad-hoc names (e.g., KOREAN, RACE.EQ.K).
• Example models: Schmid_2017_nintedanib.R (multiplicative effect 0.781 on nintedanib relative bioavailability F1; reference category Caucasian / Black / other Asian), vandenBerg_2021_finerenone.R (multiplicative effect 1.29^RACE_KOREAN on Vc/F; 2.4% of the FIDELIO-DKD cohort were Korean, and the paper Discussion notes the effect “may be a spurious finding based on limited data”).
• Notes: Operator-ratified sidecar 2026-06-21 (request-002 Q1, option B). Parallels the established RACE_CHINESE / RACE_JAPANESE entries – individual East Asian nationality indicators when the source paper distinguishes them as distinct subgroups rather than pooling under RACE_ASIAN_NORTHEAST. Korean is the third NE-Asian individual nationality canonical alongside Chinese and Japanese; together they exhaust the RACE_ASIAN_NORTHEAST composite (Chinese + Japanese + Korean) so a model that uses all three individual indicators can omit RACE_ASIAN_NORTHEAST without information loss. Distinct from RACE_ASIAN_NORTHEAST (composite) and from RACE_ASIAN_OTH (within-Asian-population sub-indicator).

RACE_INDIAN (canonical for Indian (South Asian) race indicator)

• Description: 1 = South Asian Indian heritage (subject self-identifies as Indian; refers to nationals/descendants of India and not to American Indian / Native American groups), 0 = non-Indian. Used when Indian subjects form a distinct subgroup whose PK differs from the Caucasian / Black / non-Indian Asian reference (e.g., Schmid 2017 nintedanib showed Indian patients have ~90% higher BIBF 1202 F2 than the Caucasian / Black reference).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (non-Indian; document the paper-specific reference race composition per-model in the covariate’s notes).
• Source aliases: none formally; source NONMEM control streams typically use ad-hoc names (e.g., INDIAN, RACE.EQ.I).
• Example models: Schmid_2017_nintedanib.R (multiplicative effect 1.90 on BIBF 1202 relative bioavailability F2; paired with a non-Indian-Asian indicator at 1.20; reference category Caucasian / Black).
• Notes: Added 2026-06-27 alongside RACE_IND_CHI_TWN and RACE_KOREAN for the Schmid 2017 nintedanib extraction (request-002 Q1, option B). Parallels RACE_CHINESE / RACE_JAPANESE / RACE_KOREAN – individual South / East Asian nationality indicators when the source paper distinguishes them as distinct subgroups. DISTINCT from RACE_ASIAN_AMIND_OTH and RACE_ASIAN_AMIND_MULTI, both of which use “AmInd” / “AMIND” to mean American Indian / Alaska Native (a North American indigenous population), not South Asian Indian. This is a recurring source of confusion in race-canonical lookups; reviewers should verify the meaning whenever a paper mentions “Indian” patients in a popPK cohort.

Geographic / enrollment-country indicators

Geographical study-site region indicators. Distinct from race / ethnicity (RACE_*), which describe subject ancestry; these describe the geographical location of the clinical trial site that enrolled the subject. Used in multi-regional studies (typically those including bridging analyses for Japan or East Asia) to capture region-specific clinical-practice or unmeasured-environment effects on PK that remain after accounting for body weight, race, and laboratory covariates. Encoded as a set of mutually exclusive binary indicators with US as the implicit reference category (all indicators = 0). When a paper groups some non-US regions with US (e.g., Hong 2025 groups US and Japan as the DXd CL reference), the model code uses only the indicators that distinguish the non-reference groups; the data column for the grouped region (e.g., REGION_JAPAN) is still recorded so the same dataset can serve other parameters that do separate that group.

REGION_JAPAN (canonical for Japan study-site / enrollment-country indicator)

• Description: 1 = study site in Japan (or patient enrolled in Japan, depending on source paper’s reporting), 0 = study site / enrollment country outside Japan. Geographical Japan indicator.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-Japan sites / enrollment; specific reference set varies per model – e.g., Hong 2025 Dato-DXd CL uses “any non-Japan region”, whereas Hong 2025 DXd CL groups Japan with US into the reference).
• Source aliases:
  • COUNTRY_JPN – retired canonical; used in Yin_2021_trastuzumabDeruxtecan.R as an enrollment-country (not study-site-region) indicator. Some papers report country-of-enrollment rather than site region; both map to REGION_JAPAN when the binary contrast is Japan vs. non-Japan.
• Example models: Hong_2025_datopotamab.R (multiplicative effect 1 + (-0.219) = 0.781 on Dato-DXd linear clearance), Yin_2021_trastuzumabDeruxtecan.R (multiplicative effect 0.903 on CL_intact and 0.738 on V2_intact when REGION_JAPAN = 1; Yin 2021 retained Japan enrollment-country over Japanese race because the two were highly confounded, correlation -0.81).
• Notes: Distinct from RACE_JAPANESE (subject ancestry). A subject of Japanese ancestry enrolled at a US site has RACE_JAPANESE = 1 but REGION_JAPAN = 0. Some papers (e.g., Yin 2021) report enrollment country rather than study-site region; both are encoded as REGION_JAPAN when the binary contrast is Japan vs. non-Japan. Paired with REGION_EUROPE and REGION_ROW in multi-regional studies (e.g., Hong 2025); REGION_JAPAN = 0 for a US-only cohort.

REGION_EUROPE (canonical for Europe study-site indicator)

• Description: 1 = study site in Europe, 0 = study site outside Europe. Geographical study-site region.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-Europe study sites; specific reference set varies per model).
• Source aliases: none yet; canonical name preferred.
• Example models: Hong_2025_datopotamab.R (multiplicative effect 1 + 0.240 = 1.240 on DXd clearance versus US/Japan reference), Naik_2016_vortioxetine.R (additive intercept-shift form: TVCL_EU = 39 L/hr is the typical CL/F when REGION_EUROPE = 1, versus USA reference TVCL = 51 L/hr).
• Notes: Pair with REGION_JAPAN and REGION_ROW to encode multi-regional study membership; subjects with all three indicators = 0 are in the “US” reference group.

REGION_ROW (canonical for Rest-of-World study-site indicator)

• Description: 1 = study site in Rest of World (i.e., not US, Japan, or Europe), 0 = study site in US / Japan / Europe. Residual-region indicator for multi-regional studies.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (US / Japan / Europe study sites; specific reference set varies per model).
• Source aliases:
  • REGION_RoW – mixed-case variant in some source publications (e.g., Naik 2016).
• Example models: Hong_2025_datopotamab.R (multiplicative effect 1 + 0.196 = 1.196 on DXd clearance versus US/Japan reference), Naik_2016_vortioxetine.R (additive intercept-shift form: TVCL_RoW = 38 L/hr is the typical CL/F when REGION_ROW = 1, versus USA reference TVCL = 51 L/hr; the RoW group for Naik 2016 spans study sites in Canada, Australia, and Asia).
• Notes: “Rest of the World” composition is paper-specific (e.g., Hong 2025 = study sites outside US, Japan, and Europe; Naik 2016 = Canada, Australia, and Asia). Document the subject set in covariateData[[REGION_ROW]]$notes per model.

REGION_FRANCE (canonical for France study-site / enrollment-country indicator)

• Description: 1 = study site in France (or French-population enrollment), 0 = otherwise. Country-level study-site indicator used when a multi-regional trial reports a France-vs-other contrast in popPK / PD parameters (cultural / behavioural-response differences, regional dosing-practice differences, etc.).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-France study sites; in Holford 1992 tacrine the reference cohort is the US protocol 970-01).
• Source aliases:
  • PROT == 4 – protocol-number alias used in Holford 1992 (the France cohort is protocol 970-04, encoded as PROT = 4; the US cohort is protocol 970-01, encoded as PROT = 1). Derive REGION_FRANCE = as.integer(PROT == 4).
• Example models: Holford_1992_tacrine.R (multiplicative scale factors on baseline ADAS-cog (1 + e_region_france_s0 * REGION_FRANCE) with e_region_france_s0 = 0.08; on placebo potency (1 + e_region_france_betap * REGION_FRANCE) with e_region_france_betap = 0.76; on placebo elimination half-time (1 + e_region_france_t12elp * REGION_FRANCE) with e_region_france_t12elp = 1.78 – so the France cohort has 8 percent higher baseline ADAS-cog, 76 percent larger placebo response, and 2.78x longer placebo wash-out half-time relative to the US cohort).
• Notes: Distinct from RACE_FRENCH (subject ancestry, no canonical at present). Holford 1992 introduces this as a behavioural-response covariate rather than a PK exposure covariate; the underlying mechanism is hypothesised cultural / clinical-trial-conduct differences, not pharmacology. Pair with REGION_JAPAN, REGION_EUROPE, REGION_ROW etc. when the same model needs to encode multiple geographic contrasts. Specific scope until a second model ratifies the name; at that point promote to general. Ratified canonically on 2026-05-23 alongside the Holford 1992 tacrine extraction.

REGION_MOZAMBIQUE (canonical for Mozambique study-site / enrollment-country indicator)

• Description: 1 = study site in Mozambique, 0 = otherwise. Country-level study-site indicator used in multi-country sub-Saharan African trials of intermittent preventive treatment of malaria in pregnancy (IPTp).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-Mozambique sites; specific reference set varies per model – in de Kock 2017 the reference is Mali).
• Source aliases: none yet; canonical name preferred.
• Example models: deKock_2017_sulfadoxinePyrimethamine.R (multiplicative -20.2% effect on apparent pyrimethamine clearance; +57.6% scaling on observed pyrimethamine concentrations; +21.2% scaling on observed sulfadoxine concentrations; the on-observation scalings capture residual site-specific differences in apparent bioavailability or dried-blood-spot sample handling).
• Notes: Specific scope because the canonical models a single Mozambique vs non-Mozambique contrast within a specific multi-site trial; the reference set is paper-specific (e.g., Mali in de Kock 2017). Pairs with REGION_SUDAN and REGION_ZAMBIA for a 4-country sub-Saharan African IPTp trial design with Mali as the implicit reference. Ratified canonically on 2026-05-18.

REGION_SUDAN (canonical for Sudan study-site / enrollment-country indicator)

• Description: 1 = study site in Sudan, 0 = otherwise. Country-level study-site indicator used in multi-country sub-Saharan African trials.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-Sudan sites; specific reference set varies per model – in de Kock 2017 the reference is Mali).
• Source aliases: none yet; canonical name preferred.
• Example models: deKock_2017_sulfadoxinePyrimethamine.R (+15.5% scaling on observed sulfadoxine concentrations; +33.2% scaling on observed pyrimethamine concentrations).
• Notes: Specific scope; pairs with REGION_MOZAMBIQUE and REGION_ZAMBIA. Ratified canonically on 2026-05-18.

REGION_ZAMBIA (canonical for Zambia study-site / enrollment-country indicator)

• Description: 1 = study site in Zambia, 0 = otherwise. Country-level study-site indicator used in multi-country sub-Saharan African trials.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-Zambia sites; specific reference set varies per model – in de Kock 2017 the reference is Mali).
• Source aliases: none yet; canonical name preferred.
• Example models: deKock_2017_sulfadoxinePyrimethamine.R (-24.8% scaling on observed sulfadoxine concentrations; -5.4% scaling on observed pyrimethamine concentrations).
• Notes: Specific scope; pairs with REGION_MOZAMBIQUE and REGION_SUDAN. Ratified canonically on 2026-05-18.

REGION_TANZANIA (canonical for Tanzania study-site / enrollment-country indicator)

• Description: 1 = study site in Tanzania, 0 = otherwise. Country-level study-site indicator used in multi-country sub-Saharan African trials. In Pillay-Fuentes Lorente 2024 the contrast is Tanzania (Pemba Island) versus Cote d’Ivoire, and the paper refers to it as the “study population” covariate rather than by country name.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-Tanzania sites; the specific reference set varies per model – in Pillay-Fuentes Lorente 2024 the reference is Cote d’Ivoire).
• Source aliases: Country, study population.
• Example models: PillayFuentesLorente_2024_albendazole.R (exponential categorical effect on both apparent metabolite clearances: exp(0.56) = 1.75, i.e. 75% higher albendazole sulfoxide CL/F, and exp(0.38) = 1.46, i.e. 46% higher albendazole sulfone CL/F, in the Tanzanian study population).
• Notes: Specific scope, following the same pattern as REGION_MOZAMBIQUE / REGION_SUDAN / REGION_ZAMBIA: the canonical models a single Tanzania vs non-Tanzania contrast within one multi-site trial, and the comparator set is paper-specific. Note that a country indicator in this family is a proxy for whatever differs between the study populations (in Pillay-Fuentes Lorente 2024 the authors attribute it to drug-metabolising-enzyme polymorphisms, host-parasite interaction, nutritional status and gut microbiome differences), not to geography as such.

REGION_POLAND (canonical for Poland EPPICC enrollment-country indicator)

• Description: 1 = subject enrolled at the Polish EPPICC cohort site (Medical University Warsaw / Regional Hospital of Infectious Disease), 0 = otherwise. Country-level enrollment-country indicator used in the multi-country European Pregnancy and Paediatric HIV Cohort Collaboration (EPPICC) HIV/HCV coinfection study.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-Poland EPPICC sites; specific reference set varies per model – in Majekodunmi 2017 the reference is Ukraine).
• Source aliases: none yet; canonical name preferred.
• Example models: Majekodunmi_2017_HIV_HCV_CD4_recovery.R (additive +0.44 shift on pre-ART CD4 z-score intercept; Ukraine reference).
• Notes: Specific scope; pairs with REGION_RUSSIA, REGION_SWITZERLAND, REGION_UK, REGION_SPAIN, REGION_GERMANY, REGION_ITALY for the EPPICC 8-country pediatric HIV cohort with Ukraine as the implicit reference. Distinct from a Polish race/ethnicity indicator. Ratified canonically on 2026-05-22.

REGION_RUSSIA (canonical for Russia EPPICC enrollment-country indicator)

• Description: 1 = subject enrolled at the Russian EPPICC cohort site (Republican Hospital of Infectious Diseases, St Petersburg), 0 = otherwise. Country-level enrollment-country indicator used in the EPPICC HIV/HCV coinfection study.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-Russia EPPICC sites; specific reference set varies per model – in Majekodunmi 2017 the reference is Ukraine).
• Source aliases: none yet; canonical name preferred.
• Example models: Majekodunmi_2017_HIV_HCV_CD4_recovery.R (additive +0.69 shift on pre-ART CD4 z-score intercept; Ukraine reference).
• Notes: Specific scope; pairs with REGION_POLAND, REGION_SWITZERLAND, REGION_UK, REGION_SPAIN, REGION_GERMANY, REGION_ITALY for the EPPICC 8-country pediatric HIV cohort with Ukraine as the implicit reference. Ratified canonically on 2026-05-22.

REGION_SWITZERLAND (canonical for Switzerland EPPICC enrollment-country indicator)

• Description: 1 = subject enrolled at the Swiss Mother and Child HIV Cohort Study (MoCHiV), 0 = otherwise. Country-level enrollment-country indicator used in the EPPICC HIV/HCV coinfection study.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-Switzerland EPPICC sites; specific reference set varies per model – in Majekodunmi 2017 the reference is Ukraine).
• Source aliases: none yet; canonical name preferred.
• Example models: Majekodunmi_2017_HIV_HCV_CD4_recovery.R (additive +0.02 shift on pre-ART CD4 z-score intercept; Ukraine reference).
• Notes: Specific scope; pairs with the other EPPICC REGION indicators. Ratified canonically on 2026-05-22.

REGION_UK (canonical for United Kingdom EPPICC enrollment-country indicator)

• Description: 1 = subject enrolled in the UK Collaborative HIV Paediatric Study (CHIPS), 0 = otherwise. Country-level enrollment-country indicator used in the EPPICC HIV/HCV coinfection study.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-UK EPPICC sites; specific reference set varies per model – in Majekodunmi 2017 the reference is Ukraine).
• Source aliases: none yet; canonical name preferred.
• Example models: Majekodunmi_2017_HIV_HCV_CD4_recovery.R (additive -17.5 shift on pre-ART CD4 z-score intercept with Ukraine reference; magnitude implausibly large for a z-score effect and anchored on a UK cohort of only 2 subjects – reproduced verbatim per the published table and flagged in the model file and vignette as a small-sample artifact).
• Notes: Specific scope; pairs with the other EPPICC REGION indicators. Distinct from any Britain/England/Scotland-specific indicator. Ratified canonically on 2026-05-22.

REGION_SPAIN (canonical for Spain EPPICC enrollment-country indicator)

• Description: 1 = subject enrolled at the Spanish Paediatric HIV Network (CoRISpe; Madrid and Barcelona), 0 = otherwise. Country-level enrollment-country indicator used in the EPPICC HIV/HCV coinfection study.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-Spain EPPICC sites; specific reference set varies per model – in Majekodunmi 2017 the reference is Ukraine).
• Source aliases: none yet; canonical name preferred.
• Example models: Majekodunmi_2017_HIV_HCV_CD4_recovery.R (additive +2.89 shift on pre-ART CD4 z-score intercept; Ukraine reference).
• Notes: Specific scope; pairs with the other EPPICC REGION indicators. Ratified canonically on 2026-05-22.

REGION_GERMANY (canonical for Germany EPPICC enrollment-country indicator)

• Description: 1 = subject enrolled in the German Competence Network on HIV-infected Children, 0 = otherwise. Country-level enrollment-country indicator used in the EPPICC HIV/HCV coinfection study.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-Germany EPPICC sites; specific reference set varies per model – in Majekodunmi 2017 the reference is Ukraine).
• Source aliases: none yet; canonical name preferred.
• Example models: Majekodunmi_2017_HIV_HCV_CD4_recovery.R (additive +0.34 shift on pre-ART CD4 z-score intercept; Ukraine reference).
• Notes: Specific scope; pairs with the other EPPICC REGION indicators. Ratified canonically on 2026-05-22.

REGION_ITALY (canonical for Italy EPPICC enrollment-country indicator)

• Description: 1 = subject enrolled in the Italian Register for HIV-infection in Children, 0 = otherwise. Country-level enrollment-country indicator used in the EPPICC HIV/HCV coinfection study.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-Italy EPPICC sites; specific reference set varies per model – in Majekodunmi 2017 the reference is Ukraine).
• Source aliases: none yet; canonical name preferred.
• Example models: Majekodunmi_2017_HIV_HCV_CD4_recovery.R (additive -3.63 shift on pre-ART CD4 z-score intercept; Ukraine reference; small-sample subgroup with n = 2).
• Notes: Specific scope; pairs with the other EPPICC REGION indicators. Ratified canonically on 2026-05-22.

REGION_EASTASIA (canonical for East Asian region-of-origin study-site indicator)

• Description: 1 = subject enrolled at a study site in the East Asian region, 0 = enrolled elsewhere. Multi-country regional grouping (broader than a single-country REGION_<COUNTRY> indicator, narrower than a Rest-of-World REGION_ROW bucket). The exact country membership is protocol-specific and must be documented in per-model covariateData[[REGION_EASTASIA]]$notes. Distinct from the RACE_ASIAN* family: REGION_EASTASIA records where a subject was enrolled, not self-reported race; source papers routinely test the two separately and can find one significant and the other not.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-East-Asian region).
• Source aliases:
  • EASIAFL – East Asian flag; used in Cammarata_2024_sulbactam_durlobactam.R (Table 1 rows CLEASIAFL1 and V1EASIAFL1).
  • EAST_ASIA, REGION = East Asia – variant spellings of the same regional grouping.
• Example models: Cammarata_2024_sulbactam_durlobactam.R (China, Taiwan, or South Korea; proportional shifts of -0.199 on durlobactam total CL and -0.263 on durlobactam Vc, with no sulbactam region effect; 45 of 373 pooled subjects, 12.1%).
• Notes: Scope specific, matching every other member of the REGION_* family. Sibling to the single-country REGION_JAPAN / REGION_FRANCE / … entries and to REGION_EUROPE / REGION_ROW. When a source paper reports BOTH a region effect and a race effect, register the race arm separately under the RACE_* family and keep the two columns distinct – Cammarata 2024 explicitly found race and country of origin non-significant while East Asian region was significant. Ratified canonically on 2026-07-28 alongside the Cammarata 2024 sulbactam-durlobactam extraction.

Pediatric comorbidities

DIS_CLD_PREM (canonical for chronic lung disease of prematurity)

• Description: 1 = chronic lung disease of prematurity (bronchopulmonary dysplasia, BPD), 0 = no CLD. Time-fixed per subject (diagnosis at study entry).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no CLD of prematurity).
• Source aliases:
  • CLD_PREM – prior canonical name used in Robbie_2012_palivizumab.R prior to the 2026-06-19 DIS_ prefix standardization.
  • CLD – used in Robbie_2012_palivizumab.R.
  • BPD – bronchopulmonary-dysplasia shorthand.
• Example models: Robbie_2012_palivizumab.R (fractional +20% effect on CL).
• Notes: Standard pediatric / neonatology comorbidity flag; ties to palivizumab’s label population (high-risk preterm infants) and may re-appear in future pediatric mAb PK analyses (RSV, parenteral nutrition, etc.). Renamed from CLD_PREM to DIS_CLD_PREM on 2026-06-19 per the canonical-register standardization audit (operator decision to apply the DIS_<concept> prefix uniformly to disease-state indicators).

Comorbidities

DIS_DIAB (canonical for diabetes-mellitus comorbidity indicator)

• Description: 1 = patient has diabetes mellitus comorbidity, 0 = no diabetes comorbidity. Time-fixed at study entry per subject. Type 1 vs Type 2 are pooled at the column level; record the per-model Type-2-specific detail (if any) in the per-model covariateData[[DIS_DIAB]]$source_name / notes fields rather than maintaining a parallel canonical.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no diabetes comorbidity).
• Source aliases:
  • DIAB – prior canonical name (pre-2026-06-19 DIS_ prefix standardization) used in Chen_2022_guselkumab.R, Yao_2018_guselkumab.R, Overgaard_2019_semaglutide.R.
  • T2DM – prior parallel canonical (merged into DIS_DIAB on 2026-06-19) used in NA_NA_paracetamol.R (DDMODEL00000228), Lu_2014_sglt_qsp.R, Sharma_2018_naltrexone_bupropion.R.
  • Diabetes – used in Sherer_2012_AAA.R (Sherer 2012 Methods page 2 symbol “Diabetes”).
  • T2D – used in Guiastrennec_2016_gastric_emptying.R (matched-cohort flag, 1 = T2D patient vs 0 = matched nondiabetic control).
  • ZDF – used in Han_2018_methionineMetabolismCycle.R (Zucker Diabetic Fatty rat as T2DM animal model; ZDF/Gmi fa/fa coded as DIS_DIAB = 1 vs ZDF/Gmi fa/? non-diabetic littermate control coded as DIS_DIAB = 0).
• Example models:
  • Chen_2022_guselkumab.R (multiplicative effect on CL/F: 1.15^DIS_DIAB, +15% in patients with diabetes).
  • Yao_2018_guselkumab.R (multiplicative effect on CL/F: 1.12^DIS_DIAB, +12% in patients with diabetes).
  • Overgaard_2019_semaglutide.R (multiplicative effects 1.12 on CL and 0.544 on ka per Overgaard 2019 Table 4; T2D-vs-normoglycaemic stratification).
  • Sherer_2012_AAA.R (additive shift on the first derivative of AAA growth rate with size beta2: e_diab_b2 = -0.32/year for diabetics; cohort prevalence 14%).
  • Sharma_2018_naltrexone_bupropion.R (Type-2 diabetes mellitus stratifier on Emax, kout, kpro, baseline BW; from Sharma 2018 study 6, NB-304).
  • Lu_2014_sglt_qsp.R (multiplicative +17.6% shift on the typical-value Vmax2 of SGLT2 in the renal-glucose-reabsorption QSP model: Vmax2_DIS_DIAB = 110 mmol/h vs Vmax2_healthy = 93.5 mmol/h per Lu 2014 Table 2 calibration).
  • NA_NA_paracetamol.R (DDMODEL00000228).
  • Guiastrennec_2016_gastric_emptying.R (multiplicative -81.1% depression of POTcarbC, the carbohydrate potency on CCK release; all other parameters are common across cohorts).
  • Han_2018_methionineMetabolismCycle.R (preclinical rat MMC model; multiplicative-on-log-scale T2DM effects on five rate constants K_SH (+16 %), K_HM (-92 %), K_HC (-95 %), K_HP (-86 %), K_PH (-99 %) derived from Han 2018 Table 1 ZDF/control ratios).
• Notes: Captures pre-existing diabetes mellitus as a comorbidity in non-diabetes-primary indications (e.g., psoriatic arthritis, psoriasis, vascular disease) AND as a primary stratifier in diabetes-population studies (semaglutide, SGLT-QSP, naltrexone-bupropion T2DM substudies). Distinct from a primary disease-state indicator like DIS_UC. Type 1 vs Type 2 mellitus are pooled onto the single DIS_DIAB column; if a paper distinguishes them, record “T2DM” or “Type 2” in the per-model covariateData[[DIS_DIAB]]$source_name notes, and if a future paper genuinely needs a binary Type-2-vs-Type-1 split (e.g., a popPK/PD analysis stratifying by HbA1c level), register DIS_DIAB_TYPE2 at that point. Diabetic patients tend to have higher inflammation and altered IgG turnover, which can manifest as modest changes in monoclonal-antibody clearance. In vascular populations (Sherer 2012) diabetes is associated with slower AAA growth, possibly via aberrant monocyte-matrix interactions (Golledge 2008 mechanism cited in Sherer 2012 Discussion). Renamed from DIAB to DIS_DIAB and merged the prior T2DM canonical into it on 2026-06-19 per the canonical-register standardization audit (operator decision: apply the DIS_<concept> prefix uniformly to disease-state indicators; pool T1/T2 at the column level and record the Type-2 detail per-model rather than maintaining a parallel T2DM canonical). Overgaard 2016 (weight-management liraglutide) reports T2DM as one level of a three-level “baseline glycaemic status” covariate (normoglycaemic reference, prediabetic, T2DM); the T2DM level maps to DIS_DIAB = 1 and the prediabetic level uses the sibling canonical [[DIS_PREDIAB]].

DIS_PREDIAB (canonical for prediabetes / impaired glucose regulation indicator)

• Description: 1 = subject has prediabetes / impaired glucose regulation at baseline (e.g., impaired fasting glucose 5.6-6.9 mmol/L, impaired glucose tolerance, or HbA1c 5.7-6.4 % per ADA criteria), 0 = subject is not prediabetic (typically normoglycaemic; T2DM subjects are usually stratified onto the sibling canonical DIS_DIAB in the same model). Time-fixed at study entry per subject.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (not prediabetic; typically normoglycaemic, but paper-defined).
• Source aliases:
  • Prediabetes – used in Overgaard_2016_liraglutide.R (Overgaard 2016 Fig. 2 / Table S1 “Cov. prediabetes”; one of the three levels of a “baseline glycaemic status” covariate alongside normoglycaemic reference and T2DM).
• Example models: Overgaard_2016_liraglutide.R (multiplicative log-scale effect on CL/F, coefficient +0.00 per Table S1; effect is negligible per the paper’s own analysis).
• Notes: Sibling to DIS_DIAB in the disease-state family. Used when a source paper stratifies a diabetes / obesity cohort into normoglycaemic vs prediabetic vs T2DM strata and reports covariate effects on each stratum separately. Only ratified when both DIS_PREDIAB and DIS_DIAB are needed to reproduce the paper’s three-level glycaemic decomposition – when a paper only reports a single “any dysglycaemia” indicator, use DIS_DIAB alone (or the appropriate primary-disease-state canonical). ADA prediabetes criteria: FPG 100-125 mg/dL (5.6-6.9 mmol/L), 2-h PG 140-199 mg/dL (7.8-11.0 mmol/L) on 75 g OGTT, or HbA1c 5.7-6.4 % (39-46 mmol/mol). Ratified canonically on 2026-07-10 alongside the Overgaard 2016 weight-management-liraglutide extraction.

DIS_HYPERT (canonical for hypertension comorbidity / medical-history indicator)

• Description: 1 = patient has a history of (or current) hypertension as a comorbidity; 0 = no hypertension. Time-fixed at study entry per subject (medical-history flag rather than time-varying blood-pressure measurement).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no hypertension comorbidity).
• Source aliases:
  • HYPERT – prior canonical name used in Girard_2012_pimasertib.R prior to the 2026-06-19 DIS_ prefix standardization.
  • MHHY (medical history of hypertension) – used in Girard_2012_pimasertib.R.
• Example models: Girard_2012_pimasertib.R (additive shift on the cumulative-logit AE-score model: theta_mhhy * DIS_HYPERT; +0.539 logit units in patients with prior hypertension).
• Notes: Companion to DIS_DIAB (diabetes-mellitus comorbidity); both are baseline binary medical-history flags collected from clinical-history forms. Captures any prior or current hypertension diagnosis, regardless of treatment status; if a future model needs to separate treated vs untreated hypertension, register a refinement (DIS_HYPERT_TREATED). Renamed from HYPERT to DIS_HYPERT on 2026-06-19 per the canonical-register standardization audit (operator decision to apply the DIS_<concept> prefix uniformly to disease-state indicators).

DIS_HYPERLIP (canonical for hyperlipidemia / hypercholesterolemia diagnosis indicator)

• Description: 1 = participant carries a diagnosis of hyperlipidemia (equivalently hypercholesterolemia / dyslipidemia) at study entry; 0 = no hyperlipidemia diagnosis (typically a healthy-volunteer or normolipidemic comparator arm pooled into the same analysis). Time-fixed per subject. Captures the generic acquired / polygenic lipid disorder that defines the target population of lipid-modifying therapies, as distinct from the monogenic familial forms carried by DIS_HEFH and DIS_HOFH.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no hyperlipidemia diagnosis).
• Source aliases:
  • Hyperlipidemia – Jadhav 2023 Table 2 covariate row name.
  • HLD, HCHOL, DYSLIP – common clinical-dataset abbreviations for the same diagnosis flag.
• Example models: Jadhav_2023_bempedoicAcid.R, Jadhav_2023_bempedoicAcid_ldlc.R (proportional shift CL/F * (1 + (-0.0945) * DIS_HYPERLIP), i.e. 9.5% lower apparent bempedoic acid clearance in participants with hyperlipidemia relative to the pooled healthy / non-hyperlipidemic reference; Jadhav 2023 Table 2).
• Notes: Not derivable from DIS_HEALTHY by complement, and not mutually exclusive with DIS_DIAB: the Jadhav 2023 popPK dataset contained 49 participants with diabetes alone plus 310 with hyperlipidemia and diabetes (Table 1 footnote c), so all three flags can be jointly set. Distinct from DIS_HEFH / DIS_HOFH, which identify the heterozygous and homozygous familial-hypercholesterolemia subsets; a HeFH patient normally also carries DIS_HYPERLIP = 1, so record the per-model convention in covariateData[[DIS_HYPERLIP]]$notes whenever both appear. Also distinct from the continuous lipid-panel canonicals (LDLC, HDLC), which carry the measured concentration rather than the diagnosis. Ratified canonically on 2026-07-27 alongside the Jadhav 2023 bempedoic acid extraction (sidecar request 001, operator answer A).

Surgical history / disease state

POD (canonical for post-operative day)

• Description: Days elapsed since the qualifying surgical event (e.g., solid-organ transplantation, major resection). Time-varying within subject; integer- or fractional-day valued; rises monotonically from 0 at the day of surgery. Captures time-since-surgery effects on PK that are not explained by other covariates – e.g., post-transplant clearance of immunosuppressants typically declines toward a steady value over the first weeks-to-months as graft function, fluid status, hematocrit, and corticosteroid taper stabilise.
• Units: days
• Type: continuous
• Scope: general
• Reference category: n/a – typically used in centred-deviation form (1 + e_pod_param * (POD - ref_pod)), sometimes with an upper cap (e.g., values > 180 fixed to 180 days when the residual time-varying effect plateaus).
• Source aliases:
  • POD – used in Bergmann_2014_tacrolimus.R (capped at 180 days; centred at 22.7 days, the dataset mean).
  • PTD – “posttransplantation day”; used in Suzuki_2024_mycophenolic_acid.R. Same orientation and units, no value transformation.
• Example models: Bergmann_2014_tacrolimus.R (linear deviation from POD = 22.7 days on tacrolimus CL/F; coefficient -0.0021 per day implies a 0.21% per-day decrease in apparent oral clearance with a 180-day plateau), Suzuki_2024_mycophenolic_acid.R (exponential effect on relative bioavailability, exp(0.956 * POD / 84) with the divisor being the maximum POD in the analysis dataset rather than a centring value, so the factor is 1 at POD = 0 and rises to 2.8-fold by day 90; renal-transplant recipients on mycophenolate mofetil), Zhou_2025_tacrolimus.R (median-normalised power form (POD / 49)^0.14 on tacrolimus CL/F – note the positive exponent, i.e. clearance rises with time post-transplant, the opposite direction to Bergmann 2014; see Notes).
• Notes: Time-varying within subject; the per-row value is the integer day count from the date of surgery to the observation date. Negative values are legitimate when a cohort contributes pre-surgical observations: the Suzuki 2024 renal-transplant dataset begins 14 days pre-transplantation, and its control stream gates the covariate effect off for those rows (IF (PTD.GE.0) THEN FPTD = EXP(...) ELSE FPTD = 1). A model consuming POD over a pre-surgical window must reproduce whatever gate the source applies rather than feeding a negative day count into the covariate equation. For solid-organ-transplant cohorts, POD is the conventional NONMEM $INPUT column name. When the source paper reports a different name (DPT for “days post-transplant”, TX_DAY, T_POSTOP), record the alias here. Distinct from TIME (rxode2 time clock) and from OCC (integer-valued occasion / period indicator for IOV). When a paper uses POD jointly with an IOV occasion column, both can coexist in the dataset: POD enters the typical-value covariate equation, OCC multiplexes the IOV etas. The 180-day cap in Bergmann 2014 is data-driven (most observations are within the first 90 days post-transplant, so the linear effect is identifiable only over that window) – document any per-model cap in covariateData[[POD]]$notes. Ratified canonically on 2026-05-08 alongside the Bergmann 2014 extraction.

TTD (canonical for time to death)

• Description: Days remaining from the observation time to the patient’s recorded time of death (TTD >= 0; falls to 0 on the day of death). Time-varying per subject. Available only retrospectively in observational palliative-care / end-of-life cohorts; not usable as a prospective predictor.
• Units: days
• Type: continuous
• Scope: specific
• Reference category: n/a – used in a first-order exponential decay form on a structural PK parameter. The Franken 2015 morphine model parameterises CL(TTD) = CL_pop - theta_D * exp(-theta_rate * TTD), so the decay term vanishes as TTD -> infinity (asymptotic CL far from death) and reaches its peak drop theta_D as TTD -> 0 (day of death).
• Source aliases:
  • TTD – used in Franken_2015_morphine.R (Franken 2015 NONMEM column for time-to-death in days; paper Eq. 3 and Table 2).
• Example models: Franken_2015_morphine.R (Franken 2015 Clin Pharmacokinet; first-order exponential decay term on morphine clearance with theta_D = 17.6 L/h and theta_rate = 0.13 /day; CL drops from 47.5 to 29.9 L/h as TTD goes from infinity to 0).
• Notes: Specific scope because the covariate’s semantics depend on a palliative-care / observational-cohort study design where time of death is known by retrospective abstraction. The Franken 2015 model interprets the TTD-dependent CL change as a composite of physiological end-of-life processes (e.g., reduced hepatic blood flow, cachexia) that are not captured by standard blood-chemistry covariates. For prospective simulation of a virtual cohort without a known time of death, set TTD to a large value (e.g., > 50 days) to recover the asymptotic CL_pop. Distinct from the canonical POD (post-operative day, monotonically increasing from a surgery date) – TTD counts down to death rather than up from a procedure. Ratified canonically on 2026-05-16 alongside the Franken 2015 morphine extraction.

POSTTX_DAY1 (canonical for first-24-hours-post-transplant indicator)

• Description: Binary indicator for the first 24 hours post-transplant. 1 = the observation falls within the first 24 hours (day 1) post-transplant; 0 = otherwise. Time-varying per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (any observation outside the first 24 hours post-transplant).
• Source aliases:
  • first day post-transplant – used in Storset_2014_tacrolimus.R.
• Example models: Storset_2014_tacrolimus.R (multiplicative ~2.68-fold increase in oral bioavailability on day 1: fdepot *= 2.68^POSTTX_DAY1; Storset 2014 Table 2 final theory-based model retains the day-1 factor with subject-level IIV of 57% CV on the day-1 multiplier).
• Notes: Distinct from the continuous POD (post-operative day) canonical above – POSTTX_DAY1 is a derived binary indicator (operationally POSTTX_DAY1 = as.integer(POD < 1) when both columns are present in the dataset). The two coexist in the same model when the source paper uses POD-based continuous effects on some parameters and a separate binary day-1 effect on others (Storset 2014 retains the binary day-1 factor explicitly because the day-1 oral bioavailability is ~2.68-fold higher than the rest of the post-transplant period and is not well captured by a continuous POD effect). Storset 2014 Discussion attributes the day-1 oral-bioavailability spike to candidate mechanisms including methylprednisolone-bolus inhibition of intestinal CYP3A / P-glycoprotein, surgery-related inflammation, anaesthesia / opioid effects on gut motility, and reduced food intake – but no single mechanism was identifiable in the data. The 2.68-fold factor was retained because it produced a 209-point OFV decrease and was crucial for predicting concentrations measured on the first post-transplant day. In Storset 2014 the day-1 effect carries its own subject-level eta (BSV 57% CV on the day-1 factor); only subjects with day-1 observations contribute to that eta. Ratified canonically on 2026-05-08.

PFA (canonical for perioperative intra-operative fluid administration volume)

• Description: Total volume of intra-operative fluid administered during a surgical procedure – saline / albumin / fresh-frozen-plasma / blood / platelet replacement summed across the entire procedure. Time-fixed per subject (a single per-subject scalar capturing the full intra-operative perfusion load). Used in solid-organ-transplant and other major-surgery popPK models where the volume expansion from peri-operative fluid resuscitation is large enough to perturb post-operative body composition and apparent volume of distribution beyond what pre-operative body weight alone explains.
• Units: mL
• Type: continuous
• Scope: specific
• Reference category: n/a – used algebraically. The Oualha 2018 paediatric-LT model uses PFA additively with pre-operative body weight to define a transient post-operative body-weight curve: BW(t) = (BWPREOP + PFA/1000) * (1 - (1 - fbw) * t^hill_bw / (tbw50^hill_bw + t^hill_bw)), with the /1000 conversion applying the 1 mL ~ 1 g fluid-density-1 convention so PFA in mL maps onto a kg increment of body weight. Future surgical / critical-care popPK papers may use PFA differently (additive on Vc, multiplicative on CL); the per-paper parameterisation should be recorded in covariateData[[PFA]]$notes.
• Source aliases:
  • PFA – used in Oualha_2018_enoxaparin.R (Oualha 2018 BJCP; total intra-operative fluid volume in mL summed over the entire liver-transplant procedure; cohort median 2634 mL, range 1008-6520 mL).
• Example models: Oualha_2018_enoxaparin.R (Oualha 2018 paediatric liver-transplant cohort; PFA in mL adds to BWPREOP after conversion to L, defining a transient post-operative body-weight curve BW(t) that drives the time-varying allometric scaling of V; the Hill / fBW / tBW50 parameters of the BW(t) curve are jointly estimated with the enoxaparin PK).
• Notes: Specific scope until a second surgical / critical-care popPK model ratifies the name; at that point promote to general. The 1 mL ~ 1 g (density-1) convention is conventional and adequate for resuscitation fluids (crystalloids and blood products); a more precise mass-balance accounting could account for the slightly higher density of packed RBCs (~1.08 g/mL) or 5% albumin (~1.02 g/mL), but the difference is well within the IIV captured by lfbw / ltbw50. Distinct from URINE_FLOW (instantaneous urine flow rate, time-varying within an observation interval) and from WT (the pre- or post-operative body weight scalar). Founding example: Oualha 2018 enoxaparin (paediatric liver transplantation, intra-operative fluid resuscitation typical of major paediatric surgery). When a future model needs a separate accounting of crystalloid vs colloid vs blood-product volumes, register sibling canonicals (PFA_CRYST, PFA_COLLOID, PFA_BLOOD_PROD) rather than overloading this entry.

TX_LIVER (canonical for liver (hepatic) transplant indicator)

• Description: Binary indicator for liver / hepatic-graft transplant recipients in a pooled solid-organ-transplant cohort. 1 = liver transplant; 0 = non-liver solid-organ transplant (kidney, heart, or lung). Time-fixed per subject (assigned at transplantation date).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-liver solid-organ graft – typically kidney as the most common non-liver comparator in pooled solid-organ-transplant analyses).
• Source aliases:
  • Hepatic trans_CL – used in Nanga_2019_tacrolimus_metaanalysis.R (Table 3 covariate-effect label; encoded as a multiplicative power coefficient theta^TX_LIVER on CL/F).
• Example models: Nanga_2019_tacrolimus_metaanalysis.R (multiplicative effect on apparent oral clearance: cl_typ *= 0.38^TX_LIVER, so liver-graft recipients have CL/F 62% lower than non-liver recipients at the same body weight and post-transplant day; Nanga 2019 Table 3 ‘Hepatic trans_CL’ = 0.38), Perrottet_2009_ganciclovir.R (Perrottet 2009 pools lung and liver under a single CL slope; the model applies a shared multiplicative effect via (TX_LUNG + TX_LIVER): pooled lung-or-liver theta_lung/liver = 1.17 vs theta_kidney = 1.68, so lung-or-liver recipients have a CL slope 30% lower than kidney recipients at the same GFR and sex; Perrottet 2009 Table 3).
• Notes: Specific scope because the reference complement (non-liver solid-organ graft) is paper-defined – in Nanga 2019 the complement is exclusively kidney-transplant patients (Table 5: 201 liver, 80 kidney). Future pooled solid-organ-transplant popPK models that distinguish liver from non-liver patients should reuse this canonical; if a future model decomposes the non-liver class into separate kidney / heart / lung indicators, register sibling canonicals (TX_KIDNEY, TX_HEART, TX_LUNG) rather than overloading this entry. If instead a model contrasts transplant recipients against NON-transplant patients, that is TX_ANY, not this entry – TX_LIVER = 0 does not distinguish a non-liver graft recipient from a subject who was never transplanted. Distinct from POSTTX_DAY1 (first-24-hour-post-transplant indicator, time-varying) and from POD (continuous post-transplant day); all three can coexist when a model parameterises both organ type and time-after-transplantation effects. Ratified canonically on 2026-05-18 alongside the Nanga 2019 tacrolimus meta-analysis extraction.

DONOR_DECEASED (canonical for deceased-vs-living donor indicator (solid-organ transplant))

• Description: Binary indicator for the source of the transplanted solid-organ graft. 1 = recipient received the graft from a deceased (post-mortal / cadaveric) donor; 0 = recipient received the graft from a living donor. Time-fixed per subject (assigned at transplantation date). Donor source is a graft-quality / preservation-status proxy: deceased-donor grafts typically experience longer cold-ischaemia times, more variable preservation conditions, and a higher rate of slow / delayed graft function than living-donor grafts; in solid-organ-transplant popPK studies these differences can translate into a measurable shift in apparent oral clearance of drugs that undergo hepatic / intestinal CYP3A metabolism (the mechanism is not fully understood and is hypothesised to involve recipient catabolic state, free-fraction shifts, or other unmeasured covariates).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (living-donor graft – the more common subgroup in most paediatric and adult kidney-transplant cohorts).
• Source aliases:
  • donor / Donor – common NONMEM $INPUT form (used in Andrews_2017_tacrolimus.R; Andrews 2017 codes the column as a binary living-vs-deceased indicator).
• Example models: Andrews_2017_tacrolimus.R (multiplicative effect on apparent oral clearance: living-donor recipients have CL/F 26% lower than deceased-donor recipients of the same body weight, CYP3A5 genotype, eGFR, and hematocrit; Andrews 2017 Table 2 final-model coefficient: theta_donor_living = 0.74 for the living-donor cohort relative to the deceased-donor reference, equivalent to deceased-donor recipients having ~35% higher CL/F than living-donor recipients as reported in Section 3.4).
• Notes: General scope because donor source is a universally applicable solid-organ-transplant cohort attribute, captured in transplant-registry data for kidney, liver, heart, lung, and pancreas recipients. The reference category is 0 = living donor (not 0 = deceased donor) to match the “1 = the perturbed / index condition” convention used elsewhere in the register (e.g., RRT_HEMODIAL_STATUS, POSTTX_DAY1, TX_LIVER); the index condition is “received a deceased-donor graft” because it carries the more variable graft quality and is the condition that perturbs clearance upward in published kidney-tacrolimus models. Source papers that encode the column as “1 = living donor” (e.g., Andrews 2017 itself parameterises the equation with a multiplier on living-donor recipients) should still record the column under the canonical orientation: set DONOR_DECEASED = 1 - source_living_indicator and document the value inversion in covariateData[[DONOR_DECEASED]]$notes. Distinct from the donor-genotype canonical CYP3A5_EXPR_DONOR: donor-source (deceased vs living) is a logistic / graft-procurement covariate, while donor-genotype is a pharmacogenetic covariate; both can coexist in the same dataset when the source paper genotypes both recipients and donors. Ratified canonically on 2026-05-25 alongside the Andrews 2017 tacrolimus extraction.

PRIOR_GAST (canonical for prior gastrectomy)

• Description: Prior (partial or total) gastrectomy indicator, 1 = prior gastrectomy, 0 = no prior gastrectomy. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no prior gastrectomy).
• Source aliases:
  • GAST – used in Yamada_2025_zolbetuximab.R.
• Example models: Yamada_2025_zolbetuximab.R (fractional effects on CLss, CLT, V1).
• Notes: Renamed from GAST on 2026-04-20 to follow the PRIOR_TNF / PRICORT naming pattern for prior-treatment and surgical-history indicators. Applicable to any PK model where gastrointestinal anatomy affects absorption, first-pass, or protein turnover; not inherently oncology-specific. No distinction between partial vs total gastrectomy unless the source paper separates them.

PRIOR_SPLEN (canonical for prior splenectomy)

• Description: Prior splenectomy indicator, 1 = prior splenectomy (spleen surgically removed before or concurrent with the modelled treatment period), 0 = spleen intact. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (spleen intact).
• Source aliases:
  • splenectomy – used in PerezRuixo_2015_oxaliplatin_platelet_dynamics.R (Perez-Ruixo 2015 Table I: subject-level binary covariate on the platelet random-destruction rate constant ks).
• Example models: PerezRuixo_2015_oxaliplatin_platelet_dynamics.R (multiplicative effect on ks: ks_i = ks * Phi^PRIOR_SPLEN with Phi = 0.475, i.e., splenectomized patients have a 47.5% reduction in the random platelet destruction rate constant, prolonging platelet lifespan from 3.23 days in patients with an intact spleen to 7.78 days in splenectomized patients; Perez-Ruixo 2015 The AAPS Journal Table I and Discussion).
• Notes: Follows the PRIOR_GAST / PRIOR_TNF / PRICORT naming pattern for prior surgical-history and prior-treatment indicators (short surgical stem, all uppercase, binary 0/1). General scope because the mechanism (loss of splenic platelet sequestration / phagocytic clearance) is applicable across therapeutic areas – Petrov 2024 romiplostim also identified splenectomy as a subgroup with distinct platelet PK/PD though encoded it as a subpopulation split rather than a data-driven column. Distinct from INTRAOP (active-surgery indicator, time-varying) and from POD (post-operative day, monotonically increasing continuous). Ratified canonically on 2026-07-10 alongside the Perez-Ruixo 2015 oxaliplatin platelet-dynamics extraction.

TX_HEART (canonical for heart (cardiac) transplant indicator)

• Description: Binary indicator for heart / cardiac-graft transplant recipients in a pooled solid-organ-transplant cohort. 1 = heart transplant; 0 = non-heart solid-organ transplant (kidney, lung, or liver). Time-fixed per subject (assigned at transplantation date).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-heart solid-organ graft – typically kidney as the most common non-heart comparator in pooled solid-organ-transplant analyses).
• Source aliases:
  • graft_type – pooled categorical column with levels K / H / Lu / Li; decompose into binary TX_HEART = as.integer(graft_type == "H") and document the decomposition in covariateData[[TX_HEART]]$notes. Used in Perrottet_2009_ganciclovir.R.
• Example models: Perrottet_2009_ganciclovir.R (multiplicative effect on the CL-vs-GFR slope: theta_heart = 0.86 vs theta_kidney = 1.68, so heart-transplant recipients have a CL slope 49% lower than kidney recipients at the same GFR and sex; Perrottet 2009 Table 3), Fromage_2025_mycophenolic_acid.R (pooled with TX_LUNG and TX_HCT into the source’s single group-(ii) level carrying one estimated effect on the second gamma absorption rate constant: -0.59 on log(MAT2), shortening the second-peak mean absorption time from 4.13 h to 2.29 h versus the renal / hepatic reference; Fromage 2025 Table 2).
• Notes: Specific scope because the reference complement is paper-defined; sibling to TX_LIVER, TX_LUNG, and the implicit kidney-reference cohort. Distinct from TX_ANY, whose reference category is a non-transplant patient rather than a different solid-organ graft. Ratified canonically on 2026-06-08 alongside the Perrottet 2009 ganciclovir extraction.

TX_LUNG (canonical for lung (pulmonary) transplant indicator)

• Description: Binary indicator for lung / pulmonary-graft transplant recipients in a pooled solid-organ-transplant cohort. 1 = lung transplant; 0 = non-lung solid-organ transplant (kidney, heart, or liver). Time-fixed per subject (assigned at transplantation date).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-lung solid-organ graft – typically kidney as the most common non-lung comparator in pooled solid-organ-transplant analyses).
• Source aliases:
  • graft_type – pooled categorical column with levels K / H / Lu / Li; decompose into binary TX_LUNG = as.integer(graft_type == "Lu") and document the decomposition in covariateData[[TX_LUNG]]$notes. Used in Perrottet_2009_ganciclovir.R.
• Example models: Perrottet_2009_ganciclovir.R (Perrottet 2009 pools lung and liver under a single CL slope; the model applies a shared multiplicative effect via (TX_LUNG + TX_LIVER): pooled lung-or-liver theta_lung/liver = 1.17 vs theta_kidney = 1.68, so lung-or-liver recipients have a CL slope 30% lower than kidney recipients at the same GFR and sex; Perrottet 2009 Table 3), Fromage_2025_mycophenolic_acid.R (pooled with TX_HEART and TX_HCT into the source’s single group-(ii) level carrying one estimated effect on the second gamma absorption rate constant: -0.59 on log(MAT2), shortening the second-peak mean absorption time from 4.13 h to 2.29 h versus the renal / hepatic reference; Fromage 2025 Table 2).
• Notes: Specific scope because the reference complement is paper-defined; sibling to TX_LIVER, TX_HEART, and the implicit kidney-reference cohort. Distinct from TX_ANY, whose reference category is a non-transplant patient rather than a different solid-organ graft. Future models that estimate distinct lung-vs-liver effects (rather than pooling) should keep this canonical for lung and continue to use TX_LIVER for liver. Ratified canonically on 2026-06-08 alongside the Perrottet 2009 ganciclovir extraction.

TX_LUNG_BILAT (canonical for bilateral (vs single) lung graft laterality indicator)

• Description: Binary indicator of lung-graft laterality within a cohort in which every subject is a lung transplant recipient. 1 = bilateral (double) lung transplant; 0 = single (unilateral) lung transplant. Time-fixed per subject (assigned at transplantation). Used as a proxy for the magnitude of the surgical insult: bilateral transplantation requires a longer duration of cardiopulmonary bypass, a median sternotomy / clamshell rather than a thoracotomy, and carries a greater ischemia-reperfusion burden, so it acts on early postoperative drug disposition through the associated critical-illness physiology rather than through any lung-specific elimination pathway.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (single / unilateral lung transplant).
• Source aliases:
  • transplant type – used in Miano_2024_tacrolimus.R (Miano 2024 Methods “Clinical covariates” lists “transplant type (single vs bilateral)”; Table 2 footnote a reads “Single vs bilateral”, with single-lung as the reference level carrying the factor 1.0).
• Example models: Miano_2024_tacrolimus.R (multiplicative effect on CL/F with a time-varying coefficient: theta^TX_LUNG_BILAT with theta = 0.48 on postoperative days 1-3 and theta = 0.82 on postoperative days 4-14, i.e. bilateral recipients clear tacrolimus 52% more slowly than single-lung recipients in the first three postoperative days and 18% more slowly thereafter; Miano 2024 Table 2 and the displayed CL/F equation in Results. The time-varying specification was required: a single time-invariant coefficient gave Delta-OFV = -2.87 and did not reach significance, whereas the early/late split gave Delta-OFV = -576.71 – Miano 2024 Table 3).
• Notes: Distinct in kind from TX_LUNG, whose contrast is a different organ (“1 = lung transplant; 0 = non-lung solid-organ transplant”). TX_LUNG_BILAT partitions within a lung-only cohort on graft laterality, so in such a cohort TX_LUNG = 1 for every subject and carries no information. The two can coexist in a pooled multi-organ dataset, where TX_LUNG_BILAT is defined only on the TX_LUNG = 1 subset; models that do so must state the coding for non-lung recipients (typically 0, folded into the single-lung reference) in covariateData[[TX_LUNG_BILAT]]$notes. Also distinct from SURG_SEV_MAJOR (a generic major-vs-minor procedure-severity flag that is not transplant-specific) and from INTRAOP / POD (which locate an observation in time relative to surgery rather than describing the procedure). Papers that report the reversed orientation (a single-lung indicator) should still record their column under TX_LUNG_BILAT with the values flipped and the flip documented per-model, so the bilateral-equals-1 orientation is preserved across the register. A future cohort that estimates a heart-lung or lobar-transplant contrast should register a sibling canonical rather than overloading this one. Ratified canonically on 2026-08-20 (sidecar request 001, question 2) alongside the Miano 2024 tacrolimus lung-transplant extraction.

TX_ANY (canonical for any-solid-organ-transplant indicator against a non-transplant reference)

• Description: Binary indicator that the subject is the recipient of any solid-organ graft, contrasted against a non-transplant patient. 1 = recipient of any solid-organ transplant (kidney, liver, heart, lung, pancreas, or a combination); 0 = non-transplant patient. Time-fixed per subject (assigned at transplantation date). Used when a population PK analysis pools transplant recipients with patients who have never been transplanted and tests the transplant-vs-non-transplant contrast as a PK covariate – typically because transplant recipients differ from the rest of the cohort in renal function, immunosuppressive co-medication, and general physiological reserve in ways the measured covariates do not fully capture.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-transplant patient; the complement group is defined per-model – in Yuen 1995 it is the CMV-retinitis and CMV-urine-shedding patients who make up 48 of the 53 subjects).
• Source aliases:
  • T – single-letter column name used in Yuen_1995_ganciclovir.R (Yang 2023 Table 3 footnote: “T: T = 0 for non-transplant patients and 0.76 for transplant patients”; the source folds the coefficient into the column’s coded value, so store the canonical column as a plain 0 / 1 indicator and carry 0.76 as a named ini() parameter).
  • transplant (yes/no) – the covariate label used in Yang 2023 Table 4 for the same column.
• Example models: Yuen_1995_ganciclovir.R (fractional reduction of the renal-elimination term of clearance: cl = exp(lcl) + e_wt_crcl_cl * WT * CRCL/100 * (1 - 0.76 * TX_ANY) * (1 - 0.41 * DIS_CMV_RETINITIS), i.e. a transplant recipient’s weight-and-creatinine-clearance-driven clearance term is 76% lower than a non-transplant patient’s at the same weight and creatinine clearance; Yang 2023 Table 3 and its footnote).
• Notes: Distinct in kind from the organ-specific canonicals TX_LIVER / TX_HEART / TX_LUNG, which partition within a transplant cohort (their reference category is a different solid-organ graft, not the absence of a transplant). TX_ANY and the organ-specific siblings can coexist in one dataset – TX_ANY = 1 holds for every subject with any TX_* indicator set – but TX_ANY must NOT be inferred from the organ-specific indicators when the cohort contains non-transplant patients, because a subject with TX_LIVER = 0 may be either a non-liver graft recipient or a non-transplant patient. Also distinct from DIS_CMV (transplant-recipient-with-CMV vs non-CMV subject: that canonical conflates transplant status with CMV infection and its reference group is a healthy volunteer or phase I participant, not a non-transplant patient) and from DONOR_DECEASED (graft source among transplant recipients). Scope: specific, because the reference complement is paper-defined. Registered as TX_ANY rather than TX_SOT per operator decision (sidecar request 001, question 2, 2026-07-30); if a future paper contrasts hematopoietic cell transplant recipients against solid-organ recipients, register a sibling TX_HCT and record in this entry’s notes whether that paper’s TX_ANY includes HCT. Ratified canonically alongside the Yuen 1995 ganciclovir extraction.

TX_HCT (canonical for haematopoietic cell (bone marrow / stem cell) transplant indicator)

• Description: Binary indicator for haematopoietic cell transplant recipients – bone marrow, peripheral blood stem cell, or cord blood graft – in a cohort that also contains other indications. 1 = haematopoietic cell transplant recipient; 0 = not a haematopoietic cell transplant recipient (the complement is paper-defined; in Fromage_2025_mycophenolic_acid.R it is renal or hepatic transplantation as the reference group, with cardiac and pulmonary transplantation and autoimmune disease as the other levels). Time-fixed per subject (assigned at transplantation date).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-haematopoietic-cell-transplant patient; the complement group is paper-defined).
• Source aliases:
  • bone marrow transplantation – the wording used in the covariate definition of Fromage_2025_mycophenolic_acid.R (Fromage 2025 Section 2.2.2).
  • haematopoietic stem cell (HSC) recipients, HSCT, HSC, BMT – equivalent designations for the same graft class; all map to TX_HCT.
  • Indication – pooled categorical column whose levels include renal / hepatic / cardiac / pulmonary / bone marrow transplantation and autoimmune disease; decompose into binary TX_HCT = as.integer(Indication == "bone marrow transplantation") and document the decomposition in covariateData[[TX_HCT]]$notes.
• Example models: Fromage_2025_mycophenolic_acid.R (one of three transplant indicators – TX_HEART / TX_LUNG / TX_HCT – that the source pools into a single group-(ii) level carrying one estimated effect on the second gamma absorption rate constant: beta_b2 = +0.59 on log(b2), equivalently -0.59 on log(MAT2), halving the mean absorption time of the second peak from 4.13 h to 2.29 h relative to the renal / hepatic reference; Fromage 2025 Table 2 and Discussion).
• Notes: Registered under TX_HCT rather than TX_BMT because the TX_ANY entry had already reserved this name for exactly this concept (“if a future paper contrasts hematopoietic cell transplant recipients against solid-organ recipients, register a sibling TX_HCT”), and because TX_HCT covers peripheral-blood and cord-blood grafts as well as marrow, whereas BMT names only the marrow source. Sibling to the solid-organ indicators TX_LIVER / TX_HEART / TX_LUNG; distinct in kind from them in that a haematopoietic cell graft is not a solid organ, so a model pooling both (as Fromage 2025 does) is pooling across graft classes and should say so in its covariateData notes. Fromage_2025_mycophenolic_acid.R does NOT use TX_ANY, so no statement about whether that model’s TX_ANY includes HCT is required; a future model combining the two must decide explicitly and record it in the TX_ANY entry. Where a source pools several transplant types under one estimated effect, keep the per-type columns canonical and form the pooled indicator inside model(), so a later model that separates the effects can reuse the same columns. Ratified canonically on 2026-08-21 alongside the Fromage 2025 mycophenolic acid extraction (sidecar request 001, question 1).

DIS_AUTOIMMUNE (canonical for pooled autoimmune-disease indication indicator)

• Description: Binary indicator that the treatment indication is an autoimmune disease, pooled across the specific autoimmune diagnoses present in the cohort, contrasted against a non-autoimmune indication. 1 = autoimmune-disease indication; 0 = non-autoimmune indication (the complement is paper-defined; in Fromage_2025_mycophenolic_acid.R it is transplantation). Time-fixed per subject. Use this only when the source pools two or more distinct autoimmune diagnoses into a single estimated level and reports no per-diagnosis effect; when the source names a single autoimmune disease, use that disease’s own canonical instead.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-autoimmune indication; the complement group is paper-defined – renal or hepatic transplantation in Fromage 2025).
• Source aliases:
  • autoimmune diseases – the level name used in Fromage_2025_mycophenolic_acid.R (Fromage 2025 Section 2.2.2), covering systemic lupus erythematosus, lupus nephritis and nephrotic syndrome.
  • AID – the abbreviation used in the Fromage 2025 Abstract.
  • Indication – pooled categorical column; decompose into binary DIS_AUTOIMMUNE = as.integer(Indication == "autoimmune disease") and document the decomposition in covariateData[[DIS_AUTOIMMUNE]]$notes.
• Example models: Fromage_2025_mycophenolic_acid.R (effect on the second gamma absorption rate constant: beta_b2 = -0.17 on log(b2), equivalently +0.17 on log(MAT2), lengthening the mean absorption time of the second peak from 4.13 h to 4.90 h relative to the renal / hepatic reference; Fromage 2025 Table 2 reports this effect with 188% RSE and Section 3.2.2 states it “can be disregarded”, but the authors retained it for their pcVPC simulations and the packaged model retains it accordingly).
• Notes: Distinct from the single-disease canonicals DIS_SLE, DIS_RA, DIS_UC, DIS_PBC, DIS_ALOPECIA_AREATA, DIS_VITILIGO and the like: those identify one named diagnosis, whereas DIS_AUTOIMMUNE deliberately pools several. Prefer the single-disease canonical whenever the source resolves the diagnosis; DIS_AUTOIMMUNE exists because some sources (Fromage 2025 among them, whose autoimmune group holds only 13 patients) have too few subjects per diagnosis to estimate separate effects and therefore pool lupus, lupus nephritis and nephrotic syndrome into one level. A dataset may legitimately carry both DIS_AUTOIMMUNE = 1 and DIS_SLE = 1 for the same subject; DIS_AUTOIMMUNE must NOT be inferred from the absence of the single-disease indicators, because an autoimmune diagnosis outside the registered set would then be missed. Scope: specific, because the reference complement is paper-defined. Ratified canonically on 2026-08-21 alongside the Fromage 2025 mycophenolic acid extraction (sidecar request 001, question 1).

INTRAOP (canonical for intra-operative period indicator (active surgery))

• Description: 1 = the observation time falls within the intra-operative period (active surgery, between the start of the surgical procedure and skin closure / end of surgery); 0 = pre- or post-operative. Time-varying within subject. The exact boundaries of the intra-operative window are paper-specific (e.g., “from end of induction to skin closure”, “from the post-loading-dose PK sample to end of surgery”, “from incision to last suture”) – document the per-model boundaries in covariateData[[INTRAOP]]$notes. Used as a categorical covariate to capture composite physiological changes during active surgery (hemodynamic effects of general anaesthesia, blood loss, fluid resuscitation, surgical stress, temperature management) that are jointly unidentifiable individually in typical perioperative PK datasets.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (pre- or post-operative; i.e., not during active surgery).
• Source aliases:
  • (paper-prose) – used in Stricker_2013_aminocaproicAcid.R (Stricker 2013 Methods “Full covariate model” and Table 4: the intra-operative period is defined as “the time immediately after the post-loading dose PK sample through the end of the surgery”; CL and V1 are estimated separately for pre/postoperative vs intraoperative periods, with multiplicative shifts of 0.89 on CL and 0.80 on V1 during the intra-operative window).
• Example models: Stricker_2013_aminocaproicAcid.R (multiplicative effect on CL and V1: cl = cl_postop * 0.89^INTRAOP, vc = vc_postop * 0.80^INTRAOP; reference category is pre/postoperative).
• Notes: Distinct from POD (post-operative day – continuous days since surgery, rising monotonically from 0) and POSTTX_DAY1 (binary first-24-hours-post-transplant indicator). INTRAOP captures the active-surgery window itself, whereas POD and POSTTX_DAY1 capture the post-surgical recovery time course. The three canonicals can coexist in a single dataset that pools perioperative and post-discharge PK samples. The intra-operative effect is typically interpreted as a composite of multiple confounded physiological perturbations and is not directly attributable to any single mechanism (e.g., Stricker 2013 Discussion: “the model results may be simply reflecting the net effect of a possible increased CL due to blood loss and decreased CL due to other confounding intra-operative factors”). Pairs with a future INTRAOP_BL (per-subject calculated intra-operative blood loss) canonical when a source paper reports a continuous blood-loss column alongside or instead of the binary indicator – that is a separate canonical to be proposed when needed.

SURG_SEV_MAJOR (canonical for major (vs minor) surgical procedure severity indicator)

• Description: 1 = the surgical procedure is classified as major or high-risk according to a paper-specific severity taxonomy (e.g., major orthopedic replacement, major abdominal / thoracic / vascular surgery); 0 = the procedure is classified as minor (e.g., central-line placement, small ENT / ophthalmic procedures, dental extraction, biopsy). Time-fixed per surgical procedure (a subject undergoing multiple surgeries in the study window may have different SURG_SEV_MAJOR values on each occasion). Distinct from INTRAOP (a time-varying within-surgery window indicator) – SURG_SEV_MAJOR is a per-procedure severity classification that persists across the perioperative period. Captures composite physiological differences between major and minor surgery (larger blood loss / fluid resuscitation, longer duration, higher acute-phase response, more consumption of coagulation factors) that can shift factor concentrate PK in bleeding disorders.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (minor surgical procedure).
• Source aliases:
  • severity of surgical procedure (paper-prose 0/1 indicator, 1 = major) – used in Hazendonk_2016_factor_viii.R (Hazendonk 2016 Table 5: 0.93 ^ severity on CL, so major-surgery cases show 7% lower typical FVIII clearance than minor-surgery cases; classification per Koshy et al. 1995 with major and high-risk collapsed together).
• Example models: Hazendonk_2016_factor_viii.R (multiplicative effect on CL: (1 + (-0.07) * SURG_SEV_MAJOR) – equivalent to 0.93 ^ SURG_SEV_MAJOR when binary; reference category 0 = minor surgery).
• Notes: General scope because major-vs-minor surgery severity classification recurs across perioperative popPK models for hemostatic factor concentrates and other drugs where surgical stress affects clearance. The exact severity taxonomy is paper-specific – Hazendonk 2016 follows Koshy et al. 1995 (a hemophilia-specific major / minor / high-risk classification, with major and high-risk collapsed into SURG_SEV_MAJOR = 1), while other surgical popPK models may use ASA physical-status class, RACHS-1 (cardiac only), or ad-hoc paper-specific criteria. Record the source-paper severity taxonomy in covariateData[[SURG_SEV_MAJOR]]$notes. Distinct from INTRAOP (time-varying within-surgery window indicator, applicable to any procedure regardless of severity), from RACHS1 (ordinal cardiac-surgery-specific risk score, cardiac only), and from MAJORSURG_HISTORY (would be a lifetime prior-major-surgery indicator, not yet ratified). Sign of the effect on CL is paper-specific and physiologically ambiguous: major surgery can plausibly increase FVIII clearance through blood loss and consumption OR decrease it through VWF-mediated stabilization from the surgical stress response (Hazendonk 2016 Discussion attributes the 7% decrease to confounding by age – older patients underwent more major procedures). Ratified canonically on 2026-07-11 alongside the Hazendonk 2016 perioperative FVIII extraction.

T_CPB (canonical for total cardiopulmonary bypass duration during the most recent cardiac surgery)

• Description: Total time the patient spent on cardiopulmonary bypass (CPB) during their most recent cardiac-surgery operation. Time-fixed per subject (the duration of the bypass run is a single scalar set at the close of surgery). Captures composite physiological / pharmacological effects of CPB on post-operative drug disposition (hemodilution from priming volume, systemic inflammatory response, transient hepatic / renal hypoperfusion).
• Units: minutes
• Type: continuous
• Scope: general
• Reference category: n/a – typically enters as a power-centred effect (T_CPB / ref)^exponent. Reference value is paper-specific (cohort median total CPB time): Su 2016 uses 60 minutes (overall cohort median, Table 3).
• Source aliases:
  • TBYP – used in Su_2016_dexmedetomidine.R (Su 2016 NONMEM column for total bypass time in minutes; Methods ‘Full Covariate Model’ equation and Table 4 footnote).
• Example models: Su_2016_dexmedetomidine.R (power effect on CL: (T_CPB / 60)^(-0.31); negative exponent so longer bypass time reduces post-operative CL).
• Notes: General scope because CPB time is a routine clinical covariate in any popPK study of patients undergoing open heart surgery. Distinct from T_POST_ECMO (time AFTER decannulation from ECMO – T_CPB captures the DURATION of the bypass procedure during surgery, not time since the end of it; T_POST_ECMO is time-varying within subject whereas T_CPB is time-fixed). Distinct from POD (post-operative day) and INTRAOP (binary intra-operative indicator). Per the T_ canonical family, the T_ prefix denotes a procedure-related time covariate; for T_CPB the “event” is the bypass run itself and the column carries the duration of that run. Future paediatric / adult cardiac-surgery popPK extractions that retain CPB time should reuse this canonical. Ratified canonically on 2026-06-28 alongside the Su 2016 dexmedetomidine extraction.

CPB_ON (canonical for cardiopulmonary bypass phase indicator, before rewarming begins)

• Description: 1 = the observation or dosing record falls within the cardiopulmonary bypass (CPB) phase proper, i.e. from commencement of bypass until rewarming begins; 0 = pre-CPB, during rewarming, or post-CPB. Time-varying within subject. Captures the acute intra-CPB physiological perturbation – hemodilution by the circuit priming volume, non-pulsatile flow, hypothermia, altered regional perfusion, and drug sequestration onto circuit components – as it applies during bypass but before the rewarming manoeuvre. Pairs with CPB_REWARM (below): the two are mutually exclusive, and the patient is on the bypass circuit whenever CPB_ON + CPB_REWARM == 1. A source paper that records the intra-operative CPB phase as a single multi-level column (commonly coded 0 = pre-CPB, 1 = CPB, 2 = warming, 3 = post-CPB) decomposes into these two binary indicators per the standing policy of decomposing categorical covariates rather than testing levels inside model(); the post-CPB level carries no indicator of its own and therefore collapses onto the pre-CPB reference unless a paper retains a distinct post-CPB effect (in which case register a sibling CPB_POST).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (pre-CPB or post-CPB; i.e. off the bypass circuit).
• Source aliases:
  • CPB phase (0/1/2/3, level 1) – used in Khaowroongrueng_2024_sufentanil.R (Khaowroongrueng 2024 Methods ‘CPB-adjusted model’: “CPB phases were recorded in the dataset as 0, 1, 2, and 3, denoting the pre-CPB, CPB, warming, and post-CPB phases, respectively”; the paper’s Equations 1-4 give a plain multiplicative fold-change on the typical parameter value within each phase).
• Example models: Khaowroongrueng_2024_sufentanil.R (multiplicative effect on the central volume during the CPB phase only: vc = exp(lvc) * e_cpb_vc^CPB_ON with e_cpb_vc = 2.74, a 2.74-fold hemodilution-driven increase in V1; the clearance effect spans both bypass windows and so uses e_cpb_cl^(CPB_ON + CPB_REWARM) with e_cpb_cl = 2.80; reference category is pre-/post-CPB).
• Notes: General scope because the intra-operative CPB phase is a routine time-varying covariate in any popPK study of open-heart surgery, and the CPB-versus-rewarming split recurs whenever a paper resolves the bypass run into sub-phases. Distinct in kind from T_CPB, which is the time-fixed total duration of the bypass run (continuous, minutes) rather than a within-surgery window indicator – the two can coexist in one dataset. Distinct from INTRAOP, whose window is the whole active-surgery period: in a CPB cohort every record is intra-operative, so INTRAOP would be constant 1 and carry no information, which is precisely why this finer canonical is needed. Distinct from the ECMO family (T_ECMO, T_POST_ECMO, ECMO_PUMP_SPEED) – a different extracorporeal circuit, and those time covariates are continuous rather than phase indicators. Distinct from the subject-level extracorporeal-therapy status flags RRT_CRRT_STATUS / RRT_HEMODIAL_STATUS. Because the name reads as though it should cover the whole time on the circuit, record explicitly in covariateData[[CPB_ON]]$notes that rewarming is excluded and is carried by CPB_REWARM. Ratified canonically on 2026-08-05 (sidecar request 001, question 1, option A) alongside the Khaowroongrueng 2024 sufentanil extraction.

CPB_REWARM (canonical for cardiopulmonary bypass rewarming phase indicator)

• Description: 1 = the observation or dosing record falls within the rewarming phase of cardiopulmonary bypass, i.e. from the start of active rewarming until separation from the bypass circuit; 0 = pre-CPB, during the CPB phase proper, or post-CPB. Time-varying within subject. Mutually exclusive with CPB_ON. Separates the rewarming manoeuvre from the preceding hypothermic bypass phase, which matters when a paper finds a covariate effect whose window is one but not the other (e.g. a clearance effect that persists through rewarming while a hemodilution-driven volume effect does not). The rewarming target is paper-specific (e.g. a rectal temperature of at least 36 C before separation from CPB) – document the per-model boundary in covariateData[[CPB_REWARM]]$notes.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (pre-CPB, CPB proper, or post-CPB).
• Source aliases:
  • CPB phase (0/1/2/3, level 2) – used in Khaowroongrueng_2024_sufentanil.R (Khaowroongrueng 2024 Methods ‘CPB-adjusted model’; level 2 of the paper’s single 0/1/2/3 phase column is the “warming” phase, and Methods ‘CPB system’ records that “all patients were rewarmed to a rectal temperature of >= 36 C before they were separated from CPB”).
• Example models: Khaowroongrueng_2024_sufentanil.R (enters only through the summed clearance window cl = exp(lcl + etalcl) * e_cpb_cl^(CPB_ON + CPB_REWARM) with e_cpb_cl = 2.80; Khaowroongrueng 2024 Results found the CPB effect on clearance during the CPB and rewarming phases “exhibited similarity; therefore, the CPB effect in these phases was estimated using the same typical value”, while the central-volume effect was retained for the CPB phase only, so CPB_REWARM does not appear in the vc equation).
• Notes: General scope for the same reason as CPB_ON. The pair exists so that two different covariate windows over one bypass run are separately addressable: a shared effect across both is written e_<cov>_<param>^(CPB_ON + CPB_REWARM) (the sum is 0 or 1 because the indicators are mutually exclusive), while a CPB-only or rewarming-only effect uses the single indicator. Do NOT encode a shared effect by widening CPB_ON to include rewarming – that would make the two canonicals overlap and silently break any model that needs the narrow window. Distinct from T_CPB (time-fixed total bypass duration) and INTRAOP (whole active-surgery window). If a future paper retains a distinct post-CPB effect, register a sibling CPB_POST rather than reusing either of these; in the absence of such an indicator the post-CPB phase collapses onto the pre-CPB reference. Ratified canonically on 2026-08-05 (sidecar request 001, question 1, option A) alongside the Khaowroongrueng 2024 sufentanil extraction.

ICSHUNT_R2L (canonical for right-to-left intracardiac shunt indicator)

• Description: Binary indicator that the patient’s cardiac anatomy produces a right-to-left intracardiac shunt with pulmonary blood flow to systemic blood flow ratio Qp:Qs < 1. 1 = right-to-left shunt present (e.g., single-ventricle physiology after stage 2 palliation – Glenn or hemi-Fontan); 0 = no right-to-left shunt (Qp:Qs >= 1). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no right-to-left shunt; Qp:Qs >= 1).
• Source aliases:
  • intracardiac shunt – used in Su_2016_dexmedetomidine.R (Su 2016 Methods ‘Full Covariate Model’ definition; encoded in the source dataset as a binary indicator and entered in the CL equation as a multiplicative factor when shunt = 1).
• Example models: Su_2016_dexmedetomidine.R (multiplicative effect on CL: CL is multiplied by 1.24 when ICSHUNT_R2L = 1; Su 2016 Methods hypothesise the mechanism is shunt-induced reduction in first-pass lung extraction combined with increased hepatic perfusion for a drug with relatively high hepatic extraction ratio).
• Notes: General scope because intracardiac shunting is a defining anatomical feature in any congenital-heart-disease popPK cohort and is likely to recur in future paediatric / adult cardiac-surgery popPK studies. The Qp:Qs < 1 threshold is the standard clinical definition for right-to-left shunting. Distinct from TX_HEART (heart-transplant indicator), RACHS1 (paediatric cardiac surgery risk category), and INTRAOP (binary intra-operative indicator). Future extractions that report a continuous Qp:Qs ratio could either (a) reuse ICSHUNT_R2L as a binary derived from the continuous ratio (Qp:Qs < 1) or (b) register a sibling continuous canonical QP_QS. Ratified canonically on 2026-06-28 alongside the Su 2016 dexmedetomidine extraction.

Disease state (cross-population indicators)

DIS_CMV (canonical for transplant-recipient-with-cytomegalovirus-infection disease-state indicator)

• Description: 1 = hematopoietic cell transplant (HCT) or solid organ transplant (SOT) recipient with cytomegalovirus (CMV) infection/disease; 0 = non-CMV subject (healthy volunteer, or a phase I participant such as a renal- or hepatic-impairment cohort member, pooled into the same analysis as the reference group). Time-fixed per subject. Used when a population PK model pools healthy volunteers / phase I participants with transplant recipients who have CMV infection and tests the patient-vs-healthy contrast as a PK covariate.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-CMV subject; the complement group is defined per-model – typically healthy volunteers plus the phase I special-population cohorts).
• Source aliases:
  • HSCMV – NONMEM control-stream column name used in Sun_2023_maribavir.R.
• Example models: Sun_2023_maribavir.R (log-scale additive shift on CL/F: exp(e_dis_cmv_cl * DIS_CMV) with e_dis_cmv_cl = -0.280346, i.e. CL/F is 0.756x lower in transplant recipients with CMV than in the healthy 70-kg reference subject).
• Notes: Distinct from the organ-specific transplant indicators TX_LIVER / TX_HEART / TX_LUNG, which record which organ was transplanted rather than the presence of CMV infection, and from DIS_HEALTHY, which is the complementary healthy-cohort indicator. DIS_CMV is preferred over encoding this contrast as DIS_HEALTHY = 1 - DIS_CMV because the source’s structural reference subject is explicitly the non-CMV individual (Sun 2023 Table S2: “The reference population is a 70-kg individual without CMV administered a 800 mg maribavir dose”), so keeping the patient group as the indicator preserves the published meaning of the reference THETA. Also distinct from AUC_GCV, which supplies ganciclovir exposure to CMV viral-load PD models rather than flagging CMV disease state. Also distinct from DIS_CMV_RETINITIS, which is a within-CMV-positive disease-presentation split (retinitis vs no retinitis) and is orthogonal to this entry: both indicators can appear in one dataset, and a DIS_CMV_RETINITIS cohort carries DIS_CMV = 1 throughout. Scope: specific; promote to general if a second paper pools transplant recipients with CMV against a non-CMV reference with the same semantics. Registered alongside the Sun 2023 maribavir extraction. ### DIS_CMV_RETINITIS (canonical for CMV-retinitis indicator within a CMV-positive cohort)
• Description: Binary indicator that a CMV-positive subject has CMV retinitis, i.e. symptomatic end-organ CMV disease of the retina, as opposed to CMV positivity without retinitis. 1 = CMV retinitis; 0 = CMV-positive without retinitis (in the founding study, asymptomatic CMV urine shedding). Time-fixed per subject. Used when a population PK analysis contrasts two CMV presentations inside a CMV-infected cohort, rather than contrasting CMV-infected subjects against CMV-negative ones.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (CMV-positive without retinitis; the complement group is defined per-model – in Yuen 1995 it is the 17 patients shedding CMV in urine).
• Source aliases:
  • CMV – column name used in Yuen_1995_ganciclovir.R (Yang 2023 Table 3 footnote: “CMV: CMV = 0 for CMV-shedding patients and 0.41 for patients with CMV retinitis”; the source folds the coefficient into the column’s coded value, so store the canonical column as a plain 0 / 1 indicator and carry 0.41 as a named ini() parameter).
  • CMV-shedding or CMV retinitis – the covariate label used in Yang 2023 Table 4 for the same column.
• Example models: Yuen_1995_ganciclovir.R (fractional reduction of the renal-elimination term of clearance: cl = exp(lcl) + e_wt_crcl_cl * WT * CRCL/100 * (1 - 0.76 * TX_ANY) * (1 - 0.41 * DIS_CMV_RETINITIS), i.e. a retinitis patient’s weight-and-creatinine-clearance-driven clearance term is 41% lower than an asymptomatic urine-shedding patient’s at the same weight and creatinine clearance; Yang 2023 Table 3 and its footnote).
• Notes: This is a within-CMV-positive contrast and is therefore ORTHOGONAL to DIS_CMV, which encodes transplant-recipient-with-CMV-infection versus a non-CMV reference (healthy volunteer or phase I participant). Both can appear in one dataset, and every subject in a DIS_CMV_RETINITIS cohort – retinitis and non-retinitis alike – would carry DIS_CMV = 1; reusing DIS_CMV for the retinitis split would overload it with incompatible semantics and would change the meaning of the published reference THETA in Sun_2023_maribavir.R. Registered as the retinitis-specific DIS_CMV_RETINITIS rather than a broader end-organ-disease canonical per operator decision (sidecar request 001, question 3, 2026-07-30), because retinitis was the only end-organ site present in the founding cohort. If a future paper contrasts a different symptomatic CMV site (colitis, pneumonitis, oesophagitis) against asymptomatic CMV positivity, register a sibling canonical on the same pattern (e.g. DIS_CMV_COLITIS) rather than widening this entry; if a paper pools several sites into a single “end-organ disease” indicator, that is the point at which a DIS_CMV_ORGAN parent becomes worth registering. Scope: specific, because the reference complement is paper-defined. Ratified canonically alongside the Yuen 1995 ganciclovir extraction.

DIS_COVID19 (canonical for COVID-19 patient disease-state indicator)

• Description: 1 = participant with coronavirus disease 2019 (COVID-19; laboratory-confirmed SARS-CoV-2 infection) enrolled as a patient in the source analysis; 0 = non-COVID-19 subject (healthy volunteer, or a phase I special-population participant such as a renal- or hepatic-impairment cohort member, pooled into the same analysis as the reference group). Time-fixed per subject. Used when a population PK / PD model pools phase I participants with COVID-19 patients and tests the patient-vs-non-patient contrast as a covariate.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (non-COVID-19 subject; the complement group is defined per-model – healthy volunteers plus phase I special-population cohorts in Chan 2023, a historical CML/GIST oncology cohort in Said 2025).
• Source aliases:
  • PTST (patient status) – NONMEM control-stream column name used in Chan_2023_nirmatrelvir.R (Chan 2023 Data S1 $PK: CLPTST = 1; IF(PTST.EQ.1) CLPTST = 1+THETA(17)).
  • COVID – NONMEM control-stream column name used in Said_2025_imatinib.R (Said 2025 Data S1 $PK: V = EXP(THETA(2) + THETA(8)*COVID + ETA(2))).
• Example models: Chan_2023_nirmatrelvir.R (fractional multiplier on nirmatrelvir CL: 1 + e_dis_covid19_cl * DIS_COVID19 with e_dis_covid19_cl = -0.341, i.e. CL is 34.1% lower in adults with COVID-19 than in the phase I reference participant at the same weight and renal function), Said_2025_imatinib.R (log-additive shift on the apparent central volume of a joint imatinib / N-desmethyl-imatinib model: vc = exp(lvc + e_dis_covid19_vc * DIS_COVID19 + etalvc) with e_dis_covid19_vc = log(1.2), i.e. V1/F1 is ~20% larger in pooled COVID-19 ARDS patients than in the CML/GIST oncology reference cohort, attributed to elevated capillary permeability).
• Notes: Distinct from DIS_HEALTHY, and DIS_COVID19 = 1 - DIS_HEALTHY is NOT a valid re-expression here: the reference group in Chan 2023 contains renal- and hepatic-impairment cohorts who are neither healthy nor COVID-19 patients, so the two indicators are not complements. Same structural pattern as DIS_CMV (transplant-recipient-with-CMV vs pooled phase I reference), which is the closest precedent in the DIS_ family. Also distinct from SARS_VLOAD (baseline SARS-CoV-2 viral load) and SARS_SEROPOS (baseline serostatus), which quantify infection within a COVID-19-only cohort rather than flagging cohort membership. Chan 2023 Discussion cautions that the COVID-19-on-CL effect and the 150-mg-tablet formulation effect on F1 are likely partially confounded because the 150-mg tablet was evaluated only in a single single-dose healthy-participant study. Registered alongside the Chan 2023 nirmatrelvir extraction. Scope promoted from specific to general on 2026-08-21 alongside the Said 2025 imatinib extraction, which is the second paper to pool COVID-19 patients against a non-COVID-19 reference under the same semantics and so satisfies the promotion condition this entry originally set. Note that the non-COVID-19 reference cohort is paper-defined and differs between the two example models (phase I participants in Chan 2023, CML/GIST oncology outpatients in Said 2025), so the effect size is not transferable between papers – record the actual complement in covariateData[[DIS_COVID19]]$notes. Also distinct from DIS_ARDS: Said 2025 describes its COVID-19 arm as C-ARDS throughout, but codes 1 for both the ARDS-by-Berlin-criteria InventCOVID patients and the supplemental-oxygen-only CounterCOVID patients, so the flag is cohort membership and not an ARDS diagnosis. Use DIS_ARDS only when the paper genuinely contrasts ARDS against non-ARDS.

DIS_UC (canonical for ulcerative colitis disease-state indicator)

• Description: 1 = ulcerative colitis patient, 0 = non-UC (e.g., healthy volunteer or non-IBD indication). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-UC subject; the complement group is defined per-model – typically healthy volunteers and/or patients with another indication such as asthma).
• Source aliases:
  • UC – used in Hua_2015_anrukinzumab.R.
• Example models: Hua_2015_anrukinzumab.R (multiplicative fractional increase in CL, +72.8%, on top of weight and albumin effects), Wojciechowski_2023_ritlecitinib_updated.R (two linear fractional effects from Wojciechowski 2023 Table 2: CL/F *= (1 - 0.560 * DIS_UC) and F *= (1 - 0.224 * DIS_UC); the CL/F reference category is the healthy-participant cohort while the F reference category is healthy participants plus the RA, alopecia areata and vitiligo cohorts, and the UC cohort also contributes to the pooled inflammatory-disease group that scales the IIV and proportional-residual-error magnitudes), Maleki_2024_brepocitinib.R (additive shift of -1.16 1/h on the oral absorption rate constant Ka, the only structural disease-state effect in a nine-study brepocitinib popPK model pooling healthy participants with six immuno-inflammatory indications; the reference complement is therefore healthy participants plus the alopecia-areata / hidradenitis-suppurativa / psoriatic-arthritis / plaque-psoriasis / vitiligo cohorts. Additive rather than multiplicative because Maleki 2024 Table 2 classifies Ka as a normally distributed parameter; the paper attributes the ~46% slower absorption to UC pathophysiology – gastrointestinal transit time, GI fluid composition and permeability, and altered abundance of metabolising enzymes and transporters).
• Notes: Used when a population PK model pools UC patients with a non-UC reference population (e.g., Hua 2015: healthy volunteers + asthma patients + UC patients) and UC disease status is tested as a PK covariate. Distinct from DISEXT_EP / DISEXT_OTHER, which operate within a UC-only cohort (disease extension). Start as scope: specific; promote to general if a second paper pools UC with a non-UC reference.

DIS_DUOD_ULCER (canonical for duodenal-ulcer disease-state indicator)

• Description: 1 = duodenal-ulcer patient (peptic ulcer disease of the duodenum, endoscopically confirmed), 0 = non-duodenal-ulcer reference (e.g., healthy volunteer, or other non-peptic-ulcer indication pooled in the source analysis). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-duodenal-ulcer subject; the complement group is paper-defined – for Yu 2024 the reference is the pooled healthy-subject cohort across 4 phase I trials).
• Source aliases:
  • Disease status – Yu 2024 paper narrative and Table 3 footnote (1 = duodenal ulcer, 0 = healthy).
• Example models: Yu_2024_ilaprazole.R (exponential effects exp(0.290 * DIS_DUOD_ULCER) on CL and exp(0.356 * DIS_DUOD_ULCER) on Vp of the 2-compartment IV ilaprazole model; reference category is the pooled healthy-subject cohort from CTR20132848 / CTR20140147 / CTR20150686 / CTR20150685, and the phase IIa duodenal-ulcer cohort CTR20132846 required an ulcer diameter <= 15 mm with no combined ulcer bleeding; Yu 2024 Table 3 and Eqs. 7-9).
• Notes: Distinct from ENDO_ULCER (IBD mucosal-ulcer activity indicator scored within an IBD cohort at baseline ileocolonoscopy) because DIS_DUOD_ULCER is a peptic-duodenal-ulcer-patient vs non-patient cohort indicator, following the DIS_UC / DIS_PSORIASIS / DIS_HAE pattern of specific-disease-vs-non-disease pooled-cohort covariates. Distinct from DIS_UC and any future IBD indicators because peptic ulcer disease is a non-IBD upper-GI condition. Also distinct from DIS_HEALTHY: that canonical carries a healthy-vs-pooled-patient contrast with 0 = patient, whereas a paper enrolling a single named disease cohort alongside healthy participants is encoded with the disease-specific indicator so the source coefficients and typical values transfer without a sign flip or re-baselining. The shorter forms DIS_DU (collides with other DU acronyms), DIS_PUD, and DIS_PEPTIC_ULCER (both broader than the duodenal-only cohort Yu 2024 enrolled) were considered and rejected. Scope: specific because the complement reference category is paper-defined; promote to general if a second paper pools duodenal-ulcer patients with a non-peptic-ulcer reference. Ratified canonically on 2026-07-28 alongside the Yu 2024 ilaprazole extraction.

DIS_OUD (canonical for opioid use disorder disease-state indicator)

• Description: 1 = participant with opioid use disorder (OUD; DSM-defined opioid dependence, the population enrolled in opioid-agonist-treatment trials of buprenorphine, methadone, or extended-release depot formulations), 0 = non-OUD reference (typically a healthy participant enrolled in a phase 1 cohort of the same pooled analysis). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-OUD participant; the complement group is paper-defined – for Bjornsson 2023 the reference is the pooled healthy-participant cohort from the two phase 1 trials, which is also the most common category at 62 percent and therefore the reference for the source’s fractional-difference categorical covariate model).
• Source aliases:
  • Population – Bjornsson 2023 Table 3 covariate rows (“Population covariate on Vc”, “Population covariate on Fq1w1”) with footnote a “Patients with OUD versus healthy volunteers”; the source NMTRAN column name is not separately reported.
• Example models: Bjornsson_2023_buprenorphine.R (two effects in the final buprenorphine / CAM2038 population PK model: a fractional difference of +2.69 on the central volume, vc * (1 + e_dis_oud_vc * DIS_OUD), giving 64.3 L in healthy participants versus 237 L in participants with OUD; and an additive -0.671 on the logit of the CAM2038 weekly-depot fast-pathway dose fraction Fq1w1, giving 45.5 percent versus 29.9 percent. Bjornsson 2023 Table 3 and Results 3.1.3).
• Notes: Follows the DIS_<disease> family (DIS_UC, DIS_HAE, DIS_HOFH, DIS_DUOD_ULCER, …) rather than the complementary DIS_HEALTHY canonical, per the rule recorded in the DIS_DUOD_ULCER notes: a paper enrolling a single named disease cohort alongside healthy participants is encoded with the disease-specific indicator so the source coefficients and typical values transfer without a sign flip or re-baselining. Bjornsson 2023 is exactly that shape – its reference (most common) category is the healthy cohort and both published coefficients are expressed as OUD-versus-healthy – so DIS_OUD keeps Table 3’s typical values (Vc = 64.3 L, Fq1w1 = 45.5 percent) as the model file’s structural means. Distinct from OPIOID_PATIENT_TYPE (the Mann 2022 opioid-naive versus chronic-opioid-user indicator, which stratifies respiratory-depression PD sensitivity within a non-OUD analgesia setting rather than flagging a substance-use-disorder diagnosis) and from CAR_OPIOID (time-varying fraction of mu-opioid receptors bound by an agonist, a mechanistic input rather than a cohort label). Scope: specific because the complement reference category is paper-defined; promote to general if a second paper pools an OUD cohort with a non-OUD reference. Ratified canonically alongside the Bjornsson 2023 buprenorphine / CAM2038 extraction.

DIS_SASTHMA (canonical for moderate-to-severe asthma disease-state indicator)

• Description: 1 = moderate-to-severe asthma patient, 0 = not (e.g., healthy volunteer, mild-to-moderate asthma, or other indication). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-moderate-to-severe-asthma subject; the complement group is defined per-model).
• Source aliases:
  • sAsthma – used in Hua_2015_anrukinzumab.R.
• Example models: Hua_2015_anrukinzumab.R (multiplicative fractional change in SC bioavailability, -30.9%).
• Notes: The moderate-to-severe vs. mild-to-moderate asthma cutoff is protocol-defined; Hua 2015 uses FEV1 55-80% and ACQ-5 >= 2 for “moderate to severe” (study 4) versus FEV1 > 70% and ACQ-5 <= 1 for “mild to moderate” (study 1). Scope: specific because the severity threshold is tied to a particular analysis plan; future asthma-severity indicators with different thresholds should register as separate canonicals.

DIS_PJIA (canonical for polyarticular juvenile idiopathic arthritis disease-state indicator)

• Description: 1 = polyarticular juvenile idiopathic arthritis (pJIA) patient, 0 = non-pJIA (e.g., adult rheumatoid arthritis or other indication). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-pJIA subject; the complement group is defined per-model – typically adult RA in pooled abatacept analyses).
• Source aliases:
  • JIA – used in Gandhi_2021_abatacept.R and Zhong_2026_abatacept.R.
• Example models: Gandhi_2021_abatacept.R (additive coefficient on logit-F: pJIA patients have markedly higher SC bioavailability than RA reference), Zhong_2026_abatacept.R (additive coefficient +3.08 on logit-F transferred verbatim from a previous internal JIA PPK model that matches Gandhi 2021’s published value).
• Notes: Used when a population PK model pools pJIA patients with a non-pJIA reference population (e.g., Gandhi 2021: pooled adult RA + pediatric pJIA; Zhong 2026: pooled adult RA + pediatric pJIA + adult/pediatric HM) and pJIA disease/age status is tested as a PK covariate (typically on bioavailability rather than CL). Distinct from CHILD and ADOLESCENT, which are pure age-band indicators independent of indication. Scope: specific; promote to general if a third paper pools pJIA with a non-pJIA reference and the reference category remains adult RA.

DIS_CANCER (canonical for advanced-solid-tumor / oncology cohort indicator)

• Description: 1 = patient with an advanced or metastatic solid tumor (the oncology cohort in a pooled multi-indication PK/PD analysis), 0 = non-oncology subject (healthy volunteer or non-oncology disease cohort pooled in the source analysis). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-oncology subject; the complement group is paper-defined – typically the union of healthy volunteers and a non-oncology disease cohort such as cGVHD pooled in the source analysis).
• Source aliases: none; source NONMEM / Monolix control streams typically derive the indicator from a POP or STUDY categorical alongside DIS_HEALTHY.
• Example models: Yang_2024_axatilimab.R (multiplicative effect on baseline NCMC: BL_NCMC x exp(1.22 x DIS_CANCER + 0.618 x DIS_HEALTHY); reference category cGVHD when both indicators are 0), Bonate_2004_apomine.R (log-additive shifts on baseline CL/F and Vc/F for the advanced-solid-tumor cohort vs the healthy adult-male reference: e_cancer_cl = log(10.2 / 40.7) = -1.384 and e_cancer_vc = log(7.11 / 12.3) = -0.548; reference category 0 = healthy adult male volunteer), Silva_2024_apx3330.R (log-additive shift on apparent oral clearance for the advanced-solid-tumor cohort vs the healthy Japanese male volunteer reference: e_cancer_cl = 0.409, Silva 2024 Table 2 ‘Combined data’ row beta SubjectSource,CL, applied as CL/F x exp(0.409 x DIS_CANCER) per the paper’s Eq. 2; reference category 0 = healthy Japanese male volunteer. The source paper names the covariate ‘subject source’ and chose it over serum albumin deliberately – it stands in for the whole bundle of between-cohort differences (disease state, age, ethnicity and albumin), which is exactly the paper-defined-complement semantic that makes this entry specific).
• Notes: Used together with DIS_HEALTHY to decompose a three-level “participant population” categorical (cGVHD reference, advanced solid tumor, healthy volunteer) into two orthogonal binary indicators (Yang 2024 form), or as a single binary stratifier between an oncology cohort and a healthy-volunteer reference (Bonate 2004 and Silva 2024 form). Scope: specific because the disease-pooling reference category is paper-defined (Yang 2024 reference is patients with cGVHD; Bonate 2004 reference is healthy adult males; Silva 2024 reference is healthy Japanese male volunteers). A “study source” / “data source” categorical that separates an oncology cohort from a healthy-volunteer cohort pooled in the same analysis maps to DIS_CANCER rather than to a new STUDY_* indicator, because the modelled contrast is the disease cohort rather than a specific trial protocol. Ratified canonically on 2026-04-28; extended to the Bonate 2004 apomine extraction on 2026-06-04.

DIS_CANCER_PED (canonical for pediatric oncology cohort indicator)

• Description: 1 = pediatric patient receiving cancer-directed therapy (any malignancy, including hematologic cancers such as leukemia and lymphoma as well as solid tumors / blastomas), 0 = pediatric patient admitted for a non-oncology indication (e.g. infection, surgery, transplant). Time-fixed per subject. Distinct from DIS_CANCER, which is restricted to advanced/metastatic solid tumors in adults; DIS_CANCER_PED is the pediatric variant in the DIS_CANCER* family and explicitly covers leukemia-dominant pediatric cohorts.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-oncology pediatric patient; the complement group is paper-defined – in Llanos-Paez 2020 the complement is pediatric patients admitted for various non-oncology indications, with appendicitis and kidney disease / urinary tract infection the most common).
• Source aliases:
  • ONCOLOGY – Llanos-Paez 2020 NONMEM column with the same orientation (1 = oncology, 0 = nononcology); maps directly to DIS_CANCER_PED.
• Example models: Llanos-Paez_2020_gentamicin.R (multiplicative cohort shifts on V1 (-0.154) and Q (-0.321) relative to the nononcology baseline; CL has no oncology effect), deAlwis_1998_ondansetron.R (cohort indicator used together with DIS_HEALTHY and AGE thresholds to switch the per-subject proportional-residual-error magnitude across five paper-defined sub-populations; DIS_CANCER_PED = 1 routes to the paediatric-chemotherapy stratum (Table 1 group 4, propSd 0.178), DIS_CANCER_PED = 0 paired with DIS_HEALTHY = 0 routes to the paediatric-general-anaesthesia stratum (Table 1 group 5, propSd 0.145); reference complement is the paediatric general-anaesthesia cohort (study 4) plus all non-paediatric subjects).
• Notes: Use DIS_CANCER_PED rather than DIS_CANCER whenever the source paper’s “oncology” cohort includes hematologic malignancies (leukemia / lymphoma) or pediatric blastomas, because DIS_CANCER is canonically restricted to advanced/metastatic solid tumors. Reference-category complement is paper-defined (Llanos-Paez 2020 complement is the pooled pediatric non-oncology admissions cohort; de Alwis 1998 complement is the paediatric general-anaesthesia sub-cohort plus all non-paediatric subjects). Scope: specific because the complement is paper-defined. Covariate-effect parameters drop the DIS_ prefix per the existing DIS_CANCER -> e_cancer_* convention (Yang 2024); use e_cancer_ped_<param>.

DIS_HEALTHY (canonical for healthy-participant cohort indicator)

• Description: 1 = healthy participant (no diagnosis), 0 = patient (any diagnosis represented in the pooled cohort). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (patient subject; the complement group is the union of disease cohorts pooled in the source analysis).
• Source aliases: none known; healthy-participant indicators in source NONMEM control streams typically use ad-hoc names (e.g., HV, HEALTHY, DIS_HV).
• Example models: Nikanjam_2019_siltuximab.R (multiplicative effects: 0.77 on CL, 0.83 on Vss; reference category is the pooled non-healthy oncology cohort), Okada_2025_rocatinlimab.R (multiplicative shift 1 - 0.532 on Vmax when 1; reference complement is the pooled atopic-dermatitis + ulcerative-colitis + plaque-psoriasis patient cohort), Yang_2024_axatilimab.R (multiplicative effect on baseline NCMC: BL_NCMC x exp(1.22 x DIS_CANCER + 0.618 x DIS_HEALTHY); reference category cGVHD), Goel_2016_Sonidegib.R (multiplicative power-form effect on CL/F: 2.96^DIS_HEALTHY; reference category is the pooled cancer-patient cohort across X2101 / X1101 / A2201), Brown_2017_osimertinib.R (linear factor (1 + 0.44 x DIS_HEALTHY) on apparent osimertinib clearance and (1 + 1.25 x DIS_HEALTHY) on apparent AZ5104 clearance; reference category is the pooled NSCLC cohort across AURA / AURA2), Lu_2015_vismodegib.R (additive-on-log-scale shift on ka via exp(0.671 * DIS_HEALTHY) and on F via exp(0.881 * DIS_HEALTHY) gated on the Phase I formulation indicator; reference category is the pooled cancer-patient cohort across SHH3925g / SHH4610g / SHH4476g), Gupta_2016_lenvatinib.R (multiplicative power-form effect on CL/F: 1.15^DIS_HEALTHY; reference category is the pooled solid-tumor / thyroid-cancer patient cohort across 15 phase 1-3 studies; healthy subjects show +15 percent CL/F vs cancer patients), Bienczak_2025_ligelizumab.R (log-additive exp(-0.087 * DIS_HEALTHY) on apparent ligelizumab CL/F; reference category is the pooled chronic-spontaneous-urticaria patient cohort across C2201 / C2202 / C2302 / C2303), Lu_2015_tacrolimus.R (multiplicative factor 1 / 0.562 = 1.78 on CL/F at DIS_HEALTHY = 1; reference category is the adult Chinese orthotopic liver-transplant recipient cohort; healthy-volunteer CL/F is 1.78x patient CL/F at ALT = 0. Paper Eq. 10 uses the reverse-coded SubPop indicator – the model file re-expresses it as DIS_HEALTHY = 1 - SubPop), Li_2017_CC292.R (binary stratifier on the proportional residual error magnitude: HNP cohort uses propSd = sqrt(0.234) and patient cohort uses propSd = sqrt(0.659); reference category 0 is the pooled relapsed/refractory B-cell-malignancy patient cohort from AVL-292-003; source column HNP), Yoneyama_2017_emicizumab.R (exponential effects exp(-0.232 * DIS_HEALTHY) on CL/F and exp(-0.175 * DIS_HEALTHY) on Vd/F; reference category is the pooled Japanese male adult/adolescent severe-hemophilia-A patient cohort across the Japanese MAD phase I and phase I/II extension; paper Eq. 2 uses the reverse-coded PATIENT indicator and the model file re-expresses it as DIS_HEALTHY = 1 - PATIENT, shifting the structural typicals to the patient state), Kleideiter_2017_cebranopadol.R (multiplicative effect on bioavailability: f_disease *= 0.837 for healthy volunteers relative to the LBP/OA chronic-pain reference; paired with DIS_DPN and DIS_BUNIONECTOMY to form the four-level disease-status stratification), Kleideiter_2018_cebranopadol.R (multiplicative power-form effect on bioavailability F: factor 0.837, i.e. about -16% F for healthy adults vs the pooled nociceptive-pain (LBP and OA) reference cohort; sibling indicators DIS_DPN and DIS_BUN carry the diabetic-polyneuropathy and bunionectomy effects in the same four-level disease-status encoding; Kleideiter 2018 Table 13), Taubert_2018_finafloxacin.R (log-additive effects on the canonical lcl_renal + lcl_nonren decomposition: exp(+0.985 * DIS_HEALTHY) on CL_renal and exp(+0.068 * DIS_HEALTHY) on CL_nonren; reference category is the cUTI patient cohort (Trial III), with healthy effects rederived from paper Table 3 CL_total = 20.9 L/h * patient effect (1 - 0.29) and FER1 = 0.40 / FER2 = 0.21; source column PATIENT in the paper, re-expressed as DIS_HEALTHY = 1 - PATIENT), Goggin_2004_emfilermin.R (log-additive exp(+0.4325 * DIS_HEALTHY) on apparent CL/F, where +0.4325 = -log(0.649); the patient reference category is the IVF-ET premenopausal cohort with recurrent implantation failure (Study 3, n = 39) and the model file shifts lcl to the IVF-ET state lcl = log(57 * 0.649) = log(37.0) so the +0.4325 shift restores the healthy-postmenopausal-women-typical 57 L/h at DIS_HEALTHY = 1; source column TYPE in the paper, re-expressed as DIS_HEALTHY = 1 - TYPE), Klunder_2017_upadacitinib.R (paired healthy/RA structural means lcl_h / lcl_ra and lvc_h / lvc_ra gated by DIS_HEALTHY, with cohort-specific log-normal IIV on CL/F and Vc/F; reference category 0 is the adult RA cohort. The CL/F means encode the paper’s verbatim Table 3 contrast lcl_ra = log(39.7 * 0.76); Vc/F means are identical because Klunder 2017 reports no disease-state effect on typical Vc/F, only on its ISV), Bulitta_2010_ceftazidime.R (log-additive effects on CL and on V1 / V2 / V3 of the 3-compartment ceftazidime IV model: exp(log(1 / 1.17) * DIS_HEALTHY) on CL and exp(log(1 / 1.01) * DIS_HEALTHY) shared across V1, V2, and V3; reference category 0 is the cystic-fibrosis patient cohort, and Bulitta 2010 Table 3 reports FCYFCL = 1.17 and FCYFVSS = 1.01 with the healthy-volunteer cohort as the paper’s structural reference; the model file re-expresses the paper’s CF-vs-HV scale factors onto the canonical DIS_HEALTHY orientation so the typical-value parameters equal the Table 3 CF column), Desai_2016_isavuconazole.R (multiplicative fractional effect on peripheral volume V_p: 1 + e_dis_healthy_vp * DIS_HEALTHY with e_dis_healthy_vp = -0.3765, so healthy V_p baseline is about 38% lower than the patient reference at BMI 24.80 kg/m^2; reference category is the pooled SECURE-trial invasive-aspergillosis / other-filamentous-fungi patient cohort; source column SP (1 = patient, 0 = healthy) re-expressed as DIS_HEALTHY = 1 - SP; assignment of Table 5 theta_4 = 417 L to patients and theta_11 = 260 L to healthy subjects was inferred from the Discussion typical-value report V_p ~390 L (patients) / ~292 L (healthy)), deAlwis_1998_ondansetron.R (cohort indicator used together with DIS_CANCER_PED and AGE thresholds to switch the per-subject proportional-residual-error magnitude across five paper-defined sub-populations: DIS_HEALTHY = 1 routes the subject into one of three volunteer strata selected by AGE (young < 45 y -> propSd 0.125, elderly 45-75 y -> propSd 0.133, aged >= 75 y -> propSd 0.169), DIS_HEALTHY = 0 routes the subject into one of two paediatric strata selected by DIS_CANCER_PED (chemotherapy -> propSd 0.178, anaesthesia -> propSd 0.145); reference complement under DIS_HEALTHY = 0 is the pooled paediatric-patient cohort from studies 3 and 4), Iida_2008_nicorandil.R (multiplicative log-additive effects on apparent CL, V1, Q, V2 of the 2-compartment IV nicorandil model: exp(log(1 / 1.94) * DIS_HEALTHY) on CL, exp(log(1 / 1.39) * DIS_HEALTHY) on V1, exp(log(1 / 0.519) * DIS_HEALTHY) on Q, exp(log(1 / 4.06) * DIS_HEALTHY) on V2; reference category 0 is the acute heart failure (AHF) patient cohort, and Iida 2008 Table 2 reports POP_CL, POP_V1, POP_Q, POP_V2 with multiplicative FCL = 1.94, FV1 = 1.39, FQ = 0.519, FV2 = 4.06 carrying the AHF-vs-healthy contrast with the healthy-volunteer cohort as the paper’s structural reference; the model file re-expresses the paper’s HV-vs-AHF scale factors onto the canonical DIS_HEALTHY orientation so the typical-value parameters equal the AHF cohort), Shoji_2011_pregabalin.R (binary stratifier on the combined proportional + additive residual error magnitudes: healthy cohort uses propSd = 0.220 and addSd = 0.0239 ug/mL; patient cohort uses propSd = 0.285 and addSd = 0.236 ug/mL; reference complement under DIS_HEALTHY = 0 is the pooled post-herpetic neuralgia (PT01-PT04) and diabetic peripheral neuropathy (PT05) patient cohort; Shoji 2011 Table 3 final model rows for residual variability), Farrell_2013_conestatAlfa.R (cohort indicator gating the endogenous-baseline structural mean and IIV between healthy volunteers and patients with hereditary angioedema: DIS_HEALTHY = 1 selects lrbase_hv = log(0.901) with etalrbase_hv (12.7% CV) and DIS_HEALTHY = 0 selects lrbase_hae = log(0.176) with etalrbase_hae (54.4% CV); reference complement is the pooled adolescent / adult HAE-patient cohort from Studies C1 1101-01 / 1202-01 / 1203-01 / 1205-01 / 1304-01), PerezRuixo_2006_tipifarnib.R (ratio-form multiplicative effects on five structural parameters: CL ratio 1.21, V2 ratio 0.55, Q4 ratio 8.83, V4 ratio 2.66, and Ka ratio 2.31 applied as ratio^DIS_HEALTHY; reference category is the pooled adult-cancer cohort across 14 phase 1, 2, and 3 studies; healthy subjects show +21 percent CL and 2.31-fold higher Ka than cancer patients, while V2 is 0.55-fold smaller), Yin_2020_pexidartinib.R (multiplicative effect on CL/F: 1.26x when DIS_HEALTHY = 1 corresponding to a 21 percent lower steady-state AUC0-24 in healthy subjects vs patients per Yin 2020 Results; the residual-error proportional SD also switches by DIS_HEALTHY: 0.297 for patients vs 0.196 for healthy subjects; reference category 0 is the pooled TGCT / other solid-tumour patient cohort from Studies PLX108-01 and ENLIVEN), Melhem_2018_g_csf.R (log-additive shifts on the granulopoiesis parameters KINT and STM2: KINT_HV / KINT_PT = 0.197 / 0.113 = 1.743 log-multiplier and STM2_HV / STM2_PT = 5.21 / 3.89 = 1.339 log-multiplier applied on the healthy-volunteer arm; reference category 0 is the adult cancer-patient-on-chemotherapy cohort in the primary Dataset A reference model; healthy-volunteer subjects show approximately 74 percent higher receptor internalisation rate and 34 percent higher maximum stimulation of bone-marrow transit compared with cancer patients on chemotherapy), Chen_2023_nemonoxacin.R (multiplicative power-form effect on the peripheral volume of distribution Vp: e_dis_healthy_vp^DIS_HEALTHY with e_dis_healthy_vp = 1 / 1.23 = 0.813; reference category 0 is the pooled Chinese community-acquired-pneumonia cohort from the phase II (TG-873870-C-3) and phase III (TG-873870-C-4) nemonoxacin trials, and the healthy stratum is the phase I (TG-873870-C-1) cohort; Chen 2023 Eq. 8 prints V3 = 28 x (BW/70) x 1.23^DisStat against the reverse-coded DisStat indicator (0 = healthy, 1 = CAP patient), so the model file re-expresses it as DIS_HEALTHY = 1 - DisStat and shifts lvp to the patient state log(28 x 1.23) = log(34.4) so DIS_HEALTHY = 1 restores the printed healthy typical 28 L), Galluppi_2021_ulotaront.R (log-additive shift exp(-log(0.809) * DIS_HEALTHY) = exp(+0.2119 * DIS_HEALTHY) on apparent CL/F; reference category 0 is the pooled adult-schizophrenia-patient cohort across seven phase I studies plus the phase II acute + 6-month open-label extension studies, with the structural typical lcl = log(32.5 * 0.809) = log(26.29) shifted from the paper’s HV-typical 32.5 L/h to the patient state so exp(+0.2119) restores 32.5 L/h at DIS_HEALTHY = 1; source column PATIENT in the paper, re-expressed as DIS_HEALTHY = 1 - PATIENT), Mitra_2026_ziftomenib.R (multiplicative effects on parent ziftomenib CL/F (2.59x when DIS_HEALTHY = 1, log-scale THETA(12) = 0.950; reference category is R/R AML patients), on FM (0.348x, logit-scale THETA(17) = -1.62), on KO-739 Vc (0.197x, log-scale THETA(21) = -1.62), and on KO-516 Vc (0.154x, log-scale THETA(22) = -1.87); source paper column ‘HV’), Marathe_2023_belzutifan.R (binary stratifier on the proportional residual error magnitude only, with no effect on any structural or random-effect PK parameter: healthy participants use propSd = 0.26 (RESHV) and patients use propSd = 0.29 (RES PAT) per Marathe 2023 Table 2; reference complement under DIS_HEALTHY = 0 is the pooled advanced-RCC / other-advanced-solid-tumor / VHL-RCC patient cohort from Studies 1 and 4. The source control stream switches on STUDY (PROP = THETA(8) IF (STUDY.EQ.1.OR.STUDY.EQ.4)), and because Studies 1 and 4 are exactly the two patient studies the switch is re-expressed on the canonical DIS_HEALTHY orientation. Note that disease status was separately screened as a structural covariate on CL/F and V2/F and was NOT retained, so this model carries a residual-error-only disease effect), Lee_2023_tripegfilgrastim.R (the paper’s “study population” covariate on the tripegfilgrastim PDMDD parameters VD/F and KD: Lee 2023 Table 2 reports both typical values directly rather than the exponential coefficients, so the model file encodes lvd = log(12.7) / e_hv_vd = log(4.7 / 12.7) and lkd = log(16.2) / e_hv_kd = log(42.2 / 16.2); reference category 0 is the Korean pediatric solid-tumor cohort receiving chemotherapy, matching the orientation of the upstream Melhem_2018_g_csf.R framework this model inherits. In this analysis the indicator is fully confounded with age stratum and chemotherapy exposure – every healthy participant is an adult aged 20-38 and every patient is pediatric aged 6-17 and on chemotherapy – which the paper acknowledges as its principal limitation), Ganesan_2023_tebipenem.R (cohort indicator gating (a) three multiplicative structural effects from Ganesan 2023 Eq. 4-6 – Vc/F * (1 - 0.290 * DIS_HEALTHY), Vp/F * (1 - 0.245 * DIS_HEALTHY), Ka * (1 + 0.368 * DIS_HEALTHY) – and (b) which of two cohort-specific CL/F IIV variances applies (0.0614, 24.8 %CV for the 99 healthy phase 1 subjects vs 0.328, 57.2 %CV for the 647 infected ADAPT-PO patients), hosted on paired lcl_healthy / lcl_patient anchors fixed at 0 because the source reports no infection-status effect on the typical CL/F; reference category 0 is the pooled cUTI / acute-pyelonephritis patient cohort, and the paper’s (1 - Infected) term is re-expressed as DIS_HEALTHY), Zhang_2023_brazikumab.R (linear fractional effect on CL: 1 - 0.362 * DIS_HEALTHY, i.e. healthy participants clear brazikumab 36.2 percent more slowly than patients at the same baseline albumin; reference category 0 is the pooled mild-to-severe / moderate-to-severe Crohn’s disease cohort across the phase Ib NCT01258205 and phase IIa NCT01714726 studies, and Zhang 2023 attributes the faster patient clearance to faecal protein loss through the inflamed gut wall; source column GRP coded 1 = CD patient / 2 = healthy, re-expressed as DIS_HEALTHY = GRP - 1), Zhang_2023_brazikumab_il22.R, Zhang_2023_brazikumab_crp.R (same CL effect carried forward as a fixed() parameter into the two biomarker-driven CDAI indirect-response PK/PD variants; every subject in their phase IIa efficacy cohort has DIS_HEALTHY = 0, so the term is inert there and is retained only to keep the PK layer identical to the parent popPK model), Cleary_2023_risdiplam.R (linear fractional effect on apparent clearance: CL/F x (1 + 0.524 * DIS_HEALTHY), i.e. healthy adults clear risdiplam 1.524-fold faster than patients at the same weight and age; reference category 0 is the pooled spinal-muscular-atrophy type 1 / 2 / 3 cohort across NCT03032172 / NCT02908685 / NCT02913482, and all 61 healthy participants are adults from NCT02633709 and NCT03988907. Cleary 2023 Table 2 prints the effect only as “Healthy subjects on CL/F | Factor | 0.524” and never states the functional form, so the linear encoding is an inferred assumption rather than a sourced one: the literal multiplicative reading would place healthy adults 48% below patients, contradicting ESM Fig. S4 (post-hoc CL/F 5.60 vs 3.52 L/h), the Discussion’s “approximately 30% lower” and the Mech-PPK Table 3 adult intrinsic-clearance ratios of 1.374 (CYP3A) and 1.375 (FMO3)), AitOudhia_2024_sotatercept.R (binary stratifier on the log-scale (lnorm) residual error magnitude only, with no effect on any structural or random-effect PK parameter: healthy participants use expSd = sqrt(0.0570) = 0.239 and patients use expSd = sqrt(0.0357) = 0.189 per Ait-Oudhia 2024 Table 2 rows “Residual variability in HV / in PAH (log units)”; reference complement under DIS_HEALTHY = 0 is the pooled pulmonary-arterial-hypertension cohort from PULSAR, SPECTRA, and STELLAR, and the healthy stratum is the two phase 1 post-menopausal-women studies (SAD and MAD). As in Marathe_2023_belzutifan.R, disease status was separately screened as a structural covariate, entered during forward selection, and was NOT retained during backward elimination, so this model carries a residual-error-only disease effect), Zhao_2025_paracetamol.R (multiplicative factor 1 / 1.58 = 0.633 on the apparent paracetamol volume of distribution V_pcm/F, which propagates to all three metabolite volumes through the fixed 0.18 ratio; reference category 0 is the spinal-muscular-atrophy patient cohort and the structural typical is shifted to lvc = log(63.5 * 1.58) so DIS_HEALTHY = 1 restores the paper’s printed healthy-control typical of 63.5 L/70 kg; source column disease coded 1 = SMA, re-expressed as DIS_HEALTHY = 1 - disease. Same SMA-vs-healthy contrast as Cleary_2023_risdiplam.R, on the opposite structural parameter), Maleki_2024_brepocitinib.R (no effect on any typical structural parameter; the indicator instead gates two variability layers at once – (a) the magnitude of the Box-Cox transformed interindividual variability on CL/F and Vc/F, where the tabulated omegas 0.42 and 0.19 are the healthy-participant values and patients carry multiplicative uplifts of 1 + 0.44 and 1 + 0.29 applied to the realised eta, and (b) which pair of combined residual-error SDs applies, healthy participants using addSd = 0.89 ng/mL with propSd = 0.10 and patients using addSd = 0.45 ng/mL with propSd = 0.05; reference complement under DIS_HEALTHY = 0 is the pooled alopecia-areata / hidradenitis-suppurativa / psoriatic-arthritis / plaque-psoriasis / ulcerative-colitis / vitiligo cohort. Maleki 2024 Table 2 codes the source covariate as “Health status (healthy vs patient)” with the effects reported on the patient state, so both uplifts are applied on (1 - DIS_HEALTHY)), Riccobene_2016_ceftaroline.R (multiplicative power-form effects on the active metabolite of the ceftaroline fosamil prodrug: 3.32^DIS_HEALTHY on ceftaroline CL and 3.67^DIS_HEALTHY on ceftaroline Vc; reference category 0 is the pooled infected-patient cohort of the upstream adult analysis of 10 phase 1 / 1 phase 2 / 4 phase 3 studies. Riccobene 2016 Supplemental Table 1 prints theta16^PAT = 3.32 and theta15^PAT = 3.67 against the reverse-coded PAT indicator (0 = healthy, 1 = patient) and the model file re-expresses them as DIS_HEALTHY = 1 - PAT, leaving lcl_ceftaroline = log(3.76) and lvc_ceftaroline = log(3.18) on the patient state so DIS_HEALTHY = 1 restores the healthy-adult typicals 12.5 L/h and 11.7 L. Unusually, the supplement’s own footnote asserts the opposite orientation (“theta15 and theta16 fixed to one for healthy subjects”); it is contradicted by the paper’s Table 2 noncompartmental results, which the model reproduces in BOTH dose arms only when both multipliers are active for the all-healthy ELF cohort, and which it misses by 3.5x on AUC0-tau and 2.4x on Cmax under the footnote’s orientation. Applying only one of the two multipliers fails as well, so the pair is jointly identified by the back-solve), Roepcke_2023_rezafungin.R (linear fractional effects on the 3-compartment rezafungin IV model: CL x (1 - 0.276 x DIS_HEALTHY) and V1 x (1 - 0.222 x DIS_HEALTHY); reference category 0 is the composite “disease state” cohort that Roepcke 2023 built during covariate analysis by pooling patients with candidemia and/or invasive candidiasis (STRIVE / ReSTORE) with the hepatically impaired subjects of Study CD101.IV.1.15, while that study’s matched normal-hepatic-function controls were grouped with the healthy subjects. Table 2 footnote a defines the indicator on the canonical orientation already (I_healthy, 0 = no / 1 = yes), so no re-expression was needed. The composite grouping replaced separate study on CL and infection status on V1 effects; because low albumin marks both hepatic impairment and infection, this indicator is partly collinear with the ALB effect on V23 in the same model), Yin_2024_soticlestat.R (-22.8% effect on linear elimination clearance carried by PATIENTS, i.e. on the complement (1 - DIS_HEALTHY), because the published typical CL of 4.2 L/h is the healthy-volunteer anchor and the reference complement is the pooled DEE / Dravet-syndrome / Lennox-Gastaut-syndrome / 15q-duplication-syndrome / CDKL5-deficiency-disorder cohort), Gu_2025_rivaroxaban.R (log-additive effect on apparent clearance: exp(log(6.48 / 8.35) * DIS_HEALTHY) = exp(-0.2535 * DIS_HEALTHY); reference category 0 is the Chinese NVAF cohort treated by radiofrequency ablation (Study 2, n = 105), and Gu 2025 reports the two typical clearances directly (6.48 L/h healthy, 8.35 L/h patient) rather than a coefficient, so the model file anchors lcl = log(8.35) on the patient state; the paper calls this covariate the “morbid state”), Huang_2025_dexmedetomidine.R (linear fractional effect on the absorption rate constant of intranasal dexmedetomidine: KA x (1 + 1.05 x DIS_HEALTHY), i.e. healthy-volunteer KA is 2.05-fold the patient KA, equivalently patient KA is 49% of healthy KA; reference category 0 is the phase III NCT04383418 cohort of adults undergoing elective abdominal surgery, and the healthy stratum is the phase I CTR20191868 / CTR20171118 cohort. Huang 2025 applies the Methods Eq. 7 piecewise categorical form Pi = PTV if COV = type1, PTV x (1 + theta) if COV = type2 with Table 4 state on KA = 1.05; the orientation and the additive-(1 + theta) reading are each confirmed twice over by the Abstract’s “approximately 49%” ratio, the Discussion’s “KA in healthy volunteers was approximately 1 h-1”, and the published Figure 4 healthy-to-patient Cmax ratio of 1.48-1.51 at 40/60/80/100 kg (a multiplicative x 1.05 reading would give ~1.02). In this analysis the indicator is fully confounded with study, sampling density (rich phase I vs sparse phase III) and surgical/anaesthetic context, which the authors flag as a likely partial explanation of the effect size), Ozdin_2025_dexamethasone.R (dual role: (a) multiplicative power-form effect on CL, e_dis_healthy_cl^DIS_HEALTHY with e_dis_healthy_cl = 1 / 0.899 = 1.112, i.e. healthy adults clear dexamethasone about 11% faster than patients at the same body weight, and (b) binary stratifier selecting which of two combined proportional + additive residual-error magnitudes applies (propSd 0.181 / addSd 0.0013 ng/mL for the healthy cohort versus propSd 0.295 / addSd 24.3 ng/mL for the patient cohort); reference category 0 is the ataxia-telangiectasia cohort of the phase 3 ATTeST study (NCT02770807) and the healthy stratum is the phase 1 single-dose study (NCT01925859). The source uses two reverse-coded columns that partition the pooled dataset identically – PTNT (0 = healthy, 1 = patient) for the CL effect and PHAS (1 = phase 1, 3 = phase 3) for the $ERROR branch – because every phase 1 subject is a healthy adult and every phase 3 subject is an AT patient; both are re-expressed as DIS_HEALTHY = 1 - PTNT and the structural typical is shifted to the patient state lcl = log(20.8 * 0.899) = log(18.70)), Jian_2025_peginterferon_alfa_2b.R (linear factor (1 + (-0.405) x (1 - DIS_HEALTHY)) on the SC absorption rate constant ka of peginterferon alfa-2b, i.e. chronic hepatitis B patients absorb 40.5% more slowly; note the INVERTED reference relative to most entries here – Jian 2025 uses HEALTHY as the reference category and reports the effect on the CHB arm, so the model evaluates the complement (1 - DIS_HEALTHY); reference complement under DIS_HEALTHY = 0 is the phase 2 CHB cohort NCT01143662 and the healthy stratum is the phase 1 single-ascending-dose trial), Xu_2025_aficamten.R (linear fractional effects on two structural parameters: CL/F * (1 + 0.356 * DIS_HEALTHY) and Vp/F * (1 + 0.266 * DIS_HEALTHY) per Xu 2025 Table 1 and Figure S3; reference category 0 is the pooled symptomatic obstructive-hypertrophic-cardiomyopathy cohort from REDWOOD-HCM (phase 2) and SEQUOIA-HCM (phase 3), and the healthy stratum is the 264 participants across seven phase 1 studies. Because oHCM is the reference, the paper’s typical values describe a male participant with oHCM weighing 80 kg; healthy participants show 23%-24% lower steady-state AUCtau, Cmax, and Ctau (Xu 2025 Table 2). Source column PTYPE, where PTYPE == 1 identifies healthy participants).
• Notes: Used when a population PK model pools healthy participants with patients across heterogeneous indications and the healthy-vs-patient contrast is retained as a covariate. Scope: specific because the complement reference category is paper-defined (Nikanjam 2019 reference is “all non-healthy, non-Castleman, non-SMM tumor types”; Okada 2025 reference is the pooled AD+UC+psoriasis patient cohort; Yang 2024 reference is patients with cGVHD; Goel 2016 reference is the pooled cancer-patient cohort with advanced solid tumors or BCC; Brown 2017 reference is the pooled advanced NSCLC cohort; Lu 2015 vismodegib reference is the pooled cancer-patient cohort with advanced solid tumors / metastatic or locally-advanced BCC; Lu 2015 tacrolimus reference is the adult Chinese liver-transplant recipient cohort). The retired canonical name DIS_HV (healthy-volunteer) was renamed on 2026-05-11 because “volunteer” terminology is discouraged for clinical-trial participants. Ratified canonically on 2026-04-24.

DIS_DPN (canonical for diabetic polyneuropathy disease-state indicator)

• Description: 1 = patient with painful diabetic peripheral polyneuropathy (DPN; type 1 or 2 diabetes mellitus with clinically documented painful peripheral neuropathy), 0 = non-DPN subject (e.g., chronic low back pain, osteoarthritis, post-bunionectomy acute pain, or healthy volunteer). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-DPN subject; the complement reference is paper-defined – for the Kleideiter cebranopadol models the typical-value reference is the pooled nociceptive-pain (low back pain + osteoarthritis) cohort, with the sibling canonicals DIS_HEALTHY and DIS_BUNIONECTOMY carrying the healthy-volunteer and bunionectomy effects respectively).
• Source aliases:
  • DIS – Kleideiter 2017 (paper Table 13 categorical disease status decomposed to binary DIS_DPN).
• Example models: Kleideiter_2017_cebranopadol.R (multiplicative effect on bioavailability: f_disease *= 1.132 for DPN patients relative to the LBP/OA nociceptive-pain reference; the value 1.132 reflects the 2018 erratum correction in which Table 13 rows 27-28 for bunionectomy and DPN were swapped), Kleideiter_2018_cebranopadol.R (DPN-vs-LBP/OA bioavailability ratio 1.132 applied as ratio^DIS_DPN; one level of the four-level disease-status stratification {LBP/OA reference, healthy, DPN, bunionectomy}, Kleideiter 2018 Table 13 erratum-corrected).
• Notes: Used when a population PK model pools DPN patients with a non-DPN reference cohort (typically the chronic-pain LBP/OA reference plus other strata) and DPN disease status is retained as a covariate. Pairs with DIS_HEALTHY and DIS_BUNIONECTOMY in the Kleideiter four-level disease-status stratification (LBP/OA reference, healthy, DPN, bunionectomy). Distinct from the existing DIS_DIAB canonical (binary type-1-or-type-2 diabetes-mellitus comorbidity indicator; pre-2026-06-19 names DIAB and T2DM were merged into DIS_DIAB) because DIS_DPN flags the specific painful-polyneuropathy complication of diabetes used as a chronic-pain clinical-trial enrollment category (cebranopadol phase IIa trials 10 and 12; phase II trial 14). Distinct from the DIS_HEALTHY canonical because both indicators may be 0 simultaneously in a pooled chronic-pain analysis where DIS_HEALTHY = 0 means ‘patient’ but DIS_DPN = 0 may still mean ‘non-DPN patient’ (e.g., LBP / OA or bunionectomy patients). Scope: specific because the disease-pooling reference category is paper-defined. Ratified canonically on 2026-05-25 alongside the Kleideiter 2017 cebranopadol extraction.

DIS_BUNIONECTOMY (canonical for post-bunionectomy acute-pain cohort indicator)

• Description: 1 = patient with moderate-to-severe acute pain following primary unilateral first-metatarsal bunionectomy surgery, 0 = non-bunionectomy subject (e.g., chronic low back pain, osteoarthritis, painful diabetic polyneuropathy, or healthy volunteer). Time-fixed per subject within the postoperative analgesic-dosing window.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-bunionectomy subject; the complement reference is paper-defined – for the Kleideiter cebranopadol models the typical-value reference is the pooled nociceptive-pain (low back pain + osteoarthritis) cohort, with the sibling canonicals DIS_HEALTHY and DIS_DPN carrying the healthy-volunteer and DPN effects respectively).
• Source aliases:
  • DIS – Kleideiter 2017 (paper Table 13 categorical disease status decomposed to binary DIS_BUNIONECTOMY).
• Example models: Kleideiter_2017_cebranopadol.R (multiplicative effect on bioavailability: f_disease *= 1.801 for bunionectomy patients relative to the LBP/OA nociceptive-pain reference; the value 1.801 reflects the 2018 erratum correction in which Table 13 rows 27-28 for bunionectomy and DPN were swapped; in the corrected Table 14 bunionectomy patients had 80% higher exposure than the reference, the largest disease-status effect in the analysis), Kleideiter_2018_cebranopadol.R (bunionectomy-vs-LBP/OA bioavailability ratio 1.801 applied as ratio^DIS_BUNIONECTOMY; one level of the four-level disease-status stratification {LBP/OA reference, healthy, DPN, bunionectomy}, Kleideiter 2018 Table 13 erratum-corrected).
• Notes: Bunionectomy patients are an acute-pain clinical-trial enrollment category distinct from the chronic-pain LBP / OA / DPN categories in the same analysis cohort. Use this canonical when a pooled analgesic / opioid population PK analysis retains a post-bunionectomy cohort as a covariate group; future bunionectomy-anchored analgesic models should extend the example list and document the complement reference. Pairs with DIS_HEALTHY and DIS_DPN in the Kleideiter four-level disease-status stratification. Distinct from POD (continuous post-operative day) and POSTTX_DAY1 (first-24h-post-transplant indicator) – those describe surgical-recovery time windows rather than the bunionectomy-cohort enrollment label itself. The shorter form DIS_BUN is deliberately avoided to prevent confusion with the common BUN (blood urea nitrogen) laboratory abbreviation. Scope: specific because the disease-pooling reference category is paper-defined. Ratified canonically on 2026-05-25 alongside the Kleideiter 2017 cebranopadol extraction.

DIS_CASTLEMAN (canonical for Castleman’s disease indicator)

• Description: 1 = Castleman’s disease (multicentric or unicentric), 0 = not Castleman’s disease. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-Castleman subject; the complement group is the union of healthy volunteers and other indications pooled in the source analysis).
• Source aliases: none known; source NONMEM control streams typically use ad-hoc names (e.g., CD, CASTLEMAN).
• Example models: Nikanjam_2019_siltuximab.R (multiplicative +24% effect on CL; no Vss effect).
• Notes: Castleman’s disease is a lymphoproliferative disorder strongly associated with elevated IL-6 levels; it is the only FDA-approved indication for siltuximab. Scope: specific because the disease-pooling reference category is paper-defined. Ratified canonically on 2026-04-24.

DIS_HOFH (canonical for homozygous familial hypercholesterolemia patient indicator)

• Description: 1 = patient with homozygous familial hypercholesterolemia (HoFH), 0 = non-HoFH subject (typically healthy volunteer or another reference cohort pooled in the source analysis). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-HoFH subject; the complement group is paper-defined – for Pu 2021 the reference is the pooled healthy-volunteer cohort).
• Source aliases:
  • DISTYPN – used in Pu_2021_evinacumab.R (Pu 2021 NM-TRAN $INPUT column for HoFH-vs-HV disease type, 1 = HoFH).
• Example models: Pu_2021_evinacumab.R (multiplicative exp(theta * DIS_HOFH) factor on Vmax with theta = -0.289, i.e. HoFH patients show ~25% lower target-mediated Vmax than the HV reference; biologically consistent with the LDLR-pathway disruption in HoFH altering ANGPTL3 catabolic kinetics).
• Notes: Used when a population PK model pools HoFH patients with healthy volunteers (or another non-HoFH cohort) and HoFH disease status is retained as a covariate. Distinct from a heterozygous-FH (HeFH) indicator because HoFH patients have markedly higher baseline LDL-C (untreated levels often > 500 mg/dL) and a more pronounced response to LDLR-independent therapies. Scope: specific because the reference category is paper-defined.

DIS_HAE (canonical for hereditary angioedema patient indicator)

• Description: 1 = patient with hereditary angioedema (HAE-C1INH-Type1, HAE-C1INH-Type2, or HAE-nC1INH), 0 = healthy volunteer (or other non-HAE reference cohort pooled in the source analysis). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-HAE subject; the complement group is paper-defined – for Diep 2026 the reference is the pooled healthy-volunteer cohort from NCT03263507 and ISIS 721744-CS9).
• Source aliases: paper narrative “patient with HAE” / “healthy volunteer” subgroup labels driving the Diep 2026 disease-status covariate effects on Vc/F, Q/F, baseline PKK, and IC50.
• Example models: Diep_2026_donidalorsen.R (linear (1 + theta * DIS_HAE) multiplicative effects on apparent central volume Vc/F (theta = +0.426, +42.6%), apparent intercompartmental clearance Q/F (theta = -0.261, -26.1%), baseline plasma prekallikrein BL (theta = -0.132, -13.2%), and donidalorsen IC50 on PKK production (theta = +0.770, +77.0%) for patients with HAE vs healthy volunteers), Garcia_2025_garadacimab.R, Garcia_2025_garadacimab_hae_attack.R (log-domain multiplicative effect on garadacimab clearance, exp(log(1.05) * DIS_HAE), i.e. 1.05-fold higher CL in patients with HAE than in healthy volunteers; source column PAT, which codes 2 = patient with HAE).
• Notes: Used when a population PK/PD model pools HAE patients with healthy volunteers and HAE disease status is retained as a covariate. The three molecular HAE subtypes (HAE-C1INH-Type1, HAE-C1INH-Type2, HAE-nC1INH) are pooled in this indicator following the Diep 2026 analysis; if a future paper resolves subtype-specific covariate effects, separate canonical indicators (e.g., DIS_HAE_C1INH_T1) can be added without conflicting with this pooled indicator. Scope: specific because the complement reference category is paper-defined.

DIS_PBC (canonical for primary biliary cirrhosis disease-state indicator)

• Description: 1 = patient with primary biliary cirrhosis (PBC), an autoimmune destruction of intrahepatic bile ducts causing chronic cholestasis; 0 = non-PBC subject (healthy reference cohort or other non-PBC reference). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-PBC subject; complement reference is paper-defined – for Zuo 2016 the reference is the pooled healthy-adult cohort from Xiang 2011 / Dilger 2012 / Hess 2004).
• Source aliases: paper narrative “patient with PBC” / “healthy” subgroup labels in Zuo 2016.
• Example models: Zuo_2016_UDCA.R (multiplicative scaling on liver-to-biliary rate constants when DIS_PBC = 1: K_LB,0 scaled by 0.10 – 90% reduction; K_LB,1 scaled by 0.30 – 70% reduction; K_LB,2 scaled by 0.10 – 90% reduction; reproduces the Zuo 2016 Figure 3 PBC simulation).
• Notes: Used when a systems / popPK model adapts a healthy-state structural model to a PBC population via fixed disease-state scaling on hepatic-excretion rate constants. Scope: specific because the structural adaptation form (which K parameters are scaled, by how much) is paper-defined; future PBC extractions that re-estimate or alter the scaling pattern can extend the example-models list. Distinct from DIS_HEPATIMP (hepatic-impairment severity categorical), DBIL (direct bilirubin biomarker), and ALP (cholestasis biomarker), which describe pathophysiology rather than the disease label itself.

DIS_DMD (canonical for Duchenne muscular dystrophy patient indicator)

• Description: 1 = patient with Duchenne muscular dystrophy (DMD), 0 = non-DMD subject (healthy volunteer or other reference cohort). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-DMD subject; the complement group is the reference cohort the source analysis pools alongside the DMD population – typically healthy adult volunteers).
• Source aliases: SPOP (Wojciechowski 2022 study-population indicator with the same orientation: 1 = DMD pediatric patient, 0 = healthy adult volunteer).
• Example models: Wojciechowski_2022_domagrozumab.R (additive 1 + theta shift on baseline myostatin and on the joint kdeg/kint axis; theta_BASE = -0.641, theta_kdegkint = -0.900), Hajjar_2018_DMD_6MWT.R (gates the latent-disease coefficients ALPHA and BETA – both zero for DIS_DMD = 0 – and scales the 6MWT production rate KIN by the multiplier KCOV = 0.63 for DMD subjects; reference cohort is the Henricson 2012 healthy-boy controls).
• Notes: Used when a population PK/PD model pools DMD patients with a non-DMD reference population and DMD disease status is retained as a covariate. Scope: specific because the reference category is paper-defined. Ratified canonically on 2026-04-26.

DIS_SMM (canonical for smoldering multiple myeloma indicator)

• Description: 1 = smoldering (asymptomatic) multiple myeloma, 0 = not smoldering MM. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-SMM subject; the complement group is the union of healthy volunteers and other indications pooled in the source analysis).
• Source aliases: none known; source NONMEM control streams typically use ad-hoc names (e.g., SMM, SMOLDMM).
• Example models: Nikanjam_2019_siltuximab.R (multiplicative -23% effect on Vss; no CL effect).
• Notes: Smoldering multiple myeloma is an asymptomatic plasma-cell disorder distinct from active multiple myeloma; pooled with the Nikanjam 2019 cohort that also included MGUS, multiple myeloma, RCC, ovarian, and other tumor types. Scope: specific because the disease-pooling reference category is paper-defined. Ratified canonically on 2026-04-24.

DIS_MM (canonical for active multiple myeloma disease indicator)

• Description: 1 = active (non-smoldering) multiple myeloma, 0 = other hematologic malignancy or reference group. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-MM subject; the complement group is defined per-model – in Ogasawara 2020 the reference is the union of MDS, AML, and non-Hodgkin lymphoma cohorts).
• Source aliases:
  • MM – prior canonical name (pre-2026-06-19 DIS_ prefix standardization) and source-paper column in Ogasawara_2020_durvalumab.R. The short MM token also collides with the SI unit millimolar (used in STIM_*_MM in-vitro stimulus concentrations and RIC50 in mM) and with the melanoma abbreviation in some oncology datasets (e.g., TUMTP == "MM" in Aoyama 2012 where MM means malignant melanoma); the DIS_MM form disambiguates.
• Example models: Ogasawara_2020_durvalumab.R (multiplicative factor 0.820 on Vc per Ogasawara 2020 Table 3 footnote c; active (non-smoldering) multiple myeloma cohort from studies MEDI4736-MM-002 and -MM-005).
• Notes: Renamed from MM to DIS_MM on 2026-06-19 per the canonical-register standardization audit (operator decision to apply the DIS_<concept> prefix uniformly to disease-state indicators; the bare MM token clashed with the millimolar SI unit and with the TUMTP “MM = malignant melanoma” usage). Distinct from DIS_SMM (smoldering multiple myeloma) and MM_NIGG (the immunoglobulin-subtype stratifier within multiple myeloma cohorts).

DIS_EMD (canonical for extramedullary disease indicator)

• Description: 1 = extramedullary disease (plasmacytoma or plasma-cell infiltration outside the bone marrow) present at baseline / screening, 0 = disease confined to the bone marrow. Time-fixed per subject. An aggressive-phenotype and high-tumour-burden marker in multiple myeloma and related plasma-cell malignancies; prevalence is roughly 15-20% at initial diagnosis and higher in relapsed/refractory populations.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (medullary / bone-marrow-confined disease).
• Source aliases:
  • MEDFL – used in Collins_2023_belantamab_mprotein.R; the same column name appears in the related GSK belantamab mafodotin exposure-response analyses.
  • EMD, EMD_BL, EXTRAMED – plausible alternative NONMEM $INPUT forms.
• Example models: Collins_2023_belantamab_mprotein.R (multiplicative factor 0.108 on the effect-compartment rate constant KEO of the serum M-protein tumour-growth-inhibition model when DIS_EMD = 1, per Collins 2023 Table 1; 95% CI 0.0617-0.187).
• Notes: Distinct from DIS_MM (active vs smoldering multiple-myeloma disease status) and DIS_SMM (smoldering multiple myeloma), and complementary to both: a patient with active multiple myeloma may or may not have extramedullary disease. Also distinct from the ulcerative-colitis disease-extent canonicals DISEXT_EP / DISEXT_OTHER, which describe the anatomical extent of inflammatory bowel disease rather than a plasma-cell malignancy outside the marrow. Covariate-effect parameters drop the DIS_ prefix per the DIS_CANCER -> e_cancer_* convention; use e_emd_<param>. Scope: specific because the concept is mechanistically bound to plasma-cell malignancies. Ratified canonically on 2026-07-27 alongside the Collins 2023 belantamab mafodotin M-protein extraction.

DIS_PNH (canonical for paroxysmal nocturnal hemoglobinuria indicator)

• Description: 1 = paroxysmal nocturnal hemoglobinuria (PNH) patient, 0 = non-PNH subject (healthy volunteer or another indication pooled in the source analysis). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-PNH subject; the complement group is paper-defined – for Lin 2024 it pools healthy adult volunteers and CHAPLE disease patients, while for Lee 2024 it is the healthy phase I subjects only).
• Source aliases: none known; source NONMEM control streams typically use ad-hoc names (e.g., PNH, DPNH); Lee 2024 calls the column “subject group (healthy subjects versus PNH patients)”.
• Example models: Lin_2024_pozelimab.R (additive-fractional +34.07% effect on Vc; no CL or Vp effect; reference category pools healthy volunteers and CHAPLE patients), Lee_2024_eculizumab.R (separately estimated typical values per subject group on Vc, on the terminal-complement-activity baseline E0 and on Imax, encoded as log-scale shifts e_pnh_vc, e_pnh_rbase_tca, e_pnh_imax_tca; reference category is the healthy phase I cohort), Crass_2024_pegcetacoplan.R (fractional linear effect on clearance, cl * (1 + 0.257 * DIS_PNH), i.e. patients with PNH clear pegcetacoplan about 26% faster than the pooled non-PNH reference of healthy and renal-impairment participants; Crass 2024 ESM Table 3 theta 9. The indicator additionally selects which of three log-scale residual-error strata applies, jointly with STUDY_PEGCET_PHASE3), Crass_2024_pegcetacoplan_hemoglobin.R, Crass_2024_pegcetacoplan_ldh.R (the same clearance effect carried forward as a fixed() parameter into the two biomarker PK/PD variants; every subject in their PK/PD analysis set has DIS_PNH = 1, so the term is inert there and is retained only to keep the PK layer identical to the parent popPK model).
• Notes: Paroxysmal nocturnal hemoglobinuria is a rare hematological disease characterized by uncontrolled complement activation on red blood cells; treated with C5-targeted complement inhibitors (eculizumab, ravulizumab, pozelimab). Scope: specific because the disease-pooling reference category is paper-defined. Ratified canonically on 2026-04-27.

DIS_PH1 (canonical for primary hyperoxaluria type 1 disease-state indicator)

• Description: 1 = patient with primary hyperoxaluria type 1 (PH1, autosomal-recessive AGXT mutation causing alanine-glyoxylate aminotransferase deficiency and hepatic oxalate overproduction), 0 = subject without PH1 pooled in the source analysis (primary hyperoxaluria type 2 or type 3 patient, or healthy volunteer). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-PH1 subject; the complement group is paper-defined – for Zhang 2025 it pools PH2 patients and healthy volunteers).
• Source aliases:
  • PH – Zhang 2025 Table 2 covariate-formula footnote, defined there as “PH = 1 for PH1 subjects and 0 for PH2 and healthy volunteers”.
  • PH1, PHTYPE, DPH1 – plausible alternative NONMEM $INPUT forms.
• Example models: Zhang_2025_nedosiran.R, Zhang_2025_nedosiran_uoxcr.R (power-form multiplicative factor 1.32^DIS_PH1 on the slow subcutaneous absorption rate constant ka1, per Zhang 2025 Table 2 ka1.PH = 1.32, 95% CI 1.07-1.57; no effect on CL/F, Vc/F, Q/F, Vp/F or Vmax – the paper reports PH subtype had no effect on nedosiran exposure), Zhang_2024_nedosiran.R.
• Notes: Primary hyperoxaluria is a group of three genetically distinct disorders of hepatic glyoxylate metabolism; PH1 accounts for 70-80% of cases and is the most severe. Register a sibling DIS_PH2 / DIS_PH3 only if a future paper distinguishes those subtypes as their own indicators rather than pooling them into the reference category. Covariate-effect parameters drop the DIS_ prefix per the DIS_CANCER -> e_cancer_* convention; use e_ph1_<param>. Scope: specific because the reference category is paper-defined (whether healthy volunteers, PH2 patients, or both are pooled into it varies by analysis). Distinct from DIS_HEALTHY, which is a healthy-volunteer cohort indicator: a PH2 patient is neither DIS_PH1 = 1 nor DIS_HEALTHY = 1.

DIS_MDS_AML (canonical for MDS or AML disease-type indicator)

• Description: 1 = patient with myelodysplastic syndrome (MDS) or acute myeloid leukemia (AML), 0 = other hematologic malignancy or reference group. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-MDS/AML subjects; the complement group is defined per-model – typically multiple myeloma and non-Hodgkin lymphoma in Ogasawara 2020).
• Source aliases:
  • MDSAML – prior canonical name (pre-2026-06-19 DIS_ prefix standardization) and combined indicator used directly in source analyses; MDSAML is the column name in Ogasawara_2020_durvalumab.R.
• Example models: Ogasawara_2020_durvalumab.R (multiplicative factor 1.26 on CL; reference group is the union of MM and NHL subjects), vanIersel_2018_posaconazole.R (multiplicative effect on relative bioavailability F1: fdepot *= (1 + (-0.165) * DIS_MDS_AML) – 16.5% lower F1 in AML/MDS patients relative to the non-AML/MDS reference (healthy volunteers and HSCT recipients pooled); van Iersel 2018 Table 2 final-model ‘AML/MDS on F1’ = -0.165).
• Notes: Use DIS_MDS_AML as a combined MDS+AML indicator when the source paper collapses the two diagnoses into one covariate. If a future paper separates MDS and AML as distinct indicators, register DIS_MDS and DIS_AML separately. Scope: specific because the reference category is paper-defined. Ratified canonically on 2026-04-26. Renamed from MDSAML to DIS_MDS_AML on 2026-06-19 per the canonical-register standardization audit (operator decision to apply the DIS_<concept> prefix uniformly to disease-state indicators and to insert the underscore so the combined indicator matches the sibling DIS_MDS / DIS_AML shape).

DIS_AML (canonical for acute myeloid leukemia disease-state indicator)

• Description: 1 = patient with acute myeloid leukemia (AML), 0 = non-AML subject (the complement group in a pooled multi-indication PK analysis). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-AML subject; the complement group is paper-defined – for Xu 2023 the reference is patients with advanced solid tumors, alongside the parallel DIS_MDS and DIS_CMML indicators that decompose the hematologic-malignancy cohort).
• Source aliases:
  • DISEASE_abb == "AML" – used in Xu_2023_MBG453.R (the Monolix supplement Appendix S2 encodes disease as the categorical column DISEASE_abb with categories {AML, CMML, MDS, Solid_Tumor} and reference Solid_Tumor; the canonical column carries the binary as.integer(DISEASE_abb == "AML")).
  • AML3 – used in Vaddady_2024_quizartinib.R. NOTE the source column is the complement: the NONMEM stream codes AML3 = 1 for NON-AML subjects (healthy volunteers and subjects with hepatic impairment), so the canonical column carries DIS_AML = 1 - AML3 and every reported effect is applied via (1 - DIS_AML).
• Example models: Xu_2023_MBG453.R (exponential effect on CL: exp(-0.0146 * DIS_AML); not statistically significant in the full covariate model but retained because Xu 2023 used the full-covariate-model approach), Vaddady_2024_quizartinib.R (reference category INVERTED – AML patients are the reference and the non-AML effect is estimated: Frel multiplier 1.73, fractional change -0.188 on quizartinib ka, +0.843 on AC886 CL; the column also selects which stratum-specific IIV variance applies to quizartinib Frel and to AC886 CL).
• Notes: Use DIS_AML (rather than the combined DIS_MDS_AML) when the source paper separates AML from MDS as distinct indicators. Scope: specific because the disease-pooling reference category is paper-defined – and it genuinely varies: Xu 2023 makes non-AML (advanced solid tumors) the reference, whereas Vaddady 2024 makes AML patients the reference. Always read the paper’s stated reference before assigning a sign to any effect on this column, and record the direction in covariateData[["DIS_AML"]]$reference_category.

DIS_MDS (canonical for myelodysplastic syndrome disease-state indicator)

• Description: 1 = patient with myelodysplastic syndrome (MDS), 0 = non-MDS subject (the complement group in a pooled multi-indication PK analysis). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-MDS subject; the complement group is paper-defined – for Xu 2023 the reference is patients with advanced solid tumors, alongside the parallel DIS_AML and DIS_CMML indicators).
• Source aliases:
  • DISEASE_abb == "MDS" – used in Xu_2023_MBG453.R (Monolix supplement Appendix S2 categorical column; reference category Solid_Tumor).
• Example models: Xu_2023_MBG453.R (exponential effect on CL: exp(-0.149 * DIS_MDS); statistically significant, p = 0.021 – patients with MDS have ~14% lower CL than the solid-tumor reference).
• Notes: Use DIS_MDS (rather than the combined DIS_MDS_AML) when the source paper separates MDS from AML as distinct indicators. Scope: specific because the disease-pooling reference category is paper-defined.

DIS_CMML (canonical for chronic myelomonocytic leukemia disease-state indicator)

• Description: 1 = patient with chronic myelomonocytic leukemia (CMML), 0 = non-CMML subject (the complement group in a pooled multi-indication PK analysis). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-CMML subject; the complement group is paper-defined – for Xu 2023 the reference is patients with advanced solid tumors, alongside the parallel DIS_AML and DIS_MDS indicators that decompose the hematologic-malignancy cohort).
• Source aliases:
  • DISEASE_abb == "CMML" – used in Xu_2023_MBG453.R (Monolix supplement Appendix S2 categorical column; reference category Solid_Tumor).
• Example models: Xu_2023_MBG453.R (exponential effect on CL: exp(-0.0411 * DIS_CMML); not statistically significant in the full covariate model but retained because Xu 2023 used the full-covariate-model approach).
• Notes: CMML is a clonal myeloid malignancy with overlapping features of MDS and myeloproliferative neoplasms. Scope: specific because the disease-pooling reference category is paper-defined.

DIS_BCPALL (canonical for B-cell precursor acute lymphoblastic leukemia disease-state indicator)

• Description: 1 = B-cell precursor acute lymphoblastic leukemia (BCP-ALL), 0 = B-cell non-Hodgkin’s lymphoma (NHL) or other non-BCP-ALL indication pooled in the source analysis. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-BCP-ALL; in the Wu 2024 cohort this is the adult B-cell NHL stratum).
• Source aliases:
  • ALL – used in Wu_2024_inotuzumab.R (Wu 2024 calls it the “ALL effect” and notes it bundles disease type with the corresponding bioanalytical assay difference).
• Example models: Wu_2024_inotuzumab.R (additive fractional-change effects on CL1 (-0.767) and CL2 (-0.362), and gates the BLSTABL and AGE effects on kdes; for kdes itself a -0.924 fractional change for BCP-ALL).
• Notes: Used when a population PK model pools BCP-ALL patients with a non-BCP-ALL reference (e.g., Wu 2024: pooled adult B-cell NHL + adult BCP-ALL + pediatric BCP-ALL). Scope: specific because the complement reference category is paper-defined (Wu 2024 reference is pooled adult B-cell NHL). The “ALL effect” theta in Wu 2024 conflates two physiologically distinct sources of variation – B-cell tumor type (NHL vs ALL surface CD22 burden) and bioanalytical method (ELISA for adult NHL vs HPLC-MS for ALL) – and cannot be split with the available data; document this confounding when comparing across populations. Ratified canonically on 2026-04-26.

DIS_ALL (canonical for acute lymphoblastic leukemia disease-state indicator)

• Description: 1 = patient with acute lymphoblastic leukemia (ALL) of any lineage, 0 = any other indication pooled in the source analysis. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-ALL; the complement group is paper-defined – for Schreib 2024 the reference is every other hematopoietic-stem-cell-transplantation indication in the cohort, i.e. AML, chronic granulomatous disease, HLH/XLP, hemoglobinopathies, primary immunodeficiencies, metabolic diseases, neuroblastoma, and others).
• Source aliases:
  • ALL – used in Schreib_2024_busulfan.R (Table 3 covariate theta_k5, effect of ALL on ln(k); the source dataset carries a nine-level diagnosis group factor and the canonical column is as.integer(DiaGroup == "ALL")).
• Example models: Schreib_2024_busulfan.R (exponential effect on the elimination rate constant: exp(-0.210 * DIS_ALL), about a 19% lower k and CL; 13 of 124 patients, 10%).
• Notes: Lineage-agnostic ALL indicator. Distinct from DIS_BCPALL, which is specifically B-cell precursor ALL against a paper-defined reference of B-cell non-Hodgkin lymphoma or other non-BCP-ALL indication; DIS_BCPALL additionally conflates disease type with a bioanalytical-assay difference in its founding model and is not interchangeable with DIS_ALL. Use DIS_ALL when the source pools all ALL lineages against a general non-ALL reference. Scope: specific because the complement reference category is paper-defined. Ratified canonically on 2026-08-05 (task oare_PMC11154452 sidecar question q3, answer A) alongside the Schreib 2024 busulfan extraction.

DIS_HLHXLP (canonical for hemophagocytic lymphohistiocytosis / X-linked lymphoproliferative disease indicator)

• Description: 1 = patient with hemophagocytic lymphohistiocytosis (HLH) or X-linked lymphoproliferative disease (XLP), 0 = any other indication pooled in the source analysis. Time-fixed per subject. The two diagnoses are pooled into one indicator because the source analyses that use it treat them as a single disease group.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (neither HLH nor XLP; the complement group is paper-defined – for Schreib 2024 the reference is every other hematopoietic-stem-cell-transplantation indication in the cohort).
• Source aliases:
  • HLH/XLP – used in Schreib_2024_busulfan.R (Table 3 covariate theta_dk2; the source dataset carries a nine-level diagnosis group factor and the canonical column is as.integer(DiaGroup == "HLH/XLP")).
• Example models: Schreib_2024_busulfan.R (additive effect of -0.145 on kel_exp_famp, the fractional amplitude of the decline in the elimination rate constant over a course of therapy, taking it from -0.167 to -0.312, i.e. from a 17% to a 31% fall in k and CL; 14 of 124 patients, 11%).
• Notes: Registered as a single pooled token without an internal underscore so that covariate-effect parameter names stay unambiguous (e_hlhxlp_kel_exp_famp). Splitting into separate DIS_HLH and DIS_XLP columns was considered and declined: the founding source pools the two into one 14-patient diagnosis group and estimates a single coefficient, so a split cannot be sourced from it. Register DIS_HLH / DIS_XLP separately only if a future source estimates them apart. Scope: specific because the complement reference category is paper-defined. Ratified canonically on 2026-08-05 (task oare_PMC11154452 sidecar question q3, answer A) alongside the Schreib 2024 busulfan extraction.

DIS_MAS (canonical for macrophage activation syndrome (secondary HLH) disease-state indicator)

• Description: 1 = patient whose haemophagocytic syndrome is macrophage activation syndrome (MAS), i.e. secondary HLH arising in a rheumatic disease – most commonly Still’s disease (systemic juvenile idiopathic arthritis / adult-onset Still’s disease); 0 = primary (genetic) HLH. Time-fixed per subject. Distinguishes the two aetiologies in a pooled HLH population PK analysis.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (primary HLH; the complement group is paper-defined – for Brossard 2025 the reference is the 44 primary-HLH patients of NCT01818492 pooled with the compassionate-use cohort).
• Source aliases:
  • MAS – used in Brossard_2025_emapalumab.R (supplementary Table S2 covariate CLNL_MAS).
• Example models: Brossard_2025_emapalumab.R (coefficient fixed at -1 on the non-linear, IFN-gamma-dependent clearance component, i.e. clnl = ... * (1 + e_mas_clnl * DIS_MAS) sets CLNL to exactly zero in MAS patients; this is the paper’s central PK finding – MAS patients do not reach the total-IFN-gamma levels that drive target-mediated disposition, so emapalumab clearance is linear in MAS).
• Notes: Ratified 2026-08-14 alongside the Brossard 2025 emapalumab extraction (task oare_PMC11822261 sidecar request-001 question q2, answer A – confirmed as a well-formed member of the existing DIS_<condition> family). Distinct from DIS_HLHXLP, which pools HLH with X-linked lymphoproliferative disease into a single transplant-indication group against a non-HLH reference: DIS_MAS instead partitions within a haemophagocytic population, separating secondary (MAS) from primary HLH. A model that needs all three strata should carry DIS_MAS alongside a separate primary-HLH indicator rather than overloading this column. Scope: specific because the complement reference category is paper-defined. Pairs naturally with IFNG, since the mechanism the indicator encodes is the absence of IFN-gamma-driven target-mediated clearance.

DIS_BCELLNHL (canonical for B-cell non-Hodgkin lymphoma disease-state indicator)

• Description: 1 = patient with B-cell non-Hodgkin lymphoma (B-cell NHL), 0 = non-B-cell-NHL subject (the complement group in a pooled multi-indication PK analysis of hematologic malignancies). Time-fixed per subject. Used when a population PK model treats B-cell NHL as its own indicator alongside a sibling CLL/SLL reference and other lymphoma / leukemia strata.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-B-cell-NHL; the complement group is paper-defined – for Kemal 2026 the reference is chronic lymphocytic leukemia / small lymphocytic lymphoma (CLL/SLL), alongside the parallel DIS_WM and DIS_OTHER_HEME indicators that decompose the hematologic-malignancy cohort).
• Source aliases:
  • INDIC == 1 – used in Kemal_2026_nemtabrutinib.R (Kemal 2026 NONMEM control stream in the supplement encodes disease as the categorical column INDIC with categories {B-cell NHL, CLL/SLL, WM, Other, MZL, FL, MCL, RT} and reference CLL/SLL; the canonical column carries the binary as.integer(INDIC == 1)).
• Example models: Kemal_2026_nemtabrutinib.R (fractional-change effects on CL/F (-0.166) and Vc/F (0.00224); reference category CLL/SLL).
• Notes: Distinct from DIS_BCPALL (B-cell precursor acute lymphoblastic leukemia). Use DIS_BCELLNHL when the source paper treats B-cell NHL as one indication category in a pooled analysis of chronic B-cell malignancies (CLL/SLL, WM, DLBCL, follicular, mantle-cell, marginal-zone) and the model estimates a disease-specific effect on PK. Scope: specific because the complement reference category is paper-defined.

DIS_WM (canonical for Waldenstrom’s macroglobulinemia disease-state indicator)

• Description: 1 = patient with Waldenstrom’s macroglobulinemia (WM), 0 = non-WM subject. Time-fixed per subject. WM is a rare indolent B-cell lymphoma / IgM-secreting lymphoplasmacytic disorder that is sometimes pooled with other B-cell malignancies in oncology popPK analyses.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-WM; the complement group is paper-defined – for Kemal 2026 the reference is CLL/SLL, alongside the parallel DIS_BCELLNHL and DIS_OTHER_HEME indicators).
• Source aliases:
  • INDIC == 3 – used in Kemal_2026_nemtabrutinib.R (Kemal 2026 NONMEM control stream in the supplement; reference category CLL/SLL).
• Example models: Kemal_2026_nemtabrutinib.R (fractional-change effects on CL/F (0.0718) and Vc/F (0.152); reference category CLL/SLL).
• Notes: Scope: specific because the complement reference category is paper-defined.

DIS_OTHER_HEME (canonical for pooled ‘other hematologic malignancy’ indicator)

• Description: 1 = patient with a hematologic malignancy other than the explicitly-modeled sibling indications (typically pools some subset of MZL, FL, MCL, Richter’s transformation, or unclassified ‘other’), 0 = one of the explicitly-modeled indications or the reference disease. Time-fixed per subject. The exact composition of the pool is paper-specific and must be documented in per-model covariateData[[DIS_OTHER_HEME]]$notes.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (subject falls into one of the sibling indication indicators or the reference; the reference disease is paper-defined – for Kemal 2026 the reference is CLL/SLL).
• Source aliases:
  • INDIC IN (4, 5, 6, 7, 8) – used in Kemal_2026_nemtabrutinib.R (Kemal 2026 NONMEM control stream pools ‘Other’, marginal zone lymphoma (MZL), follicular lymphoma (FL), mantle cell lymphoma (MCL), and Richter’s transformation (RT) into a single indicator).
• Example models: Kemal_2026_nemtabrutinib.R (fractional-change effects on CL/F (-0.0244) and Vc/F (-0.0200); reference category CLL/SLL).
• Notes: Different-composition pools do not share coefficients; every model that uses DIS_OTHER_HEME must document the exact set of pooled diseases in covariateData[[DIS_OTHER_HEME]]$notes so downstream users know what the coefficient represents. When a paper analyzes MZL, FL, MCL, RT, or a specific ‘other’ indication as its own indicator, register a sibling per-disease canonical (e.g. DIS_MZL, DIS_FL, DIS_MCL, DIS_RT) rather than reusing DIS_OTHER_HEME. Scope: specific because the pool composition is paper-defined.

DIS_SAD (canonical for secondary antibody deficiency indicator)

• Description: 1 = secondary antibody deficiency (SAD) patient (hypogammaglobulinaemia from external causes such as B-cell-depleting therapy, haematological malignancy, or other immunosuppression), 0 = primary immunodeficiency (PID) patient. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (PID patient; the complement category is the genetic / inborn-error-of-immunity primary immunodeficiency cohort pooled with SAD in the source analysis).
• Source aliases: none known; source NONMEM control streams typically use ad-hoc names (e.g., SAD, IMD, DIS).
• Example models: Cheng_2026_immunoglobulin.R (multiplicative theta^DIS_SAD factors on CL (0.542) and on baseline IgG (CBAS, 0.541); reference category PID).
• Notes: Used when a population PK model pools PID and SAD pediatric or adult patients receiving immunoglobulin replacement therapy (IgRT) and tests SAD-vs-PID as a covariate. Distinct from the disease-state indicators that pool oncology / autoimmune indications: DIS_SAD specifically partitions hypogammaglobulinaemia by its underlying mechanism (genetic vs. acquired). Scope: specific because the SAD cohort composition is paper-defined (in Cheng 2026, 75% post-rituximab and 25% post-CAR-T cell therapy). Ratified canonically on 2026-04-28.

DIS_AD (canonical for Alzheimer’s disease patient indicator)

• Description: 1 = participant with Alzheimer’s disease (clinical AD diagnosis), 0 = non-AD subject (typically healthy volunteer pooled in the source analysis). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-AD subject; the complement group is paper-defined – for Perez-Ruixo 2025 the reference is the pooled healthy-volunteer cohort).
• Source aliases: none known; source NONMEM control streams typically use ad-hoc names (e.g., AD, STATUS, DISGRP).
• Example models: PerezRuixo_2025_posdinemab.R (acts on baseline free p217+tau in CSF, R0; healthy R0 = 0.793 pmol/L vs AD R0 = 5.995 pmol/L, a 656% relative increase, no PK-parameter effects).
• Notes: Used when a population PK/PD model pools healthy volunteers with Alzheimer’s disease patients and the AD-vs-HV contrast is retained as a covariate on a target-related parameter (e.g., baseline p-tau, baseline p217+tau). Scope: specific because the complement reference category is paper-defined. Ratified canonically on 2026-04-28.

DIS_COPD (canonical for chronic obstructive pulmonary disease patient indicator)

• Description: 1 = patient with chronic obstructive pulmonary disease (clinical COPD diagnosis, typically moderate-to-severe per GOLD criteria), 0 = non-COPD subject (typically healthy volunteer pooled in the source analysis). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-COPD subject; the complement group is paper-defined – for Lahu 2010 the reference is the pooled phase I healthy-volunteer cohort).
• Source aliases:
  • COPD – used in Lahu_2010_roflumilast.R (paper text covariate symbol in equation 6 and 7).
• Example models: Lahu_2010_roflumilast.R (linear additive effects on roflumilast parent CL (-39.4%) and V1 (+184%) and on roflumilast N-oxide CL (-7.9%) and Vd (-21.4%); reference category 0 = pooled phase I healthy volunteers, 1 = pooled phase II/III moderate-to-severe COPD patient), Facius_2018_roflumilast.R (linear additive phase II-III patient effects on KA (-73.3%), parent CL (-55.2%), N-oxide CL (-24.4%), and N-oxide central V3 (-20.7%) on the joint Lahu 2010 base model re-estimated on the OPTIMIZE + REACT phase III COPD dataset; reference category 0 = the implicit phase I healthy-volunteer cohort that backed the upstream Lahu 2010 fixed structural parameters), Yang_2013_losmapimod.R (mutually-exclusive proportional residual variance switch: sigma^2_prop = 0.061 for non-COPD subjects vs sigma^2_prop,COPD = 0.268 for COPD subjects, reproducing the NONMEM IF (COPD.EQ.1) EPS(2) ELSE EPS(1) $ERROR pattern; reference category 0 = pooled healthy volunteers plus rheumatoid arthritis patients).
• Notes: Used when a population PK/PD model pools healthy volunteers (with or without additional non-COPD patient cohorts such as rheumatoid arthritis in Yang 2013) with COPD patients and the COPD-vs-non-COPD contrast is retained either as a covariate on structural PK parameters or as a switch on residual-error magnitude. Scope: specific because the complement reference category and the COPD-severity inclusion criteria are paper-defined.

DIS_OBESE_MORBID (canonical for morbidly obese cohort indicator)

• Description: 1 = morbidly obese patient (BMI > 40 kg/m^2 in the canonical definition; typical pooled-analysis enrollment criterion is bariatric-surgery patients), 0 = non-obese subject (typically healthy volunteer pooled in the source analysis). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-obese subject; the complement group is paper-defined – for de Hoogd 2017 the reference is the pooled healthy-volunteer cohort from Sarton 2000 and Romberg 2004).
• Source aliases: none known; source NONMEM control streams typically use ad-hoc names (e.g., OBESE, MO, COHORT).
• Example models: deHoogd_2017_morphine.R (selects per-cohort proportional residual error magnitudes for each of three observed species – morphine, M3G, M6G – after a pooled-cohort fit of 20 morbidly obese surgical patients and 20 healthy volunteers).
• Notes: Used when a population PK or PK/PD model pools morbidly obese patients with a non-obese reference population (typically healthy volunteers) and the cohort indicator selects per-cohort parameter values (residual error magnitudes, study-specific bioavailability, or similar). Distinct from BMI (which is the continuous body-mass-index covariate used for parameter scaling) – DIS_OBESE_MORBID is the binary cohort-membership flag and does not encode a specific BMI threshold for general use; the threshold is paper-defined. Scope: specific because the complement reference category is paper-defined. Ratified canonically on 2026-05-11.

HSCT_URD_7OF8 (canonical for hematopoietic stem cell transplant from a 7-of-8 HLA-matched unrelated donor)

• Description: 1 = patient received an allogeneic hematopoietic stem cell transplant (HSCT) from an unrelated donor (URD) HLA-matched at 7 of 8 alleles (single-allele mismatch), 0 = otherwise (the union of patients not in this transplant cohort, including non-HSCT patients and HSCT recipients matched at all 8 alleles). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (not in the 7-of-8-matched HSCT cohort; the complement group is paper-defined – for Zhong 2026 it pools the RA + pJIA studies and the 8-of-8-matched HSCT cohort, with the latter encoded by the parallel HSCT_URD_8OF8 indicator).
• Source aliases:
  • COHORT7 – used in Zhong_2026_abatacept.R (Zhong 2026 NM-TRAN indicator for ABA2 study Cohort 7/8).
• Example models: Zhong_2026_abatacept.R (exponential coefficient -0.326 on CL; the single-allele-mismatch HSCT cohort exhibits ~28% lower abatacept clearance than the reference complement).
• Notes: Used together with HSCT_URD_8OF8 to decompose a three-level “transplant cohort” categorical (non-HSCT-cohort / 7-of-8 / 8-of-8) into two orthogonal binary indicators. The 7-of-8 cohort represents a higher GvHD-risk population because of the single-allele HLA mismatch. Scope: specific because the reference complement (the union of non-transplant disease cohorts pooled in the source analysis) is paper-defined. Ratified canonically on 2026-04-29.

HSCT_URD_8OF8 (canonical for hematopoietic stem cell transplant from an 8-of-8 HLA-matched unrelated donor)

• Description: 1 = patient received an allogeneic hematopoietic stem cell transplant (HSCT) from an unrelated donor (URD) HLA-matched at all 8 alleles (full match), 0 = otherwise (the union of patients not in this transplant cohort, including non-HSCT patients and HSCT recipients matched at 7 of 8 alleles). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (not in the 8-of-8-matched HSCT cohort; the complement group is paper-defined – for Zhong 2026 it pools the RA + pJIA studies and the 7-of-8-matched HSCT cohort, with the latter encoded by the parallel HSCT_URD_7OF8 indicator).
• Source aliases:
  • COHORT8 – used in Zhong_2026_abatacept.R (Zhong 2026 NM-TRAN indicator for ABA2 study Cohort 8/8).
• Example models: Zhong_2026_abatacept.R (exponential coefficient -0.0934 on CL and +0.257 on VC; the fully-HLA-matched HSCT cohort exhibits a small CL decrease and a larger VC increase relative to the reference complement).
• Notes: Used together with HSCT_URD_7OF8 to decompose a three-level “transplant cohort” categorical (non-HSCT-cohort / 7-of-8 / 8-of-8) into two orthogonal binary indicators. The 8-of-8 cohort is the lower-risk HLA-matching configuration. Scope: specific because the reference complement (the union of non-transplant disease cohorts pooled in the source analysis) is paper-defined. Ratified canonically on 2026-04-29.

DIS_PSORIASIS (canonical for plaque psoriasis disease-state indicator)

• Description: 1 = plaque psoriasis patient, 0 = non-psoriasis subject (e.g., atopic dermatitis, ulcerative colitis, or healthy volunteer). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-psoriasis subject; the complement group is paper-defined – the union of other disease cohorts pooled in the source analysis).
• Source aliases: none known; source NONMEM control streams typically use a categorical DIS indicator (e.g., Okada 2025: DIS=1 for psoriasis, DIS=0 for healthy, DIS=2 for UC, DIS=3 for AD), decomposed into a binary DIS_PSORIASIS indicator at ingestion.
• Example models: Okada_2025_rocatinlimab.R (multiplicative shift 1 - 0.372 on linear CL when 1; reference complement is the pooled atopic dermatitis + ulcerative colitis + healthy-volunteer cohort), Warren_2025_apremilast.R (multiplicative factor 1.09 on CL/F when 0; reference complement is “other disease or missing”, i.e. the FDA Otezla popPK model treats unknown disease status the same as a non-psoriasis indication, and Warren 2025 simulates atopic dermatitis patients at DIS_PSORIASIS = 0).
• Notes: Used when a population PK model pools plaque-psoriasis patients with a non-psoriasis reference population and psoriasis disease status is retained as a covariate. Scope: specific because the disease-pooling reference category is paper-defined – the two registered models differ in it (Okada 2025 pools three named non-psoriasis cohorts; the FDA Otezla model behind Warren 2025 pools every non-psoriasis indication together with subjects whose disease status is missing), so the 1-side is transferable across papers but the 0-side is not. Ratified canonically on 2026-04-27.

DIS_RA (canonical for rheumatoid arthritis disease-state indicator)

• Description: 1 = adult rheumatoid arthritis patient, 0 = non-RA subject (e.g., healthy volunteer, Crohn’s disease, systemic lupus erythematosus, or other indication). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-RA subject; the complement group is paper-defined – the union of other cohorts pooled in the source analysis).
• Source aliases: none known; source NONMEM control streams typically derive the indicator from a POPULATION / STUDY categorical alongside other DIS_* indicators.
• Example models: Li_2018_PF04236921.R (log-shift e_ra_cl = log(0.00588 / 0.00546) = 0.0741 on linear CL, log-shifts on baseline CRP / IC50 / logit-Imax; one of three orthogonal indicators (DIS_RA / DIS_CD / DIS_SLE) decomposing the four-level HV / RA / CD / SLE cohort with HV as the reference category), Wojciechowski_2023_ritlecitinib_base.R, Wojciechowski_2023_ritlecitinib_updated.R (linear fractional effect on apparent clearance CL/F *= (1 + e_ra_cl * DIS_RA) with e_ra_cl = -0.439 in the base model and -0.496 in the updated model; reference category is the healthy-participant cohort, and the RA cohort also contributes to the pooled inflammatory-disease group that scales the IIV and proportional-residual-error magnitudes; one of four orthogonal indicators (DIS_RA / DIS_UC / DIS_ALOPECIA_AREATA / DIS_VITILIGO) decomposing the source’s multi-level PTST patient-type column).
• Notes: Use when a population PK model pools adult RA patients with a non-RA reference population and RA disease status is retained as a covariate distinct from CD / SLE / other indications. Distinct from DIS_PJIA (polyarticular juvenile idiopathic arthritis, a pediatric-cohort sibling indicator). Scope: specific because the disease-pooling reference category is paper-defined. Ratified canonically on 2026-06-01 alongside the Li 2018 PF-04236921 extraction.

DIS_CD (canonical for Crohn’s disease state indicator (multi-indication pooled analyses))

• Description: 1 = Crohn’s disease patient, 0 = non-CD subject (e.g., healthy volunteer, rheumatoid arthritis, systemic lupus erythematosus, or other indication). Time-fixed per subject. Distinct from IBD_CD, which is a pooled-UC+CD discriminator with UC as the reference category; DIS_CD is used when the complement group is a heterogeneous non-IBD cohort rather than UC specifically.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-CD subject; the complement group is paper-defined – the union of other cohorts pooled in the source analysis).
• Source aliases: none known; source NONMEM control streams typically derive the indicator from a POPULATION / STUDY categorical alongside other DIS_* indicators.
• Example models: Li_2018_PF04236921.R (log-shift e_cd_cl = log(0.00946 / 0.00546) = 0.5499 on linear CL – a 73 percent higher typical CL in CD vs the HV reference, consistent with the paper’s reported 60 percent higher CL in CD when other covariates are held at reference; also log-shifts on baseline CRP, IC50, and logit-Imax).
• Notes: Use when a population PK model pools CD patients with a non-IBD reference population and Crohn’s disease status is retained as a covariate. When the analysis pools CD with UC only (and tests CD-vs-UC as a discriminator), use IBD_CD instead. Scope: specific because the disease-pooling reference category is paper-defined. Ratified canonically on 2026-06-01 alongside the Li 2018 PF-04236921 extraction.

DIS_SLE (canonical for systemic lupus erythematosus disease-state indicator)

• Description: 1 = systemic lupus erythematosus patient, 0 = non-SLE subject (e.g., healthy volunteer, rheumatoid arthritis, Crohn’s disease, or other indication). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-SLE subject; the complement group is paper-defined – the union of other cohorts pooled in the source analysis).
• Source aliases: none known; source NONMEM control streams typically derive the indicator from a POPULATION / STUDY categorical alongside other DIS_* indicators.
• Example models: Li_2018_PF04236921.R (log-shift e_sle_cl = log(0.00643 / 0.00546) = 0.1632 on linear CL, log-shifts on baseline CRP / IC50 / logit-Imax, and a Hill coefficient effect e_sle_gamma = log(1.55) = 0.4383 shifting gamma from 1 in the HV/RA/CD reference to 1.55 in SLE).
• Notes: Use when a population PK model pools SLE patients with a non-SLE reference population and SLE disease status is retained as a covariate. Future SLE-anchored extractions (e.g., anifrolumab, belimumab) that include SLE as a covariate against a non-SLE comparator should extend the example list. Distinct from BGENE21 / BGENE21_HIGH (continuous / binary IFN-21-gene scores within an SLE cohort) – those operate within an SLE-only population whereas DIS_SLE is the across-cohort disease-state flag. Scope: specific because the disease-pooling reference category is paper-defined. Ratified canonically on 2026-06-01 alongside the Li 2018 PF-04236921 extraction.

DIS_INFECT_CSSSI_SEV (canonical for complicated skin and skin-structure infection severity indicator)

• Description: 1 = severe complicated skin and skin-structure infection (cSSSI), 0 = not severe cSSSI. Within-cohort severity indicator: stratifies severity inside an already-defined cSSSI / acute bacterial skin and skin-structure infection (ABSSSI) population, not a disease-vs-non-disease cohort indicator. Time-fixed per subject in the source analysis. The exact clinical criteria that classify a patient as severe vs not severe are protocol-defined within the trial dataset and may differ across antimicrobial development programs; per-model covariateData[[DIS_INFECT_CSSSI_SEV]]$notes should document the underlying definition when the source paper provides one.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (not severe cSSSI).
• Source aliases:
  • SOI (severity-of-infection indicator; same orientation, 1 = severe / 0 = not severe) – used in Lodise_2018_iclaprim.R (ASSIST-1 / ASSIST-2 phase 3 cSSSI trials).
• Example models: Lodise_2018_iclaprim.R (additive-linear shift on inter-compartmental clearance Q: q_typ = exp(lq) + e_infect_csssi_sev_q * DIS_INFECT_CSSSI_SEV with e_infect_csssi_sev_q = +13.5 L/h, so severe-cSSSI patients have Q rise from 1.85 L/h to 15.35 L/h relative to non-severe patients).
• Notes: Specific scope because the “severe cSSSI” definition is protocol-defined; future antimicrobial popPK papers that test a severity-of-infection contrast in a different infection class (pneumonia, HABP/VABP, bloodstream infection, bone and joint infection) should register sibling canonicals (e.g., DIS_INFECT_PNEUM_SEV, DIS_HABP_SEV) rather than overloading this entry. Distinct from DIS_SASTHMA and other disease-state indicators (which contrast a disease cohort with a non-disease reference) – DIS_INFECT_CSSSI_SEV operates within an already-cSSSI cohort. The covariate-effect parameter naming drops the DIS_ prefix per the existing DIS_CANCER -> e_cancer_* / DIS_CANCER_PED -> e_cancer_ped_* convention; here that gives e_infect_csssi_sev_<param>. Ratified canonically on 2026-05-30 alongside the Lodise 2018 iclaprim extraction.

DIS_HABP (canonical for hospital-acquired bacterial pneumonia infection-type indicator)

• Description: 1 = the subject’s index infection is hospital-acquired bacterial pneumonia (HABP), 0 = otherwise. Infection-TYPE indicator (which infection the subject has), not a within-class severity indicator. Member of the mutually exclusive DIS_<infection> cohort-indicator set used by antimicrobial popPK analyses that pool healthy volunteers with several infected cohorts; the shared reference category (all indicators 0) is normally the uninfected healthy-volunteer stratum, but the reference must be documented per model because some analyses use one infection type as reference instead.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no HABP; in Cammarata_2024_sulbactam_durlobactam.R and Xie_2025_aztreonam_avibactam.R alike the shared all-zero reference is the uninfected Phase 1 subject).
• Source aliases:
  • INFTYPN = 1 – numeric infection-type flag level; used in Cammarata_2024_sulbactam_durlobactam.R (Table 1 rows CLINFTYPN1, V3INFTYPN1, and the merged V1INFTYPN1&2).
• Example models: Cammarata_2024_sulbactam_durlobactam.R (proportional shifts of -0.424 on sulbactam total CL and +0.836 on sulbactam Vc; durlobactam Vc uses a merged HABP-or-VABP coefficient of +1.52 applied to DIS_HABP + DIS_VABP), VanWart_2025_telavancin.R (a single coefficient shared across DIS_BACTEREMIA + DIS_HABP + DIS_VABP: +0.418 on total CL, +0.578 on Vc, +0.329 on Vp; second ratifying model).
  • Cammarata_2024_sulbactam_durlobactam.R (proportional shifts of -0.424 on sulbactam total CL and +0.836 on sulbactam Vc; durlobactam Vc uses a merged HABP-or-VABP coefficient of +1.52 applied to DIS_HABP + DIS_VABP).
  • Xie_2025_aztreonam_avibactam.R (shares the merged +0.931 cIAI-or-HAP-or-VAP coefficient applied to DIS_CIAI + DIS_HABP + DIS_VABP on the central volume of BOTH aztreonam and avibactam; neither drug carries a pneumonia-specific clearance term).
• Notes: Plain DIS_<disease> cohort indicator in the same shape as DIS_HEALTHY / DIS_CANCER / DIS_MDS / DIS_AML. Sibling to DIS_CIAI, DIS_VABP, DIS_CUTI, DIS_BACTEREMIA, and DIS_AP. The DIS_INFECT_<TYPE> prefix suggested by the DIS_INFECT_CSSSI_SEV Notes was considered and not chosen, because DIS_INFECT_CSSSI_SEV is a severity-WITHIN-cohort indicator while these are type-of-infection COHORT indicators (operator decision recorded in the Cammarata 2024 extraction sidecar agcand_13067668 request-001 q1, answered A on 2026-07-27). Covariate-effect naming drops the DIS_ prefix: e_habp_<param>, or e_habp_vabp_<param> / e_ciai_habp_vabp_<param> when a model shares one coefficient across several infection types. Ratified canonically on 2026-07-28 alongside the Cammarata 2024 sulbactam-durlobactam extraction, at Scope: specific pending a second ratifying model; promoted to Scope: general on 2026-08-17 when the Xie 2025 aztreonam-avibactam extraction became that second model, exactly as these Notes anticipated.

DIS_VABP (canonical for ventilator-associated bacterial pneumonia infection-type indicator)

• Description: 1 = the subject’s index infection is ventilator-associated bacterial pneumonia (VABP), 0 = otherwise. Member of the mutually exclusive DIS_<infection> cohort-indicator set. See DIS_HABP for the shared-reference-category discipline.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no VABP; shared all-zero reference documented per model).
• Source aliases:
  • INFTYPN = 2 – numeric infection-type flag level; used in Cammarata_2024_sulbactam_durlobactam.R (Table 1 rows CLINFTYPN2, V3INFTYPN2, and the merged V1INFTYPN1&2).
• Example models: Cammarata_2024_sulbactam_durlobactam.R (proportional shifts of -0.298 on sulbactam total CL and +1.43 on sulbactam Vc; shares the merged +1.52 HABP-or-VABP coefficient on durlobactam Vc because the separate HABP and VABP durlobactam terms ‘did not yield significantly different effects on PK from one another’), VanWart_2025_telavancin.R (shares one coefficient with DIS_HABP and DIS_BACTEREMIA on CL, Vc and Vp; Van Wart 2025 Table 1 pools HABP and VABP into a single demographic stratum and never reports them separately; second ratifying model).
  • Cammarata_2024_sulbactam_durlobactam.R (proportional shifts of -0.298 on sulbactam total CL and +1.43 on sulbactam Vc; shares the merged +1.52 HABP-or-VABP coefficient on durlobactam Vc because the separate HABP and VABP durlobactam terms ‘did not yield significantly different effects on PK from one another’).
  • Xie_2025_aztreonam_avibactam.R (shares the merged +0.931 cIAI-or-HAP-or-VAP coefficient on the central volume of both drugs; Xie 2025 reports that steady-state exposures were similar between VAP and cIAI patients while HAP patients had the highest, yet the final model carries no VAP-specific coefficient distinct from HAP).
• Notes: Sibling to DIS_HABP and DIS_CIAI. When a source paper merges the pneumonia arms into one coefficient, keep the covariate COLUMNS distinct and apply the shared coefficient to their sum inside model() – do not collapse to a single DIS_HABP_VABP column, because sibling analyses (and the same paper’s other analyte) may separate them again. Both ratifying models so far merge them, in different groupings (Cammarata 2024 across HABP + VABP, Xie 2025 across cIAI + HAP + VAP), which is exactly why the columns stay separate. Ratified canonically on 2026-07-28 alongside the Cammarata 2024 sulbactam-durlobactam extraction, at Scope: specific pending a second ratifying model; promoted to Scope: general on 2026-08-17 alongside the Xie 2025 aztreonam-avibactam extraction.

DIS_CUTI (canonical for complicated urinary tract infection infection-type indicator)

• Description: 1 = the subject’s index infection is a complicated urinary tract infection (cUTI), 0 = otherwise. Member of the mutually exclusive DIS_<infection> cohort-indicator set. Papers that enroll a combined “cUTI including acute pyelonephritis” cohort but report separate model coefficients for the two should pair this column with DIS_AP; papers that report a single pooled coefficient should use this column alone and say so in the per-model notes.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no cUTI; shared all-zero reference documented per model).
• Source aliases:
  • INFTYPN = 3 – numeric infection-type flag level; used in Cammarata_2024_sulbactam_durlobactam.R (Table 1 rows V1INFTYPN3, CLINFTYPN3, V3INFTYPN3).
• Example models: Cammarata_2024_sulbactam_durlobactam.R (proportional shifts of +0.343 on durlobactam Vc, -0.157 on sulbactam total CL, and +0.17 on sulbactam Vc; cUTI subjects came from the Phase 2 study CS2514-2017-0003, in which acute pyelonephritis was reported as a separate infection-type level).
  • Cammarata_2024_sulbactam_durlobactam.R (proportional shifts of +0.343 on durlobactam Vc, -0.157 on sulbactam total CL, and +0.17 on sulbactam Vc; cUTI subjects came from the Phase 2 study CS2514-2017-0003, in which acute pyelonephritis was reported as a separate infection-type level).
  • Xie_2025_aztreonam_avibactam.R (proportional shifts of +0.222 on avibactam CL and +1.5 on avibactam Vc; both terms are avibactam-only because only 3 of the 431 subjects in the aztreonam data set had cUTI against 707 of 2,635 in the avibactam data set, so the aztreonam coefficients are absent and must not be borrowed).
• Notes: Sibling to DIS_CIAI / DIS_HABP / DIS_VABP / DIS_BACTEREMIA / DIS_AP. Both ratifying models are fixed-ratio beta-lactam / beta-lactamase-inhibitor combinations in which the cUTI effect is carried by one analyte only, for the same reason in each case – the two analytes have very differently sized cUTI sub-cohorts. Check the per-analyte subject counts before assuming a missing coefficient is an omission. Ratified canonically on 2026-07-28 alongside the Cammarata 2024 sulbactam-durlobactam extraction, at Scope: specific pending a second ratifying model; promoted to Scope: general on 2026-08-17 alongside the Xie 2025 aztreonam-avibactam extraction.

DIS_BACTEREMIA (canonical for bacteremia / bloodstream-infection infection-type indicator)

• Description: 1 = the subject’s index infection is bacteremia (bloodstream infection), 0 = otherwise. Member of the mutually exclusive DIS_<infection> cohort-indicator set. Whether bacteremia secondary to another focus counts as bacteremia or as the primary focus is protocol-specific and must be documented per model.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no bacteremia; shared all-zero reference documented per model).
• Source aliases:
  • INFTYPN = 4 – numeric infection-type flag level; used in Cammarata_2024_sulbactam_durlobactam.R (Table 1 rows V1INFTYPN4, CLINFTYPN4, V3INFTYPN4).
• Example models: Cammarata_2024_sulbactam_durlobactam.R (proportional shifts of +3.32 on durlobactam Vc – the largest single covariate effect in the model – plus -0.444 on sulbactam total CL and +1.85 on sulbactam Vc; bacteremia due to Acinetobacter baumannii-calcoaceticus complex was one of the Phase 3 enrollment categories), VanWart_2025_telavancin.R (uncomplicated Staphylococcus aureus bacteremia from a single Phase 2 study, 18 of 1,205 subjects; shares one coefficient with DIS_HABP and DIS_VABP on CL, Vc and Vp; second ratifying model).
• Notes: Scope specific until a second model ratifies it; sibling to DIS_HABP / DIS_VABP / DIS_CUTI / DIS_AP / DIS_CSSSI. Ratified canonically on 2026-07-28 alongside the Cammarata 2024 sulbactam-durlobactam extraction.

DIS_AP (canonical for acute pyelonephritis infection-type indicator)

• Description: 1 = the subject’s index infection is acute pyelonephritis (AP), 0 = otherwise. Member of the mutually exclusive DIS_<infection> cohort-indicator set. Used when a source paper reports AP as a model level distinct from DIS_CUTI, which is common because AP trials enroll under a combined “cUTI including AP” protocol but the two present with materially different renal and distributional physiology.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no AP; shared all-zero reference documented per model).
• Source aliases:
  • INFTYPN = 5 – numeric infection-type flag level; used in Cammarata_2024_sulbactam_durlobactam.R (Table 1 rows CLINFTYPN5, V3INFTYPN5; the table footnote defines ‘AP, acute pyelonephritis’).
• Example models: Cammarata_2024_sulbactam_durlobactam.R (proportional shifts of -0.382 on sulbactam total CL and -0.704 on sulbactam Vc; durlobactam carries no AP term because no V1INFTYPN5 row appears in the durlobactam half of Table 1).
• Notes: Scope specific until a second model ratifies it; sibling to DIS_CUTI. A model may legitimately carry an AP coefficient for one analyte and not the other – do not synthesise a missing coefficient by borrowing the cUTI value. Ratified canonically on 2026-07-28 alongside the Cammarata 2024 sulbactam-durlobactam extraction.

DIS_CIAI (canonical for complicated intra-abdominal infection infection-type indicator)

• Description: 1 = the subject’s index infection is a complicated intra-abdominal infection (cIAI), 0 = otherwise. Member of the mutually exclusive DIS_<infection> cohort-indicator set used by antimicrobial popPK analyses that pool healthy volunteers with several infected cohorts; the shared reference category (all indicators 0) is normally the uninfected healthy-volunteer stratum, but the reference must be documented per model. cIAI is a broad protocol-defined class – perforated viscus, intra-abdominal abscess, complicated appendicitis, complicated cholecystitis, secondary peritonitis – and is the most commonly enrolled infection type across beta-lactam / beta-lactamase-inhibitor development programs.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no cIAI; in Xie_2025_aztreonam_avibactam.R the shared all-zero reference is the uninfected Phase 1 healthy subject).
• Source aliases:
  • cIAI – printed row label in Xie_2025_aztreonam_avibactam.R (Table S3 rows cIAI on CL_ATM, cIAI on CL_AVI, and the shared cIAI/NP on Vc).
• Example models: Xie_2025_aztreonam_avibactam.R (proportional shifts of +0.279 on aztreonam CL and +0.115 on avibactam CL, plus a share in the merged +0.931 cIAI-or-HAP-or-VAP coefficient applied to the central volume of both drugs; cIAI raises the clearance of both analytes, which is why Xie 2025 chose cIAI as the most conservative population for its dose-selection simulations).
• Notes: Plain DIS_<disease> cohort indicator, sibling to DIS_HABP, DIS_VABP, DIS_CUTI, DIS_BACTEREMIA and DIS_AP, and registered in exactly the shape those five were ratified in on 2026-07-28. Distinct from DIS_PERIT: DIS_PERIT is a peritonitis indicator (inflammation of the peritoneum specifically, usable in any critically-ill cohort), whereas DIS_CIAI is the broader trial-protocol infection-TYPE class of which peritonitis is one presentation; a paper that enrols a cIAI cohort and separately flags which of those subjects had peritonitis would legitimately carry both columns. Covariate-effect naming drops the DIS_ prefix per the family convention: e_ciai_<param>, or e_ciai_habp_vabp_<param> when a model shares one coefficient across cIAI and the two pneumonia types. Scope specific until a second model ratifies it. Ratified canonically on 2026-08-17 alongside the Xie 2025 aztreonam-avibactam extraction.

CARRAGEENAN (canonical for intraplantar-carrageenan inflammatory-challenge indicator)

• Description: Binary indicator for intraplantar injection of carrageenan suspension as an experimental inflammatory / hyperalgesic challenge. 1 = subject received an intraplantar carrageenan injection at the start of the experiment (the carrageenan-induced peripheral inflammation / thermal-hyperalgesia paradigm); 0 = subject received an intraplantar saline injection (sham control). Time-fixed per subject within an experiment.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (saline-injected sham animal; no induced inflammation).
• Source aliases:
  • CARRAGEENAN – used in VasquezBahena_2009_lumiracoxib_rat.R (1 = groups II-IX, 100 uL of 1% carrageenan suspension into the right hind paw; 0 = group I, 100 uL of 0.9% saline solution).
• Example models: VasquezBahena_2009_lumiracoxib_rat.R (switches the COX-2 synthesis-rate model: CARRAGEENAN = 0 selects the constant saline synthesis rate ks_cox2_saline and CARRAGEENAN = 1 selects the time-variant gamma function ks_cox2(t) = A * t^alpha * exp(-beta * t) driving the carrageenan-induced inflammation profile).
• Notes: The carrageenan-induced peripheral inflammation / hyperalgesia model (Winter 1962; Hargreaves 1988 thermal-hyperalgesia variant) is one of the most widely used preclinical assays for screening anti-inflammatory and analgesic drugs in rodents, so the canonical name is reusable for future preclinical extractions. Scope: specific until a second model ratifies the binary-switch semantics on a different PD framework. The Hargreaves-test thermal-hyperalgesia readout (LT paw withdrawal latency, seconds) is the typical observable when this indicator is in use, but other readouts (paw oedema, mechanical-allodynia von Frey threshold) are equally valid. Distinct from disease-state indicators (DIS_*) because the inflammatory state is experimentally induced at a defined time, not a chronic patient condition.

MENT_DISABLED (canonical for severe mental-disability indicator (paper-defined diagnosis))

• Description: 1 = subject diagnosed with severe mental disability by the pediatrics department per the source-paper clinical criteria; 0 = mentally intact (ASA class 1, normal mental ability). Time-fixed per subject. Subject-level indicator; the underlying definition is broad (severe developmental / cognitive impairment with impairment of daily-living activities irrespective of primary neurologic cause, per Shin 2014 Results) rather than a single diagnostic category.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (mentally intact; ASA class 1).
• Source aliases:
  • MEN – used in Shin_2014_sevoflurane.R (Shin 2014 Appendix 1 NONMEM $INPUT column; same orientation: MEN = 1 mentally disabled, MEN = 0 mentally intact).
• Example models: Shin_2014_sevoflurane.R (stratifies both C50 and the Hill coefficient of the sigmoid-Emax probability of return of consciousness during emergence from sevoflurane anesthesia: typical-value C50 0.37 vol % vs 0.19 vol % and gamma 16.4 vs 4.53 for intact vs disabled).
• Notes: Specific scope because the “severe mental disability” diagnostic criterion is paper-defined (Shin 2014 enrolled pediatric patients diagnosed with severe mental disability by the pediatrics department, without specifying a structured instrument); per-model covariateData[[MENT_DISABLED]]$notes should document the diagnostic basis when the source paper provides one. Distinct from DIS_* disease-state indicators (which name a specific disease) and from cognitive-score covariates (SCORE_ADAS_COG, SCORE_MMSE, SCORE_CDR_SOB, SCORE_FAQ) which are continuous measurements; MENT_DISABLED is a binary developmental / cognitive-impairment cohort indicator. Future emergence-from-anesthesia or perioperative PK/PD models that use a similar binary cognitive-impairment indicator may extend this entry; promote to general scope once a second model ratifies the name with the same diagnostic criterion family. Ratified canonically alongside the Shin 2014 sevoflurane emergence-PD extraction.

DIS_CF (canonical for cystic-fibrosis disease-state indicator)

• Description: 1 = cystic fibrosis patient, 0 = healthy participant (or non-CF reference cohort pooled in the source analysis). Time-fixed per subject. Distinct from the Harun_2019_cysticFibrosis.R model which is single-cohort (all CF) and therefore carries no DIS_CF column; DIS_CF is the canonical for popPK / PD analyses that pool a CF patient cohort with a non-CF reference (typically healthy volunteers) and retain the CF-vs-non-CF contrast as a covariate.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-CF subject; the complement is paper-defined – for Bulitta 2007 the reference is the pooled adult healthy-volunteer cohort).
• Source aliases: none known; source NONMEM control streams typically encode the cohort indicator with ad-hoc names (e.g., CF, CYS, PATIENT, or via a STUDY / GROUP categorical).
• Example models: Bulitta_2007_piperacillin.R (multiplicative power-form effects on volumes via fcyf_vss^DIS_CF with fcyf_vss = 0.926, i.e. about a 7.4% reduction in V1 and V2 for CF patients vs healthy adults of the same lean body mass; CL carries the boundary-estimated fcyf_cl^DIS_CF with fcyf_cl = 1.00, retained from the published model for traceability though it has no numerical effect at the published point estimate; reference category is the pooled adult healthy-volunteer cohort), Bulitta_2011_cefpirome.R (log-scale effects on the canonical lcl_renal + lcl_nonren decomposition and on the central / shallow-peripheral / deep-peripheral volumes: e_cf_cl_renal = log(1.07) = +0.06766 on CL_R, e_cf_cl_nonren = log(1.13) = +0.12222 on CL_NR, and e_cf_vc_vp_vp2 = log(0.98) = -0.02020 applied uniformly to V1, V2, V3; typical values anchored to the HV column of paper Table 3 with FCYF effects from paper Table 4 LBM-allometric row).
• Notes: Used when a population PK / PD analysis pools CF patients with a non-CF reference (typically healthy volunteers) and the CF-vs-non-CF contrast is retained as a covariate. Scope: specific until a second model ratifies the canonical; promote to general at that point. Pair with LBM rather than WT as the size descriptor when the source paper found LBM to reduce unexplained BSV more than total body weight, which is common in CF cohorts whose body composition differs substantially from healthy adults. Ratified canonically on 2026-06-07 alongside the Bulitta 2007 piperacillin extraction.

DIS_SEPSIS (canonical for active-sepsis co-condition indicator)

• Description: 1 = active sepsis / septic syndrome at the start of (or during) the modeled PK interval, 0 = no sepsis. Time-fixed per subject for the typical popPK use-case (Han 2013), although time-varying use is permitted; document per-model in covariateData[[DIS_SEPSIS]]$notes whether the indicator is fixed at PK-sampling onset or updated dynamically over the observation period. Used as a binary co-condition indicator on PK parameters (clearance, volume) when a study population pools septic and non-septic subjects, typically in ICU / burn-ICU / critically ill cohorts.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no sepsis).
• Source aliases:
  • SEPS – used in Han_2013_fluconazole.R (Han 2013 Methods / Table 1 covariate with values 0 = no sepsis, 1 = sepsis; same orientation as the canonical).
• Example models: Han_2013_fluconazole.R (additive shift on the non-RRT CL arm: CL_norrt = theta1 + (CLCR/120)*theta4 + TBI*theta7 + SEPS*theta9 with theta9 = -0.369 L/h – septic patients have about 0.37 L/h lower CL in the non-RRT arm), Jeon_2014_piperacillin.R (additive shift on central volume V1: TVV1 = exp(lvc) + e_sepsis_vc * DIS_SEPSIS with e_sepsis_vc = 14.8 L – septic patients have V1 = 25.3 + 14.8 = 40.1 L versus 25.3 L in non-septic patients, consistent with capillary leakage and interstitial edema in sepsis).
• Notes: Distinct from chronic disease-state indicators (e.g., DIS_UC, DIS_HAE) – sepsis is an acute / transient co-condition rather than a chronic indication label. Distinct from CRP / IL6 / SAPS_II, which are biomarker / severity-score columns that may co-vary with clinical sepsis but are conceptually distinct from a binary clinical-sepsis diagnostic flag. Ratified canonically on 2026-06-09 alongside the Han 2013 fluconazole extraction.

DIS_INFECT_ACTIVE (canonical for active clinical infection episode indicator)

• Description: 1 = the record falls within an active clinical infection episode; 0 = no active infection. Time-varying per record (an acute, transient episode), in contrast to chronic disease-state indicators. The clinical criterion is paper-specific and MUST be recorded in covariateData[[DIS_INFECT_ACTIVE]]$notes – e.g. Kloos 2021 used “fever (>38 degrees Celsius) and hospital admission or prescription of antibiotics”. Used as a multiplicative or additive shift on clearance (and occasionally volume) when acute infection / inflammation alters drug elimination, e.g. via activation of the mononuclear phagocyte system.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no active infection).
• Source aliases:
  • INFECTION – used in Kloos_2021_pegasparaginase.R (Kloos 2021 Table 2 / Table 3 covariate; same orientation as the canonical).
• Example models: Kloos_2021_pegasparaginase.R (multiplicative on CL: 1.38^DIS_INFECT_ACTIVE, i.e. 38 percent higher PEGasparaginase clearance during an infection, attributed by the authors to activation of the mononuclear phagocyte system that clears PEGylated asparaginase), AbdullahKoolmees_2024_voriconazole_pbpk.R (divides each hepatic CYP Vmax by a per-isoenzyme fold decrease – CYP2C19 1.79, CYP3A4 4.6, CYP2C9 1.5 – so severe infection lowers voriconazole intrinsic clearance, the opposite direction to Kloos 2021; the clinical criterion is the paper’s C-reactive-protein banding of its simulated patient types rather than a fever / antibiotic rule).
• Notes: Distinct from [[DIS_SEPSIS]], which denotes a specific septic syndrome – DIS_INFECT_ACTIVE is the broader “any active clinical infection episode” flag and is typically time-varying where DIS_SEPSIS is often time-fixed at PK-sampling onset. Distinct from DIS_INFECT_CSSSI_SEV (an infection-severity grade in a specific-scope cSSSI indication). Distinct from its constituent measurements BODYTEMP, CRP, and the CONMED_<INN> antibiotic columns, which are biomarker / comedication columns that may co-vary with a clinical infection flag but are conceptually separate. Ratified canonically alongside the Kloos 2021 PEGasparaginase extraction (sidecar request-001 / response-001, question q2, option A).

DIS_EDEMA (canonical for clinical edema presence indicator)

• Description: 1 = clinical edema present (typical clinical definition: puffy face and/or pitting peripheral edema), 0 = no clinical edema. Time-fixed per subject in the typical popPK use-case (Han 2013), although time-varying use is permitted; document per-model in covariateData[[DIS_EDEMA]]$notes whether the indicator is fixed at PK-sampling onset or updated dynamically. Used as a binary co-condition indicator on PK volume (most commonly) when third-space / extravascular fluid expansion is expected to alter drug distribution.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no clinical edema).
• Source aliases:
  • EDEM – used in Han_2013_fluconazole.R (Han 2013 Methods / Table 1 covariate with values 0 = no edema, 1 = clinical edema by the paper’s puffy-face and leg-pitting criterion; same orientation as the canonical).
• Example models: Han_2013_fluconazole.R (additive shift on V: V = (WT/65)*theta5 + EDEM*theta6 + TBI*theta8 with theta6 = 13.6 L – edematous patients have about 13.6 L larger V).
• Notes: Conceptually distinct from DIS_HAE (hereditary angioedema disease-state indicator) – DIS_EDEMA is a generic clinical-sign flag (e.g., burn-related capillary leak, congestive heart failure, hypoalbuminemia) rather than a disease classification. Distinct from a continuous fluid-balance / weight-gain column. Ratified canonically on 2026-06-09 alongside the Han 2013 fluconazole extraction.

DIS_BURN_RECENT (canonical for recent-postburn-injury hypermetabolic-phase indicator)

• Description: 1 = within the paper-defined recent-postburn window (i.e., the acute hypermetabolic phase shortly after burn injury), 0 = beyond the window. Time-varying within a subject is permitted as time since injury crosses the cutoff; in Han 2013 the cutoff is 30 days postburn (the hypermetabolic response is maximized between days 7-17 and substantially resolved by day 30; the binary recoding captures patients whose physiology is dominated by the hypermetabolic period). The exact recency threshold is paper-specific – document the threshold in each model’s covariateData[[DIS_BURN_RECENT]]$notes. Used as a piecewise covariate on PK parameters (CL and / or V) that change during the acute hypermetabolic phase.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (beyond the recent-postburn window; i.e., resolved hypermetabolic phase).
• Source aliases:
  • TBI – used in Han_2013_fluconazole.R (Han 2013 Methods / Tables 2 and 3 covariate with values 1 = within 30 days of burn injury, 0 = at least 30 days postburn; same orientation as the canonical, with the recency threshold = 30 days documented per-model). NOTE: TBI in this register is a recoded binary covariate; it does NOT denote traumatic brain injury (the common medical abbreviation) and is NOT a continuous time-from-burn-injury value.
• Example models: Han_2013_fluconazole.R (recency threshold = 30 days; additive shifts on both CL and V: CL_norrt += TBI*theta7 with theta7 = 0.504 L/h and V += TBI*theta8 with theta8 = 9.61 L – recent-postburn patients have about 0.5 L/h higher CL and 9.6 L larger V relative to the resolved-phase reference).
• Notes: Generalised binary recoding rather than a continuous time-from-burn-injury column. A future paper that uses a continuous days-from-burn-injury covariate (analogous to TPP for time-postpartum) would warrant its own canonical (suggested name: POSTBURN_DAYS); DIS_BURN_RECENT is reserved for binary phase indicators. The paper’s source-column name TBI is preserved as a documented alias even though it collides with the medical abbreviation for traumatic brain injury – this is the author’s chosen column name and the canonical avoids the collision. Ratified canonically on 2026-06-09 alongside the Han 2013 fluconazole extraction.

DIS_ARDS (canonical for acute respiratory distress syndrome indicator)

• Description: 1 = acute respiratory distress syndrome (ARDS) present at the start of (or during) the modeled PK interval, 0 = no ARDS. Diagnostic standard is paper-specific (Berlin 2012 criteria, the older American-European Consensus Conference / AECC criteria, or a clinician-adjudicated diagnosis on the chart) – document per-model in covariateData[[DIS_ARDS]]$notes which definition was used. Used as a binary co-condition indicator on PK parameters (most often clearance and / or volume) when ARDS is hypothesized to alter drug disposition through pulmonary inflammation, increased pulmonary reactive oxygen species, ventilator-induced lung injury, or altered fluid balance and capillary leak.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no ARDS).
• Source aliases:
  • ARDS – used in Taubert_2016_linezolid.R (Taubert 2016 patient-group-1 baseline indicator; ARDS = 1 multiplies CL by 1.82, encoded inside model() as a log-additive shift e_dis_ards_cl = log(1.82) per Taubert 2016 page 5256 covariate-model equation).
• Example models: Taubert_2016_linezolid.R (multiplicative factor on CL: typical-value CL is multiplied by 1.82 in ARDS patients, equivalent to a ~82% increase in linezolid clearance; the proposed mechanism is non-enzymatic oxidation of linezolid by elevated pulmonary reactive oxygen species in ARDS lungs per Taubert 2016 Discussion).
• Notes: Distinct from DIS_SEPSIS (acute septic syndrome co-condition) and from DIS_CKD / DIS_HEPATIC chronic organ-failure indicators – ARDS is an acute pulmonary syndrome that may coexist with sepsis but has its own pathophysiology and is captured separately when both are tested as covariates. General scope because ARDS has a stable internationally-standardised clinical definition (Berlin criteria) and is universally applicable as a critically-ill PK covariate. Time-varying within a subject is permitted as the syndrome resolves or worsens. Ratified canonically on 2026-06-30 alongside the Taubert 2016 linezolid extraction.

DIS_PERIT (canonical for peritonitis indicator)

• Description: 1 = peritonitis (infection / inflammation of the peritoneum, including primary spontaneous bacterial peritonitis, secondary peritonitis from a perforated viscus or anastomotic leak, and tertiary / persistent peritonitis) present at the start of (or during) the modeled PK interval, 0 = no peritonitis. Used as a binary co-condition indicator on PK parameters when peritoneal inflammation, intra-abdominal fluid sequestration, third-space loss, or altered hepatic synthetic function is hypothesized to change drug disposition.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no peritonitis).
• Source aliases:
  • Peritonitis – used in Taubert_2016_linezolid.R (Taubert 2016 patient-group-1 baseline indicator; peritonitis = 1 multiplies Vc by 1.53, encoded inside model() as a log-additive shift e_dis_perit_vc = log(1.53) per Taubert 2016 page 5256 covariate-model equation).
• Example models: Taubert_2016_linezolid.R (multiplicative factor on Vc: typical-value central volume is multiplied by 1.53 in peritonitis patients, equivalent to a ~53% larger central volume of distribution, attributed in Taubert 2016 Discussion to third-space fluid accumulation in the peritoneal cavity).
• Notes: Distinct from PERIT_DIAL (chronic peritoneal-dialysis modality indicator) – DIS_PERIT is an acute infection / inflammation of the peritoneum, while PERIT_DIAL is a chronic therapeutic dialysis modality; the two have entirely different pathophysiologies and PK implications. Future papers that retain peritonitis as a covariate may extend Example models. General scope because peritonitis has a clear universal clinical definition and any future critically-ill PK model with the same encoding may reuse the canonical. Ratified canonically on 2026-06-30 alongside the Taubert 2016 linezolid extraction.

DIS_SCOL_IDIO (canonical for idiopathic-scoliosis aetiology indicator)

• Description: 1 = subject has idiopathic scoliosis (no underlying systemic / syndromic cause; classic adolescent idiopathic scoliosis or idiopathic kyphoscoliosis), 0 = otherwise (subject is in either the non-idiopathic-scoliosis cohort or the reference complement cohort). Time-fixed per subject. Used as a binary cohort indicator in popPK / popPD models that pool an adolescent scoliosis population with another paediatric surgical reference cohort and the scoliosis-aetiology effect on PK is retained as a covariate.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (not idiopathic-scoliosis; the complement reference is paper-defined – in Stricker 2015 the implicit reference cohort is the pooled infant craniofacial-reconstruction cohort from the earlier Stricker 2013 BJA paper).
• Source aliases:
  • paper narrative idiopathic scoliosis / idiopathic kyphoscoliosis diagnosis labels in Stricker 2015 Table 1, decomposed from a 3-level diagnosis categorical {craniofacial, idiopathic scoliosis, non-idiopathic scoliosis} into binary DIS_SCOL_IDIO with value 1 for the idiopathic-scoliosis cohort.
• Example models: Stricker_2015_aminocaproic_acid.R (multiplicative power-form effect on CL: cl *= 1.10^DIS_SCOL_IDIO; idiopathic-scoliosis adolescents have ~10% higher CL than the craniofacial-cohort reference at the same body weight and post-natal age, after allometric scaling; Stricker 2015 Table 5 ‘Impact of idiopathic spines’ = 1.10; effect retained for magnitude / precision reporting even though the authors note it is not clinically meaningful).
• Notes: Specific scope because the reference complement (infant craniofacial reconstruction cohort) is paper-defined. Pairs with DIS_SCOL_NONIDIO in the Stricker 2015 three-level surgical-cohort stratification {craniofacial reference, idiopathic scoliosis, non-idiopathic scoliosis}; both = 0 selects the craniofacial reference. Distinct from ADOLESCENT (pure age-cohort indicator independent of aetiology) and from CHILD (broader paediatric age-cohort indicator). Ratified canonically on 2026-06-12 alongside the Stricker 2015 epsilon-aminocaproic acid extraction.

DIS_SCOL_NONIDIO (canonical for non-idiopathic / syndromic scoliosis aetiology indicator)

• Description: 1 = subject has non-idiopathic / syndromic scoliosis (cerebral palsy, Marfan syndrome, Ehlers-Danlos syndrome, neurofibromatosis type 1, spina bifida, congenital neuromuscular scoliosis, syringomyelia, 22q deletion, cortical dysgenesis, and similar systemic / syndromic aetiologies), 0 = otherwise. Time-fixed per subject. Used as a binary cohort indicator in popPK / popPD models that pool a syndromic-scoliosis paediatric population with another paediatric surgical reference cohort and the scoliosis-aetiology effect on PK is retained as a covariate.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (not non-idiopathic-scoliosis; the complement reference is paper-defined – in Stricker 2015 the implicit reference cohort is the pooled infant craniofacial-reconstruction cohort from the earlier Stricker 2013 BJA paper).
• Source aliases:
  • paper narrative diagnosis labels in Stricker 2015 Table 1 (cerebral palsy, Marfan, Ehlers-Danlos, neurofibromatosis, spina bifida, congenital neuromuscular scoliosis, syringomyelia, 22q deletion, cortical dysgenesis), decomposed from the 3-level diagnosis categorical {craniofacial, idiopathic scoliosis, non-idiopathic scoliosis} into binary DIS_SCOL_NONIDIO with value 1 for any of the syndromic-scoliosis aetiologies.
• Example models: Stricker_2015_aminocaproic_acid.R (multiplicative power-form effect on CL: cl *= 0.97^DIS_SCOL_NONIDIO; non-idiopathic-scoliosis adolescents have ~3% lower CL than the craniofacial-cohort reference at the same body weight and post-natal age, after allometric scaling; Stricker 2015 Table 5 ‘Impact of non-idiopathic spines’ = 0.97; the authors describe this effect as not clinically meaningful but retain it in the final model for precision estimation).
• Notes: Specific scope because the reference complement (infant craniofacial reconstruction cohort) is paper-defined and because the non-idiopathic class pools many aetiologically heterogeneous syndromes (cerebral palsy, connective-tissue disorders, neuromuscular disorders, neurocutaneous disorders, etc.) that future papers may want to resolve into separate sub-cohort indicators. Pairs with DIS_SCOL_IDIO in the Stricker 2015 three-level surgical-cohort stratification {craniofacial reference, idiopathic scoliosis, non-idiopathic scoliosis}; both = 0 selects the craniofacial reference. Ratified canonically on 2026-06-12 alongside the Stricker 2015 epsilon-aminocaproic acid extraction.

DIS_CHF (canonical for compensated congestive heart failure indicator)

• Description: 1 = subject has compensated congestive heart failure (chronic CHF stabilised on diuretics, cardiac glycosides, or other background therapy; NYHA class typically not stratified at this level), 0 = no diagnosed CHF. Time-fixed per subject in popPK / popPK-PD analyses that include CHF as a baseline disease-state covariate.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no CHF).
• Source aliases:
  • chf – used in Thomson_1989_lisinopril.R (Thomson 1989 Table 2 cardiac-failure indicator; treated as a multiplicative factor 0.645 raised to chf on CL/F so the unaffected reference group keeps factor 1).
• Example models: Thomson_1989_lisinopril.R (multiplicative power-form effect on CL/F: e_chf_cl^DIS_CHF with e_chf_cl = 0.645; ~35% lower apparent clearance with compensated cardiac failure).
• Notes: Use this canonical when the source paper tests a binary cardiac-failure indicator (compensated, stable, on background therapy) as a structural covariate on PK parameters. Distinct from concomitant-medication indicators such as CONMED_SPIRON (which captures the diuretic / aldosterone-antagonist effect typically given alongside CHF therapy) and from haemodynamic / autonomic covariates such as HR and the blood-pressure outputs. For papers that decompose CHF severity into NYHA classes, prefer a per-class encoding (e.g., DIS_CHF_NYHA3, DIS_CHF_NYHA4) when the strata are retained in the final model. The covariate-effect parameter form is e_chf_<param> (drops the DIS_ prefix per the existing DIS_CANCER -> e_cancer_<param> precedent). Ratified canonically on 2026-06-10 alongside the Thomson 1989 lisinopril extraction.

DIS_RETT (canonical for Rett syndrome disease-state indicator)

• Description: 1 = participant has Rett syndrome (RTT), a rare X-linked neurodevelopmental disorder caused by loss-of-function variants in MECP2; 0 = healthy volunteers and any other disease cohort. Subject-level, time-fixed. Rett syndrome occurs almost exclusively in females, so in a pooled analysis this indicator is effectively collinear with female sex within the patient cohort; document that collinearity per model rather than fitting both a sex and an RTT term on the same PK parameter unless the source paper did.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (healthy volunteers, per the usual pooled healthy-volunteer-plus-patient design).
• Source aliases:
  • Rett – used in Darwish_2025_trofinetide.R (Darwish 2025 Results, definition of the Rett_I indicator variable in the typical-value equations for CL and Vp).
  • RTT – the standard abbreviation used throughout the Rett syndrome literature and in the Darwish 2025 narrative text.
• Example models: Darwish_2025_trofinetide.R (two proportional shifts from Darwish 2025 Table 2: cl * (1 + e_rett_cl * DIS_RETT) with e_rett_cl = -0.169 (16.9% lower clearance) and vp * (1 + e_rett_vp * DIS_RETT) with e_rett_vp = 0.616 (61.6% larger peripheral volume). The indicator additionally selects the disease-cohort residual-error magnitude, which Darwish 2025 pooled across Rett syndrome, fragile X syndrome, and traumatic brain injury – founding example).
• Notes: Member of the DIS_<CONDITION> family of disease-state indicators. The covariate-effect parameter form is e_rett_<param> (drops the DIS_ prefix, per the DIS_CANCER -> e_cancer_<param> and DIS_CHF -> e_chf_<param> precedents). Distinct from DIS_FXS (fragile X syndrome) even though both are monogenic neurodevelopmental disorders studied with overlapping trofinetide programmes – the two cohorts have opposite sex distributions and Darwish 2025 estimated separate, differently-signed effects on different PK parameters, so they must not be collapsed into a single “neurodevelopmental disorder” indicator. Ratified canonically alongside the Darwish 2025 trofinetide extraction.

DIS_TBI (canonical for traumatic brain injury disease-state indicator)

• Description: 1 = participant has a traumatic brain injury (TBI); 0 = healthy volunteers and any other disease cohort. Subject-level, time-fixed. Used where a source paper pools a TBI cohort with healthy volunteers or other patient groups and retains a TBI shift on a PK parameter.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (healthy volunteers).
• Source aliases:
  • TBI – used in Darwish_2025_trofinetide.R (Darwish 2025 Results, definition of the TBI_I indicator variable in the typical-value equations for CL and Vp).
• Example models: Darwish_2025_trofinetide.R (two proportional shifts from Darwish 2025 Table 2: cl * (1 + e_tbi_cl * DIS_TBI) with e_tbi_cl = 0.235 (23.5% higher clearance) and vp * (1 + e_tbi_vp * DIS_TBI) with e_tbi_vp = -0.752 (75.2% smaller peripheral volume). The indicator additionally selects the disease-cohort residual-error magnitude – founding example).
• Notes: Member of the DIS_<CONDITION> family; covariate-effect parameter form e_tbi_<param>. Name-collision warning: DIS_BURN_RECENT documents a source alias also spelled TBI (used in Han_2013_fluconazole.R), where the author’s column name abbreviates a recoded burn-injury recency flag rather than traumatic brain injury. The two canonicals are unrelated. When a source paper uses a bare TBI column, read the paper’s own variable definition before mapping it – map to DIS_TBI only when the paper defines it as traumatic brain injury, and to DIS_BURN_RECENT when it defines it as time since burn injury. Ratified canonically alongside the Darwish 2025 trofinetide extraction.

DIS_FXS (canonical for fragile X syndrome disease-state indicator)

• Description: 1 = participant has fragile X syndrome (FXS), the X-linked trinucleotide-repeat expansion disorder of FMR1 and the most common inherited cause of intellectual disability; 0 = healthy volunteers and any other disease cohort. Subject-level, time-fixed. Clinically penetrant FXS predominates in males, so in a pooled analysis this indicator tends to be collinear with male sex within the patient cohort.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (healthy volunteers).
• Source aliases:
  • FXS – used in Darwish_2025_trofinetide.R (Darwish 2025 Results, definition of the FXS_I indicator variable in the typical-value equation for Vc).
• Example models: Darwish_2025_trofinetide.R (proportional shift on central volume from Darwish 2025 Table 2: vc * (1 + e_fxs_vc * DIS_FXS) with e_fxs_vc = 1.15, i.e. a 115% larger central volume – more than a doubling, the largest single covariate effect in that model. The indicator additionally selects the disease-cohort residual-error magnitude – founding example).
• Notes: Member of the DIS_<CONDITION> family; covariate-effect parameter form e_fxs_<param>. See the DIS_RETT notes for why the two neurodevelopmental-disorder cohorts are registered separately rather than pooled. Ratified canonically alongside the Darwish 2025 trofinetide extraction.

DIS_CSSSI (canonical for complicated skin and skin-structure infection infection-type indicator)

• Description: 1 = the subject’s index infection is a complicated skin and skin-structure infection (cSSSI, also published as acute bacterial skin and skin-structure infection, ABSSSI), 0 = otherwise. Infection-TYPE indicator (which infection the subject has), not a within-cSSSI severity indicator. Member of the mutually exclusive DIS_<infection> cohort-indicator set used by antimicrobial popPK analyses that pool healthy volunteers with several infected cohorts; see DIS_HABP for the shared-reference-category discipline.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no cSSSI; in VanWart_2025_telavancin.R the shared all-zero reference across DIS_CSSSI / DIS_HABP / DIS_VABP / DIS_BACTEREMIA is the uninfected healthy subject).
• Source aliases: none known; source NONMEM control streams normally derive the indicator from an infection-type categorical alongside the sibling DIS_* columns.
• Example models: VanWart_2025_telavancin.R (proportional shifts of +0.228 on total CL, +0.313 on Vc and +0.118 on Vp; cSSSI is the largest infected stratum of the pooled analysis at 557 of 1,205 subjects, and carries its own coefficient distinct from the pooled bacteremia / HABP / VABP coefficient on the same three parameters).
• Notes: Sibling to DIS_HABP, DIS_VABP, DIS_CUTI, DIS_BACTEREMIA and DIS_AP; adds the cSSSI member to that set using the DIS_<infection> pattern the operator ratified in the Cammarata 2024 extraction sidecar (agcand_13067668 request-001 q1, answered A on 2026-07-27), where the alternative DIS_INFECT_<TYPE> prefix was offered and not chosen. Distinct from DIS_INFECT_CSSSI_SEV, which is a severity-WITHIN-cohort indicator (severe vs non-severe cSSSI, Lodise_2018_iclaprim.R) rather than a type-of-infection cohort indicator: a model may legitimately carry both, DIS_CSSSI selecting the cSSSI cohort against a non-cSSSI reference and DIS_INFECT_CSSSI_SEV stratifying severity inside it. Covariate-effect naming drops the DIS_ prefix: e_csssi_<param>. Scope specific until a second model ratifies it. Ratified canonically on 2026-08-17 alongside the Van Wart 2025 telavancin extraction.

DIS_GERD (canonical for gastroesophageal-reflux-disease patient indicator)

• Description: 1 = patient with gastroesophageal reflux disease (GERD), 0 = non-GERD reference (e.g., healthy volunteer, or another indication pooled in the source analysis). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-GERD subject; the complement group is paper-defined – for Yang 2024 the reference is the 68-subject phase 1 healthy-volunteer cohort screened free of any gastrointestinal disorder).
• Source aliases:
  • disease status / PT – Yang 2024 narrative and Table 2 footnote a (PT = 1 for patient, 0 for healthy volunteer).
• Example models: Yang_2024_zastaprazan.R (linear fractional effect on apparent clearance of the 2-compartment Erlang-absorption oral zastaprazan model: CL/F * (1 + e_dis_gerd_cl * DIS_GERD) with e_dis_gerd_cl = -0.414, i.e. CL/F 41.4% lower in patients with erosive GERD (17.2 L/h) than in healthy volunteers (29.4 L/h); reference category is the phase 1 healthy-volunteer cohort of Study 1, and the patient cohort is the 92-subject phase 2 erosive-GERD cohort of Study 2; Yang 2024 Table 2 and footnote a).
• Notes: Follows the DIS_UC / DIS_DUOD_ULCER / DIS_PSORIASIS / DIS_HAE pattern of specific-disease-vs-non-disease pooled-cohort indicators. Distinct from DIS_DUOD_ULCER, which is a peptic-duodenal-ulcer cohort indicator: both are upper-GI acid-related conditions treated with the same drug classes (PPIs and P-CABs), but they are different diagnoses and a subject can carry either independently. Also distinct from DIS_HEALTHY, which carries a healthy-vs-pooled-patient contrast with 0 = patient; a paper enrolling a single named disease cohort alongside healthy participants is encoded with the disease-specific indicator so the source coefficients and typical values transfer without a sign flip or re-baselining (the same reasoning recorded under DIS_DUOD_ULCER). Registered as the broad DIS_GERD rather than the cohort-specific DIS_GERD_EROSIVE even though Yang 2024 enrolled only erosive GERD, because erosive and non-erosive reflux disease are severity strata of one diagnosis rather than distinct diseases, and because the mechanism the source paper invokes (delayed gastric emptying raising bioavailability, reported for vonoprazan and lansoprazole as well as zastaprazan) is not specific to the erosive stratum; record the per-paper erosive / non-erosive composition in covariateData[[DIS_GERD]]$notes. If a future paper contrasts erosive against non-erosive GERD within a GERD-only cohort, register a separate within-cohort severity indicator on the DIS_CMV_RETINITIS pattern rather than widening or re-pointing this entry. Scope: specific because the complement reference category is paper-defined; promote to general if a second paper pools GERD patients with a non-GERD reference.

Treatment-emergent adverse-event indicators

Binary indicators flagging that a participant is currently experiencing a specific treatment-emergent adverse event, used where the event itself perturbs the drug’s pharmacokinetics (most often gastrointestinal events altering oral absorption or bioavailability). Naming follows AE_<EVENT>, where <EVENT> is the MedDRA-style preferred term in upper case.

These are deliberately not registered under the DIS_<CONDITION> family. DIS_* entries describe the participant’s underlying disease or comorbid state – the reason they are in the trial – and are almost always time-fixed. An AE_* entry describes a drug-caused, typically transient and time-varying event occurring during treatment. The distinction matters because both can appear in the same model on the same parameter: Darwish_2025_trofinetide.R carries DIS_RETT (the disease under study) and AE_DIARRHEA (the drug’s most common adverse event) simultaneously, and conflating them would obscure which effect is disease-driven and which is treatment-emergent. Record per model in covariateData[[AE_<EVENT>]]$notes whether the source carried the flag per record (time-varying) or per subject (ever-experienced).

AE_DIARRHEA (canonical for concurrent treatment-emergent diarrhea indicator)

• Description: 1 = participant is experiencing treatment-emergent diarrhea at the time of the record; 0 = no diarrhea. Diarrhea shortens gastrointestinal transit time and reduces the window available for absorption, so it typically enters oral popPK models as a negative effect on bioavailability or a positive effect on the absorption rate constant.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no diarrhea).
• Source aliases:
  • Diar – used in Darwish_2025_trofinetide.R (Darwish 2025 Results, definition of the Diar_I indicator variable in the typical-value equation for F1; the paper explicitly describes it as a time-varying indicator).
  • DIAR – the all-caps form used in the Darwish 2025 Fig. 5 forest-plot panel labels.
• Example models: Darwish_2025_trofinetide.R (proportional shift on oral bioavailability from Darwish 2025 Table 2: fdepot * (1 + e_diarrhea_f * AE_DIARRHEA) with e_diarrhea_f = -0.148, a 14.8% reduction in F1 while diarrhea is present. Diarrhea is trofinetide’s most common adverse event, affecting 52.4% of the Rett syndrome participants in the analysis dataset at some point during the studies, which is why the sponsor carried it as a structural covariate rather than screening it out – founding example).
• Notes: Founding member of the AE_<EVENT> family. Carry the flag per dose record when the source models it as time-varying (the Darwish 2025 form) and per subject only when the source collapses it to an ever-experienced flag; state which in the per-model notes. Distinct from DIS_UC, DIS_DUOD_ULCER, and the other gastrointestinal DIS_* entries, which denote a pre-existing gastrointestinal disease rather than a treatment-emergent event. Ratified canonically alongside the Darwish 2025 trofinetide extraction.

Ophthalmology: geographic atrophy lesion characteristics

Baseline morphology and laterality descriptors of a geographic atrophy (GA) lesion secondary to age-related macular degeneration, as graded on fundus autofluorescence imaging of the study eye. All members are time-fixed per subject and are graded on the study eye; the fellow (non-randomised) eye carries no covariate in the models that use these columns. Distinct from the TUM* oncology lesion-burden family (TUM_SLD, TUMSZ, NTARGET_GE3), which describes solid-tumour burden under RECIST, and from BCVA_ETDRS (a functional visual-acuity score rather than an anatomic lesion descriptor).

DIS_GA_UNILATERAL (canonical for unilateral vs bilateral geographic atrophy indicator)

• Description: 1 = unilateral geographic atrophy at baseline (GA present in the randomised study eye only); 0 = bilateral GA (GA present in both eyes at baseline). Time-fixed per subject. Bilateral GA is an established indicator of accelerated atrophy (Desai 2022 Eye), so the unilateral stratum is the slower-progressing group.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (bilateral GA – the majority stratum). Reference values observed: 81% bilateral in the pooled FILLY + OAKS + DERBY pegcetacoplan population (Crass 2025 Table 1); 19% unilateral.
• Source aliases:
  • UNILATGA – Crass 2025 Data S1 equation notation for the indicator entering INIT_i,study (theta_11) and SLOPE_i,study (theta_12).
  • GA laterality – the Crass 2025 Table 1 demographic row name, reported as the complementary Unilateral / Bilateral pair.
• Example models: Crass_2025_pegcetacoplan_ga_progression.R, Crass_2025_pegcetacoplan_ga_doseresponse.R, Crass_2025_pegcetacoplan_ga_exposureresponse.R (prespecified structural covariate; proportional shifts (1 + e_unilat_rbase_study * DIS_GA_UNILATERAL) on the study-eye initial lesion area and (1 + e_unilat_slope_study * DIS_GA_UNILATERAL) on the study-eye progression rate; in the final PK/PD fit the slope coefficient -0.139 gives the paper’s 0.860-fold lower lesion growth for unilateral GA).
• Notes: General scope – laterality of GA is a routinely collected baseline characteristic in every GA natural-history and interventional trial, so future ophthalmology extractions should reuse this column. Because a subject with unilateral GA has no atrophic fellow eye, joint study-eye / fellow-eye models drop the fellow-eye records for that subject (Crass 2025 excluded fellow-eye observations for patients with unilateral GA at baseline who converted to bilateral disease during the study). The covariate-effect parameter form is e_unilat_<param> (drops the DIS_GA_ prefix per the existing DIS_CANCER -> e_cancer_<param> precedent). Ratified canonically alongside the Crass 2025 pegcetacoplan geographic-atrophy extraction.

DIS_GA_NONSUBFOVEAL (canonical for nonsubfoveal geographic atrophy lesion location indicator)

• Description: 1 = the study-eye GA lesion does not involve the fovea (nonsubfoveal / extrafoveal); 0 = subfoveal involvement. Time-fixed per subject; graded at baseline on fundus autofluorescence. Encoded in the “absence of foveal involvement” direction because that is the direction the source literature reports the effect in (nonsubfoveal lesions progress faster, so the coefficient is positive on the lesion growth rate).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (subfoveal involvement). Reference values observed: 63% subfoveal, 37% nonsubfoveal in the pooled Crass 2025 population (Table 1).
• Source aliases:
  • NOFOV – Crass 2025 Data S1 equation notation (theta_19).
  • GA lesion location – the Crass 2025 Table 1 demographic row name, reported as the complementary Subfoveal / Nonsubfoveal pair. A source paper reporting SUBFOVEAL = 1 for foveal involvement is the value-inverted alias: set DIS_GA_NONSUBFOVEAL = 1 - SUBFOVEAL and flip the sign convention of the effect accordingly.
• Example models: Crass_2025_pegcetacoplan_ga_progression.R, Crass_2025_pegcetacoplan_ga_doseresponse.R, Crass_2025_pegcetacoplan_ga_exposureresponse.R (proportional shift (1 + e_nonsubfov_slope_study * DIS_GA_NONSUBFOVEAL) on the study-eye progression rate only; in the final PK/PD fit the coefficient 0.118 gives the paper’s 1.12-fold higher lesion growth for nonsubfoveal lesions).
• Notes: General scope – foveal involvement is graded in every GA trial because it governs the visual-acuity consequences of atrophy. Registered in the nonsubfoveal-is-1 direction to match the source-paper convention; document the inversion in covariateData[[DIS_GA_NONSUBFOVEAL]]$notes when a future paper reports the complementary column. The covariate-effect parameter form is e_nonsubfov_<param>. Distinct from DIS_GA_UNIFOCAL (lesion multiplicity rather than lesion position). Ratified canonically alongside the Crass 2025 pegcetacoplan geographic-atrophy extraction.

DIS_GA_UNIFOCAL (canonical for unifocal vs multifocal geographic atrophy lesion indicator)

• Description: 1 = unifocal study-eye GA lesion (a single atrophic patch); 0 = multifocal lesion (two or more separate atrophic patches). Time-fixed per subject; graded at baseline on fundus autofluorescence. Multifocal lesions have a larger total perimeter for a given area and progress faster, so the unifocal stratum is the slower-progressing group.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (multifocal). Reference values observed: 70% multifocal, 30% unifocal in the pooled Crass 2025 population (Table 1; one FILLY patient had missing data).
• Source aliases:
  • UNIFOC – Crass 2025 Data S1 equation notation (theta_20).
  • GA focality – the Crass 2025 Table 1 demographic row name, reported as the complementary Unifocal / Multifocal pair.
• Example models: Crass_2025_pegcetacoplan_ga_progression.R, Crass_2025_pegcetacoplan_ga_doseresponse.R, Crass_2025_pegcetacoplan_ga_exposureresponse.R (proportional shift (1 + e_unifocal_slope_study * DIS_GA_UNIFOCAL) on the study-eye progression rate only; in the final PK/PD fit the coefficient -0.164 gives the paper’s 0.837-fold lower lesion growth for unifocal lesions).
• Notes: General scope. GA trial entry criteria commonly impose a minimum patch size for multifocal lesions (Crass 2025 required at least one lesion >= 1.25 mm^2 if GA was multifocal), so the multifocal stratum is not simply “any number of small patches”; record the per-paper entry criterion in covariateData[[DIS_GA_UNIFOCAL]]$notes. The covariate-effect parameter form is e_unifocal_<param>. Ratified canonically alongside the Crass 2025 pegcetacoplan geographic-atrophy extraction.

DRUSEN_GT20 (canonical for baseline drusen burden binarised at 20 intermediate or large drusen groups)

• Description: Binary indicator dichotomising baseline drusen burden in the study eye at 20 groups: 1 = more than 20 intermediate or large drusen groups (individual drusen diameter >= 63 micrometres, the AREDS simplified-severity-scale size threshold of Ferris 2005 AREDS report No. 18); 0 = 20 or fewer. Time-fixed per subject. Drusen are extracellular deposits between the retinal pigment epithelium and Bruch’s membrane and are the defining lesion of early / intermediate AMD; a higher baseline drusen burden is associated with slower subsequent GA lesion growth.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (20 or fewer intermediate or large drusen groups). Reference values observed: 45% of study eyes above the threshold and 55% at or below it in the pooled Crass 2025 population (Table 1; two OAKS patients had missing data).
• Source aliases:
  • MOREDR – Crass 2025 Data S1 equation notation (theta_21).
  • No. of intermediate or large drusen groups – the Crass 2025 Table 1 demographic row name, reported as the complementary <= 20 / > 20 pair.
• Example models: Crass_2025_pegcetacoplan_ga_progression.R, Crass_2025_pegcetacoplan_ga_doseresponse.R, Crass_2025_pegcetacoplan_ga_exposureresponse.R (proportional shift (1 + e_drusen_slope_study * DRUSEN_GT20) on the study-eye progression rate only; in the final PK/PD fit the coefficient -0.130 gives the paper’s 0.869-fold lower lesion growth for the high-drusen-burden stratum).
• Notes: Follows the count-covariate policy (“count covariate -> decomposed binary indicators, not a single integer count”) and the NTARGET_GE3 precedent of binarising a lesion count at a paper-defined threshold. No DIS_ prefix because drusen are an AMD imaging feature that is graded independently of the GA diagnosis, in the same way NTARGET_GE3 records RECIST lesion multiplicity without a disease-state prefix. Source papers supply the already-dichotomised indicator rather than a raw count; if a future paper reports the integer count, derive DRUSEN_GT20 = as.integer(count > 20) and record the raw column as a source alias. If a future paper uses a different split (e.g. > 10 or > 30) or a different size threshold, register a sibling canonical rather than overloading this name. The covariate-effect parameter form is e_drusen_<param>. Ratified canonically alongside the Crass 2025 pegcetacoplan geographic-atrophy extraction.

DIS_CHF_NYHA2 (canonical for NYHA class II heart-failure indicator)

• Description: 1 = subject has mild chronic heart failure classified as New York Heart Association (NYHA) functional class II, 0 = otherwise. Time-fixed per subject. Member of the mutually-exclusive DIS_CHF_NYHA2 / DIS_CHF_NYHA3 / DIS_CHF_NYHA4 trio prescribed by the DIS_CHF entry’s Notes for papers that retain NYHA strata in the final model; all three = 0 selects the non-heart-failure reference. NYHA class I is not given its own indicator because published HF pharmacokinetic cohorts almost never separate it from the healthy reference.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no heart failure, when DIS_CHF_NYHA2, DIS_CHF_NYHA3 and DIS_CHF_NYHA4 are all 0).
• Source aliases:
  • HF-II – used in Gu_2025_*_pbpk.R (Gu 2025 Table 1 and Table S3 heart-failure grade column).
• Example models: Gu_2025_digoxin_pbpk.R.
• Notes: Distinct from DIS_CHF, which is an unstratified compensated-CHF indicator; use DIS_CHF when the source treats heart failure as a single binary and this trio when it resolves NYHA classes. Distinct from the paediatric SCORE_ROSS severity instrument (NYHA is adult-only) and from DIS_HF_OR_LF_SEV (which pools severe heart failure with severe liver failure). The covariate-effect parameter form is e_nyha2_<param>, dropping the DIS_CHF_ prefix per the DIS_CANCER -> e_cancer_<param> precedent. Ratified canonically on 2026-08-19 alongside the Gu 2025 heart-failure PBPK extraction.

DIS_CHF_NYHA3 (canonical for NYHA class III heart-failure indicator)

• Description: 1 = subject has moderate chronic heart failure classified as New York Heart Association (NYHA) functional class III, 0 = otherwise. Time-fixed per subject. Member of the mutually-exclusive DIS_CHF_NYHA2 / DIS_CHF_NYHA3 / DIS_CHF_NYHA4 trio prescribed by the DIS_CHF entry’s Notes for papers that retain NYHA strata in the final model; all three = 0 selects the non-heart-failure reference. NYHA class I is not given its own indicator because published HF pharmacokinetic cohorts almost never separate it from the healthy reference.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no heart failure, when DIS_CHF_NYHA2, DIS_CHF_NYHA3 and DIS_CHF_NYHA4 are all 0).
• Source aliases:
  • HF-III – used in Gu_2025_*_pbpk.R (Gu 2025 Table 1 and Table S3 heart-failure grade column).
• Example models: Gu_2025_digoxin_pbpk.R.
• Notes: Distinct from DIS_CHF, which is an unstratified compensated-CHF indicator; use DIS_CHF when the source treats heart failure as a single binary and this trio when it resolves NYHA classes. Distinct from the paediatric SCORE_ROSS severity instrument (NYHA is adult-only) and from DIS_HF_OR_LF_SEV (which pools severe heart failure with severe liver failure). The covariate-effect parameter form is e_nyha3_<param>, dropping the DIS_CHF_ prefix per the DIS_CANCER -> e_cancer_<param> precedent. Ratified canonically on 2026-08-19 alongside the Gu 2025 heart-failure PBPK extraction.

DIS_CHF_NYHA4 (canonical for NYHA class IV heart-failure indicator)

• Description: 1 = subject has severe chronic heart failure classified as New York Heart Association (NYHA) functional class IV, 0 = otherwise. Time-fixed per subject. Member of the mutually-exclusive DIS_CHF_NYHA2 / DIS_CHF_NYHA3 / DIS_CHF_NYHA4 trio prescribed by the DIS_CHF entry’s Notes for papers that retain NYHA strata in the final model; all three = 0 selects the non-heart-failure reference. NYHA class I is not given its own indicator because published HF pharmacokinetic cohorts almost never separate it from the healthy reference.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no heart failure, when DIS_CHF_NYHA2, DIS_CHF_NYHA3 and DIS_CHF_NYHA4 are all 0).
• Source aliases:
  • HF-IV – used in Gu_2025_*_pbpk.R (Gu 2025 Table 1 and Table S3 heart-failure grade column).
• Example models: Gu_2025_digoxin_pbpk.R.
• Notes: Distinct from DIS_CHF, which is an unstratified compensated-CHF indicator; use DIS_CHF when the source treats heart failure as a single binary and this trio when it resolves NYHA classes. Distinct from the paediatric SCORE_ROSS severity instrument (NYHA is adult-only) and from DIS_HF_OR_LF_SEV (which pools severe heart failure with severe liver failure). The covariate-effect parameter form is e_nyha4_<param>, dropping the DIS_CHF_ prefix per the DIS_CANCER -> e_cancer_<param> precedent. Ratified canonically on 2026-08-19 alongside the Gu 2025 heart-failure PBPK extraction.

Epilepsy baseline seizure-severity indicators

Baseline seizure-severity indicators derived from a pre-trial seizure count (typically the number of seizures in the 3 months before study entry, decomposed by the source paper into severity bins). The count itself is decomposed into a set of mutually-exclusive binary indicators per the standing operator policy for count covariates (“count covariate -> decomposed binary indicators, not a single integer count”). Members are jointly restricted to at most one being = 1 for any subject; the reference category is 2-6 seizures in the previous 3 months (all indicators = 0 selects the reference).

NSP3M_LT2 (canonical for baseline seizure count fewer than 2 in the previous 3 months)

• Description: Binary indicator that the subject reported fewer than 2 seizures in the 3 months before trial start (baseline period). 1 = <2 baseline seizures (the low-severity bin), 0 = otherwise. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (2-6 baseline seizures per the Lindauer 2017 NSP3M decomposition; the reference bin). Subjects in the 7-50 or >50 bins have NSP3M_LT2 = 0 and are identified by NSP3M_7_50 = 1 or NSP3M_GT50 = 1 instead.
• Source aliases:
  • NSP3M (categorical, the <2 bin) – used in Lindauer_2017_lacosamide_seizure.R (Lindauer 2017 Section 2.5 splits NSP3M into four categories: <2, 2-6, 7-50, >50, with 2-6 as reference; the low-severity bin is registered here as NSP3M_LT2).
• Example models: Lindauer_2017_lacosamide_seizure.R (log-hazard shifts on both first-seizure and subsequent-seizure Weibull scale: e_nsp3m_lt2_1st = -1.12 (first event), e_nsp3m_lt2_2nd = -1.37 (subsequent events); the associated hazard ratios vs the 2-6 reference are 0.58 and 0.38 per Lindauer 2017 Table 4).
• Notes: Specific scope because the concept is tied to the pre-trial-seizure-count decomposition used in the SP0993 / N01061 monotherapy epilepsy trials (Lindauer 2017 references Abrantes et al. for the two-Weibull-sub-model approach with NSP3M-derived binary categories). Sibling canonicals NSP3M_7_50 and NSP3M_GT50 complete the 4-level decomposition. Data assemblers must enforce mutual exclusivity: at most one of {NSP3M_LT2, NSP3M_7_50, NSP3M_GT50} is 1 for each subject; if all three are 0 the subject belongs to the reference 2-6 bin. The covariate-effect parameter form is e_nsp3m_lt2_<param> (drops _LT2 structure into the coefficient name). Ratified canonically on 2026-07-03 alongside the Lindauer 2017 lacosamide time-to-seizure extraction.

NSP3M_7_50 (canonical for baseline seizure count 7 to 50 in the previous 3 months)

• Description: Binary indicator that the subject reported 7 to 50 seizures in the 3 months before trial start (baseline period). 1 = 7-50 baseline seizures (the moderate-high-severity bin), 0 = otherwise. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (2-6 baseline seizures per the Lindauer 2017 NSP3M decomposition; the reference bin).
• Source aliases:
  • NSP3M (categorical, the 7-50 bin) – used in Lindauer_2017_lacosamide_seizure.R.
• Example models: Lindauer_2017_lacosamide_seizure.R (log-hazard shifts on both first-seizure and subsequent-seizure Weibull scale: e_nsp3m_7_50_1st = +1.94 (first event), e_nsp3m_7_50_2nd = +1.36 (subsequent events); the associated hazard ratios vs the 2-6 reference are 2.60 and 2.63 per Lindauer 2017 Table 4).
• Notes: Specific scope. Sibling of NSP3M_LT2 and NSP3M_GT50 in the 4-level NSP3M decomposition; see NSP3M_LT2 notes for the mutual-exclusivity rule and covariate-effect naming. The Lindauer 2017 Table 4 hazard ratio of 2.60 (90% CI 2.02-3.31) for the first-seizure hazard was highlighted in the paper Abstract as the primary NSP3M-severity finding. Ratified canonically on 2026-07-03 alongside the Lindauer 2017 lacosamide time-to-seizure extraction.

NSP3M_GT50 (canonical for baseline seizure count greater than 50 in the previous 3 months)

• Description: Binary indicator that the subject reported more than 50 seizures in the 3 months before trial start (baseline period). 1 = >50 baseline seizures (the highest-severity bin), 0 = otherwise. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (2-6 baseline seizures per the Lindauer 2017 NSP3M decomposition; the reference bin).
• Source aliases:
  • NSP3M (categorical, the >50 bin) – used in Lindauer_2017_lacosamide_seizure.R.
• Example models: Lindauer_2017_lacosamide_seizure.R (log-hazard shifts on both first-seizure and subsequent-seizure Weibull scale: e_nsp3m_gt50_1st = +3.30 (first event), e_nsp3m_gt50_2nd = +2.53 (subsequent events); the associated hazard ratios vs the 2-6 reference are 5.09 and 6.09 per Lindauer 2017 Table 4).
• Notes: Specific scope. Sibling of NSP3M_LT2 and NSP3M_7_50 in the 4-level NSP3M decomposition; see NSP3M_LT2 notes for the mutual-exclusivity rule and covariate-effect naming. Ratified canonically on 2026-07-03 alongside the Lindauer 2017 lacosamide time-to-seizure extraction.

Pulmonary / lung-disease biomarkers

FEV1 (canonical for forced expiratory volume in 1 second)

• Description: Baseline forced expiratory volume in 1 second (FEV1) reported as an absolute volume in litres. Pulmonary-function spirometry endpoint; reflects large-airway airflow obstruction and is a standard covariate in chronic obstructive pulmonary disease, alpha-1 antitrypsin deficiency, asthma, and cystic-fibrosis disease-progression analyses. Distinct from FEV1 percent-predicted (the % predicted value standardises absolute FEV1 by reference equations from sex / age / height / ethnicity); use this canonical only for the absolute-litre value supplied as a covariate column.
• Units: L (absolute volume; document the exhalation-effort standard the source paper cites in covariateData[[FEV1]]$notes if non-default – ATS / ERS post-bronchodilator is the typical convention).
• Type: continuous
• Scope: specific
• Reference category: n/a – used with linear-deviation forms (theta + e_fev1_param * (FEV1 - ref)) or power forms (FEV1 / ref)^exponent. Reference values observed: 1.6 L (Tortorici 2017, population median across the RAPID-RCT/RAPID-OLE A1-PI augmentation cohort).
• Source aliases: none yet; canonical name preferred. Source papers typically use the abbreviation FEV1 directly.
• Example models: Tortorici_2017_a1pi.R (linear-deviation effect on the lung-density decline rate: theta5 * (FEV1 - 1.6) with theta5 = +0.56 (g/L/year per L FEV1); lower-FEV1 patients have steeper natural decline rates independent of A1-PI exposure).
• Notes: Distinct from FEV1 percent-predicted (which is a derived ratio with the reference-equation denominator built in; FEV1% is the outcome variable in Harun_2019_cysticFibrosis.R rather than a covariate column). Use FEV1 only when the source paper supplies the absolute-volume value; if the source supplies a percent-predicted value as a covariate, the canonical for that surface is FEV1_PCTPRED. Scope: specific until a second model ratifies the absolute-litre semantics; promote to general at that point. Ratified canonically on 2026-05-09 alongside the Tortorici 2017 extraction.

FEV1_PCTPRED (canonical for baseline FEV1 as percent of the predicted value)

• Description: Baseline forced expiratory volume in 1 second expressed as a percent of the sex / age / height / ethnicity reference-equation predicted value (FEV1% predicted). The percent-predicted scaling normalises the absolute-litre FEV1 measurement against a healthy-reference standard so a single covariate value is comparable across patient ages and body sizes. Standard pulmonary-function covariate in cystic-fibrosis disease-severity and inhaled-therapy popPK analyses. Time-fixed at study entry / baseline.
• Units: % predicted (numeric percentage, e.g. 62.1 for the Ting 2014 population median; not a fraction 0.621).
• Type: continuous
• Scope: specific
• Reference category: n/a – used with power-form effects (FEV1_PCTPRED / ref)^exponent or linear-deviation forms (1 + e * (FEV1_PCTPRED - ref)). Reference values observed: 62.1 % (Ting 2014, population median across the combined three-study cystic-fibrosis cohort).
• Source aliases:
  • FEV1% predicted – used in Ting_2014_tobramycin_inhaled.R (paper Table 1 / Table 2). Free-text label, not a typical NMTRAN column header; downstream data sets should use the canonical column name FEV1_PCTPRED.
• Example models: Ting_2014_tobramycin_inhaled.R (power-form effect on apparent central volume of distribution: (FEV1_PCTPRED / 62.1)^-0.303; lower lung-function patients have larger apparent Vd/F, consistent with the paper’s hypothesis that worsening lung disease increases central-airway aerosol deposition).
• Notes: Distinct from absolute-litre FEV1 (canonical for the unstandardised volume) and from FEV1 as an outcome variable in disease-progression models (e.g. Harun_2019_cysticFibrosis.R, where percent-predicted FEV1 is the dependent variable rather than a covariate). The reference equation used to derive the percent-predicted value is paper-specific; document any non-default reference standard (Hankinson 1999 NHANES III, GLI 2012, Wang 1993, etc.) in covariateData[[FEV1_PCTPRED]]$notes per model. Scope: specific until a second model ratifies the percent-predicted semantics; promote to general at that point. Ratified canonically on 2026-05-12 alongside the Ting 2014 extraction.

A1PI (canonical for serum alpha-1 proteinase inhibitor concentration)

• Description: Baseline serum alpha-1 proteinase inhibitor (A1-PI; also known as alpha-1 antitrypsin, AAT) concentration. Used in alpha-1 antitrypsin deficiency (AATD) augmentation-therapy modelling as a per-subject pre-treatment exposure covariate (the subject’s endogenous A1-PI level at study entry, before any augmentation infusions). Time-fixed per subject. Distinct from a time-course of A1-PI used as a state variable – when the source paper carries A1-PI as the dynamic dependent variable (the augmentation model’s PD output), use Cc rather than A1PI.
• Units: umol/L (typical SI-convention reporting; also reported as mg/dL in US-convention papers – document the unit used in each model via covariateData[[A1PI]]$units). Conversion: 1 umol/L A1-PI ~= 5.2 mg/dL (using MW ~52 kDa). The 11 umol/L “putative protective threshold” used clinically corresponds to ~57 mg/dL.
• Type: continuous
• Scope: specific
• Reference category: n/a – used with power-form effects (A1PI / ref)^exponent on the post-treatment exposure intercept and slope. Reference values observed: 5.5 umol/L (Tortorici 2017, approximate median pre-treatment A1-PI among RAPID-RCT placebo-randomised patients, used as the normalisation denominator for power-form covariate effects).
• Source aliases: Cbase – used in Tortorici 2017’s published equation 6 to denote the baseline pre-treatment A1-PI value; the column name in the modelled dataset would be A1PI.
• Example models: Tortorici_2017_a1pi.R (two power-form effects: (A1PI/5.5)^theta5 with theta5 = +0.73 on the placebo-arm post-treatment exposure intercept, and (A1PI/5.5)^theta4 with theta4 = -0.12 on the dose-rate slope; together they encode the modest dose-exposure dependence on each subject’s endogenous A1-PI level).
• Notes: AATD enrolment criteria typically restrict A1PI to <= 11 umol/L (severe deficiency); reference / heterozygous PI*MZ phenotypes have higher levels. Document the source paper’s AATD-genotype enrolment criteria in covariateData[[A1PI]]$notes per model. Specific scope because the canonical is tied to AATD augmentation-therapy modelling; future PK / PD analyses of A1-PI (or AAT) in non-AATD contexts (acute-phase response, smoking-induced inflammation) should ratify general scope at that time. Ratified canonically on 2026-05-09 alongside the Tortorici 2017 extraction.

Cystic fibrosis lung-disease indicators

AIR_TRAP_5Y (canonical for severe air trapping on chest HRCT scan at age 5 years)

• Description: 1 = subject had a non-zero “air trapping” component score on the validated Brody-II chest high-resolution computed tomography (HRCT) scan performed at age 5 years; 0 = air trapping component score of 0 (absent). Time-fixed per subject (the indicator captures the single end-of-study HRCT performed at age 5 in the Australasian Cystic Fibrosis Bronchoalveolar Lavage (ACFBAL) study). The Brody-II scoring system reports air trapping as a percentage of maximum possible HRCT score; the binary “present vs absent” dichotomy follows the same convention as Rosenow 2015 (Am J Respir Crit Care Med 191:1158-65).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no air trapping detected on the age-5 HRCT scan).
• Source aliases:
  • ATS5C – used in Harun_2019_cysticFibrosis.R (Harun 2019 NMTRAN $INPUT column, “presence of air trapping at age 5”; values 0 = absent, 1 = present).
• Example models: Harun_2019_cysticFibrosis.R (linear-deviation effect on baseline FEV1% predicted at age 5: (1 + e_at_baseline * AIR_TRAP_5Y) with coefficient -0.0417, i.e., subjects with severe air trapping at age 5 have a baseline FEV1% predicted approximately 4.17% lower than those without).
• Notes: Specific scope because the indicator is tied to the ACFBAL study’s standardised HRCT-at-age-5 protocol and the Brody-II scoring rubric; future paediatric CF lung-disease studies that score HRCT at a different age or use a different scoring system should register a separate canonical (AIR_TRAP_8Y, AIR_TRAP_PRAGMA, etc.) rather than overload this name. Ratified canonically on 2026-05-08 alongside the Harun 2019 extraction.

HOSPRA (canonical for hospitalisation due to a pulmonary exacerbation)

• Description: Time-varying binary indicator of inpatient hospitalisation for management of a pulmonary exacerbation at the time of the FEV1% predicted measurement: 1 = subject is hospitalised because of a pulmonary exacerbation when the spirometry value is recorded, 0 = not hospitalised at that visit. Used as a per-visit covariate in disease-progression models of FEV1% decline in cystic fibrosis where pulmonary exacerbations are tracked from the Australian Cystic Fibrosis Data Registry (ACFDR) inpatient records.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (not hospitalised for a pulmonary exacerbation at the time of the FEV1% measurement).
• Source aliases:
  • HOSPRA – used in Harun_2019_cysticFibrosis.R (Harun 2019 NMTRAN $INPUT column, “hospitalisation at the time of FEV1% predicted measurement; 0=no, 1=yes”).
• Example models: Harun_2019_cysticFibrosis.R (linear-deviation effects on the disease-progression maximum drop and on the half-effect age: (1 + e_hpe_dmax * HOSPRA) with coefficient -0.22 on the maximum FEV1% drop and (1 + e_hpe_t50max * HOSPRA) with coefficient -0.235 on the age at which 50% of the maximum drop occurs; hospitalised visits accelerate both the magnitude and the onset of FEV1% decline).
• Notes: Specific scope because the canonical encoding pools all pulmonary exacerbation-driven hospitalisations into a single binary regardless of severity, duration, or treatment intensity; future CF / chronic-respiratory-disease studies that need to distinguish exacerbation severity (e.g., requiring intravenous antibiotics vs oral) should register a finer-grained canonical. Ratified canonically on 2026-05-08 alongside the Harun 2019 extraction.

Infectious disease

LNPC (canonical for log-transformed admission Plasmodium parasitaemia)

• Description: Natural logarithm of the asexual Plasmodium parasite count (parasites per microlitre of blood) at study admission. Time-fixed per subject (one value per subject, captured at enrolment).
• Units: log(parasites/uL)
• Type: continuous
• Scope: specific
• Reference category: n/a – used with linear-deviation forms (1 + e * (LNPC - ref)). Reference values observed: 5.88 log(parasites/uL) (Birgersson 2019, population median in the pooled pregnant + non-pregnant Burkina Faso cohort).
• Source aliases: none formally; companion column PARA (raw asexual parasite count per microlitre) is provided alongside LNPC in the Birgersson 2019 NONMEM dataset but the model uses LNPC = log(PARA) as the active covariate.
• Example models: Birgersson_2019_artesunate.R (linear-deviation effect on relative bioavailability F1: F1LNPC = 1 + e_lnpc_f * (LNPC - 5.88); positive coefficient e_lnpc_f = +0.138 per unit increase in log-parasite-count, reflecting increased oral artesunate bioavailability with higher parasite burden).
• Notes: Disease-severity covariate specific to malaria PK models. Higher parasitaemia is a marker of more severe acute malaria infection and has been associated in the source publication with altered oral bioavailability of artesunate (presumably via gut-mucosal / first-pass effects of the febrile parasitised state). Scope: specific because the canonical reference value (5.88) is the Birgersson 2019 cohort median; future malaria-in-pregnancy or malaria-in-children PK models may legitimately reuse LNPC but should document their own cohort-specific reference value in covariateData[[LNPC]]$notes. Distinct from PARA (raw parasitaemia in parasites/uL), which is the companion canonical for models that apply the log transform inside model() rather than pre-computing it at dataset-assembly time, and that use a time-varying (last-observation-carried-forward) parasitaemia trajectory rather than the admission-only fixed value. Ratified canonically on 2026-05-07.

PARA (canonical for raw Plasmodium parasitaemia (parasites/uL))

• Description: Asexual Plasmodium parasite count per microlitre of blood. May be admission-only (time-fixed) or time-varying with serial counts across follow-up (last-observation-carried-forward is the typical NONMEM idiom in this lab’s malaria models). Document time-varying vs time-fixed mode in covariateData[[PARA]]$notes per model.
• Units: parasites/uL
• Type: continuous
• Scope: specific
• Reference category: n/a – used with log-transformed linear forms (1 + e * log10(PARA)) or (1 + e * (log10(PARA) - ref)) when the source paper applies the log transform inside the model. Reference values observed: implicit log10(PARA) = 0 (i.e. PARA = 1 parasite/uL) in Kloprogge 2014 (the linear form anchors F at the typical population estimate when PARA = 1).
• Source aliases:
  • PARA – used in Kloprogge_2014_quinine.R (raw parasitaemia in parasites/uL, time-varying via last-observation-carried-forward across the 7-day quinine treatment course), in Kloprogge_2018_lumefantrine.R (admission-only / time-fixed parasitaemia, centered at the model-building geometric-mean 15,800 parasites/uL = log10 4.2), in Tarning_2012_dihydroartemisinin.R (admission-only / time-fixed parasitaemia, centered at the typical-patient log10(PARA) = 3.98 reported in the Table 4 footnote of Tarning 2012 AAC), and in Hien_2017_cipargamin.R (admission-only / time-fixed parasitaemia in parasites/uL, used to seed the initial-condition split of the two-population parasite clearance PD model into sensitive and refractory subpools).
• Example models: Kloprogge_2014_quinine.R (linear effect on relative bioavailability with the log10 transform applied inside model(): F1PARA = 1 + e_para_f * log10(max(PARA, 1)) with e_para_f = +0.389 per log10 parasitaemia; gated by max(PARA, 1) so that PARA values below 1 parasite/uL (effectively zero / below detection) collapse to no covariate effect, matching the source paper’s “effect only during the acute phase when parasitaemia was above the limit of detection” wording).
  • Kloprogge_2014_quinine.R – linear effect on relative bioavailability with the log10 transform applied inside model(): F1PARA = 1 + e_para_f * log10(max(PARA, 1)) with e_para_f = +0.389 per log10 parasitaemia; gated by max(PARA, 1) so that PARA values below 1 parasite/uL (effectively zero / below detection) collapse to no covariate effect, matching the source paper’s “effect only during the acute phase when parasitaemia was above the limit of detection” wording.
  • Kloprogge_2018_lumefantrine.R – exponential effect on relative bioavailability with the log10 transform applied inside model(), centered on log10(15,800) = 4.2: F1PARA = exp(e_lnpc_f * (log10(max(PARA, 1)) - 4.2)) with e_lnpc_f = -0.643 per log10 parasitaemia (higher pre-treatment parasitaemia is associated with lower relative bioavailability, consistent with reduced visceral blood flow in more severe disease). Same max(PARA, 1) gating convention as Kloprogge 2014.
  • Tarning_2012_dihydroartemisinin.R – linear-deviation effect on relative bioavailability with the log10 transform applied inside model(), centered on log10(PARA) = 3.98 (the pooled-cohort median in the Thai-Myanmar-border dihydroartemisinin-piperaquine combination trial): fpara = 1 + e_para_f * (log10(max(PARA, 1)) - 3.98) with e_para_f = +0.278 per log10 unit (Results: ‘27.8% linear increase per unit logarithmic parasitemia’). Higher pre-treatment parasitaemia is associated with higher relative bioavailability of dihydroartemisinin (Tarning 2012 Discussion: ‘…consistent with a disease-related decrease in first-pass metabolism possibly compounded by reduced hepatic blood flow…’). Same max(PARA, 1) gating convention as the Kloprogge models; admission-only / time-fixed.
  • Hien_2017_cipargamin.R – admission-only / time-fixed parasitaemia in parasites/uL (enrolment inclusion criterion 5,000-50,000 asexual parasites/uL per Hien 2017 Methods ‘Patients’). Not used as a bioavailability covariate; instead PARA seeds the parasite ODE compartments at simulation start: parasite_sensitive(0) = fsen * PARA and parasite_refractory(0) = (1 - fsen) * PARA. The ODE states carry the same parasites/uL units as PARA (no total-burden conversion applied inside the model), matching the Fig 3 individually-predicted-clearance-profile reporting scale.
• Notes: Companion to LNPC – both canonicals describe the same biological quantity (asexual Plasmodium parasite count) but differ in the transform location: LNPC is pre-transformed to natural log at dataset-assembly time and used as (LNPC - ref), whereas PARA is the raw count with the log transform applied inside model(). Use PARA when the source paper reports the covariate coefficient on the log10 scale (per log10 parasitaemia) and / or when parasitaemia is time-varying. Use LNPC when the source dataset pre-computes the natural log at admission. Scope: specific because the gating-at-1 convention and the log10-inside-model() idiom reflect the Mahidol-Oxford malaria popPK lab’s specific implementation; a future malaria PK model that uses a different gating threshold (e.g., LOQ-aware, or log base-e inside the model) or a different reference value should document its own convention in covariateData[[PARA]]$notes. Ratified canonically on 2026-05-21 alongside the Kloprogge 2014 quinine extraction; extended to Kloprogge 2018 lumefantrine on 2026-05-22 (same log10-inside-model() idiom, distinct centering value: 4.2 = log10(15,800) for Kloprogge 2018 vs 0 = log10(1) for Kloprogge 2014); extended to Tarning 2012 dihydroartemisinin on 2026-06-01 (same log10-inside-model() idiom with the source paper’s typical-patient centering value 3.98 = log10(pooled-cohort-median ~ 9,550 parasites/uL)).

SARS_VLOAD (canonical for SARS-CoV-2 baseline viral load)

• Description: Baseline (pre-treatment) SARS-CoV-2 viral load measured from nasopharyngeal swab by RT-qPCR, reported as log10 RNA copies/mL.
• Units: log10 copies/mL
• Type: continuous
• Scope: specific
• Reference category: n/a – used with power scaling (SARS_VLOAD / ref)^exponent. Reference values observed: 6.4 log10 copies/mL (Lin 2024, median in pooled COVID-19 cohort).
• Source aliases:
  • VIRAL – used in Lin_2024_casirivimab.R.
• Example models: Lin_2024_casirivimab.R (small negative exponent -0.0075 on CL).
• Notes: SARS-CoV-2-specific. For non-infected subjects, the value is encoded as 0 in the source dataset (below assay detection); the population-PK exponent is small enough that this 0 is absorbed by the reference shift. Register a parallel canonical for any future paper that uses a different infection (e.g., RSV, influenza).

HEV_VLOAD (canonical for hepatitis E virus baseline viral load)

• Description: Pre-treatment (per-subject anchor-time) hepatitis E virus (HEV) RNA concentration in plasma, reported by RT-qPCR. Used as a static per-subject covariate that supplies the initial condition for a virion-state ODE in target-cell-limited HEV viral-dynamics models (virus(0) <- HEV_VLOAD) and that feeds the steady-state derivation of the infection-rate and virion-production-rate constants (beta = kloss * rho / HEV_VLOAD, p = elim * HEV_VLOAD / rho) so that all three viral compartments (healthy hepatocytes, infected hepatocytes, virions) start at pre-treatment steady state.
• Units: IU/mL (international units per millilitre; the WHO-standardized HEV-RNA reporting unit since the 2011 first WHO international standard for HEV RNA, NIBSC code 6329/10).
• Type: continuous
• Scope: specific
• Reference category: n/a – subject-level baseline supplied as a covariate column. Reference values observed: 1.886e6 IU/mL (Mulder 2025 chronic-HEV SOT cohort median; range 527-1.68e8 IU/mL).
• Source aliases:
  • VLBASE – used in Mulder_2025_ribavirin.R.
• Example models: Mulder_2025_ribavirin.R (IU/mL; initial condition for the virus compartment; used in the baseline steady-state derivations for the infection-rate beta and virion-production rate p; cohort median 1,886,058 IU/mL).
• Notes: HEV-specific (parallel to SARS_VLOAD for SARS-CoV-2 and to viral-load canonicals for HCV / HBV / HIV that should be registered separately when first encountered). The IU/mL reporting convention is the WHO-standardized one for HEV; older HEV literature may report viral load in genomic copies/mL with a study-specific copies-to-IU conversion factor (roughly 1 IU ~ 0.3-2.5 copies depending on assay). The Mulder 2025 dataset is chronically infected subjects only (no zero baselines); papers that pool infected and non-infected subjects should encode the non-infected value as the assay LOD and document the convention in covariateData[[HEV_VLOAD]]$notes. Specific scope; promote to general only if a second paper ratifies the same HEV-RNA-baseline-as-initial-condition pattern.

SARS_SEROPOS (canonical for SARS-CoV-2 baseline serostatus positive)

• Description: 1 = SARS-CoV-2 spike or nucleocapsid antibody positive at baseline (prior infection or prior vaccination), 0 = seronegative or other / unknown.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (seronegative; “Other” / unknown serostatus is typically pooled into the reference per the source paper’s analysis plan).
• Source aliases:
  • SERPOS – used in Lin_2024_casirivimab.R.
• Example models: Lin_2024_casirivimab.R (multiplicative fractional change on CL).
• Notes: SARS-CoV-2-specific. The exact assay (anti-spike vs anti-nucleocapsid; vendor) varies by study; document per-model in covariateData[[SARS_SEROPOS]]$notes.

OXYSUP_LOW (canonical for low-flow supplemental oxygen indicator)

• Description: 1 = subject is receiving low-flow supplemental oxygen at baseline (e.g., nasal cannula, simple face mask), 0 = no supplemental oxygen at baseline.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no supplemental oxygen at baseline; the high-flow / mechanical-ventilation categories are encoded by the parallel OXYSUP_HIGH indicator).
• Source aliases:
  • OXYSTAT1 – used in Lin_2024_casirivimab.R.
• Example models: Lin_2024_casirivimab.R (multiplicative fractional change on CL; +10.6%).
• Notes: Decomposed indicator from a 4-level ordered categorical (no oxygen / low-flow / high-flow / mechanical ventilation). Use with the parallel OXYSUP_HIGH indicator. Register a separate OXYSUP_VENT canonical if a future analysis splits mechanical ventilation from high-flow oxygen.

OXYSUP_HIGH (canonical for high-flow supplemental oxygen indicator)

• Description: 1 = subject is receiving high-flow supplemental oxygen at baseline (high-flow nasal cannula, non-rebreather mask, non-invasive positive-pressure ventilation, OR mechanical ventilation pooled into the high-flow category), 0 = otherwise (no supplemental oxygen or low-flow).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no supplemental oxygen at baseline; document whether the source paper pooled mechanical ventilation into this indicator or treated it separately).
• Source aliases:
  • OXYSTAT2 – used in Lin_2024_casirivimab.R.
• Example models: Lin_2024_casirivimab.R (multiplicative fractional change on CL; +38.0%).
• Notes: Companion indicator to OXYSUP_LOW. In Lin 2024 the rare mechanical-ventilation cases were pooled into the high-flow indicator (n = 24 across the 7598-subject dataset).

HIV_POS (canonical for HIV-positive comorbidity indicator)

• Description: 1 = HIV-1 antibody positive at study entry, 0 = HIV-negative. Time-fixed per subject. Used as a binary comorbidity indicator on PK parameters (typically bioavailability or clearance) when a study population pools HIV-positive and HIV-negative subjects on a non-HIV primary indication (tuberculosis treatment, hepatitis treatment, etc.).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (HIV-negative).
• Source aliases:
  • HIV – used in Jonsson_2011_ethambutol.R (DDMODEL00000220 NMTRAN $INPUT column with values 0 = HIV negative, 1 = HIV positive; same orientation as the canonical).
• Example models: Jonsson_2011_ethambutol.R (multiplicative 1 + e_hiv_pos_f * HIV_POS shift on bioavailability; HIV-positive patients exhibit a 15.5% reduction in ethambutol bioavailability versus HIV-negative reference), Jonsson_2011_ethambutol_ddmore.R (multiplicative 1 + e_hiv_pos_f * HIV_POS shift on bioavailability; HIV-positive patients exhibit a 15.4% reduction in ethambutol bioavailability versus HIV-negative reference).
• Notes: Parallels the _POS suffix convention used by ADA_POS, SARS_SEROPOS, and other serostatus / antibody-positivity indicators. Distinct from a primary disease-state indicator like DIS_HIV (not yet registered) – HIV_POS is a comorbidity flag in non-HIV-primary indications where HIV-vs-non-HIV is tested as a PK covariate. Ratified canonically on 2026-05-06.

TB_POS (canonical for active tuberculosis co-infection indicator)

• Description: 1 = active tuberculosis (typically pulmonary) at study entry, 0 = no active TB. Time-fixed per subject. Used as a binary comorbidity indicator on PK or PD parameters (typically albumin secretion, hepatic clearance, bioavailability, or disease-progression rates) when a study population pools TB-positive and TB-negative subjects on a non-TB primary indication (HIV ART, hepatitis treatment, malnutrition, etc.) or when a TB-cohort study pools TB subjects against a separately enrolled non-TB comparator group.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no active TB).
• Source aliases:
  • TB – used in Bisaso_2014_albumin.R (paper text and Table 1 stratification column with values 0 = HIV only, 1 = HIV + TB co-infection; same orientation as the canonical).
• Example models: Bisaso_2014_albumin.R (multiplicative additive shift on baseline albumin secretion rate Q0: Q0 = exp(lq0) * (1 + e_tb_pos_q0 * TB_POS) with e_tb_pos_q0 = -0.308; TB-positive subjects have ~30.8% lower Q0 than the HIV-only reference – equivalent to the paper text’s “44.2% lower” framing relative to the TB-HIV cohort).
• Notes: Parallels the _POS suffix convention used by HIV_POS, ADA_POS, SARS_SEROPOS, and other serostatus / disease-state indicators. Distinct from any TB-treatment-regimen indicator (e.g. CONMED_RIF_LPVR4 for concomitant rifampicin) – TB_POS is the active-disease flag; the medication exposure is a separate concept. In Bisaso 2014 all 158 TB-positive subjects were also on rifampicin-based anti-TB therapy, so the two are confounded in that single cohort; the canonical preserves the conceptual distinction for future studies that decouple them. Ratified canonically on 2026-05-20 alongside the Bisaso 2014 albumin extraction.

HCV_POS (canonical for HCV coinfection / hepatitis C virus positive indicator)

• Description: 1 = chronic hepatitis C virus (HCV) coinfection at study entry (HCV antibody positive and/or >= 2 positive HCV RNA detections on separate visits at least 3 months apart, with subjects of known spontaneous HCV clearance excluded), 0 = HCV uninfected / HCV cleared. Time-fixed per subject. Used as a binary comorbidity / coinfection indicator on PD parameters (e.g., immune-reconstitution recovery rates) or PK parameters when a study pools HCV-coinfected and HCV-uninfected subjects on a non-HCV primary indication (HIV ART, transplant medicine, IBD biologics, etc.).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (HCV uninfected).
• Source aliases:
  • HCV – used in Majekodunmi_2017_HIV_HCV_CD4_recovery.R (paper Methods ‘Definitions’ and Table 2 covariate ‘C:Coinf’ for HIV/HCV coinfected vs HIV monoinfected; same orientation as the canonical).
• Example models: Majekodunmi_2017_HIV_HCV_CD4_recovery.R (multiplicative fractional reduction on the CD4 z-score recovery-rate constant c: c = (c_pop + etac) * (1 + e_hcv_pos_c * HCV_POS) with e_hcv_pos_c = -0.77; HCV-coinfected children recover at 23% of the HIV-monoinfected rate – 0.357 /year versus 1.55 /year typical).
• Notes: Parallels the _POS suffix convention used by HIV_POS, TB_POS, ADA_POS, SARS_SEROPOS, and other serostatus / disease-state indicators. Distinct from any anti-HCV treatment-regimen indicator (e.g., pegylated interferon + ribavirin in Majekodunmi 2017’s coinfected subset of 10 children) and from any HCV-genotype indicator (1/2/3/4 distribution reported in Majekodunmi 2017 Table 1 but not used as a covariate). Distinct from a primary disease-state indicator like DIS_HCV (not yet registered) – HCV_POS is the coinfection / comorbidity flag in non-HCV-primary indications. Ratified canonically on 2026-05-22 alongside the Majekodunmi 2017 CD4 recovery extraction.

EARLY_ART (canonical for early-vs-delayed antiretroviral-treatment-initiation arm indicator)

• Description: Trial randomization-arm indicator: 1 = subject was randomized to initiate antiretroviral treatment (ART) early (within the first 14 days of admission, before nutritional recovery), 0 = subject was randomized to delayed ART initiation (after nutritional recovery, > 14 days from admission). Time-fixed per subject within the trial. The indicator captures the early-vs-delayed-ART contrast tested in the Archary 2019 / MATCH (Malnutrition and ART Timing in Children with HIV) trial in severely malnourished HIV-infected children; the early-ART arm exhibits ~31% higher abacavir bioavailability than the delayed-ART arm.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (delayed ART initiation; F = 1 typical-value reference).
• Source aliases:
  • EARLY – Archary 2019 source description (“randomized to early ART”); same orientation as the canonical (1 = early arm).
• Example models: Archary_2019_abacavir.R (multiplicative additive shift on bioavailability F: f(depot) <- (1 + e_earlyart_f * EARLY_ART) * exp(etalfdepot) with e_earlyart_f = 0.31; the early-arm IIV on F is 21.4%, the delayed-arm IIV on F is fixed at 0 in the source – see model-file vignette Errata).
• Notes: Specific scope because the early/delayed cutoff (14 days from admission) and the underlying nutritional-rehabilitation context are tied to the MATCH-trial design; future studies that test a similar early-vs-delayed contrast with a different time cutoff or non-nutritional context should register a new canonical (e.g., EARLY_ART_28D). Distinct from TRT_PHASE (which gates active-vs-baseline study-phase contributions on a per-record basis) and from DAY14 (which is a within-subject time-since-treatment-initiation landmark, not an enrolment arm). Ratified canonically on 2026-05-08.

DIS_TB_XDR (canonical for (pre-)XDR vs MDR tuberculosis drug-resistance stratum indicator)

• Description: 1 = subject’s Mycobacterium tuberculosis isolate is classified as pre-extensively-drug-resistant (pre-XDR; resistant to isoniazid + rifampicin plus a second-line fluoroquinolone OR an injectable, but not both) or extensively-drug-resistant (XDR; resistant to isoniazid + rifampicin plus a second-line fluoroquinolone AND an injectable); 0 = multidrug-resistant (MDR; resistant to isoniazid + rifampicin only), drug-susceptible, or unclassified TB. Time-fixed per subject (one drug-susceptibility profile per subject from baseline sputum culture). The dichotomisation pools pre-XDR with XDR into the “1” stratum because Svensson 2017 found these two categories share the same slower-clearance bacterial-load phenotype (28.1% longer half-life of mycobacterial load decline relative to MDR).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (MDR / susceptible / missing-treated-as-MDR; the typical-value reference). Per Svensson 2017 Results paragraph 4, the 19% of subjects with missing TB-type information were assigned to the reference (MDR) group after testing that the missing group did not differ significantly from MDR.
• Source aliases: none formally; the Svensson 2017 source describes the variable in prose as the “(pre-)XDR” stratum, operationally encoded as 1 when TB type = pre-XDR or XDR and 0 otherwise (MDR + susceptible + missing).
• Example models: Svensson_2017_bedaquiline.R (multiplicative (1 + e_xdr_hl * DIS_TB_XDR) shift on the half-life of mycobacterial load decline; pre-XDR/XDR patients have a 28.1% longer MBL half-life than MDR/susceptible patients, translating to 2-4 weeks longer median time-to-sputum-culture-conversion per Svensson 2017 Table 3).
• Notes: Specific scope because the dichotomisation is tied to the Svensson 2017 multidrug-resistant tuberculosis cohort (TMC207-C208 Phase IIb registration trial) and the pre-XDR + XDR pooling convention may not transfer to studies that report a different TB-resistance categorisation (e.g., separate XDR vs pre-XDR stratification, or rifampicin-monoresistant TB as a distinct category). Future TB drug-resistance models should consider sibling canonicals (e.g., DIS_TB_XDR_STRICT for XDR-only, DIS_TB_RIFRES for rifampicin-monoresistant) rather than overloading this name; the DIS_TB_XDR_STRICT sibling anticipated here was subsequently ratified for Lin_2024_TB_multistate.R, which places pre-XDR in the reference group. Distinct from HIV_POS (a comorbidity indicator on TB-coinfected patients, not a drug-resistance stratum). Ratified canonically on 2026-05-21 alongside the Svensson 2017 bedaquiline extraction.

DIS_TB_XDR_STRICT (canonical for XDR-only tuberculosis drug-resistance stratum indicator)

• Description: 1 = subject’s Mycobacterium tuberculosis isolate is classified as extensively-drug-resistant (XDR; resistant to isoniazid + rifampicin plus a second-line fluoroquinolone AND a second-line injectable); 0 = every other resistance stratum, including pre-XDR (resistant to isoniazid + rifampicin plus a fluoroquinolone OR an injectable, but not both), MDR, drug-susceptible and unclassified TB. Time-fixed per subject (one drug-susceptibility profile per subject from baseline sputum culture). Pre-2021 WHO drug-resistance definitions.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-XDR: pre-XDR + MDR + drug-susceptible + missing-treated-as-MDR).
• Source aliases:
  • XDR (Lin 2024 NONMEM control-stream variable, derived as XDR = 0; IF(TBTYPE.EQ.4) XDR = 1 from a four-level TBTYPE column) – used in Lin_2024_TB_multistate.R.
• Example models: Lin_2024_TB_multistate.R (log-hazard effect on the surge amplitude of the sputum-culture-conversion hazard, SA12 = exp(lsa12 + ... + e_xdr_sa12 * DIS_TB_XDR_STRICT) with e_xdr_sa12 = -0.622792; exp(-0.622792) = 0.536 reproduces the paper’s reported “lambda12 decreased by 46% (95% CI, 24%-62%)” for XDR patients).
• Notes: The strict sibling of DIS_TB_XDR, and the two are NOT interchangeable: DIS_TB_XDR pools pre-XDR with XDR into the “1” stratum, whereas DIS_TB_XDR_STRICT places pre-XDR in the reference group. Choosing the wrong one misclassifies the pre-XDR patients, who are a substantial fraction of a typical drug-resistant TB cohort (95 of 402, 28%, in Lin 2024 Table 1). Read the source paper’s own contrast wording before picking: Lin 2024 states the effect for XDR-TB patients “compared with those with non-XDR-TB”, while Svensson 2017 reports a pooled “(pre-)XDR” effect. Specific scope because the dichotomisation depends on which resistance categories the source study enumerated. Distinct from HIV_POS (a comorbidity indicator on TB-coinfected patients, not a drug-resistance stratum). The sibling name was anticipated in the DIS_TB_XDR Notes at its 2026-05-21 ratification and is realised here for the Lin 2024 multistate extraction; patients with a missing resistance profile (~16% in Lin 2024) were assigned to the MDR reference group after the source tested that they did not differ significantly from MDR.

TTP_MGIT_BASE (canonical for baseline mean time-to-positivity in mycobacterial growth indicator tube)

• Description: Subject-specific baseline (pre-treatment) mean time-to-positivity (TTP) in the mycobacterial growth indicator tube (MGIT) liquid-culture system, expressed in days. Computed in the source paper as the mean of three replicate spot-sputum-sample TTP values collected the day before the start of treatment. The MGIT system automatically records the time in hours from inoculation to a positive signal indicating presence of M. tuberculosis (cap at 42 days = negative); the per-subject baseline TTP is then used as a covariate driving the model’s starting mycobacterial load (a longer baseline TTP corresponds to fewer viable bacteria in the inoculum and a lower starting MBL). Time-fixed per subject in the Svensson 2017 source.
• Units: days
• Type: continuous
• Scope: specific
• Reference category: n/a – used in power form (TTP_MGIT_BASE / ref)^exponent. Reference value observed: 6.8 days (Svensson 2017 Table 1 cohort median, n = 191).
• Source aliases:
  • mTTP0 (Svensson 2017 paper symbol, also written mTTP_{0,i}) – used in Svensson_2017_bedaquiline.R.
• Example models: Svensson_2017_bedaquiline.R (power-form covariate on the starting mycobacterial load mbl0_i = exp(lmbl0) * (TTP_MGIT_BASE / 6.8)^e_ttp_mbl0 with e_ttp_mbl0 = -3.69; a four-fold longer median TSCC is predicted in patients with the lowest baseline TTP versus the highest, per Svensson 2017 Discussion paragraph 3), Lin_2024_TB_multistate.R (exponential-deviation covariate shared between the peak time and the surge width of the sputum-culture-conversion hazard, PT12 = exp(lpt12 - e_ttp_pt12sw12 * (TTP_MGIT_BASE - 9.06944)/7) and SW12 = exp(lsw12 + e_ttp_pt12sw12 * (TTP_MGIT_BASE - 9.06944)/7) with e_ttp_pt12sw12 = 0.442668; a longer baseline TTP moves the conversion peak earlier and widens the surge; cohort-specific reference 9.06944 days for the pooled C208 + C209 cohort, n = 402, and the divisor 7 converts the day deviation to weeks so the coefficient is per week of baseline TTP).
• Notes: Specific scope because the cohort-median reference value (6.8 days) is tied to the Svensson 2017 cohort (TMC207-C208 Phase IIb registration trial); future MGIT-based mycobacterial-load models should reuse this canonical but document their own cohort-specific reference in covariateData[[TTP_MGIT_BASE]]$notes (as Lin_2024_TB_multistate.R does for its 9.06944-day pooled-cohort median). Source columns are frequently recorded in HOURS by the MGIT instrument (Lin 2024’s MTTP column is centered at 217.6667 h = 9.06944 days); always convert to days when populating this canonical. Distinct from LNPC (log-transformed admission Plasmodium parasitaemia, a different pathogen-quantification method for malaria) and from SARS_VLOAD (SARS-CoV-2 RT-qPCR log10 copies/mL, a different infectious-disease baseline biomarker). MGIT samples without a positive signal within 42 days are classified as negative in the source paper; for the per-subject baseline mean, only the positive triplicate values are averaged (a subject with all-negative baseline samples has missing TTP_MGIT_BASE). Ratified canonically on 2026-05-21 alongside the Svensson 2017 bedaquiline extraction.

MBL_HL_WK2 (canonical for model-derived half-life of mycobacterial-load decline through week 2)

• Description: Per-subject half-life of the decline of the model-derived mycobacterial load (MBL) over the first 2 weeks of anti-tuberculosis treatment, expressed in weeks. A model-derived covariate: it is not measured directly but is a secondary metric read out of an upstream longitudinal mycobacterial-load PK-PD model, which integrates baseline bacterial burden, drug-resistance profile, prior anti-TB treatment and individual drug exposure over time into a single early-bactericidal-activity summary. A shorter half-life means faster bacterial clearance. Time-fixed per subject in the models that use it: the source computes the metric prospectively from data up to week 2 only (to avoid immortal-time bias) and carries the week-2 value forward thereafter, so a single scalar per subject reproduces the model exactly.
• Units: week
• Type: continuous
• Scope: specific
• Reference category: n/a – used as an exponential deviation exp(beta * (MBL_HL_WK2 - ref)). Reference value observed: 0.69443 weeks (Lin 2024 cohort median, hardcoded in the source NONMEM control stream and confirmed by Lin 2024 Figure 3, whose reference individual has “0.69 weeks of half-life of bacterial clearance at Week 2”). Lin 2024 Figure 6 reports the 5th and 95th percentiles as 0.33 weeks (high clearance) and 1.1 weeks (low clearance).
• Source aliases:
  • HL2 (Lin 2024 paper symbol and data column; the subscript denotes “up to week 2”) – used in Lin_2024_TB_multistate.R.
• Example models: Lin_2024_TB_multistate.R (enters BOTH surge parameters of the sputum-culture-conversion hazard through a single shared coefficient of opposite sign, SA12 = exp(lsa12 + e_hl2_sa12pt12 * (MBL_HL_WK2 - 0.69443) + ...) and PT12 = exp(lpt12 - ... - e_hl2_sa12pt12 * (MBL_HL_WK2 - 0.69443)) with e_hl2_sa12pt12 = -0.686145; because the coefficient is negative, a shorter half-life simultaneously raises the surge amplitude and moves the peak earlier).
• Notes: Must be strictly positive. The intended source is the half-life of the mycobacterial-load state of modellib('Svensson_2017_bedaquiline'), whose hl_i is already in weeks – no conversion is needed; supplying it from any other longitudinal bacterial-load model is acceptable provided the half-life is expressed in weeks and evaluated at week 2. Paired with MBL_END, the end-of-treatment load from the same upstream model; the two share the MBL_ prefix precisely because they are different readouts of one underlying mycobacterial-load trajectory, and the family extends naturally (MBL_BASE, MBL_HL_WK8, …). Distinct from TTP_MGIT_BASE, which is a directly measured baseline culture-growth time rather than a model-derived rate of on-treatment decline; the two are complementary and Lin 2024 retains both. Specific scope because the cohort-median reference value is tied to the pooled TMC207-C208 + TMC207-C209 cohort; models reusing this canonical should document their own reference in covariateData[[MBL_HL_WK2]]$notes.

MBL_END (canonical for model-derived mycobacterial load at the end of treatment)

• Description: Per-subject model-derived mycobacterial load (MBL) at the end of the investigational anti-tuberculosis treatment period, on the natural (untransformed) scale, in number of bacteria per sample inoculum. A model-derived covariate read out of an upstream longitudinal mycobacterial-load PK-PD model; the source describes it as “a composite of disease severity at baseline and on-treatment response throughout the whole treatment period”, because the upstream model integrates baseline burden, resistance profile and the full longitudinal individual drug exposure. Time-fixed per subject.
• Units: n bacteria per sample inoculum
• Type: continuous
• Scope: specific
• Reference category: n/a – used as a natural-log deviation exp(beta * (log(MBL_END) - log(ref))). Reference value observed: 5.5726e-05 bacteria per inoculum (Lin 2024 cohort median, hardcoded in the source NONMEM control stream as LOG(0.000055726)); log10(5.5726e-05) = -4.25, matching the “-4.3 log10(MMBLend)” reference individual of Lin 2024 Figure 3.
• Source aliases:
  • MMBLend (Lin 2024 paper symbol and data column; “model-derived Mycobacterial Load at end of treatment”) – used in Lin_2024_TB_multistate.R.
• Example models: Lin_2024_TB_multistate.R (log-linear effect on the recurrence hazard, gated to act only after week 26: lambda23 = exp(llambda23 + e_sexf_lambda23 * SEXF + e_mblend_lambda23 * (t > 26) * (log(MBL_END) - log(5.5726e-05))) with e_mblend_lambda23 = 0.0371081).
• Notes: Store on the NATURAL scale and let model() take the log – the coefficient is per unit of natural log, so a per-log10-unit hazard ratio is exp(beta * ln(10)) (for Lin 2024, exp(0.0371081 * ln(10)) = 1.089). Confusing the natural-log and log10 conventions is the obvious failure mode here because the source paper’s figures are labelled in log10(MMBLend) while the control stream uses LOG() (natural log). Must be strictly positive: log(0) is undefined and would propagate NaN through the entire ODE solve even at times before the gate opens, since 0 * -Inf is NaN. Units match the mycobacterial-load state of modellib('Svensson_2017_bedaquiline') (n bacteria per sample inoculum, typical starting MBL_0 = 2.14e3), so that model’s mbl state at the end of treatment can be supplied directly. Paired with MBL_HL_WK2 (see that entry for the shared MBL_ family rationale). Specific scope because both the cohort-median reference and the “end of treatment” timepoint (24 weeks in Lin 2024) are study-design-dependent; models reusing this canonical should document their own reference value and end-of-treatment definition in covariateData[[MBL_END]]$notes.

CD19_ABS (canonical for absolute CD19+ B-lymphocyte count)

• Description: Absolute peripheral-blood CD19+ B-lymphocyte count. Baseline or time-varying; document the time resolution per model via covariateData[[CD19_ABS]]$notes. Used as a continuous target-antigen-burden / B-cell-mediated-clearance biomarker in anti-CD20 (rituximab, ofatumumab, obinutuzumab) and other B-cell-targeted antibody popPK models, where the therapeutic mAb elimination increases with the size of the on-target B-cell pool via target-mediated drug disposition.
• Units: cells/mm^3 (equivalently cells/uL; the two labels are numerically equivalent).
• Type: continuous
• Scope: general – the absolute CD19+ B-cell count is the standard flow-cytometry biomarker of circulating B-cell burden and is expected to reuse across anti-CD20 / anti-CD79b / anti-CD22 popPK extractions where the source paper carries the count as an exogenous exposure covariate rather than as a modelled PD state.
• Reference category: n/a – enters as a centred power term (CD19_ABS / ref)^exponent against a population reference. Document the form and reference value in each model’s covariateData[[CD19_ABS]]$notes.
• Source aliases:
  • CD19 – bare-name source-paper column; common in clinical-PK papers (Lioger 2017 prints “CD19+ count” throughout Methods, Results, and Table 2).
  • CD19_COUNT – explicit-suffix variant.
• Example models: Lioger_2017_rituximab.R (time-varying pre-course CD19+ count as a covariate on the elimination rate constant k10; power exponent 0.035, reference 100 cells/mm^3; sign is positive because higher CD19+ counts correlate with faster rituximab elimination via target-mediated drug disposition).
• Notes: Distinct from BLBCELL (baseline-only CD19+ B-cell count used as a baseline covariate in ofatumumab / polatuzumab vedotin PK-PD models, where the count is measured prior to first dose only) – CD19_ABS is the general canonical for CD19+ counts that are carried as time-varying exogenous covariates measured at multiple visits. BLBCELL’s notes (“Distinct from a time-varying B cell count, which is the PD response variable rather than a covariate”) reflected the earlier register where the only registered anti-CD20 CD19+ use case was baseline; CD19_ABS fills the exogenous time-varying covariate slot (analogous to how CD4_ABS covers both baseline and time-varying CD4+ counts). Distinct from a modelled time-varying B-cell state, which is a PD response variable and would be encoded as an ODE state, not a covariate column. Ratified canonically on 2026-07-09 alongside the Lioger 2017 rituximab extraction.

CD4_ABS (canonical for absolute CD4+ T-lymphocyte count)

• Description: Absolute peripheral-blood CD4+ T-lymphocyte count. Baseline or time-varying; document the time resolution per model via covariateData[[CD4_ABS]]$notes. Used as a continuous disease-severity / immunosuppression-depth biomarker in HIV / AIDS popPK models and as a covariate on clearance for drugs whose disposition is altered by advanced HIV immunosuppression.
• Units: cells/mm^3 (equivalently cells/uL; the two labels are numerically equivalent).
• Type: continuous
• Scope: general – the absolute CD4 count is a textbook HIV-severity covariate that future popPK extractions in HIV / AIDS cohorts are expected to reuse.
• Reference category: n/a – enters either as a raw additive linear slope (McLachlan 1996, CL = exp(lcl) + e_crcl_cl * CRCL + e_cd4_abs_cl * CD4_ABS; coefficient in L/h per cell/mm^3) or as a centred linear / power term (CD4_ABS / ref)^exponent against a population reference (typical reference 50 or 200 cells/mm^3 in HIV cohorts). Document the form and reference value in each model’s covariateData[[CD4_ABS]]$notes.
• Source aliases:
  • CD4 – bare-name source-paper column; common in clinical-PK papers (McLachlan 1996 prints “CD4 cell count” in the abstract and “CD4+ T-lymphocyte count” in the Methods).
  • CD4_COUNT – explicit-suffix variant.
  • CD4_T_COUNT – verbatim “CD4+ T-lymphocyte count” form used in some clinical-PK papers.
• Example models: McLachlan_1996_fluconazole.R (raw additive linear slope on the CL covariate model: 0.00068 L/h per cell/mm^3; cohort mean 69 cells/mm^3 with 97 of 109 covariate-evaluable subjects below 200 cells/mm^3 per Table 3; entered as e_cd4_abs_cl * CD4_ABS in the additive intercept-plus-slopes CL regression alongside e_crcl_cl * CRCL).
• Notes: Distinct from the CD4 z-score family (e.g., the age-standardised z-score modelled by Majekodunmi_2017_HIV_HCV_CD4_recovery.R), which is a paediatric-recovery standardised metric on the natural-z-score scale and is not directly interchangeable with the absolute count. Distinct from CD4 percentage (CD4_PCT, the proportion of total lymphocytes that are CD4+; commonly reported in paediatric HIV studies and not yet registered) – register CD4_PCT separately if a future paper retains it. Distinct from HIV_POS (the binary serostatus indicator), which captures HIV-positive-vs-negative rather than the depth of immunosuppression within an HIV-positive cohort. The two can be paired in a single model when an HIV-positive subset is further stratified by CD4 count, but most HIV-popPK papers focus on either the comorbidity flag (mixed HIV+/HIV- cohort) or the absolute count (HIV+-only cohort), not both. Ratified canonically on 2026-06-10 alongside the McLachlan 1996 fluconazole extraction.

DIS_ANTHRAX (canonical for active inhalational-anthrax infection indicator)

• Description: 1 = the subject has active inhalational anthrax (in the founding example, following aerosol challenge with a target 200 LD50 of Bacillus anthracis Ames-strain spores); 0 = healthy / unexposed. Time-fixed per subject in challenge-model designs, where an animal is allocated to either the challenged or the unexposed arm for the whole study. Distinguishes the infected cohort from the healthy cohort when a population PK model is fit simultaneously to both in order to estimate a disease effect on disposition.
• Units: (binary)
• Type: binary
• Scope: general – a disease-state indicator in the established DIS_<condition> family; anthrax medical countermeasures developed under the FDA Animal Rule (obiltoxaximab, raxibacumab, anthrax immune globulin) are a recurring extraction class.
• Reference category: 0 (healthy / unexposed).
• Source aliases:
  • INFECTED – used in Nagy_2017_obiltoxaximab.R (Nagy 2017 Results: “TMDD in infected NZW rabbits and cynomolgus macaques was approximated via parallel nonlinear elimination for infected animals only”).
• Example models: Nagy_2017_obiltoxaximab.R (switches on a parallel Michaelis-Menten elimination arm approximating protective-antigen target-mediated drug disposition: vmax <- DIS_ANTHRAX * vmaxRef * (WT / wtRefVmax)^e_wt_cl).
• Notes: In anthrax monoclonal-antibody models the biological content of this indicator is the presence of the drug’s binding target: protective antigen (PA) circulates only during active infection, so target-mediated disposition exists only when DIS_ANTHRAX = 1. That is why the founding paper’s human population PK model – fit to healthy volunteers only – has no fitted nonlinear arm, and why setting DIS_ANTHRAX = 1 for a human subject invokes a component transferred from the macaque rather than one estimated in humans. Distinct from DIS_INFECT_CSSSI_SEV (a severity grade for complicated skin and skin-structure infection) and from the pathogen-load covariate BACT_PTT_LOG10CFU, which quantifies how advanced the infection is rather than whether it is present; the two are complementary and a model may carry both. Ratified canonically alongside the Nagy 2017 obiltoxaximab extraction.

BACT_PTT_LOG10CFU (canonical for prior-to-treatment quantitative bacteremia on the log10 CFU/mL scale)

• Description: Quantitative blood bacterial burden measured immediately prior to the start of treatment (“prior-to-treatment”, PTT), expressed as log10 colony-forming units per mL of blood. Per-subject scalar captured at the treatment-trigger time point. Serves as the disease-severity covariate in trigger-to-treat infection models, where treatment is initiated on a physiological trigger rather than at a fixed time and the bacterial burden at that moment is the dominant predictor of outcome.
• Units: log10 CFU/mL
• Type: continuous
• Scope: general – prior-to-treatment bacteremia is the standard severity covariate across bacterial-challenge medical-countermeasure programmes, not specific to anthrax.
• Reference category: n/a – enters on its natural log10 scale. In the founding example it appears twice in the same model: as an exponentiated penalty on the cure fraction, logit(psurv) = theta0 - (theta1 * BACT_PTT_LOG10CFU)^theta2 + Emax * dose / (ED50 + dose), and as a log-linear effect on the Weibull death rate, log(lambda) = lambda0 + lambda1 * BACT_PTT_LOG10CFU. A value of 0 denotes no detectable PTT bacteremia and makes the penalty term vanish.
• Source aliases:
  • PTT – used in Nagy_2017_obiltoxaximab_survival.R (Nagy 2017 Results, “Animal survival modeling”; the paper writes the covariate as “log10(PTT bacteremia)” and “PTT quantitative bacteremia”).
• Example models: Nagy_2017_obiltoxaximab_survival.R (Weibull cure-rate survival model; Supplementary Figure S2 stratifies the observed and predicted dose-response into quartiles of this covariate – [BLQ, 3.02], [3.03, 3.95], [3.96, 4.87], (4.87, 8.56] log10 CFU)….
• Notes: Values below the assay’s limit of quantification are reported by the founding paper as “BLQ” and pooled into the lowest quartile; a model consuming this covariate must decide how to impute BLQ records and document the choice in covariateData[[BACT_PTT_LOG10CFU]]$notes (the founding vignette uses the quartile midpoint when reproducing the published figure). The _PTT token marks the measurement timing (at treatment trigger), which matters because bacteremia rises steeply during untreated infection – a post-treatment or peak bacteremia value is a different covariate and should be registered separately (e.g. BACT_PEAK_LOG10CFU) rather than folded into this name. The _LOG10CFU token marks the scale explicitly so that a raw-CFU column is never silently substituted; register BACT_PTT_CFU if a paper models the untransformed count. Distinct from LNPC (natural-log Plasmodium parasitaemia per microlitre), which is the malaria analogue on a different log base, a different organism and a different volume unit. Ratified canonically alongside the Nagy 2017 obiltoxaximab extraction.

Oncology

RCFB1MAX (canonical for week-1 maximum across-lesion relative change from baseline in SUVmax)

• Description: Per-subject scalar predictor entering the overall-survival hazard in the Schindler 2016 sunitinib joint SUVmax / SLD / OS-TTE / dropout model. Defined as the maximum (across the up-to-five tracked target lesions) of (SUVmax(t = 168 h) - SUVmax(0)) / SUVmax(0), i.e., the most negative (largest reduction) relative change in [18F]FDG-PET standardized uptake value at one week of sunitinib therapy. The OS Weibull hazard is lambh * alphh * t^(alphh - 1) * exp(theta_pred * RCFB1MAX) with alphh = 1 (degenerates to constant baseline hazard).
• Units: unitless (relative change; typically negative for responders).
• Type: continuous
• Scope: specific
• Reference category: n/a – entered directly into the OS hazard exponent. Sign convention: more negative RCFB1MAX (greater week-1 SUVmax suppression) reduces the OS hazard (Schindler 2016 reports a positive theta_pred = 5.36, so exp(5.36 * RCFB1MAX) is < 1 for negative RCFB1MAX).
• Source aliases:
  • RCFB1MAX (Schindler 2016 NONMEM $ERROR block intermediate; “max relative change in SUVmax from baseline at week 1 across lesions”). Computed inline in the source .mod from the on-the-fly SUVmax states at TIME = 168 h and reused in subsequent records.
• Example models: Schindler_2016_sunitinib.R (DDMODEL00000221).
• Notes: Specific scope because the metric is tied to the Schindler 2016 GIST-on-sunitinib joint biomarker / OS analysis. In the source NONMEM .mod RCFB1MAX is a record-loop state, not a true subject-level covariate – it is captured at FLAG = 1 / TIME = 168 h from the running SUVmax compartment values and reused on every subsequent record. nlmixr2 / rxode2 do not have an idiomatic equivalent of NONMEM’s record-loop persistent state, so the model file consumes RCFB1MAX as a per-subject input covariate; reproducing the source’s behavior requires a two-stage simulation (run the SUVmax + SLD ODEs first, compute RCFB1MAX per subject from the t = 168 h SUVmax values, then run the OS / dropout TTE arms with RCFB1MAX bound). The vignette virtual cohort follows this pattern.

PFS_EVENT (canonical for progression-free-survival event indicator observed during follow-up)

• Description: Binary indicator of a documented disease-progression event observed during the trial follow-up window (1 = the patient experienced a progression event during follow-up per the study’s response-assessment criteria; 0 = no progression event observed through end of follow-up). Time-fixed per subject in a post-hoc analysis (the indicator is assigned once follow-up is complete). Used by Rozman 2017 as a stratifying covariate on the target-mediated CL decay-rate parameter kdes of rituximab – patients who later experienced progression had 82.2% lower kdes (slower decay of the time-varying CL arm), interpreted mechanistically as sustained CD20 target burden in poor responders. Standard oncology-trial terminology derived from progression-free-survival endpoints (PFS event = first documented disease progression or death from any cause during follow-up).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no progression event observed during follow-up).
• Source aliases:
  • Rozman 2017 uses the prose descriptor “disease progression” / “with disease progression” / “no disease progression” throughout Methods / Results / Table 2; the paper does not disclose the NONMEM $INPUT column name. The canonical PFS_EVENT follows the widely used oncology-trial convention.
• Example models: Rozman_2017_rituximab.R (multiplicative effect on the time-varying CL decay-rate kdes: kdes = exp(lkdes + e_pfs_event_kdes * PFS_EVENT + etalkdes) with e_pfs_event_kdes = log(1 - 0.822) = log(0.178), reproducing the paper’s 82.2% reduction from 0.143/day in non-progressors to 0.0254/day in progressors per Rozman 2017 Table 2).
• Notes: This is an outcome-derived covariate: the indicator is assigned from longitudinal response-assessment data (typically CT / PET imaging plus clinical follow-up per the study’s response criteria) and then used post-hoc as a stratifying covariate on a PK parameter. That is unusual but is a legitimate published modeling choice; models using PFS_EVENT should document the post-hoc nature and the associated selection-bias caveat in the vignette Assumptions and deviations section. Time-fixed by construction. If a downstream paper treats progression as a time-varying Markov state (progression-free at time t vs. progressed by time t), register a distinct time-varying canonical rather than reusing PFS_EVENT. Distinct from LMET (baseline liver metastases – a baseline burden indicator, not a follow-up outcome) and from TUMSZ / TUM_SLD (baseline continuous tumor-size measures).

TUMSZ (canonical for baseline tumor size)

• Description: Baseline tumor size. For solid tumors, the sum of diameters of target lesions per RECIST; for classical Hodgkin lymphoma and lymphoma generally, the sum of products of perpendicular diameters (SPPD) or the sum of linear diameters of target lesions, depending on the source paper.
• Units: mm (for linear-diameter constructs); mm^2 (for SPPD constructs; record the per-model convention in covariateData[[TUMSZ]]$units and notes).
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (TUMSZ / ref)^exponent for continuous effects, or with a paper-specific threshold for categorical-stratum indicators (e.g., Gibiansky 2014 splits BSIZ at 1750 mm^2 into low- vs high-burden strata). Reference values observed: 41 mm (Zhou 2025); 63 mm (Budha 2023); 90 mm (Lu 2014, source reference 9 cm converted to mm); 1750 mm^2 threshold (Gibiansky 2014, SPPD).
• Source aliases:
  • LDIAM (Zhou 2025; pediatric lymphoma “linear diameter” of target lesions in mm).
  • TMBD (originally in cm; TUMSZ_mm = TMBD_cm * 10) – used in Lu_2014_trastuzumabemtansine.R.
  • BSIZ (Gibiansky 2014; baseline tumor size as the sum of products of perpendicular diameters of target lesions, mm^2; used as the categorical indicator (BSIZ <= 1750) in the obinutuzumab popPK model rather than as a continuous power covariate).
  • SIZE (He 2025; preoperative largest-lesion diameter of a liver tumor by imaging, originally in cm, converted to mm on ingestion). Not a RECIST sum of diameters – a single-lesion diameter used as an indirect surrogate for the remaining functional liver mass, so TUMSZ (the pooled tumor-burden register) applies rather than TUM_SLD.
• Example models: Budha_2023_tislelizumab.R (reference 63 mm), He_2025_lidocaine.R (reference 50 mm, the Table 1 median 5 cm converted to mm; negative exponent -0.382 on lidocaine clearance, i.e. clearance falls as tumor size rises – the only extraction so far where tumor size acts as a liver-function proxy in a non-oncology-therapy popPK model), Lu_2014_trastuzumabemtansine.R (reference 90 mm; source column TMBD in cm, values converted to mm on ingestion), Zhou_2025_brentuximab.R (reference 41 mm; source column LDIAM is the sum of linear diameters of target lesions; effect on ADC clearance only), Gibiansky_2014_obinutuzumab.R (SPPD in mm^2; used as a categorical indicator (TUMSZ <= 1750) on the time-dependent clearance decay rate kdes, not as a continuous power covariate), Hansson_2013b_sunitinib.R (DDMODEL00000198; observed baseline tumor SLD used as the per-subject IC of the tumor-size ODE via the IPP-style proportional baseline-residual construction tumor(0) = TUMSZ * (1 + etaibase * propSd); the source .mod reads OBASE from DV at TIME=0/FLAG=4, but nlmixr2 / rxode2 cannot replicate the in-record assignment idiom and consumes the observed baseline as a covariate instead).
• Notes: Promoted to scope: general on 2026-04-20 as a conventional oncology baseline-tumor-size measure (RECIST for solid tumors, SPPD or sum-of-linear-diameters for lymphomas). The SPPD vs sum-of-diameters vs sum-of-linear-diameters convention is pooled onto a single column; document the per-model mixture where relevant. When the source paper reports tumor size in cm, convert to mm (the canonical unit) on data ingestion and scale the per-model reference accordingly so (TUMSZ / ref)^exp is numerically invariant. For SPPD constructs the natural unit is mm^2 (a product of two perpendicular diameters in mm); record that in the per-model covariateData[[TUMSZ]]$units field and do NOT cross-mix mm and mm^2 within a single ingest. When a source paper specifically reports the RECIST 1.1 “sum of longest diameters” of target lesions, use the more specific TUM_SLD canonical instead – TUMSZ remains the pooled-tumor-burden register.

TUM_SLD (canonical for sum of longest diameters of target lesions)

• Description: Baseline sum of longest diameters of target lesions per RECIST 1.1. More specific than the pooled TUMSZ canonical; use TUM_SLD when the source paper explicitly reports “sum of longest diameters” (or “sum of lesions”) as the tumor-burden metric, distinct from the pooled “sum of diameters / SPPD / sum of linear diameters” mixture covered by TUMSZ.
• Units: mm
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (TUM_SLD / ref)^exponent. Reference values observed: 70.0 mm (de Vries Schultink 2020 zenocutuzumab population median).
• Source aliases:
  • SoL / “sum of lesions” (de Vries Schultink 2020 zenocutuzumab) – same construct, mm.
• Example models: deVriesSchultink_2020_zenocutuzumab.R (reference 70.0 mm; power exponent 0.447 on Vmax of the parallel non-linear / Michaelis-Menten clearance).
• Notes: Distinct from TUMSZ (pooled tumor-size canonical covering RECIST sum-of-diameters / SPPD / sum-of-linear-diameters); TUM_SLD is the precise RECIST 1.1 sum-of-longest-diameters metric. Ratified canonically on 2026-04-29 alongside the pilot bispecific extraction (de Vries Schultink 2020 zenocutuzumab). When the source paper reports tumor burden in cm, convert to mm (the canonical unit) on data ingestion and scale the per-model reference accordingly so (TUM_SLD / ref)^exp is numerically invariant. Also used as the per-subject initial-condition input for tumour-growth / angiogenesis-inhibition (TGI) ODE models where the source paper sets the SLD state at time zero from the observed baseline SLD rather than estimating a typical-value baseline (e.g., Ouerdani_2015_pazopanib.R uses tumorSize(0) <- TUM_SLD).

TUM_VOL (canonical for volumetric tumor burden)

• Description: Tumor burden expressed as a volume, pooling every measurement modality that yields one. Two established sources: (a) baseline tumor volume measured by handheld caliper in subcutaneous-xenograft preclinical studies and similar small-animal models, computed from longest length and orthogonal width via volume = (length * width^2) / 2 (the standard ellipsoid-approximation formula used in xenograft pharmacology); and (b) clinical total tumor volume delineated on a tomographic image (CT, or the CT component of a SPECT/CT or PET/CT acquisition) and summed over all lesions. Used both as a covariate stratifier (size at randomisation, or tumor burden as a covariate on an uptake parameter) and as the per-subject initial-condition input for TGI ODE models where the source paper sets the tumor-volume state at time zero from the observed measurement rather than estimating a typical-value baseline. Usually a per-subject baseline held constant, but may be time-varying when the source re-measures it per treatment cycle.
• Units: mm^3
• Type: continuous
• Scope: general
• Reference category: n/a – typically used directly as the ODE initial state (tumorSize(0) <- TUM_VOL); not a covariate effect coefficient.
• Source aliases:
  • P0 (Ouerdani 2015 mouse model symbol for the observed initial tumour volume; mm^3 in the CAKI-2 xenograft cohort; range 100-250 mm^3 at randomisation per the paper’s preclinical Methods).
  • total tumor volume (Siebinga 2023 clinical CT-delineated volume summed over all PSMA-positive lesions; reported in mL, so multiply by 1000 to reach the canonical mm^3).
• Example models: Ouerdani_2015_pazopanib_mouse.R (Ouerdani 2015 preclinical TGI in CAKI-2 xenograft mice; per-subject TUM_VOL initialises the tumorSize state and is held constant per individual across the 24-day dosing window), Siebinga_2023_lu177psma617.R (clinical CT-delineated total tumor volume as a power covariate on the tumor uptake rate constant, (TUM_VOL / 1730)^0.705; re-measured before each treatment cycle).
• Notes: General scope because tumor-volume measurements have a shared meaning across papers regardless of drug, cell line, or measurement modality. Carried in mm^3 regardless of the unit the source reports (clinical papers usually report mL; 1 mL = 1000 mm^3), so convert on data ingestion and scale any per-model reference accordingly to keep (TUM_VOL / ref)^exp numerically invariant. Use TUM_SLD for clinical RECIST sum-of-longest-diameters (a length, not a volume) and TUMSZ for the pooled “baseline tumor burden as a covariate on PK” use case where the source does not resolve to a volume. Ratified canonically on 2026-05-12 alongside the Ouerdani 2015 pazopanib mouse extraction, initially scoped to caliper-measured preclinical xenograft volumes; scope promoted on 2026-07-30 to any volumetric tumor-burden measurement (operator-ratified alongside the Siebinga 2023 extraction, which supplies the founding clinical volumetric example). The promotion follows the CRCL precedent, where creatinine-based and tracer-measured GFR are pooled onto one canonical because they play the same operational role. For clinical functional-imaging-segmented tumor volumes where a paper reports BOTH the whole-body burden and the segmented target-lesion subset separately, use the finer-grained siblings TUM_VOL_TOTAL / TUM_VOL_TARGET below instead, so the two volumes stay distinguishable; TUM_VOL remains correct when a paper reports a single volumetric tumor burden with no target-lesion subset.

TUM_VOL_TOTAL (canonical for total clinical imaging-segmented tumor volume over all lesions)

• Description: Total tumor volume summed over ALL identified lesions, delineated on clinical functional imaging (PET/CT, SPECT/CT) rather than by caliper. Represents whole-body tumor burden as a volume. The founding example uses semi-automatic threshold segmentation at 50% SUVmax on [68Ga]Ga-HA-DOTATATE PET/CT. Per-subject baseline measurement, held constant per individual.
• Units: mL
• Type: continuous
• Scope: general
• Reference category: n/a – the founding example uses it uncentred in an exponential tumor-sink term, kin_spleen = kin_spleen_pop * exp(0.4 * (-TUM_VOL_TOTAL / 1000)), with the volume converted to litres. Population median observed: 283 mL (Siebinga 2023 EJNMMI Phys Table 1, range 22.4-644).
• Source aliases:
  • V tumor total, i / “total tumor volume” (Siebinga 2023 EJNMMI Phys, Equation 4 and Table 1) – same construct, mL, no transformation.
• Example models: Siebinga_2023_ga68hadotatate.R, Siebinga_2023_lu177hadotatate.R (drives the tumor-sink effect on spleen uptake in the six-compartment semi-physiological HA-DOTATATE radioligand models).
• Notes: Distinct from TUM_VOL (preclinical caliper volume in mm^3, whose own notes redirect clinical models elsewhere) and from TUMSZ / TUM_SLD (both LENGTHS in mm, not volumes). Distinct from its sibling TUM_VOL_TARGET, which covers only the segmented target-lesion subset; the distinction is load-bearing whenever a model uses both, because the whole-burden volume and the modelled-compartment volume drive different effects with different coefficients. Canonical unit is mL because clinical functional imaging reports volumes in mL; when a source reports cm^3 the values are numerically identical, and when a source reports mm^3 divide by 1000 on ingestion. Note that Siebinga_2023_lu177psma617.R pre-dates this entry and carries its clinical total tumor volume in TUM_VOL (converted to mm^3); new clinical extractions should use TUM_VOL_TOTAL. Ratified canonically on 2026-07-31 (sidecar request 001 of task oare_PMC10449733, question q1 option A) alongside the Siebinga 2023 HA-DOTATATE extraction.

TUM_VOL_TARGET (canonical for clinical imaging-segmented volume of the target lesions only)

• Description: Tumor volume summed over the segmented TARGET lesions only – the protocol-defined subset of lesions that a model carries as its tumor compartment – delineated on clinical functional imaging (PET/CT, SPECT/CT). In the founding example the target lesions are those with a diameter above 2 cm, to a maximum of five segmented lesions and two per organ system per patient, segmented at a 50% SUVmax threshold. Per-subject baseline measurement, held constant per individual.
• Units: mL
• Type: continuous
• Scope: general
• Reference category: n/a – the founding example uses it in a power term referenced to the cohort median, (TUM_VOL_TARGET / 80.0)^eff, and ALSO as the tumor compartment volume itself (v_tumor = TUM_VOL_TARGET / 1000 L). Population median observed: 80.0 mL (Siebinga 2023 EJNMMI Phys Table 1, range 7.81-212).
• Source aliases:
  • V tumor cmt, i / “tumor volume of target tumors (representing the tumor compartment)” (Siebinga 2023 EJNMMI Phys, Equation 5 and Table 1) – same construct, mL, no transformation.
• Example models: Siebinga_2023_ga68hadotatate.R, Siebinga_2023_lu177hadotatate.R (sets the tumor compartment volume and drives the power effect of tumor burden on tumor uptake in the six-compartment semi-physiological HA-DOTATATE radioligand models).
• Notes: A strict subset of TUM_VOL_TOTAL; register both when a paper reports both, because a model may legitimately use the target-lesion volume as the modelled compartment volume while using the whole-body burden to drive a sink effect on healthy tissue. Where the model uses this column as a compartment volume, a concentration-valued binding capacity automatically scales the compartment’s capacity with tumor size – record that dependency in the per-model covariateData notes so a downstream user does not double-count it. Distinct from TUM_SLD (RECIST sum of longest diameters, a length in mm) even when both describe target lesions: TUM_SLD is a length and TUM_VOL_TARGET is a volume, and they are not interconvertible without a shape assumption. Ratified canonically on 2026-07-31 (sidecar request 001 of task oare_PMC10449733, question q1 option A) alongside the Siebinga 2023 HA-DOTATATE extraction.

ORGVOL_KIDNEY (canonical for measured total kidney volume)

• Description: Measured total volume of the kidneys (both kidneys combined unless the model states otherwise), typically delineated on CT or the CT component of a SPECT/CT or PET/CT acquisition.
• Units: mL
• Type: continuous
• Scope: general
• Reference category: n/a – used directly as a physiologic input that sets a compartment’s sub-volumes, its perfusion and its permeability-surface-area product; not a covariate-effect coefficient.
• Source aliases:
  • Kidneys (measured volume) – Kletting 2016 J Nucl Med Table 1 column header; reported in mL, no value transformation.
• Example models: Budiansah_2025_dotatate_pbpk.R, Golzaryan_2025_lu177psmaIT_pbpk.R.
• Notes: Founding member of the ORGVOL_<ORGAN> family, whose members carry a per-patient measured organ volume that a physiologically-based model uses to build that organ’s sub-volumes rather than scaling it allometrically from body size. In the founding model the measured volume is split into vascular (5.5 %), interstitial (15 %) and intracellular (two thirds of the remainder) sub-volumes and multiplies the fitted sst2 binding-site density. Cohort range 125-233 mL. Distinct from KBF (renal blood flow, mL/min) – that is a flow, this is a volume.

ORGVOL_LIVER (canonical for measured total liver volume)

• Description: Measured total liver volume, typically delineated on CT or the CT component of a SPECT/CT or PET/CT acquisition.
• Units: mL
• Type: continuous
• Scope: general
• Reference category: n/a – used directly as a physiologic input that sets a compartment’s sub-volumes, its perfusion and its permeability-surface-area product; not a covariate-effect coefficient.
• Source aliases:
  • Liver (measured volume) – Kletting 2016 J Nucl Med Table 1 column header; reported in mL, no value transformation.
• Example models: Budiansah_2025_dotatate_pbpk.R.
• Notes: Member of the ORGVOL_<ORGAN> family; see ORGVOL_KIDNEY for the family rationale. In the founding model the measured volume is split into vascular (8.5 %) and interstitial (20 %) sub-volumes and multiplies the fitted hepatic sst2 binding-site density. Cohort range 1500-4876 mL, the upper end being a patient whose liver was largely replaced by neuroendocrine metastases. Distinct from LIVER_VOL_FRAC or any tumour-burden covariate: this is the whole organ including any intrahepatic disease.

ORGVOL_SPLEEN (canonical for measured total spleen volume)

• Description: Measured total spleen volume, typically delineated on CT or the CT component of a SPECT/CT or PET/CT acquisition.
• Units: mL
• Type: continuous
• Scope: general
• Reference category: n/a – used directly as a physiologic input that sets a compartment’s sub-volumes, its perfusion and its permeability-surface-area product; not a covariate-effect coefficient.
• Source aliases:
  • Spleen (measured volume) – Kletting 2016 J Nucl Med Table 1 column header; reported in mL, no value transformation.
• Example models: Budiansah_2025_dotatate_pbpk.R.
• Notes: Member of the ORGVOL_<ORGAN> family; see ORGVOL_KIDNEY for the family rationale. In the founding model the measured volume is split into vascular (12 %) and interstitial (20 %) sub-volumes and multiplies the fitted splenic sst2 binding-site density. The spleen is the highest-density normal-tissue somatostatin-receptor organ, so its volume is a material determinant of whole-body peptide distribution. Cohort range 110-320 mL. Splenectomised patients contribute no spleen observations; set the volume to a small positive placeholder rather than zero so that the sub-volume divisions in the ODEs stay finite, and exclude the spleen output from those subjects.

CTDNA (canonical for baseline circulating tumor DNA burden)

• Description: Baseline (pre-treatment, cycle 1 day 1) circulating tumor DNA burden in plasma, quantified by next-generation sequencing as the average number of mutant tumor molecules per millilitre of plasma (MMPM). ctDNA is shed into the circulation when tumor cells die by apoptosis or necrosis, so MMPM acts as a liquid-biopsy surrogate for total tumor burden that is independent of the RECIST target-lesion selection captured by TUM_SLD.
• Units: MMPM (mutant molecules per mL of plasma)
• Type: continuous
• Scope: general
• Reference category: n/a – used as the per-subject regressor / ODE initial-condition input rather than as a covariate effect coefficient. Models that fit ctDNA on the base-10 logarithmic scale (the common convention, because raw MMPM spans several orders of magnitude) derive the state initial condition inside model() as rbase_ctdna <- log10(CTDNA); the register stores the untransformed MMPM value so the transformation is visible at the call site.
• Source aliases:
  • y0 (Ribba 2022 Eq. 1 symbol for the observed baseline used as a Monolix regressor).
  • ctDNA0 (Ribba 2022 Eq. 2 symbol for the same quantity in the joint ctDNA / SLD model).
• Example models: Ribba_2022_ctdna.R (Stein bi-exponential on log10 ctDNA; growth_ctdna(0) <- log10(CTDNA)), Ribba_2022_ctdna_sld_joint.R (joint ctDNA / SLD model; same initial-condition use alongside TUM_SLD).
• Notes: Ratified canonically on 2026-07-28 alongside the Ribba 2022 ctDNA extraction, the first ctDNA-modality model in the library. Deliberately NOT pooled with variant-allele-frequency (VAF) or ctDNA-tumor-fraction (cTF) readouts: MMPM is an absolute concentration of mutant molecules whereas VAF and cTF are dimensionless ratios of mutant to wild-type (or aneuploidy-derived) signal, so the two are not interconvertible without the wild-type denominator. A future VAF / cTF canonical should be registered separately (e.g. CTDNA_VAF) rather than aliased onto CTDNA. The assay platform matters for cross-study pooling – Ribba 2022 used the Roche AVENIO panel for the MMPM cohorts (Weber 2021 and OAK) and the FMI panel for the cTF cohort (IMspire170) – so record the panel in the per-model covariateData[[CTDNA]]$notes.

TUMTP_HODGKIN_CLASSICAL (canonical for classical Hodgkin lymphoma tumor-type indicator)

• Description: 1 = classical Hodgkin lymphoma (cHL) or Hodgkin lymphoma generally, 0 = other tumor types.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = all other tumor types (e.g., NSCLC, EC, HCC, UC, GC, CRC, NPC, OC, “Other” solid tumors in the Budha 2023 cohort; systemic anaplastic large-cell lymphoma in the Zhou 2025 pediatric cohort).
• Source aliases:
  • TUMTP_CHL – prior canonical name (pre-2026-06-19 standardization audit).
  • TUMTP (categorical column with levels like cHL, GC, …) – decompose into TUMTP_HODGKIN_CLASSICAL = as.integer(TUMTP == "cHL").
  • DIS (Zhou 2025; integer code with DIS == 1 flagging HL) – decompose into TUMTP_HODGKIN_CLASSICAL = as.integer(DIS == 1). Zhou 2025 calls the complement “non-HL”; in the Zhou 2025 cohort the non-HL group is exclusively sALCL.
• Example models: Budha_2023_tislelizumab.R, Zhou_2025_brentuximab.R (effects on ADC Q2, MMAE central volume VM, and the ADC->MMAE conversion-decay rate ALFM; the Zhou 2025 paper anchors typical-value parameters to HL patients so the model uses (1 - TUMTP_HODGKIN_CLASSICAL) as the on-effect indicator with reference category 1 = HL).
• Notes: Paired with TUMTP_GASTRIC in Budha 2023; a patient can have at most one of the indicators set to 1 (the remaining tumor types collapse into the reference 0 group). The reference category is the off-encoded value (0) by definition; when a source paper anchors typical-value parameters to the HL group rather than the non-HL group (as Zhou 2025 does), encode the effect as coef^(1 - TUMTP_HODGKIN_CLASSICAL) so the canonical column meaning (1 = cHL/HL) is preserved while the paper’s reference (HL) still receives multiplier 1.

HER2_ECD (canonical for HER2 shed extracellular domain concentration)

• Description: Baseline serum concentration of the shed extracellular domain of human epidermal growth factor receptor 2 (HER2). Serves as a soluble-antigen biomarker of HER2-mediated target-mediated drug disposition for HER2-directed mAbs / ADCs.
• Units: ng/mL
• Type: continuous
• Scope: specific
• Reference category: n/a – used with power scaling (HER2_ECD / ref)^exponent. Reference 25 ng/mL used in Lu 2014; reference 8.23 ng/mL (population median) used in Bruno 2005, with a 200 ng/mL plateau cap.
• Source aliases:
  • ECD – used in Lu_2014_trastuzumabemtansine.R and Bruno_2005_trastuzumab.R.
• Example models: Lu_2014_trastuzumabemtansine.R (reference 25 ng/mL; exponent 0.035 on CL), Bruno_2005_trastuzumab.R (reference 8.23 ng/mL; power exponents 0.041 on CL and 0.105 on V; HER2_ECD capped at 200 ng/mL inside model() to reflect the Bruno 2005 plateau observation), deVriesSchultink_2018_trastuzumab_LVEF.R (inherits the Bruno 2005 trastuzumab popPK as a deterministic forcing function with the same 8.23 ng/mL reference and 200 ng/mL plateau cap; PK IIV is omitted per the source paper’s ‘fixed effect parameters’ clause, so HER2_ECD only enters via the typical-value PK covariate equations).
• Notes: Scoped specific because the covariate is meaningful only for HER2-targeted agents; if a non-HER2 paper uses a shed-antigen analog for a different target, register a target-specific canonical (e.g., EGFR_ECD) rather than reusing this one. Disambiguated from the covariate-columns register by the explicit HER2_ prefix.

TRAST_BL (canonical for baseline trastuzumab concentration from prior therapy)

• Description: Baseline serum concentration of residual unconjugated trastuzumab remaining from prior trastuzumab-containing therapy, measured at the start of a subsequent anti-HER2 treatment (e.g., trastuzumab emtansine). Encodes the magnitude of residual HER2-site competition from a previous trastuzumab exposure.
• Units: ug/mL
• Type: continuous
• Scope: specific
• Reference category: n/a – used as a linear covariate on log(CL) via exp(coef * TRAST_BL). TRAST_BL = 0 corresponds to no detectable residual trastuzumab (reference condition used in Lu 2014).
• Source aliases:
  • TBL – used in Lu_2014_trastuzumabemtansine.R.
• Example models: Lu_2014_trastuzumabemtansine.R (linear-on-log coefficient -0.002 per ug/mL on CL).
• Notes: Scoped specific because the covariate is meaningful only for drugs that compete with trastuzumab at the HER2 binding site. Expect values clustered at 0 for trastuzumab-naive patients; Lu 2014 observed 0 at the 5th percentile and 54 ug/mL at the 95th percentile.

TROPONIN_T_MAX (canonical for peak post-anthracycline high-sensitive serum troponin T concentration)

• Description: Peak (maximum) high-sensitive serum troponin T concentration observed per subject during or after the anthracycline-containing portion of an adjuvant or neoadjuvant chemotherapy regimen, in ng/L. Used as a covariate on subsequent trastuzumab-induced LVEF decline (the peak captures the cumulative anthracycline-driven myocyte damage that sensitises the heart to subsequent HER2-directed therapy).
• Units: ng/L
• Type: continuous
• Scope: specific
• Reference category: n/a – median-centered power covariate in de Vries Schultink 2018: EC50_i = EC50_pop * (TROPONIN_T_MAX / 18)^(-1.16), where 18 ng/L is the cohort-median TROPONIN_T_MAX. A TROPONIN_T_MAX value at the cohort median leaves EC50 unchanged from its population value.
• Source aliases:
  • TRPmax – used in deVriesSchultink_2018_trastuzumab_LVEF.R.
• Example models: deVriesSchultink_2018_trastuzumab_LVEF.R (power covariate effect on EC50 with median-centered normalisation, exponent -1.16, explaining 15.1% of inter-individual variability in EC50; per de Vries Schultink 2018 Table 2 and the EC50 covariate equation in Results).
• Notes: Derived per-subject from the upstream K-PD anthracycline-troponin T model output (see deVriesSchultink_2018_anthracycline_troponinT.R). Treated as a time-fixed baseline covariate for the LVEF model: even though the troponin T peak occurs during anthracycline treatment, by the time the LVEF observations start (typically a few weeks after the last anthracycline dose), the peak is a determined historical scalar per subject. Specific scope until a second cardiotoxicity / cardiac-biomarker model legitimately reuses this indicator. Values are positive; sub-LLOQ peak values are imputed at LLOQ/2 (= 1.5 ng/L for the Roche Modular E hs-TnT assay used in de Vries Schultink 2018).

TUMTP_GASTRIC (canonical for gastric-cancer tumor-type indicator)

• Description: 1 = gastric cancer (GC) or adenocarcinoma of the gastroesophageal junction (GEJ), 0 = other tumor types.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = all other tumor types (same reference group as TUMTP_HODGKIN_CLASSICAL).
• Source aliases:
  • TUMTP_GC – prior canonical name (pre-2026-06-19 standardization audit).
  • TUMTP (categorical column) – decompose into TUMTP_GASTRIC = as.integer(TUMTP == "GC").
  • TTYPE (Quartino 2019; categorical column with levels MBC, EBC, HV, AGC, Others) – decompose into TUMTP_GASTRIC = as.integer(TTYPE == "AGC").
  • TTYPE4 (Wang 2024; level 4 of a five-level tumor-type factor labelled “GCGEJ” in the source) – decompose into TUMTP_GASTRIC = as.integer(TTYPE4 == 1).
• Example models: Budha_2023_tislelizumab.R, Quartino_2019_trastuzumab.R (advanced gastric cancer; per-group typical-value switch on linear CL and Vc rather than an exponential multiplier), Wang_2024_sugemalimab.R (gastric + GEJ adenocarcinoma pooled; exponential coefficient log(1.13) on CL and log(1.14) on Vc).
• Notes: Follows the RACE_<GROUP> indicator-decomposition pattern. New oncology tumor types should be added as additional TUMTP_<GROUP> entries so the reference set stays explicit. “Advanced gastric cancer” (AGC), “gastric cancer” (GC), and “GC or adenocarcinoma of the gastroesophageal junction” (GCGEJ) are pooled onto a single TUMTP_GASTRIC indicator; document the per-paper stage-of-disease and GEJ-inclusion detail in covariateData[[TUMTP_GASTRIC]]$notes. ESCC (squamous histology) is captured by the separate TUMTP_ESCC indicator and is not pooled here.

TUMTP_OTHER (canonical for ‘other tumor types’ residual indicator)

• Description: 1 = heterogeneous “other” tumor-type pool (typically NSCLC plus miscellaneous solid tumors such as prostate, ovarian, and colorectal), 0 = one of the named tumor-type groups in the same analysis.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = named tumor-type groups defined per-paper (e.g., MBC, EBC, HV, AGC in Quartino 2019). The complement of all TUMTP_<GROUP> indicators defined in the same model.
• Source aliases:
  • TUMTP_OTH – prior canonical name (pre-2026-06-19 standardization audit).
  • TTYPE (Quartino 2019) – decompose into TUMTP_OTHER = as.integer(TTYPE == "Others").
  • PAT2 (Sathe 2024, Sathe 2025) – integer-coded tumor type column with levels 1 (mTNBC), 2 (mUC or HR+/HER2- mBC), 4 (Other epithelial); the source NONMEM control stream collapses PAT2 = 1 and PAT2 = 2 into the reference (no effect) and applies the deviation only when PAT2 = 4. Decompose into TUMTP_OTHER = as.integer(PAT2 == 4).
• Example models: Quartino_2019_trastuzumab.R (per-group typical-value switch on linear CL; NSCLC plus a small residual group of prostate, ovarian, and other histologies), Sathe_2024_sacituzumab.R (multiplicative effect on tAB CL: -13.4% when TUMTP_OTHER = 1; “Other” pool = small-cell and non-small-cell lung cancer, colorectal cancer, esophageal cancer, pancreatic ductal adenocarcinoma, etc., n = 184; reference = pooled mTNBC + mUC + HR+/HER2- mBC, n = 345), Sathe_2025_sacituzumab.R (updated 3-study pooled analysis of the same drug adding TROPiCS-02 HR+/HER2- mBC data to Sathe 2024; multiplicative effect on tAB CL: -11.2% when TUMTP_OTHER = 1; “Other” pool composition and n = 184 unchanged from Sathe 2024; reference = pooled mTNBC + mUC + HR+/HER2- mBC, n = 605), Lacy_2018_cabozantinib.R (multiplicative fractional effect on CL/F = +0.178 and on Vc/F = -0.186 for “other malignancies” relative to the healthy-volunteer reference; n = 40 / 1534 = 2.6% of the cohort, all from Study XL184-001 mixed-malignancy first-in-human cohort), Wang_2024_sugemalimab.R (multiplicative effect exp(e_oth_cl * TUMTP_OTHER) on CL and exp(e_oth_vc * TUMTP_OTHER) on Vc, with the “Other” pool = the residual tumor-type bucket complementing TUMTP_LYMPH / TUMTP_GASTRIC / TUMTP_ESCC in the same model), Gastonguay_2005_efaproxiral.R (categorical power-model multiplier on SLPp50 = 0.854 for “other” cancer type vs lung-cancer reference; CATP level 4; cancer types not in {lung, breast, GBM} pooled into “other”, n = 72 / 451 = 16.0% of cohort).
• Notes: Scope: specific because the set of histologies collapsed into “Others” is defined by the analysis plan of the source paper; two papers’ TUMTP_OTHER columns are not interchangeable. Document the exact per-paper composition (e.g., “NSCLC + prostate + ovarian + other, n = 107 in Quartino 2019”; “small-cell + non-small-cell lung + CRC + esophageal + pancreatic ductal adenocarcinoma, n = 184 in Sathe 2024 and Sathe 2025 (same PAT2 = 4 pool carried across the two analyses)”; “advanced mixed malignancies enrolled in the FIH Study XL184-001, n = 40 in Lacy 2018”; “all non-{lung, breast, GBM} cancer types in the radiation-therapy cohort, n = 72 in Gastonguay 2005”) in covariateData[[TUMTP_OTHER]]$notes. A given subject can have at most one of the TUMTP_<GROUP> indicators (including TUMTP_OTHER) set to 1; all-zero means the reference group.

SPDL1 (canonical for soluble PD-L1 concentration)

• Description: Baseline (or time-varying) serum concentration of soluble programmed death-ligand 1 (sPD-L1). Serves as a circulating biomarker of target burden and immune activation for anti-PD-1/PD-L1 antibodies.
• Units: pg/mL
• Type: continuous
• Scope: specific
• Reference category: n/a – used with power scaling (SPDL1 / ref)^exponent. Reference value observed: 173.8 pg/mL (study-population median in Ogasawara 2020).
• Source aliases: none; SPDL1 is the standard abbreviation used directly in source analyses.
• Example models: Ogasawara_2020_durvalumab.R (power effect on CL, exponent 0.0617, reference 173.8 pg/mL; time-varying; values below LOD imputed as LOD/2 = 33.55 pg/mL), Quartino_2019_trastuzumab.R (per-group typical-value switch on linear CL; NSCLC plus a small residual group of prostate, ovarian, and other histologies), Wang_2024_sugemalimab.R (heterogeneous solid-tumor residual group of n = 174; exponential coefficient log(0.885) on CL and log(0.926) on Vc; NSCLC is the reference group, not part of TUMTP_OTHER), deVries_2025_durvalumab.R (centred linear effect on the Michaelis-Menten Vmax rather than on linear CL: (1 + 0.00336 * (SPDL1 - 124.8)), reference 124.8 pg/mL; baseline, time-fixed; the only registered model that places sPD-L1 on the saturable elimination arm).
• Notes: Scope: specific because sPD-L1 is meaningful only for drugs targeting the PD-1/PD-L1 pathway. For other checkpoint biomarkers (e.g., soluble CTLA-4, soluble LAG-3) register new dedicated canonicals rather than reusing this one. Ratified canonically on 2026-04-26.
  • TTYPE3 (Wang 2024; level 3 of a five-level tumor-type factor labelled “Other” in the source) – decompose into TUMTP_OTHER = as.integer(TTYPE3 == 1).
• Example models: Quartino_2019_trastuzumab.R (per-group typical-value switch on linear CL; NSCLC plus a small residual group of prostate, ovarian, and other histologies), Wang_2024_sugemalimab.R (heterogeneous solid-tumor residual group of n = 174; exponential coefficient log(0.885) on CL and log(0.926) on Vc; NSCLC is the reference group, not part of TUMTP_OTHER), Ogasawara_2020_durvalumab.R (power effect on CL, exponent 0.0617, reference 173.8 pg/mL; time-varying; values below LOD imputed as LOD/2 = 33.55 pg/mL), deVries_2025_durvalumab.R (centred linear effect on the Michaelis-Menten Vmax rather than on linear CL: (1 + 0.00336 * (SPDL1 - 124.8)), reference 124.8 pg/mL; baseline, time-fixed; the only registered model that places sPD-L1 on the saturable elimination arm).
• Notes: Scope: specific because the set of histologies collapsed into “Others” is defined by the analysis plan of the source paper; two papers’ TUMTP_OTHER columns are not interchangeable. Document the exact per-paper composition (e.g., “NSCLC + prostate + ovarian + other, n = 107 in Quartino 2019”; “miscellaneous solid tumors excluding NSCLC, lymphoma, GCGEJ, and ESCC, n = 174 in Wang 2024”) in covariateData[[TUMTP_OTHER]]$notes. A given subject can have at most one of the TUMTP_<GROUP> indicators (including TUMTP_OTHER) set to 1; all-zero means the reference group.

MCPROT (canonical for serum monoclonal (M) protein concentration)

• Description: Serum monoclonal (M) protein concentration. Multiple-myeloma plasma-cell-burden marker secreted by the tumor clone; elevated MCPROT reflects higher tumor burden and (for IgG-secreting MM) competes with therapeutic IgG mAbs for FcRn-mediated salvage and target-mediated elimination. Typically time-varying – measured at multiple visits over the treatment course and supplied at every PK observation time via linear interpolation between measurements.
• Units: g/dL (US-convention; equivalent to 10 g/L SI). Document the unit used in each model via covariateData[[MCPROT]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – used as a continuous, log-linear effect on the Vmax of target-mediated elimination via exp(theta * MCPROT) (i.e., MCPROT enters un-log-transformed). Reference values observed: 0 g/dL (Ide 2020 Vmax,REF reference) and 2.0 g/dL (Ide 2020 figure-1 reference patient).
• Source aliases:
  • TMCPROT (time-varying serum M-protein concentration) – used in Ide_2020_elotuzumab.R. NONMEM column with imputation sentinel -99 for missing observations, replaced by population median 2.1 g/dL via IF(TMCPROT.EQ.-99) TMCPROT = 2.1.
• Example models: Ide_2020_elotuzumab.R (g/dL, time-varying; entered un-log-transformed as exp(0.277 * MCPROT) on Vmax of the Michaelis-Menten target-mediated elimination from the central compartment), Collins_2023_belantamab_mprotein.R (g/L, time-fixed baseline; seeds the initial condition of the modelled M-protein tumor state and is binarized as (MCPROT < 20) to carry a multiplicative factor 1.41 on the kill rate constant KD).
• Notes: Specific scope because the column is mechanistically meaningful only for plasma-cell-targeting therapies in multiple myeloma (e.g., elotuzumab anti-SLAMF7, daratumumab anti-CD38, isatuximab anti-CD38, belantamab anti-BCMA, and BCMA-bispecifics / CAR-T). MCPROT decreases with treatment response; the time-varying form is the only correct way to capture the diminishing target-mediated-elimination component as the tumor regresses. In NONMEM datasets MCPROT is supplied at each event-row time, with linear interpolation between observations and last-observation-carried-forward beyond the last sample (Ide 2020 Methods). Distinct from MM_NIGG (which is the immunoglobulin subtype, an MM-disease stratifier that is time-fixed), SBCMA (soluble BCMA, a different MM tumor-burden biomarker for BCMA-targeting drugs), and B2M (beta-2-microglobulin, a renal-function-and-MM-disease-burden marker). The 1 g/dL = 10 g/L conversion lets future SI-convention papers register the same canonical with their own unit string.

PDL1_TUM (canonical for tumor PD-L1 expression (tumor proportion score))

• Description: Baseline tumor PD-L1 expression measured by immunohistochemistry, reported as Tumor Proportion Score (TPS) – the percent of viable tumor cells with complete or partial membrane staining at any intensity. Used as a prognostic and predictive covariate for anti-PD-1 / anti-PD-L1 therapies. Distinct from SPDL1 (soluble serum PD-L1 in pg/mL); PDL1_TUM is a tissue biomarker, SPDL1 is a circulating biomarker.
• Units: percent (0-100)
• Type: continuous
• Scope: general (oncology)
• Reference category: n/a – enters typically as exp(theta * PDL1_TUM) (uncentred multiplicative) or 1 + theta * (PDL1_TUM - ref) (centred linear deviation). Reference values observed: 15 (Struemper 2025 population median across PD-1 inhibitor arms); 70 (Struemper 2025 INTR@PID imputed value when the 22C3 assay was unavailable).
• Source aliases: PD-L1, PDL1, TPS, PDL1_TPS – paper notation varies; map to canonical PDL1_TUM with no value transformation.
• Example models: Struemper_2025_tumorsize_OS_nsclc.R (exponential effect on the tumor shrinkage rate ks of a Stein bi-exponential TGI model; coefficient 0.00902 per percent; applied only to PD-1 inhibitor-containing treatment arms via a derived has_pd1 indicator that gates the effect when TRT is one of the eight pembrolizumab- or dostarlimab-containing categories).
• Notes: The PD-L1 IHC antibody clone (22C3, 73-10, SP142, SP263, etc.) determines cross-study comparability; document the assay per-model in covariateData[[PDL1_TUM]]$notes. Tumor Proportion Score (TPS) is the percent of TUMOR cells stained; Combined Positive Score (CPS) and Immune Cell (IC) score are distinct percent metrics that should be registered separately if a paper relies on them. PDL1_TUM is a tissue-level biomarker that may coexist in a model with the serum-level SPDL1. Scope: general because PDL1 IHC is a widely reused oncology covariate. Founding example: Struemper 2025 (exp(PDL1_TUM * 0.00902) on ks, paper Table 3 footnote b).

LMET (canonical for baseline presence of liver metastases)

• Description: Binary indicator of radiologically documented liver metastases at baseline, 1 = liver metastases present, 0 = no liver metastases.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no liver metastases at baseline).
• Source aliases: none; LMET is the common NONMEM / clinical-PK abbreviation used directly by the source papers.
• Example models: Quartino_2019_trastuzumab.R (exponential effect on linear CL; +16.4% typical CL when LMET = 1, per Quartino 2019 Table 1 theta12 = 0.152).
• Notes: Liver metastases are associated with hepatic protein-synthesis impairment and altered IgG catabolism, making LMET a commonly tested covariate in oncology mAb population PK analyses. Scope: general so future oncology papers can reuse the canonical column. Time-fixed baseline indicator; if a source paper treats it as time-varying (progression during treatment), document in covariateData[[LMET]]$notes.

MET_GE4 (canonical for baseline number of metastatic sites >= 4 indicator)

• Description: Binary indicator dichotomising the count of baseline metastatic sites at 4, 1 = patient has four or more documented metastatic sites at baseline, 0 = patient has zero to three metastatic sites at baseline. Time-fixed per subject. Treated as a surrogate for tumor burden in oncology mAb popPK analyses.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (fewer than four metastatic sites at baseline).
• Source aliases:
  • MET – used in Bruno_2005_trastuzumab.R (Bruno 2005 Methods: “MET=1 if number of metastatic sites=4 or greater; otherwise MET=0”). When a source paper supplies the raw integer count column (NMET, N_METS, etc.) rather than the pre-binarised indicator, derive MET_GE4 = as.integer(NMET >= 4).
• Example models: Bruno_2005_trastuzumab.R (multiplicative effect on linear CL: typical CL multiplied by (1 + 0.221 * MET_GE4), i.e. +22.1% CL in MET_GE4 = 1 patients; reference MET_GE4 = 0 per Bruno 2005 Table 3).
• Notes: The >= 4 split is the dichotomisation used by Bruno 2005 to capture “patients with four or more metastatic sites” as a high-tumor-burden subgroup. Scope: general so future oncology popPK papers using the same dichotomisation can reuse this canonical column. If a future paper uses a different split (e.g. >= 2 or >= 5), register a separate MET_GEN canonical rather than overloading this entry. A MET_GE4 = 1 patient may also have LMET = 1; the two columns are not mutually exclusive (liver-only metastases would have LMET = 1, MET_GE4 = 0).

MET_GE3 (canonical for baseline number of metastatic sites >= 3 indicator)

• Description: Binary indicator dichotomising the count of baseline metastatic sites at 3, 1 = patient has three or more documented metastatic sites at baseline, 0 = patient has zero to two metastatic sites at baseline. Time-fixed per subject. Treated as a surrogate for tumor burden in oncology popPK / TGD analyses that centre the source-paper continuous integer count at 3.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (fewer than three metastatic sites at baseline).
• Source aliases:
  • NumMet (raw integer count 0-12; used in Terranova_2022_TGD_OS_gastric.R). When a source paper supplies the raw integer count column, derive MET_GE3 = as.integer(NumMet >= 3).
• Example models: Terranova_2022_TGD_OS_gastric.R (multiplicative-additive effect on Gompertzian baseline tumor size: BASE = exp(ltvBase + etalBase) * exp(0.143 * MET_GE3) – single-step binarisation of the paper’s continuous per-metastasis coefficient exp(0.143 * (NumMet - 3)); deviation from paper’s continuous form documented in vignette Errata per the count-covariate policy).
• Notes: Sibling of MET_GE4 (Bruno 2005) and NTARGET_GE3 (Struemper 2025). The >= 3 split matches Terranova 2022’s centering value of 3 (avelumab-arm median NumMet). Auto-approved under the count-covariate-decomposed-to-binary policy on 2026-07-24 (agcand_13066655). If a future paper uses a different split (e.g. >= 2 or >= 5), register a separate sibling (MET_GE2, MET_GE5) rather than overloading this entry. A MET_GE3 = 1 patient may also have LMET = 1 (liver metastasis) or PERIT_CARC = 1 (peritoneal carcinomatosis); the three columns are not mutually exclusive.

PERIT_CARC (canonical for peritoneal carcinomatosis indicator)

• Description: Binary indicator of radiologically or surgically documented peritoneal carcinomatosis (diffuse peritoneal-surface metastatic spread) at baseline / re-baseline in a solid-tumor oncology cohort, 1 = peritoneal carcinomatosis present, 0 = absent. Time-fixed per subject at the assessed baseline. Distinct from PERIT_DIAL (peritoneal-dialysis treatment-status indicator; entirely different clinical concept) and from DIS_PERIT (peritonitis, an infectious inflammation of the peritoneum).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no peritoneal carcinomatosis at baseline).
• Source aliases:
  • Peritoneal carcinomatosis – used in Terranova_2022_TGD_OS_gastric.R (Terranova 2022 Table S2 categorical covariate on log-median OS, coded 1/0).
• Example models: Terranova_2022_TGD_OS_gastric.R (linear-additive effect on log-median OS: log_median_OS += -0.181 * PERIT_CARC; peritoneal carcinomatosis shortens median OS by ~16.6% relative to patients without it, one of the covariates flagged as meaningful in the paper’s +/-15% posterior-median threshold).
• Notes: Specific scope because peritoneal carcinomatosis is a gastrointestinal-cancer-associated metastasis pattern (colon, gastric, ovarian, appendiceal); future GI-oncology or ovarian-cancer popPK models retaining this covariate should extend the Example-models list rather than register a sibling. Ratified canonically on 2026-07-24 alongside the Terranova 2022 avelumab JAVELIN Gastric 100 extraction (agcand_13066655 sidecar request-001 q1=A). Sibling to LMET (liver metastasis) and MET_GE3 / MET_GE4 (aggregate metastatic-site count binarised at different thresholds); all four indicators can coexist for a patient with widespread advanced disease.

RESP_SD (canonical for RECIST re-baseline response = stable disease indicator)

• Description: Binary indicator, 1 = the patient’s RECIST 1.1 best response at re-baseline (typically the end of induction chemotherapy or the pre-maintenance assessment) is stable disease (SD); 0 = any other response category (CR, PR, PD, NED, NE, or non-CR/non-PD). Time-fixed at the re-baseline assessment. Used as one leaf of a decomposed RECIST-response categorical covariate under the count-covariate-decomposed-to-binary policy.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (RECIST response is anything other than SD – typically responder [CR/PR], non-CR/non-PD, PD, or NE).
• Source aliases:
  • Re-baseline stable disease vs other – used in Terranova_2022_TGD_OS_gastric.R (Terranova 2022 Table S2 categorical covariate; also enters TGD BASE equation).
  • RESSD – source NONMEM column name derived from the RECIST assessment.
• Example models: Terranova_2022_TGD_OS_gastric.R (multiplicative effect on Gompertzian baseline tumor size BASE = ... * (1 + 0.644 * RESP_SD) – SD patients have +64% larger baseline tumor size than the responder+PD+NE reference at re-baseline; and additive linear effect on log-median OS: log_median_OS += -0.0919 * RESP_SD).
• Notes: One of three parallel RECIST-response indicators (RESP_SD, RESP_NONPDCR, RESP_RESPONDER) that decompose the RECIST 1.1 response categorical into binary flags per the count-covariate-decomposed-to-binary policy. The three flags share the same source RECIST-response column and different subsets are used by different sub-models within the same paper (Terranova 2022 TGD uses RESP_SD + RESP_NONPDCR with implicit reference = responder/PD/NE; Terranova 2022 OS TTE uses RESP_SD + RESP_RESPONDER with implicit reference = non-responder-non-SD). Ratified canonically on 2026-07-24 alongside the Terranova 2022 avelumab JAVELIN Gastric 100 extraction (agcand_13066655 sidecar request-001 q1=A).

RESP_NONPDCR (canonical for RECIST re-baseline response = neither CR nor PR indicator)

• Description: Binary indicator, 1 = the patient’s RECIST 1.1 best response at re-baseline is NOT complete response (CR) and NOT partial response (PR) – i.e., the patient is a non-responder by RECIST criteria (any of stable disease [SD], non-CR/non-PD, no evidence of disease [NED], progressive disease [PD], or not evaluable [NE]); 0 = responder (CR or PR). Time-fixed at the re-baseline assessment. Complement of RESP_RESPONDER from a different reference-category framing; both are needed because different sub-models within the same analysis use different reference categories.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (responder: CR or PR at re-baseline).
• Source aliases:
  • RES_nonPR/nonCR – Terranova 2022 Supplementary Methods notation; equivalent to “response at re-baseline was not complete or partial response” per the paper’s definition. Coded 1/0.
• Example models: Terranova_2022_TGD_OS_gastric.R (multiplicative effect on Gompertzian baseline tumor size: BASE = ... * (1 - 0.0769 * RESP_NONPDCR) – non-responders have -7.69% smaller baseline tumor size than responders at re-baseline; used in the TGD sub-model only).
• Notes: Sibling of RESP_SD and RESP_RESPONDER. The name is deliberately explicit about “not CR / not PR” rather than the shorter RESP_NONRESP because the source paper’s coding is defined on the exact CR/PR complement rather than an arbitrary “responder / non-responder” binarisation of a broader ordinal. Ratified canonically on 2026-07-24 alongside the Terranova 2022 avelumab JAVELIN Gastric 100 extraction (agcand_13066655 sidecar request-001 q1=A).

RESP_RESPONDER (canonical for RECIST re-baseline response = responder (CR or PR) indicator)

• Description: Binary indicator, 1 = the patient’s RECIST 1.1 best response at re-baseline is either complete response (CR) or partial response (PR) – i.e., the patient is a responder by RECIST criteria; 0 = non-responder (any of SD, non-CR/non-PD, NED, PD, NE). Time-fixed at the re-baseline assessment. Complement of RESP_NONPDCR from a different reference-category framing; both are needed because different sub-models within the same analysis use different reference categories.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (non-responder at re-baseline; typically the pooled reference alongside a separate RESP_SD flag).
• Source aliases:
  • Re-baseline responder vs other – Terranova 2022 Table S2 OS TTE covariate label; coded 1/0.
• Example models: Terranova_2022_TGD_OS_gastric.R (additive linear effect on log-median OS: log_median_OS += 0.146 * RESP_RESPONDER; responder patients have ~15.7% longer median OS than non-responder-non-SD reference).
• Notes: Sibling of RESP_SD and RESP_NONPDCR. Explicitly RESPONDER rather than CRPR because responder-vs-other is the clinically meaningful contrast used by the source paper’s OS sub-model. Ratified canonically on 2026-07-24 alongside the Terranova 2022 avelumab JAVELIN Gastric 100 extraction (agcand_13066655 sidecar request-001 q1=A).

NTARGET_GE3 (canonical for baseline number of target lesions >= 3 indicator)

• Description: Binary indicator dichotomising the count of target lesions at baseline at 3, 1 = three or more target lesions at baseline (per RECIST 1.1), 0 = one or two target lesions at baseline. Time-fixed per subject. Distinct from TUM_SLD (continuous sum of longest diameters, mm), which captures tumor burden magnitude; NTARGET_GE3 captures lesion multiplicity / spread.
• Units: (binary)
• Type: binary
• Scope: general (oncology)
• Reference category: 0 (one or two target lesions at baseline).
• Source aliases:
  • NTARGET – raw integer count of target lesions per RECIST 1.1 (paper notation in Struemper 2025). When a source paper supplies the integer count column, derive NTARGET_GE3 = as.integer(NTARGET >= 3).
• Example models: Struemper_2025_tumorsize_OS_nsclc.R (multiplicative effect on the typical-value baseline tumor size TVTSb: TVTSb * (1 + 0.288 * NTARGET_GE3); +28.8% TVTSb for patients with three or more target lesions vs one or two).
• Notes: The >= 3 split aligns with the Struemper 2025 NSCLC analysis equation centring (NTARGET = 3 reference) even though the population median is 2 lesions (paper Table S3); the binarisation preserves the qualitative effect direction (more lesions => larger baseline tumor) while avoiding the linear extrapolation past the data range (paper data: NTARGET 1-8) that the source paper’s linear form implies. If a future oncology paper uses a different split (e.g. >= 2 or >= 5), register a separate NTARGET_GEN canonical rather than overloading this entry. Scope: general so future oncology popPK / popPD papers using the same dichotomisation can reuse this column. Founding example: Struemper 2025.

JOINT_SMALL (canonical for small-joint tenosynovial giant cell tumor (TGCT) involvement indicator)

• Description: 1 = the primary TGCT tumor is located in a small joint (e.g., hand / wrist / foot / ankle), 0 = the tumor is located in a large joint (e.g., knee / hip / shoulder / elbow; reference category). Time-fixed per subject; the anatomic joint-size classification is investigator-assigned per patient in TGCT trials. Small-joint TGCT is associated with lower baseline tumor size and greater response to CSF1R inhibition.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (large joint; the reference joint-size category in Yin 2021 Figure 2 caption reference patient).
• Source aliases:
  • JOINT_SIZE (with values small / large) – decompose into JOINT_SMALL = as.integer(JOINT_SIZE == "small") (Yin 2021 codes joint size as a binary categorical stratifier without publishing the raw column name).
• Example models: Yin_2021_pexidartinib.R (multiplicative effects exp(theta4)^JOINT_SMALL on baseline Y0 (0.581), exp(theta10)^JOINT_SMALL on kdrug (2.14), and exp(theta14)^JOINT_SMALL on konset (1.82) in the longitudinal RECIST tumor-size model; small-joint TGCT reduces baseline tumor size and increases drug-effect rate constant kdrug relative to large-joint reference).
• Notes: Specific scope because the small-vs-large joint-size stratifier is tied to musculoskeletal-oncology (TGCT) analyses; future TGCT popPK / popPD papers using the same dichotomisation should extend this entry’s example list rather than register a new canonical. The paper does not enumerate which anatomic joints are pooled as small vs large; this is an investigator-assigned per-patient classification. Ratified canonically on 2026-07-24 alongside the Yin 2021 pexidartinib TGCT exposure-response extraction. Distinct from SWOL_28JOINT (rheumatoid-arthritis 28-joint swollen-joint count from the DAS28 composite; integer 0-28) and from NTARGET_GE3 (RECIST target-lesion count binarised at 3).

TUMEXT_UPPER (canonical for upper-extremity tenosynovial giant cell tumor (TGCT) location indicator)

• Description: 1 = the primary TGCT tumor is located in an upper extremity (e.g., shoulder / elbow / wrist / hand), 0 = the tumor is located in a lower extremity (e.g., hip / knee / ankle / foot; reference category). Time-fixed per subject; the anatomic extremity classification is investigator-assigned per patient in TGCT trials. TGCT is overwhelmingly lower-extremity (~90% in the Yin 2021 cohort).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (lower extremity; the reference extremity category in Yin 2021 Figure 2 caption reference patient).
• Source aliases:
  • EXTREMITY (with values upper / lower) – decompose into TUMEXT_UPPER = as.integer(EXTREMITY == "upper") (Yin 2021 codes tumor extremity as a binary categorical stratifier without publishing the raw column name).
• Example models: Yin_2021_pexidartinib.R (multiplicative effects exp(theta3)^TUMEXT_UPPER on baseline Y0 (0.692), exp(theta9)^TUMEXT_UPPER on kdrug (0.527), and exp(theta13)^TUMEXT_UPPER on konset (7.82) in the longitudinal RECIST tumor-size model; upper-extremity TGCT has wide-CI non-significant effects on kdrug and konset in this cohort due to small upper-extremity cell counts, but the covariate is retained in the paper’s final model).
• Notes: Specific scope because the upper-vs-lower extremity stratifier is tied to musculoskeletal-oncology (TGCT) analyses; future TGCT popPK / popPD papers using the same dichotomisation should extend this entry’s example list rather than register a new canonical. Ratified canonically on 2026-07-24 alongside the Yin 2021 pexidartinib TGCT exposure-response extraction. Distinct from a hypothetical TUMEXT_LOWER canonical (would be the polar complement); the register uses the paper-published direction (upper as the non-reference indicator) to match Yin 2021 Table S2 sign convention. Complementary to JOINT_SMALL (small-vs-large joint-size stratifier); the two indicators describe orthogonal anatomic-location features of TGCT and may coexist in the same model.

ECOG_GE1 (canonical for Eastern Cooperative Oncology Group performance-status indicator, >= 1)

• Description: 1 if baseline Eastern Cooperative Oncology Group (ECOG) performance status is greater than or equal to 1, 0 if ECOG = 0. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (ECOG performance status = 0, i.e., fully active / asymptomatic).
• Source aliases:
  • PS / BPS – used in Bajaj_2017_nivolumab.R (BPS = “baseline performance status”; the one study using Karnofsky Performance Status was mapped to ECOG via Oken 1982 before binarization) and Zhang_2019_nivolumab.R (paper’s binary collapse PS=0 vs. PS>0).
  • ECOG_1 – alternative explicit form; equivalent to ECOG_GE1 when ECOG only takes values 0, 1, 2 in the analysis dataset (the typical oncology case).
  • ECOG_PS_GT0 – retired name used in earlier register drafts; semantically identical (>= 1 equals > 0 for integer ECOG scores).
  • ECOG101 (categorical 0/1/2 score with thresholding IF(ECOG101.GT.0.5)) – used in Ide_2020_elotuzumab.R. Decompose: ECOG_GE1 = as.integer(ECOG101 >= 1).
• Example models: Bajaj_2017_nivolumab.R (exponential effect on CL with coefficient 0.172), Zhang_2019_nivolumab.R (exponential effect exp(0.181) on baseline CL; additive effect -0.138 on the time-varying-CL Emax parameter), Ide_2020_elotuzumab.R (multiplicative effect on CL = 1.03; paired with ECOG_GE2 for separate ECOG=1 vs ECOG>=2 effects), Netterberg_2017_docetaxel.R (multiplicative effect on baseline ANC of the Friberg myelosuppression chain: BACOV *= (1 + theta * ECOG_GE1) with theta = 0.130; source column PERF with ordinal ECOG 0/1/2 values, binarized via ECOG_GE1 = as.integer(PERF >= 1) per Kloft 2006).
• Notes: Oncology papers conventionally report ECOG as an integer (0-5) but binarize at >= 1 because ECOG >= 2 is rare in trial cohorts. When a source paper provides the ordinal ECOG score separately, derive ECOG_GE1 = as.integer(ECOG >= 1). Zhang 2019 uses ECOG_GE1 on both baseline CL and the time-varying Emax parameter (unlike Bajaj 2017, which uses it on CL only); document the structural role in each model’s covariateData[[ECOG_GE1]]$notes. When a paper retains separate effects for ECOG = 1 vs ECOG >= 2 (Ide 2020), pair this column with ECOG_GE2 and supply both indicators in the event dataset.

ECOG_GE2 (canonical for Eastern Cooperative Oncology Group performance-status indicator, >= 2)

• Description: 1 if baseline Eastern Cooperative Oncology Group (ECOG) performance status is greater than or equal to 2, 0 if ECOG <= 1. Time-fixed per subject. Used in models that retain separate effects for ECOG = 1 vs ECOG >= 2 by pairing this column with ECOG_GE1.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (ECOG performance status <= 1; in models that pair ECOG_GE1 and ECOG_GE2, both indicators = 0 corresponds to ECOG = 0 and (ECOG_GE1 = 1, ECOG_GE2 = 0) corresponds to ECOG = 1).
• Source aliases:
  • ECOG101 (categorical 0/1/2 score with thresholding IF(ECOG101.GT.1.5)) – used in Ide_2020_elotuzumab.R. Decompose: ECOG_GE2 = as.integer(ECOG101 >= 2).
• Example models: Ide_2020_elotuzumab.R (multiplicative effect on CL = 1.15; paired with ECOG_GE1 to retain separate ECOG = 1 vs ECOG >= 2 effects).
• Notes: Parallels ECOG_GE1. Use only when the source paper reports a separate effect for ECOG >= 2 in addition to ECOG_GE1; otherwise ECOG_GE1 alone is sufficient. The paired (ECOG_GE1, ECOG_GE2) decomposition reproduces a three-level (ECOG = 0, ECOG = 1, ECOG >= 2) ordinal effect with two binaries.

TUMTP_SCLC (canonical for small-cell-lung-cancer tumor-type indicator)

• Description: 1 = small cell lung cancer (SCLC), 0 = other tumor types.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = all other tumor types (e.g., melanoma, NSCLC, RCC, HCC, CRC in the Sanghavi 2020 cohort; reference category is melanoma).
• Source aliases:
  • TUMTP (categorical column with levels including melanoma, NSCLC, SCLC, CRC, HCC, RCC) – decompose into TUMTP_SCLC = as.integer(TUMTP == "SCLC").
• Example models: Sanghavi_2020_ipilimumab.R (exponential coefficient -0.124 on CL).
• Notes: Follows the TUMTP_HODGKIN_CLASSICAL / TUMTP_GASTRIC decomposition pattern. SCLC is the only retained tumor-type indicator in the Sanghavi 2020 final model after backward elimination; the other tumor types collapse into the reference (melanoma) group.

TUMTP_NSCLC (canonical for non-small-cell-lung-cancer tumor-type indicator)

• Description: 1 = non-small cell lung cancer (NSCLC), 0 = other tumor types.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 = all other tumor types (in Ahamadi 2017 the implicit reference is melanoma, with the rare “other” cancer type pooled into the reference).
• Source aliases:
  • TUMTP (categorical column with levels including melanoma, NSCLC, other) – decompose into TUMTP_NSCLC = as.integer(TUMTP == "NSCLC").
• Example models: Ahamadi_2017_pembrolizumab.R (proportional change of +14.5% on CL for NSCLC patients relative to melanoma; the “other” cancer type cohort – 1.01% of the population – is pooled into the melanoma reference per the paper’s model description), Aoyama_2012_sepantronium.R.
• Notes: Follows the TUMTP_HODGKIN_CLASSICAL / TUMTP_GASTRIC / TUMTP_SCLC decomposition pattern. Scope: general because NSCLC is a high-frequency tumor-type contrast (with melanoma or “other” reference) across PD-1 / PD-L1 / chemotherapy popPK analyses, and is likely to recur in future extractions. Ratified canonically on 2026-05-17 alongside the Ahamadi 2017 pembrolizumab extraction.

TUMTP_NSCLC_NONADENO (canonical for non-adenocarcinoma-histology NSCLC sub-indicator)

• Description: 1 = NSCLC of a histology OTHER than adenocarcinoma (squamous-cell carcinoma, large-cell carcinoma, or other non-adenocarcinoma NSCLC subtype); 0 = adenocarcinoma NSCLC, NSCLC of unknown histology, or a non-NSCLC diagnosis. Within-NSCLC histology sub-indicator paired with TUMTP_NSCLC (which distinguishes NSCLC patients overall from other tumor types).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (adenocarcinoma NSCLC, unknown-histology NSCLC, or non-NSCLC subject; in particular IPF / non-cancer subjects in a mixed cohort are assigned 0 because they have no NSCLC histology).
• Source aliases:
  • Schmid 2017 categorical column with levels “NSCLC-adenocarcinoma” / “NSCLC-no adenocarcinoma” / “IPF or NSCLC of unknown histology” – decompose into TUMTP_NSCLC_NONADENO = as.integer(NSCLC_histology == "non-adenocarcinoma").
• Example models: Schmid_2017_nintedanib.R (multiplicative effect 1.36 on BIBF 1202 ka2 absorption rate; reference category adenocarcinoma NSCLC pooled with IPF and unknown-histology NSCLC).
• Notes: Follows the TUMTP_<type> decomposition pattern (auto-approved family). Within-NSCLC histology distinctions arise when a popPK / PD model in NSCLC differentiates adenocarcinoma from squamous-cell carcinoma; the paired adenocarcinoma reference can be made explicit by adding a TUMTP_NSCLC_ADENO sibling in a future extraction if needed. Ratified canonically on 2026-06-27 alongside the Schmid 2017 nintedanib extraction.

TUMTP_HRPC (canonical for hormone-refractory prostate cancer tumor-type indicator)

• Description: 1 = hormone-refractory prostate cancer (HRPC), 0 = other tumor types. The historical term HRPC has since been displaced by CRPC (castration-resistant prostate cancer); the two terms refer to the same clinical entity. TUMTP_HRPC is the canonical column for both wordings (no separate TUMTP_CRPC is registered; if a future paper distinguishes hormone-naive metastatic prostate cancer from CRPC, register a more specific canonical at that time).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 = all other tumor types (per source paper; in Aoyama 2012 the implicit reference is NSCLC when paired with TUMTP_MEL = 0).
• Source aliases:
  • TUMTP (categorical column with levels including HRPC, NSCLC, MM) – decompose into TUMTP_HRPC = as.integer(TUMTP == "HRPC").
  • STUDY / CANCER_TYPE factor columns where one level is HRPC or CRPC – decompose identically.
• Example models: Aoyama_2012_sepantronium.R (proportional change of -4.5% on CL for HRPC patients relative to the NSCLC reference; ratio THETA_HRPC = 0.955 in the paper’s power form), Lacy_2018_cabozantinib.R (multiplicative fractional effect on CL/F = -0.009 and on Vc/F = -0.241 for CRPC patients relative to the healthy-volunteer reference; Lacy 2018 enrolled 823 CRPC patients across Studies XL184-203, XL184-306, and XL184-307, the largest cancer-type cohort in the integrated popPK analysis).
• Notes: Follows the TUMTP_HODGKIN_CLASSICAL / TUMTP_GASTRIC / TUMTP_SCLC / TUMTP_NSCLC decomposition pattern. Scope: general because prostate-cancer cohorts (under either HRPC or CRPC nomenclature) recur across small-molecule and targeted-therapy popPK analyses. The “hormone-refractory” wording reflects the 2010s convention; modern papers using CRPC map onto the same canonical. Ratified canonically on 2026-05-20 alongside the Aoyama 2012 sepantronium extraction.

TUMTP_MTC (canonical for medullary thyroid carcinoma tumor-type indicator)

• Description: 1 = medullary thyroid carcinoma (MTC), 0 = other tumor types. Time-fixed per subject. MTC is a distinct C-cell-derived thyroid malignancy (not papillary / follicular / anaplastic thyroid cancer); cabozantinib and vandetanib are the principal MTC-approved tyrosine-kinase inhibitors.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 = all other tumor types (per source paper; in Lacy 2018 the implicit reference is healthy volunteer when paired with all other TUMTP_* indicators = 0).
• Source aliases:
  • POP (Lacy 2018 NONMEM categorical population indicator with levels HV / RCC / CRPC / MTC / GB / Other) – decompose into TUMTP_MTC = as.integer(POP == "MTC").
• Example models: Lacy_2018_cabozantinib.R (multiplicative fractional effect on CL/F = +0.928 and on Vc/F = -0.07 for MTC patients relative to the healthy-volunteer reference; the +93% CL/F increase in MTC is the load-bearing finding of Lacy 2018 and explains why the MTC capsule label dose is 140 mg/day while the RCC tablet label dose is only 60 mg/day).
• Notes: Follows the TUMTP_HODGKIN_CLASSICAL / TUMTP_GASTRIC / TUMTP_SCLC / TUMTP_NSCLC decomposition pattern. Scope: general because MTC is the approved indication for Cometriq (cabozantinib capsule) and Caprelsa (vandetanib), and future TKI popPK papers in MTC populations are likely to recur. Ratified canonically on 2026-05-25 alongside the Lacy 2018 cabozantinib extraction.

TUMTP_RCC (canonical for renal cell carcinoma tumor-type indicator)

• Description: 1 = renal cell carcinoma (RCC), 0 = other tumor types. Time-fixed per subject. Includes both clear-cell and non-clear-cell histologies pooled at the cohort level; if a future paper distinguishes clear-cell from non-clear-cell, register a finer-grained canonical at that time.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 = all other tumor types (per source paper; in Lacy 2018 the implicit reference is healthy volunteer when paired with all other TUMTP_* indicators = 0).
• Source aliases:
  • POP (Lacy 2018 NONMEM categorical population indicator with levels HV / RCC / CRPC / MTC / GB / Other) – decompose into TUMTP_RCC = as.integer(POP == "RCC").
• Example models: Lacy_2018_cabozantinib.R (multiplicative fractional effect on CL/F = -0.129 and on Vc/F = -0.63 for RCC patients relative to the healthy-volunteer reference; Lacy 2018 enrolled 282 RCC patients in Study XL184-308 dosed at 60 mg/day cabozantinib tablet).
• Notes: Follows the TUMTP_HODGKIN_CLASSICAL / TUMTP_GASTRIC / TUMTP_SCLC / TUMTP_NSCLC decomposition pattern. Scope: general because RCC cohorts recur frequently across TKI, anti-PD-1, and anti-VEGF popPK analyses. Ratified canonically on 2026-05-25 alongside the Lacy 2018 cabozantinib extraction.

TUMTP_DTC (canonical for differentiated thyroid cancer tumor-type indicator)

• Description: 1 = differentiated thyroid cancer (DTC, i.e. papillary or follicular thyroid carcinoma including their variants; in most modelling cohorts specifically radioiodine-refractory DTC, RR-DTC), 0 = other tumor types or healthy subject. Time-fixed per subject. Distinct from TUMTP_MTC (medullary thyroid carcinoma), which arises from parafollicular C cells rather than the follicular epithelium and is a separate disease entity; do not pool the two.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 = all other tumor types (per source paper; in Majid 2024 the reference stratum is cancer patients with a solid tumor other than DTC, RCC or HCC, obtained by setting TUMTP_DTC, TUMTP_RCC, TUMTP_HCC and DIS_HEALTHY all to 0).
• Source aliases:
  • DTC (Majid 2024 Text S1 NONMEM flag derived from the study number: IF (STUD.EQ.303.OR.STUD.EQ.211.OR.STUD.EQ.201) DTC=1).
  • TUMTYP (Majid 2024 Text S1 categorical column with levels 1 = DTC, 2 = RCC, 3 = HCC, 4 = other solid tumor) – decompose into TUMTP_DTC = as.integer(TUMTYP == 1).
  • TUM / POP / CANCER_TYPE factor columns where one level is DTC, RR-DTC, papillary or follicular thyroid cancer – decompose identically.
• Example models: Majid_2024_lenvatinib.R (multiplicative power-form effect on CL/F: 0.951^TUMTP_DTC, i.e. -4.9 percent relative to the other-solid-tumor reference; 542 of 1921 pooled subjects were DTC).
• Notes: Follows the TUMTP_HODGKIN_CLASSICAL / TUMTP_GASTRIC / TUMTP_SCLC / TUMTP_NSCLC / TUMTP_RCC decomposition pattern. Scope: general because DTC cohorts recur across multikinase-inhibitor popPK analyses (lenvatinib, sorafenib, cabozantinib, vandetanib) and thyroid-cancer PK/PD work. Gupta_2016_lenvatinib.R records its DTC cohort only in population$disease_state because that model has no DTC covariate effect; that is not an omission of this column. Ratified canonically alongside the Majid 2024 lenvatinib PK/PD extraction.

TUMTP_HCC (canonical for hepatocellular carcinoma tumor-type indicator)

• Description: 1 = hepatocellular carcinoma (HCC), 0 = other tumor types or healthy subject. Time-fixed per subject. Note that an HCC cohort usually also carries altered hepatic-function laboratory covariates (albumin, alkaline phosphatase, bilirubin, Child-Pugh class); when a model fits both this indicator and continuous liver-function markers, the indicator captures the residual tumor-type effect after those markers are accounted for.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 = all other tumor types (per source paper; in Majid 2024 the reference stratum is cancer patients with a solid tumor other than DTC, RCC or HCC).
• Source aliases:
  • HCC (Majid 2024 Text S1 NONMEM flag derived from the study number: IF (STUD.EQ.202.OR.STUD.EQ.304) HCC=1).
  • TUMTYP (Majid 2024 Text S1 categorical column with levels 1 = DTC, 2 = RCC, 3 = HCC, 4 = other solid tumor) – decompose into TUMTP_HCC = as.integer(TUMTYP == 3).
  • POP / CANCER_TYPE factor columns where one level is HCC or hepatocellular – decompose identically.
• Example models: Majid_2024_lenvatinib.R (multiplicative power-form effect on CL/F: 0.862^TUMTP_HCC, i.e. -13.8 percent relative to the other-solid-tumor reference; 534 of 1921 pooled subjects were HCC).
• Notes: Follows the TUMTP_HODGKIN_CLASSICAL / TUMTP_GASTRIC / TUMTP_SCLC / TUMTP_NSCLC / TUMTP_RCC decomposition pattern. Scope: general because HCC cohorts recur across TKI, anti-PD-(L)1 and anti-VEGF popPK analyses. Distinct from the HEPIMP_* (NCI ODWG hepatic-impairment) family, which classifies hepatic function by bilirubin and AST regardless of tumor type; an HCC patient may have normal hepatic function and a non-HCC patient may be hepatically impaired, so the two families are not substitutes. Ratified canonically alongside the Majid 2024 lenvatinib PK/PD extraction.

TUMTP_MEL (canonical for melanoma tumor-type indicator)

• Description: 1 = melanoma (any anatomic site; in advanced-solid-tumor cohorts typically unresectable stage III or IV cutaneous melanoma), 0 = other tumor types.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 = all other tumor types (per source paper; in Aoyama 2012 the implicit reference is NSCLC when paired with TUMTP_HRPC = 0).
• Source aliases:
  • TUMTP (categorical column with levels including MM, NSCLC, HRPC) – decompose into TUMTP_MEL = as.integer(TUMTP == "MM"). NOTE: in Aoyama 2012 the abbreviation MM denotes malignant (unresectable) melanoma, NOT multiple myeloma; do not confuse with the canonical MM register entry (which is specifically active multiple myeloma).
  • STUDY / CANCER_TYPE factor columns where one level is melanoma or MM (melanoma sense) – decompose identically.
• Example models: Aoyama_2012_sepantronium.R (proportional change of +24% on CL for melanoma patients relative to the NSCLC reference; ratio THETA_MM = 1.24 in the paper’s power form).
• Notes: Follows the TUMTP_HODGKIN_CLASSICAL / TUMTP_GASTRIC / TUMTP_SCLC / TUMTP_NSCLC decomposition pattern. The canonical name uses MEL (not MM) to disambiguate from the existing MM register entry for multiple myeloma. Scope: general because melanoma cohorts recur across PD-1 / PD-L1 / BRAF-inhibitor / small-molecule popPK analyses. Ratified canonically on 2026-05-20 alongside the Aoyama 2012 sepantronium extraction.

TUMTP_LYMPH (canonical for lymphoma (pooled) tumor-type indicator)

• Description: 1 = lymphoma (heterogeneous lymphoma pool spanning multiple lymphoma histologies – e.g., classical Hodgkin lymphoma combined with extranodal NK/T-cell lymphoma; or any-histology lymphoma pooled with solid-tumor and leukemia cohorts), 0 = solid tumor or other tumor type.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 = non-lymphoma tumor type (per source paper; e.g., NSCLC, GC/GEJ, ESCC, “Other” solid tumors in the Wang 2024 cohort, with NSCLC as the implicit reference when paired with the other Wang 2024 TUMTP_* indicators; or leukemia as the implicit reference in the Akbar 2025 cohort).
• Source aliases:
  • TTYPE1 (Wang 2024) – decompose into TUMTP_LYMPH = as.integer(TTYPE1 == 1). The Wang 2024 source paper uses a multi-level TTYPE factor with levels 1 = lymphoma, 2 = lung cancer (reference), 3 = other, 4 = GCGEJ, 5 = ESCC.
  • Categorical column “type of cancer” with level “Lymphoma” (Akbar 2025) – decompose into TUMTP_LYMPH = as.integer(cancer_type == "Lymphoma").
• Example models: Wang_2024_sugemalimab.R (exponential coefficient log(0.877) on baseline CL and log(0.879) on Vc), Akbar_2025_voriconazole.R (additive-fractional +1.91% effect on CL relative to leukemia reference; 95% CI spans zero).
• Notes: Distinct from TUMTP_HODGKIN_CLASSICAL (which is specifically classical Hodgkin lymphoma). Wang 2024 pools two lymphoma histologies (extranodal NK/T-cell lymphoma from CS1001-201 / NCT03595657 and classical Hodgkin lymphoma from CS1001-202 / NCT03505996) into a single lymphoma indicator; the indicator therefore captures a generic “hematologic-vs-solid-tumor” contrast rather than a histology-specific effect. Akbar 2025 uses a single “Lymphoma” category alongside leukemia, sarcoma, breast cancer, myeloma, and glioma in a heterogeneous-cancer TDM cohort. When a future paper studies a single lymphoma histology distinct from cHL, register a more specific canonical (e.g., TUMTP_ENKTL, TUMTP_NHL) rather than overloading this one. Document the per-paper histology composition in covariateData[[TUMTP_LYMPH]]$notes. Promoted from Scope: specific to Scope: general on 2026-05-09 alongside the Akbar 2025 voriconazole extraction so that any heterogeneous-cancer-cohort PK analysis can use this canonical name without scope-violation.

TUMTP_BREAST (canonical for breast-cancer tumor-type indicator)

• Description: 1 = breast cancer (any histology / receptor status), 0 = other tumor types.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 = all other tumor types (per source paper; in Lu 2022 the implicit reference is NSCLC, with colorectal cancer pooled into the reference because its CL effect was found insignificant relative to NSCLC).
• Source aliases:
  • TUMTP_BC – prior canonical name (pre-2026-06-19 standardization audit).
  • TUMTP (categorical column with levels including BC, NSCLC, CRC) – decompose into TUMTP_BREAST = as.integer(TUMTP == "BC").
• Example models: Lu_2022_patritumab.R (multiplicative fractional effect 0.811 on CLlin of DXd-conjugated antibody for breast-cancer patients vs the NSCLC reference; CRC effect was tested and found insignificant so CRC is pooled into the reference).
• Notes: Follows the TUMTP_HODGKIN_CLASSICAL / TUMTP_GASTRIC / TUMTP_SCLC decomposition pattern. Registers the breast-cancer arm of an oncology-cohort tumor-type contrast; pair with sister TUMTP_<GROUP> indicators (e.g., TUMTP_NSCLC, TUMTP_CRC) when a future paper retains separate effects for additional tumor types beyond the implicit reference. Ratified canonically on 2026-04-28.

TUMTP_CRC (canonical for colorectal-cancer tumor-type indicator)

• Description: 1 = colorectal cancer (any site / histology, including metastatic CRC), 0 = other tumor types.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 = all other tumor types (per source paper; in Zhang 2024 the reference is a healthy participant, obtained when TUMTP_CRC = 0 and every sister TUMTP_<GROUP> indicator is also 0).
• Source aliases:
  • TUM (Zhang 2024 tucatinib; integer tumor-type code where TUM == 1 is HER2+ mCRC, TUM == 0 is HER2+ mBC and TUM < 0 is a healthy participant) – decompose into TUMTP_CRC = as.integer(TUM == 1).
  • TUMTP (categorical column with levels including BC, NSCLC, CRC) – decompose into TUMTP_CRC = as.integer(TUMTP == "CRC").
• Example models: Zhang_2024_tucatinib.R (multiplicative fractional effect -0.705 on CL and -0.637 on relative bioavailability for HER2+ metastatic colorectal cancer versus the healthy-participant reference; the two partly offset, giving a net 18.7% lower apparent CL/F; n = 69 / 283 = 24.4% of the pooled cohort, all from study SGNTUC-017 / MOUNTAINEER).
• Notes: Follows the TUMTP_HODGKIN_CLASSICAL / TUMTP_GASTRIC / TUMTP_SCLC decomposition pattern, and is the sister indicator anticipated by the TUMTP_BREAST entry. Registers the colorectal arm of an oncology-cohort tumor-type contrast. General scope because “colorectal cancer” is a stable histology-agnostic disease label rather than a per-paper pooled bucket; use TUMTP_OTHER instead when a paper collapses CRC into a heterogeneous residual pool (as Sathe 2024 / Sathe 2025 do), and pool CRC into the reference when a paper tests it and finds no effect (as Lu 2022 does). A given subject can have at most one of the TUMTP_<GROUP> indicators set to 1; all-zero means the reference group. Receptor status (e.g. HER2+) and RAS status are not encoded here – record them in covariateData[[TUMTP_CRC]]$notes when the source cohort is selected on them.

TUMTP_ESCC (canonical for oesophageal-squamous-cell-carcinoma tumor-type indicator)

• Description: 1 = oesophageal squamous cell carcinoma (ESCC), 0 = other tumor types.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = all other tumor types (per source paper; in Wang 2024, the implicit reference is NSCLC when all the other TUMTP_* indicators are also 0).
• Source aliases:
  • TTYPE5 (Wang 2024) – decompose into TUMTP_ESCC = as.integer(TTYPE5 == 1).
• Example models: Wang_2024_sugemalimab.R (exponential coefficient log(0.99) on baseline CL and log(1.08) on Vc).
• Notes: Follows the TUMTP_HODGKIN_CLASSICAL / TUMTP_GASTRIC / TUMTP_SCLC decomposition pattern. Distinct from gastroesophageal-junction adenocarcinoma (which is captured by the broader TUMTP_GASTRIC indicator that pools GC and GEJ adenocarcinomas) – ESCC is a squamous-cell histology, not adenocarcinoma. Document the per-paper histology composition in covariateData[[TUMTP_ESCC]]$notes.

TUMTP_PCALCL (canonical for primary cutaneous anaplastic large-cell lymphoma indicator)

• Description: 1 = primary cutaneous anaplastic large-cell lymphoma (pcALCL), 0 = other tumor types.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = all other tumor types (in Suri 2018, the non-pcALCL reference comprises Hodgkin lymphoma, systemic ALCL, mycosis fungoides, and other CD30+ hematologic malignancies pooled together).
• Source aliases:
  • PCALCL – used in Suri_2018_brentuximab.R. Suri 2018 reports the effect as a power-form multiplier cl_adc *= 0.728^TUMTP_PCALCL (pcALCL CL ~27% lower than non-pcALCL).
• Example models: Suri_2018_brentuximab.R (effect on ADC clearance only).
• Notes: Follows the TUMTP_HODGKIN_CLASSICAL / TUMTP_GASTRIC / TUMTP_SCLC decomposition pattern. Distinct from TUMTP_LYMPH (heterogeneous lymphoma pool) and TUMTP_HODGKIN_CLASSICAL (classical Hodgkin lymphoma). pcALCL is one of two histologies pooled into the broader CTCL category in Suri 2018 (alongside mycosis fungoides); the model singles out pcALCL because Suri 2018 backward elimination retained pcALCL as a separate effect on ADC clearance after exploring the broader CTCL contrast. Ratified canonically on 2026-04-28.

TUMTP_SARC (canonical for sarcoma tumor-type indicator)

• Description: 1 = sarcoma (any histology – soft-tissue or bone sarcoma pooled), 0 = other tumor types.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = all other tumor types (per source paper; in Akbar 2025 the implicit reference is leukemia when paired with the other Akbar TUMTP_* indicators all = 0).
• Source aliases:
  • Categorical column “type of cancer” with level “Sarcoma” – decompose into TUMTP_SARC = as.integer(cancer_type == "Sarcoma"). Used in Akbar_2025_voriconazole.R.
• Example models: Akbar_2025_voriconazole.R (additive-fractional +18.5% effect on CL relative to leukemia reference).
• Notes: Follows the TUMTP_HODGKIN_CLASSICAL / TUMTP_GASTRIC / TUMTP_SCLC decomposition pattern. Akbar 2025 pools soft-tissue and bone sarcoma histologies into a single sarcoma category. Scope: specific because the reference category (leukemia in Akbar 2025) is paper-defined. Ratified canonically on 2026-05-09.

TUMTP_MYELO (canonical for multiple myeloma tumor-type indicator (used in heterogeneous-cancer pooled cohorts))

• Description: 1 = multiple myeloma, 0 = other tumor types.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = all other tumor types (per source paper; in Akbar 2025 the implicit reference is leukemia when paired with the other Akbar TUMTP_* indicators all = 0).
• Source aliases:
  • Categorical column “type of cancer” with level “Myeloma” – decompose into TUMTP_MYELO = as.integer(cancer_type == "Myeloma"). Used in Akbar_2025_voriconazole.R.
• Example models: Akbar_2025_voriconazole.R (additive-fractional -2.33% effect on CL relative to leukemia reference; the 95% CI spans zero).
• Notes: Distinct from the stub MM entry (which is reserved for multiple-myeloma-as-primary-indication PK studies; the MM definition lacks a complete schema and predates the TUMTP_* convention) and from DIS_SMM (smoldering multiple myeloma, an asymptomatic plasma-cell disorder). Use TUMTP_MYELO when the source paper pools multiple myeloma alongside other tumor types in a heterogeneous oncology cohort and treats cancer type as a many-level categorical covariate. Scope: specific because the reference category (leukemia in Akbar 2025) is paper-defined. Ratified canonically on 2026-05-09.

TUMTP_GLIO (canonical for glioma tumor-type indicator)

• Description: 1 = glioma (any grade / histology – e.g., glioblastoma, anaplastic astrocytoma, oligodendroglioma pooled), 0 = other tumor types.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = all other tumor types (per source paper; in Akbar 2025 the implicit reference is leukemia when paired with the other Akbar TUMTP_* indicators all = 0; in Lacy 2018 the reference is healthy volunteer when paired with the other TUMTP_* indicators = 0).
• Source aliases:
  • Categorical column “type of cancer” with level “Glioma” – decompose into TUMTP_GLIO = as.integer(cancer_type == "Glioma"). Used in Akbar_2025_voriconazole.R.
  • POP (Lacy 2018 NONMEM categorical population indicator) – decompose into TUMTP_GLIO = as.integer(POP == "GB") (Lacy 2018 enrolled glioblastoma multiforme patients in Study XL184-201 dosed at 140 mg/day cabozantinib capsule).
• Example models: Akbar_2025_voriconazole.R (additive-fractional +8.81% effect on CL relative to leukemia reference; the 95% CI spans zero), Lacy_2018_cabozantinib.R (multiplicative fractional effect on CL/F = +0.216 and on Vc/F = -0.569 for GB patients relative to the healthy-volunteer reference; n = 39 GB patients in Study XL184-201), Gastonguay_2005_efaproxiral.R (categorical power-model multiplier on SLPp50 = 1.28 for cranial glioblastoma multiforme vs lung-cancer reference; CATP level 3; n = 72 GBM patients / 451 = 16.0% of radiation-therapy cohort).
• Notes: Follows the TUMTP_HODGKIN_CLASSICAL / TUMTP_GASTRIC / TUMTP_SCLC decomposition pattern. Akbar 2025 reports a single “glioma” category without further subdivision by histology or grade. Scope: specific because the reference category (leukemia in Akbar 2025; healthy volunteer in Lacy 2018; lung cancer in Gastonguay 2005) is paper-defined. Ratified canonically on 2026-05-09.

TUMTP_CNS_PRIM (canonical for primary CNS tumour umbrella indicator)

• Description: 1 = primary central nervous system (CNS) tumour (an umbrella pooling glioma, medulloblastoma, ependymoma, brainstem glioma, atypical teratoid / rhabdoid tumour, and other tumours arising within the brain or spinal cord), 0 = non-primary-CNS tumour (per-source-paper reference cohort; e.g. sarcomas in Han 2015). Distinct from TUMTP_GLIO, which is glioma-only; use TUMTP_CNS_PRIM when the source paper’s binary tumour-type contrast pools glioma with non-glioma primary CNS histologies. The PRIM suffix distinguishes primary CNS tumours from metastatic CNS involvement (which is classified by primary site of origin under the other TUMTP_* entries). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = non-primary-CNS tumour (paper-defined; for Han 2015 the complement cohort is the pooled paediatric sarcomas: refractory sarcomas AVF2771s + newly-diagnosed osteosarcoma AVF4117s + metastatic soft-tissue sarcoma BO20924).
• Source aliases:
  • Study-based indicator “Primary CNS tumour” as defined by paper narrative (Han 2015 Table 1 splits the paediatric cohort into two clinical groups: primary-CNS-tumour patients in AVF3842s and sarcoma patients in AVF2771s + AVF4117s + BO20924, with n = 76 in each group of the model-building population).
• Example models: Han_2015_bevacizumab.R (multiplicative fractional effect: CL x 0.725^TUMTP_CNS_PRIM and V1 x 0.854^TUMTP_CNS_PRIM relative to the paediatric-sarcoma reference; Han 2015 Table 2 rows ‘Primary CNS tumour on CL’ = 0.725 and ‘Primary CNS tumour on V1’ = 0.854 with RSE 4.3% and 3.7% respectively).
• Notes: Follows the TUMTP_CHL / TUMTP_GC / TUMTP_SCLC decomposition pattern. Use this canonical when the source paper reports a single umbrella indicator for primary CNS tumours as a group rather than resolving individual histologies (glioma / medulloblastoma / ependymoma). When a future paper resolves specific primary-CNS-tumour histologies separately, register the finer-grained canonical(s) alongside; the umbrella indicator can coexist with the specific ones because the pooling is a paper-level analytic decision. Scope: specific because the reference category is paper-defined. Ratified canonically on 2026-06-20 alongside the Han 2015 bevacizumab paediatric-popPK extraction.

TUMTP_NET (canonical for neuroendocrine-tumour tumor-type indicator)

• Description: 1 = neuroendocrine tumour (NET, including metastasised gastroenteropancreatic and other well-differentiated NETs), 0 = other tumour types (per-source-paper reference cohort). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = other tumour types (paper-defined; in the Budiansah 2025 cohort the complement is meningioma, the other somatostatin-receptor-positive tumour type enrolled for peptide receptor radionuclide therapy).
• Source aliases:
  • Net / NETs – Kletting 2016 J Nucl Med Table 1 ‘Disease’ column, whose two levels are Net and Men (meningioma); decompose into TUMTP_NET = as.integer(Disease == "Net").
• Example models: Budiansah_2025_dotatate_pbpk.R (whole-body sst2 PBPK in which the tumour is the only tumour-type-specific region: the indicator switches the tumour interstitial fraction 0.3 vs 0.23, vascular fraction 0.1 vs 0.11, serum flow density 1.0 vs 0.9 mL/min/g and permeability-surface density 0.2 vs 0.31 mL/min/g).
• Notes: Follows the TUMTP_HODGKIN_CLASSICAL / TUMTP_GASTRIC / TUMTP_SCLC decomposition pattern. Distinct from TUMTP_MTC (medullary thyroid carcinoma), which is a specific neuroendocrine histology that a paper pooling all NETs would subsume; register the finer-grained canonical alongside when a source resolves NET subtypes. In radioligand-therapy papers the tumour type usually enters as a physiologic switch (perfusion, permeability, interstitial fraction) rather than as a covariate-effect coefficient on a PK parameter. Ratified canonically on 2026-08-14 alongside the Budiansah 2025 [111In]In-DOTA-TATE PBPK extraction.

TUMTP_LEUK (canonical for leukemia tumor-type indicator)

• Description: 1 = leukemia (any subtype – AML / ALL / CLL / CML pooled), 0 = other tumor types. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = all other tumor types (per source paper). In Akbar 2025 leukemia is the implicit reference (column not used directly), but the canonical name is registered so that future papers that retain leukemia as a non-reference contrast can use it.
• Source aliases:
  • Categorical column “type of cancer” with level “Leukemia” – decompose into TUMTP_LEUK = as.integer(cancer_type == "Leukemia"). Implicit reference category in Akbar_2025_voriconazole.R (so the model file does not consume this column directly; it is registered for future heterogeneous-cancer-cohort analyses).
• Example models: Akbar_2025_voriconazole.R.
• Notes: Distinct from the more specific DIS_AML, DIS_BCPALL, DIS_CMML, DIS_MDS_AML entries – those are for leukemia-only or leukemia-vs-leukemia contrasts; TUMTP_LEUK is for heterogeneous-cancer pooled cohorts where leukemia is one of several tumor types and the analysis treats cancer type as a many-level categorical. Akbar 2025 had leukemia as 56.8% of the cohort and used it as the reference category. Scope: specific because the reference category in any source paper is paper-defined. Ratified canonically on 2026-05-09.

TUMTP_BCL (canonical for B-cell lymphoma (pooled residual) tumor-type indicator)

• Description: 1 = B-cell lymphoma (BCL), 0 = other tumor types. Time-fixed per subject. In Gibiansky 2014 the BCL category is a pooled residual indolent-B-cell-lymphoma group that includes follicular lymphoma (FL was the primary indication in GAUDI; the four-level DIS column in the NONMEM control stream splits B-cell histologies into CLL = 1, BCL = 2 (residual indolent B-cell-lymphoma pool including FL), DLBCL = 3, MCL = 4).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = all other tumor types (in Gibiansky 2014 the implicit reference is CLL when paired with TUMTP_DLBCL and TUMTP_MCL all = 0; the residual indolent-B-cell-lymphoma pool in this paper is dominated by follicular lymphoma).
• Source aliases:
  • DIS (Gibiansky 2014; integer code with DIS == 2 flagging BCL) – decompose into TUMTP_BCL = as.integer(DIS == 2).
• Example models: Gibiansky_2014_obinutuzumab.R (effect on time-dependent clearance decay rate kdes via the composite (TUMTP_BCL + TUMTP_DLBCL + TUMTP_MCL) (any-NHL effect; ratio 2.08) and on both time-dependent CL_T and steady-state CL_inf via the composite (TUMTP_BCL + TUMTP_DLBCL) (shared BCL/DLBCL effect; ratio 0.834 in the reverse direction, i.e., 16.6% lower CL than CLL)).
• Notes: Follows the TUMTP_HODGKIN_CLASSICAL / TUMTP_GASTRIC / TUMTP_SCLC decomposition pattern. Distinct from TUMTP_LYMPH (a broader heterogeneous lymphoma pool that lumps cHL with NHL histologies) – TUMTP_BCL is specifically B-cell lymphoma and pairs with sibling TUMTP_DLBCL and TUMTP_MCL for histology-specific contrasts within the NHL family. When a future paper studies follicular lymphoma in isolation (rather than pooled into BCL), register a more specific canonical (e.g., TUMTP_FL) rather than overloading this one. Ratified canonically on 2026-05-11.

TUMTP_DLBCL (canonical for diffuse large B-cell lymphoma indicator)

• Description: 1 = diffuse large B-cell lymphoma (DLBCL), 0 = other tumor types. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = all other tumor types (in Gibiansky 2014 the implicit reference is CLL when paired with TUMTP_BCL and TUMTP_MCL all = 0).
• Source aliases:
  • DIS (Gibiansky 2014; integer code with DIS == 3 flagging DLBCL) – decompose into TUMTP_DLBCL = as.integer(DIS == 3).
• Example models: Gibiansky_2014_obinutuzumab.R (effect on kdes via the any-NHL composite indicator; effect on CL_T and CL_inf via the shared BCL/DLBCL composite indicator), Lu_2017_polatuzumab_neuropathy.R (multiplicative effect on the grade >= 2 peripheral neuropathy hazard via exp(theta_DLBCL * TUMTP_DLBCL) with theta_DLBCL = -0.0697, SE 0.365; the median hazard ratio of DLBCL vs FL is 0.931 per Figure 3).
• Notes: Follows the TUMTP_HODGKIN_CLASSICAL / TUMTP_GASTRIC / TUMTP_SCLC decomposition pattern. Distinct from TUMTP_LYMPH (broader lymphoma pool) and TUMTP_PCALCL (primary cutaneous anaplastic large-cell lymphoma; a CD30+ T-cell-lineage entity unrelated to DLBCL). DLBCL is the most common high-grade B-cell-NHL subtype; the Gibiansky 2014 cohort had only 30 DLBCL patients (4.4%), so a single estimated effect on CL is shared with BCL (a much larger pooled group; see TUMTP_BCL notes). Ratified canonically on 2026-05-11.

TUMTP_MCL (canonical for mantle cell lymphoma indicator)

• Description: 1 = mantle cell lymphoma (MCL), 0 = other tumor types. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = all other tumor types (in Gibiansky 2014 the implicit reference is CLL when paired with TUMTP_BCL and TUMTP_DLBCL all = 0).
• Source aliases:
  • DIS (Gibiansky 2014; integer code with DIS == 4 flagging MCL) – decompose into TUMTP_MCL = as.integer(DIS == 4).
• Example models: Gibiansky_2014_obinutuzumab.R (effect on kdes via the any-NHL composite indicator; separate effect on CL_T and CL_inf via the standalone MCL indicator (ratio 1.75, i.e., 75% higher CL than CLL)).
• Notes: Follows the TUMTP_HODGKIN_CLASSICAL / TUMTP_GASTRIC / TUMTP_SCLC decomposition pattern. Distinct from TUMTP_LYMPH (broader lymphoma pool). The Gibiansky 2014 cohort had only 20 MCL patients (2.9%); the paper reports the highest obinutuzumab CL among the four B-cell-malignancy histologies for MCL, consistent with the highest CD20 expression density on MCL B-cells relative to the other histologies. Ratified canonically on 2026-05-11.

TUMTP_FL (canonical for follicular lymphoma indicator)

• Description: 1 = follicular lymphoma (FL), 0 = other tumor types. Time-fixed per subject. FL is the most common indolent non-Hodgkin lymphoma (NHL) subtype, derived from germinal-center B cells and characterized by the t(14;18) BCL2-IGH translocation in most cases.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = all other tumor types (in Lu 2017 the implicit reference is the union of DLBCL and other non-FL NHL when paired with TUMTP_DLBCL = 0 and TUMTP_OTHER_NHL = 0; the Lu 2017 covariate model uses FL itself as the reference category, so the indicator does not appear explicitly in model() – both TUMTP_DLBCL and TUMTP_OTHER_NHL are 0 for FL patients).
• Source aliases:
  • Categorical “tumor histology” column with level FL (Lu 2017) – decompose into TUMTP_FL = as.integer(tumor_histology == "FL").
• Example models: Lu_2017_polatuzumab_neuropathy.R (FL is the implicit reference category; the model file declares TUMTP_FL in covariatesDataExcluded to record FL as the reference without referencing it in model()).
• Notes: Follows the TUMTP_HODGKIN_CLASSICAL / TUMTP_GASTRIC / TUMTP_SCLC decomposition pattern. Distinct from TUMTP_BCL (a broader pooled-residual indolent-B-cell-lymphoma indicator in Gibiansky 2014 that lumps FL with other indolent histologies); use TUMTP_FL when the source paper names follicular lymphoma in isolation. Ratified canonically on 2026-06-24 alongside the Lu 2017 polatuzumab vedotin TTE extraction.

TUMTP_OTHER_NHL (canonical for ‘other non-FL non-DLBCL NHL’ tumor-histology indicator)

• Description: 1 = a non-follicular-lymphoma (non-FL) non-diffuse-large-B-cell-lymphoma (non-DLBCL) NHL histology (e.g., mantle cell lymphoma, marginal-zone lymphoma, small-lymphocytic lymphoma, transformed FL, chronic lymphocytic leukemia, T-cell NHL, etc., pooled together in the paper’s residual histology category), 0 = FL or DLBCL or any non-NHL tumor. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (FL or DLBCL or any non-NHL tumor; in Lu 2017 the implicit reference is FL when paired with TUMTP_DLBCL = 0).
• Source aliases:
  • Categorical “tumor histology” column with level not in {FL, DLBCL} (Lu 2017 “others” category) – decompose into TUMTP_OTHER_NHL = as.integer(!tumor_histology %in% c("FL", "DLBCL")).
• Example models: Lu_2017_polatuzumab_neuropathy.R (multiplicative effect on hazard of grade >= 2 peripheral neuropathy via the exp(theta_otherNonFL * TUMTP_OTHER_NHL) proportional-hazard term with theta_otherNonFL = 0.688, SE 0.758; the median hazard ratio of “other non-FL” vs FL is 1.98 per Figure 3 with very wide uncertainty given the small “others” sub-cohort).
• Notes: Captures the residual NHL-histology bucket in papers that decompose tumor histology into a three-level categorical (FL reference, DLBCL, otherNonFL). Distinct from TUMTP_BCL (Gibiansky 2014 four-level B-cell-lymphoma decomposition: CLL, BCL, DLBCL, MCL – where BCL is the indolent-B-cell-lymphoma residual including FL). When a future paper retains a different pool of non-FL non-DLBCL histologies, document the per-paper histology composition in covariateData[[TUMTP_OTHER_NHL]]$notes rather than overloading the canonical. Ratified canonically on 2026-06-24 alongside the Lu 2017 polatuzumab vedotin TTE extraction.

LINE_1L (canonical for first-line-therapy indicator)

• Description: 1 = first-line therapy (1L) / treatment-naive, 0 = second-line or greater (2L+) / relapsed-or-refractory.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (2L+, second-line or greater / relapsed-refractory).
• Source aliases:
  • LINE (categorical column with levels 1L, 2L, 3L+, …) – decompose into LINE_1L = as.integer(LINE == "1L").
  • RRFN (relapsed/refractory flag; treatment-naive corresponds to RRFN == 0) – used in Lu_2019_polatuzumab.R. Decompose: LINE_1L = as.integer(RRFN == 0).
• Example models: Sanghavi_2020_ipilimumab.R (exponential coefficient -0.0949 on CL), Lu_2019_polatuzumab.R (multiplicative effects on V1 = 1.20, kdes = 3.38, CL_T = 3.53, FRAC_NS = 0.756; the same pooled-trial NHL cohort mixes 415 R/R and 45 first-line patients).
• Notes: Promoted to scope: general on 2026-04-26 after Lu 2019 polatuzumab vedotin ratified the same 1L vs 2L+ binarization that Sanghavi 2020 ipilimumab introduced. The two papers use different indicator semantics (Sanghavi reports the effect as exp(-0.0949 * LINE_1L) and Lu reports theta^LINE_1L with theta < or > 1 depending on the parameter); both reduce to the same canonical 0/1 column. If a future paper requires finer resolution (separate effects for 2L vs 3L+), add a parallel LINE_2L canonical rather than overloading this one.

NIVO_1Q3W (canonical for nivolumab 1 mg/kg every 3 weeks co-administration indicator)

• Description: 1 = ipilimumab co-administered with nivolumab 1 mg/kg every 3 weeks; 0 = otherwise (monotherapy or any other nivolumab regimen).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no nivolumab or any non-1Q3W nivolumab regimen).
• Source aliases:
  • NIVO_REGIMEN (categorical column with levels none, 0.3 mg/kg Q3W, 1 mg/kg Q2W, 1 mg/kg Q3W, 3 mg/kg Q2W, 3 mg/kg Q3W) – decompose into NIVO_1Q3W = as.integer(NIVO_REGIMEN == "1 mg/kg Q3W").
• Example models: Sanghavi_2020_ipilimumab.R (exponential coefficient 0.0950 on ipilimumab CL).
• Notes: Paired with NIVO_3Q2W in the Sanghavi 2020 final model; both decomposed indicators are 0 for ipilimumab monotherapy. Other nivolumab regimens (0.3 mg/kg Q3W, 1 mg/kg Q2W, 3 mg/kg Q3W) were tested but not retained in the final model and collapse into the reference 0 group.

NIVO_3Q2W (canonical for nivolumab 3 mg/kg every 2 weeks co-administration indicator)

• Description: 1 = ipilimumab co-administered with nivolumab 3 mg/kg every 2 weeks; 0 = otherwise (monotherapy or any other nivolumab regimen).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no nivolumab or any non-3Q2W nivolumab regimen).
• Source aliases:
  • NIVO_REGIMEN (categorical column) – decompose into NIVO_3Q2W = as.integer(NIVO_REGIMEN == "3 mg/kg Q2W").
• Example models: Sanghavi_2020_ipilimumab.R (exponential coefficient 0.191 on ipilimumab CL).
• Notes: Paired with NIVO_1Q3W; same reference grouping convention.

COMBO_NIVO (canonical for any-regimen nivolumab combination-therapy indicator)

• Description: 1 = ipilimumab co-administered with any nivolumab regimen, 0 = ipilimumab monotherapy.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (ipilimumab monotherapy).
• Source aliases:
  • COMBO – used in Sanghavi_2020_ipilimumab.R. Equivalently derivable from NIVO_REGIMEN as COMBO_NIVO = as.integer(NIVO_REGIMEN != "none").
• Example models: Sanghavi_2020_ipilimumab.R (additive effect -0.202 on the Emax parameter of the time-varying CL function).
• Notes: Distinct from the per-regimen NIVO_1Q3W / NIVO_3Q2W indicators on baseline CL: COMBO_NIVO aggregates across all nivolumab regimens and acts on the time-varying-CL Emax parameter, whereas the per-regimen indicators act on baseline (time-zero) CL.

BLSTABL (canonical for baseline absolute blast counts in peripheral blood)

• Description: Baseline absolute count of blasts (immature lymphoid/myeloid precursor cells) circulating in peripheral blood. Time-fixed baseline value.
• Units: 10^9 counts/L (equivalently 10^9 counts; reported as “x 10^9 counts” in Wu 2024 Table 2).
• Type: continuous
• Scope: specific
• Reference category: n/a – used with power scaling (BLSTABL / <ref>)^exponent. Reference value observed: 0.352 x 10^9 counts (Wu 2024 Table 3, BCP-ALL median).
• Source aliases:
  • BLSTABL – used in Wu_2024_inotuzumab.R.
• Example models: Wu_2024_inotuzumab.R (power exponent -0.0484 on kdes for BCP-ALL patients only; the effect is gated off for B-cell NHL by multiplying the exponent by DIS_BCPALL).
• Notes: Distinct from blasts in bone marrow (different specimen) and from BLSTPB (percentage of blasts in peripheral blood, used by the predecessor Garrett 2019 adult model). Not applicable for B-cell NHL patients in pooled BCP-ALL + NHL analyses (Wu 2024 retains the effect only in BCP-ALL patients via the DIS_BCPALL gate). When supplying BLSTABL for an NHL subject, set the value to the BCP-ALL reference (0.352) so the gated power term evaluates to 1 numerically. Scope: specific because the covariate is most meaningful in B-cell-leukemia population PK analyses; promote to general if a second paper retains it.

COMBO_RG (canonical for anti-CD20 (rituximab or obinutuzumab) combination-therapy indicator)

• Description: 1 = polatuzumab vedotin co-administered with rituximab OR obinutuzumab, 0 = single-agent polatuzumab vedotin (or any other regimen lacking an anti-CD20 partner). Time-fixed per subject in the source paper’s analysis cohort.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (single-agent polatuzumab vedotin or no anti-CD20 partner).
• Source aliases:
  • COMBO (categorical: 0 = single agent, 1 = + rituximab, 2 = + obinutuzumab) – used in Lu_2019_polatuzumab.R. Decompose: COMBO_RG = as.integer(COMBO == 1 | COMBO == 2). The Lu 2019 NONMEM separately defines RTX = as.integer(COMBO == 1) and GA101 = as.integer(COMBO == 2) and applies effects as theta^(RTX + GA101); because RTX and GA101 are mutually exclusive, RTX + GA101 takes values {0, 1} and the effect collapses to theta^COMBO_RG.
• Example models: Lu_2019_polatuzumab.R (multiplicative effects on CL_INF = 0.844, kdes = 0.932, FRAC_NS = 0.709).
• Notes: Rituximab and obinutuzumab both bind CD20 on B cells (rituximab is a Type I anti-CD20 mAb, obinutuzumab a glycoengineered Type II), so co-administration is hypothesized to alter polatuzumab vedotin disposition through depletion of CD79b+ target B cells. The Lu 2019 final model fits a single combined effect rather than separate rituximab- and obinutuzumab-specific effects. Scope: specific because the relevant combination partners (CD20-directed mAbs) are tied to NHL pathway; if a future paper distinguishes rituximab from obinutuzumab combinations, register COMBO_R and COMBO_G separately rather than overloading this canonical.

COMBO_DURVA (canonical for durvalumab combination-therapy indicator)

• Description: 1 = the analyzed therapeutic mAb is co-administered with durvalumab (anti-PD-L1 IgG1), 0 = monotherapy.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (monotherapy).
• Source aliases:
  • COMB – used in Hwang_2022_tremelimumab.R ($INPUT NM-TRAN data item; control-stream switch IF(COMB.EQ.0) selects monotherapy parameters and IF(COMB.EQ.1) selects combination-therapy parameters).
• Example models: Hwang_2022_tremelimumab.R (selects between monotherapy and combination-with-durvalumab values of the time-varying-CL Tmax and lambda parameters).
• Notes: Parallels COMBO_NIVO but for durvalumab rather than nivolumab co-administration. Acts on the time-varying-CL component (Tmax and lambda); baseline CL is shared between monotherapy and combination groups in Hwang 2022.

COMBO_LEN_DEX (canonical for lenalidomide plus dexamethasone combination-therapy indicator)

• Description: 1 = the analyzed therapeutic mAb (or other agent under PK study) is co-administered with the lenalidomide + low-dose-dexamethasone (Ld) backbone, 0 = monotherapy or any non-Ld regimen. Lenalidomide is an immunomodulatory imide (IMiD) that activates natural killer cells; dexamethasone is an immunosuppressant glucocorticoid. The Ld backbone is a standard combination partner in multiple-myeloma and other hematologic-malignancy regimens.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (monotherapy or any non-Ld regimen). When a paper reports the reference patient as “with Ld coadministration” (as Ide 2020 does), the model still stores the canonical 0/1 column and applies the effect as exp(theta * (COMBO_LEN_DEX - 1)) so that COMBO_LEN_DEX = 1 yields factor 1 (paper’s reference) and COMBO_LEN_DEX = 0 activates the effect.
• Source aliases:
  • LENDEX (1 = with Ld, 0 = without Ld; in Ide 2020 derived from STUDY != 204011 because study 204011 was the Ld-free elotuzumab-monotherapy cohort) – used in Ide_2020_elotuzumab.R.
  • COMBO_LD (retired canonical name; renamed to COMBO_LEN_DEX on 2026-04-27 for clarity).
• Example models: Ide_2020_elotuzumab.R (multiplicative effects: CLLd = 0.74 on nonspecific CL, encoded as exp(log(0.74) * (COMBO_LEN_DEX - 1)); KINTLd = 10.1 on the second-order target-mediated elimination rate from the peripheral compartment, encoded as exp(log(10.1) * (COMBO_LEN_DEX - 1))).
• Notes: Specific scope because the canonical’s mechanistic relevance is hematologic-malignancy-domain-bound (multiple myeloma and related plasma-cell or B-cell disorders). Distinct from COMBO_BELAMAF (which pools Ld with bortezomib-dex and pomalidomide-dex into a single broader “any-combination” belantamab indicator); COMBO_LEN_DEX is the per-backbone Ld-only flag. If a future paper distinguishes “Ld-only” from a broader “any-IMiD-plus-dex” backbone with separate effects, register a parallel canonical (e.g., COMBO_PD for pomalidomide-dex, COMBO_VD for bortezomib-dex). Sign of the exponential coefficient is paper-dependent: Ide 2020 reports CLLd = 0.74 so the Ld-coadministration arm has 26% lower nonspecific CL than the Ld-free arm, but KINTLd = 10.1 so the Ld arm has 10x higher second-order target-mediated elimination – both are mechanistically interpretable (dexamethasone suppresses non-specific catabolic clearance; lenalidomide-activated NK cells increase target-cell-binding-mediated elimination).

COMBO_BELAMAF (canonical for any-combination belantamab mafodotin therapy indicator)

• Description: 1 = belantamab mafodotin administered as part of a combination regimen (with bortezomib + dexamethasone, lenalidomide + dexamethasone, or pomalidomide + dexamethasone) in the relapsed/refractory multiple myeloma setting; 0 = belantamab mafodotin monotherapy.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (belantamab mafodotin monotherapy).
• Source aliases:
  • COMBO (when the source dataset uses a generic combination flag for belantamab mafodotin pooled regimens) – used in Papathanasiou_2025_belantamab.R.
• Example models: Papathanasiou_2025_belantamab.R (multiplicative factor theta = 1.44 on the Imax parameter of the time-varying CL function – combination therapy increases the steady-state CL reduction from 33.2 % to 44.0 %).
• Notes: Pools the three combination backbones tested in DREAMM-6 / DREAMM-7 / DREAMM-8 (Bor-Dex, Len-Dex, Pom-Dex) into a single binary because Papathanasiou 2025 reports no meaningful per-backbone difference in cycle-1 ADC exposure. If a future paper tests per-backbone combination effects, register dedicated indicators (COMBO_BELAMAF_BORDEX, etc.) rather than overloading this aggregate.

KG (canonical for subject-specific tumour-growth rate constant from a prior IPP fit)

• Description: Empirical-Bayes posterior estimate of the subject-specific tumour-size first-order growth rate constant carried over from an upstream tumour-size-dynamics population model. Supplied per subject in the dataset and used directly inside a downstream model (e.g., overall-survival hazard) that integrates the tumour-size ODE inline conditional on each subject’s growth/death rates.
• Units: internal scaled rate; the Zecchin 2016 OS model carries the source convention KG / 1000 * tumorSize in the SLD ODE so the column units are (1/day) * 1000 as published in the source NONMEM run. Document per-model via covariateData[[KG]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – used directly inside the SLD ODE (see Zecchin_2016_survival.R).
• Source aliases:
  • KG (NONMEM $INPUT column in DDMODEL00000218; identical column shipped in the bundle’s Simulated_OS.csv).
• Example models: Zecchin_2016_survival.R (Zecchin 2016 OS model, DDMODEL00000218).
• Notes: Specific scope because the column is the empirical-Bayes output of a particular upstream fit (modellib('Zecchin_2016_tumorovarian') / DDMODEL00000217). When this OS model is used standalone, the user must supply KG per subject – typically by first fitting the SLD model and extracting the per-subject empirical-Bayes posterior. The internal /1000 scaling is preserved verbatim from the source $DES block to maintain numerical equivalence with the published estimates.

KD0 (canonical for subject-specific carboplatin-related tumour-death rate constant from a prior IPP fit)

• Description: Empirical-Bayes posterior estimate of the subject-specific carboplatin-driven tumour-size death rate constant carried over from an upstream tumour-size-dynamics population model. Pairs with the time-varying AUC_CARBO covariate inside the SLD ODE term KD0 * AUC_CARBO * tumorSize.
• Units: internal scaled rate; the Zecchin 2016 OS model carries the source convention KD0 / 1000 * AUC_CARBO * tumorSize so the column units are (1/day per AUC unit) * 1000 as published in the source NONMEM run. Document per-model via covariateData[[KD0]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – used directly inside the SLD ODE (see Zecchin_2016_survival.R).
• Source aliases:
  • KD0 (NONMEM $INPUT column in DDMODEL00000218).
• Example models: Zecchin_2016_survival.R (Zecchin 2016 OS model, DDMODEL00000218).
• Notes: Specific scope because the column is tied to a specific drug (carboplatin) and a specific upstream IPP fit. The internal /1000 scaling is preserved verbatim from the source $DES block.

KD1 (canonical for subject-specific gemcitabine-related tumour-death rate constant from a prior IPP fit)

• Description: Empirical-Bayes posterior estimate of the subject-specific gemcitabine-driven tumour-size death rate constant carried over from an upstream tumour-size-dynamics population model. Pairs with the time-varying AUC_GEM covariate inside the SLD ODE term KD1 * AUC_GEM * tumorSize.
• Units: internal scaled rate; the Zecchin 2016 OS model carries the source convention KD1 / 100 * AUC_GEM * tumorSize so the column units are (1/day per AUC unit) * 100 as published in the source NONMEM run. Document per-model via covariateData[[KD1]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – used directly inside the SLD ODE (see Zecchin_2016_survival.R).
• Source aliases:
  • KD1 (NONMEM $INPUT column in DDMODEL00000218).
• Example models: Zecchin_2016_survival.R (Zecchin 2016 OS model, DDMODEL00000218).
• Notes: Specific scope because the column is tied to a specific drug (gemcitabine) and a specific upstream IPP fit. The internal /100 scaling is preserved verbatim from the source $DES block.

IBASE (canonical for subject-specific baseline tumour-size estimate from a prior IPP fit)

• Description: Empirical-Bayes posterior estimate of the subject-specific baseline sum-of-longest-diameters (SLD) tumour size carried over from an upstream tumour-size-dynamics population model. Used both to set the SLD ODE initial state (tumorSize(0) <- IBASE * 1000 in the Zecchin 2016 model: source convention multiplies by 1000 to convert the internal value to mm) and to scale the time-varying tumour-size ratio (mmbas <- IBASE * 1000) inside the OS hazard.
• Units: internal scaled length; the Zecchin 2016 OS model carries the source convention IBASE * 1000 = mm so the column itself is in metres (1 m = 1000 mm). Document per-model via covariateData[[IBASE]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – used as the SLD ODE initial condition and as the denominator in (tumorSize - mmbas) / mmbas.
• Source aliases:
  • IBASE (NONMEM $INPUT column in DDMODEL00000218; identical column shipped in the bundle’s Simulated_OS.csv with values typically in the 0.04-0.50 m range).
• Example models: Zecchin_2016_survival.R (Zecchin 2016 OS model, DDMODEL00000218).
• Notes: Distinct from the canonical TUM_SLD column. TUM_SLD carries the measured baseline tumour size in mm (used to compute the time-fixed NSLD0 = TUM_SLD / 70 covariate term in the Zecchin 2016 OS hazard), whereas IBASE carries the empirical-Bayes fitted baseline from the upstream SLD model (used to initialise the integrated SLD trajectory and to define the time-varying TSR(t) reference). The two are correlated but not equal because the upstream IPP fit smooths measurement noise away from the observed SLD0. Specific scope because the column is the empirical-Bayes output of a specific upstream model fit and the internal *1000 unit-conversion is tied to the source NONMEM coding convention.

NEW_LESION (canonical for time-varying new-lesion appearance indicator)

• Description: Time-varying binary indicator of whether a new (non-target) RECIST lesion has appeared since enrolment. 1 = new lesion present at the current observation time; 0 = no new lesion as of the current observation time. Once NEW_LESION flips to 1 it stays 1 for subsequent observation times in that subject (a step-function flag, not a transient pulse).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no new lesion appeared as of the current time).
• Source aliases:
  • NWLS – prior canonical name (pre-2026-06-19 readability standardization) and NONMEM $INPUT column in DDMODEL00000218; the bundle’s Simulated_OS.csv re-labels the same column NWLSCOV. Downstream consumers should map NWLSCOV -> NEW_LESION.
• Example models: Zecchin_2016_survival.R (Zecchin 2016 OS model, DDMODEL00000218; multiplicative effect on the Weibull hazard via exp(e_nwls_haz * NEW_LESION) with e_nwls_haz = 1.23 per Output_real_OS.lst FINAL TH5 / Table 2 of Zecchin 2016).
• Notes: Specific scope because the column encodes a per-paper RECIST-style binary that is supplied by the dataset; it is not a generic “any new lesion” indicator the user can populate from routine clinical data without an explicit lesion-appearance imaging schedule. The Zecchin 2016 OS model uses NEW_LESION directly (no time-gating), which is faithful to the simulated dataset shipped in the DDMORE bundle. The source Output_real_OS.lst (the listing on the original real dataset) gates the indicator with an additional TNWLS (lesion-appearance-time) column not shipped in the bundle’s simulated dataset; the two encodings are functionally equivalent when the dataset’s NEW_LESION column is constructed as a 0/1 step that flips at the lesion-appearance time. The bundle’s simulated dataset uses the simpler step-function form, and that is the form the nlmixr2lib model expects. Renamed from NWLS to NEW_LESION on 2026-06-19 per the canonical-register standardization audit (operator decision: readability over abbreviation, since NWLS is opaque and not a universally-recognized clinical-trial term; no DIS_ prefix because this is a RECIST-imaging event indicator rather than a disease-state indicator).

MM_NIGG (canonical for non-IgG multiple myeloma immunoglobulin-type indicator)

• Description: 1 = patient with non-IgG-secreting multiple myeloma (e.g., IgA, IgD, IgE, IgM, light-chain-only / Bence Jones, or non-secretory MM), 0 = patient with IgG-secreting multiple myeloma.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (IgG MM).
• Source aliases:
  • Ig_type – used in Fau_2020_isatuximab.R. Values 0 / 1 with the same orientation as the canonical (1 = non-IgG MM).
• Example models: Fau_2020_isatuximab.R (exponential effect on the steady-state linear CL CLinf with coefficient -0.751, and on the time-varying-CL half-time KCL with coefficient -0.931), Xu_2020_daratumumab.R (additive shift (1 + 0.806 * (1 - MM_NIGG)) on linear CL – Xu 2020 parameterises with non-IgG MM as reference, so an IgG MM patient receives an 80.6% higher linear CL than a non-IgG MM patient; canonical column semantics 1 = non-IgG / 0 = IgG are preserved).
• Notes: Within-disease (multiple-myeloma) immunoglobulin-subtype stratifier. The mechanistic rationale (Fau 2020) is that endogenous IgG monoclonal protein in IgG-MM patients competes with the therapeutic IgG mAb for FcRn-mediated salvage, raising the therapeutic mAb’s catabolic clearance; non-IgG-MM patients lack that competition and exhibit lower therapeutic-mAb clearance. Distinct from the disease-state indicators (DIS_SMM = smoldering MM); applies only after a multiple-myeloma diagnosis is established. Scope: specific because the comparison is a within-MM stratifier rather than a cross-population indicator. Reference category at the model level (which value of MM_NIGG corresponds to TVCL = base) varies between papers: Fau 2020 anchors to 0 (IgG MM) and Xu 2020 anchors to 1 (non-IgG MM); the canonical column orientation (1 = non-IgG) is fixed across papers and the per-model covariateData[[MM_NIGG]]$reference_category field records which anchor each model uses.

TUM_TP53_MUT (canonical for tumour TP53 / p53 mutation indicator)

• Description: Binary indicator of tumour-cell TP53 mutational status as assessed in the source paper. 1 = TP53 mutant tumour (typically a missense mutation detected by sequencing, or p53 protein overexpression by immunohistochemistry used as a surrogate for TP53 missense mutation per Gillet et al. J Neurooncol 2014); 0 = TP53 wild-type tumour. Time-fixed per subject (a somatic tumour-genotype call made at diagnosis, not a germline genotype).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (TP53 wild-type).
• Source aliases:
  • “p53 mutation” / “p53 mutated” (Mazzocco 2015, paper text; p53 overexpression by IHC used as a surrogate for TP53 missense mutation).
• Example models: Mazzocco_2015_temozolomide.R (exponential effect on the temozolomide tumour-cell-death rate constant gamma: gamma = gamma0 * exp(beta_p53 * TUM_TP53_MUT) with beta_p53 = log(0.143 / 0.254) = -0.574; TP53-mutant LGG tumours are 44% less sensitive to TMZ than TP53-wild-type tumours).
• Notes: Specific scope because the column encodes a somatic tumour-genotype call (mechanism = altered DNA-damage response in tumour cells) rather than a germline pharmacogenomic variant. Distinct from the SNP_<GENE>_<RSID> family, which encodes inherited host germline genotypes affecting drug PK; TUM_TP53_MUT encodes a tumour-cell mutation and only makes mechanistic sense for drugs whose effect depends on a functional p53 pathway in the tumour. The Mazzocco 2015 cohort assayed p53 status by IHC overexpression; future extractions that use direct TP53 sequencing should still record their values under this canonical and document the assay method in covariateData[[TUM_TP53_MUT]]$notes. Ratified canonically on 2026-05-17 alongside the Mazzocco 2015 temozolomide extraction.

TUM_1P19Q_CODEL (canonical for tumour 1p/19q chromosomal codeletion indicator)

• Description: Binary indicator of tumour-cell 1p/19q chromosomal codeletion status. 1 = tumour carries the combined loss of the short arm of chromosome 1 (1p) and the long arm of chromosome 19 (19q); 0 = non-codeleted tumour (intact 1p and/or 19q). Time-fixed per subject (a somatic tumour-genotype call made at diagnosis, typically by fluorescence in-situ hybridisation, comparative genomic hybridisation, or loss-of-heterozygosity assay).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-codeleted; intact 1p and/or 19q).
• Source aliases:
  • “1p/19q codeletion” / “1p/19q codeleted” (Mazzocco 2015, paper text).
• Example models: Mazzocco_2015_temozolomide.R (exponential effect on the damaged-quiescent-to-proliferative repair rate constant kQpP: kQpP = kQpP0 * exp(beta_1p19q * TUM_1P19Q_CODEL) with beta_1p19q = log(0.00807 / 0.00947) = -0.160; 1p/19q-codeleted LGG tumours have 15% lower kQpP than non-codeleted tumours, consistent with longer reported duration of response in codeleted patients).
• Notes: Specific scope because the column encodes a brain-tumour-specific somatic chromosomal alteration with mechanistic relevance to DNA-repair capacity in glioma cells; it is not a generic “any tumour-chromosomal-alteration” indicator. The 1p/19q codeletion is a defining molecular feature of oligodendrogliomas (per the 2016 WHO classification of CNS tumours) and is mutually exclusive with TP53 missense mutation in the Mazzocco 2015 cohort (Ricard 2007, ref 12 of Mazzocco 2015). Ratified canonically on 2026-05-17 alongside the Mazzocco 2015 temozolomide extraction.

TUM_IGHV_MUT (canonical for immunoglobulin heavy-chain variable region (IGHV) mutational status)

• Description: Binary indicator of the somatic hypermutation status of the immunoglobulin heavy-chain variable region (IGHV) gene rearrangement in a mature B-cell clone, most commonly assayed in chronic lymphocytic leukaemia (CLL). 1 = IGHV-mutated (the clone carries >= 2% divergence from the closest germline IGHV sequence); 0 = IGHV-unmutated (< 2% divergence). Time-fixed per subject – IGHV status is fixed at the point of clonal transformation and does not change with therapy. Note the prognostic polarity: unlike most mutation indicators, the MUTATED state is the FAVOURABLE one in CLL (IGHV-mutated disease has an indolent course, IGHV-unmutated disease is aggressive), so a positive coefficient on this column usually corresponds to a lower disease burden.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (IGHV-unmutated).
• Source aliases:
  • IMPIGVHMS (Ibrahim 2023 nlmixr control stream, Supporting Information S4 run100: igvh <- IMPIGVHMS) – the IMP prefix records that missing categorical covariates (n <= 6) were mode-imputed per Ibrahim 2023 Appendix S1 section 2. Note the source column transposes the VH / HV letters relative to the standard IGHV abbreviation.
  • “IGHV mutational status” / “IGVH-mutated” / “IGVH-unmutated” (Ibrahim 2023 paper text).
• Example models: Ibrahim_2023_ibrutinib_leukocyte_spd.R (selects the baseline-SPD typical value: spdbase = spdbase_unm * (1 - TUM_IGHV_MUT) + spdbase_m * TUM_IGHV_MUT, giving 48.9 cm^2 for unmutated vs 19.5 cm^2 for mutated – a 2.5-fold higher baseline lymph-node burden in IGHV-unmutated patients).
• Notes: Specific scope; a somatic B-cell-clone genotype call rather than a host germline variant, so it belongs to the TUM_<MARKER> family (TUM_TP53_MUT, TUM_1P19Q_CODEL) and NOT to the SNP_<GENE>_RS<rsid> germline-pharmacogenomics family. Encoded as an effect on a disease-burden parameter rather than on a PK parameter, which is the typical use: IGHV status is a disease-biology marker, not a metabolism marker. When a source paper reports the covariate with the opposite polarity (an “IGHV-unmutated = 1” column, which some CLL papers use because unmutated is the high-risk group), convert on ingestion with TUM_IGHV_MUT = 1 - <source column> and record the transformation in covariateData[[TUM_IGHV_MUT]]$notes. Ratified canonically on 2026-07-30 alongside the Ibrahim 2023 ibrutinib extraction.

TUM_17P_DEL (canonical for deletion(17p) chromosomal abnormality indicator)

• Description: Binary indicator of the deletion(17p) chromosomal abnormality in a tumour / leukaemic clone. 1 = del(17p) present, 0 = absent. The deleted region carries the TP53 locus, so del(17p) is functionally a TP53-loss marker and defines the highest-risk cytogenetic subgroup in chronic lymphocytic leukaemia (10-year overall survival 29%; Ibrahim 2023 Discussion). Time-fixed per subject; a baseline cytogenetic call, typically by fluorescence in-situ hybridisation (FISH).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no del(17p)).
• Source aliases:
  • del(17p) / “deletion (17p)” (Ibrahim 2023 paper text and Table 2 footnote f).
• Example models: Ibrahim_2023_ibrutinib_competing_risk.R (multiplicative effect on the alive-to-death transition rate of the competing-risk multistate model: exp(1.42 * TUM_17P_DEL), a hazard ratio of 4.14).
• Notes: Specific scope; a somatic chromosomal deletion, so it belongs to the TUM_<MARKER> family alongside TUM_1P19Q_CODEL (the other registered chromosomal-alteration indicator) and NOT to the germline SNP_<GENE>_RS<rsid> family. Distinct from TUM_TP53_MUT: TUM_TP53_MUT records a point mutation / IHC-overexpression call at the TP53 locus, whereas TUM_17P_DEL records loss of the whole chromosome arm carrying it. The two are frequently but not always concordant (del(17p) and TP53 mutation can occur independently, and papers that assay both usually report them as separate columns), so do not alias one onto the other – register a paper’s del(17p) column here and its TP53 sequencing / IHC column under TUM_TP53_MUT. Ibrahim 2023 also screened deletion(11q) and deletion(13q) as candidate covariates but retained neither in any final model, so no canonicals are registered for them. Ratified canonically on 2026-07-30 alongside the Ibrahim 2023 ibrutinib extraction.

WHO_PS (canonical for ordinal-numeric performance-status integer (WHO / ECOG))

• Description: World Health Organization (WHO) performance-status integer score, identical-by-construction with the Eastern Cooperative Oncology Group (ECOG) PS per the Oken 1982 cross-walk (Am J Clin Oncol 5:649-655). Scale: 0 = fully active / asymptomatic, 1 = ambulatory and able to do light work, 2 = ambulatory and capable of all self-care but unable to work, 3 = bedridden > 50% of waking hours, 4 = completely disabled / bedridden, 5 = dead. Used as a continuous-numeric covariate when the source paper enters the integer score directly into a linear / exponential / power covariate model (e.g. CL ~ (1 - theta_PS * WHO_PS) or CL ~ exp(theta_PS * WHO_PS)) rather than binarizing into ECOG_GE1 / ECOG_GE2. Time-fixed per subject in the typical baseline-PS application; document time-varying use in per-model notes.
• Units: (integer score; document the empirical range and reference value in covariateData[[WHO_PS]]$notes)
• Type: continuous (semantically ordinal but treated as continuous in the covariate model)
• Scope: general
• Reference category: n/a – used with linear or power scaling. Reference value observed: WHO_PS = 0 (Leger 2004 reference patient implicit; the typical CL formula (1 - 0.12 * WHO_PS) evaluates to 1 at WHO_PS = 0).
• Source aliases:
  • PS – WHO performance status integer; used in Leger_2004_topotecan.R and historically in Mould 2002 and Gallo 2000 topotecan popPK papers (cited by Leger 2004) using the same linear-ordinal form.
  • ECOG – equivalent ECOG performance status integer.
  • PERF – common NONMEM column header for the ordinal performance-status integer.
• Example models: Leger_2004_topotecan.R (linear-ordinal effect (1 - 0.12 * WHO_PS) on the renal-plus-non-renal CL formula (theta1 + theta2 * CrCl_Lh) * (1 - e_who_ps_cl * WHO_PS); PS distribution in cohort 0 / 1 / 2 / 3 = 79 / 98 / 11 / 2 patients).
• Notes: Distinct from ECOG_GE1 / ECOG_GE2 (binary indicators used when the source paper collapses the ordinal score). Use WHO_PS when the paper retains the integer score in a linear / exponential / power covariate form. If a future paper provides the ordinal integer column but binarizes the effect downstream, derive ECOG_GE1 = as.integer(WHO_PS >= 1) and ECOG_GE2 = as.integer(WHO_PS >= 2); do not duplicate the same patient-level integer under two columns. WHO and ECOG PS are operationally identical and cross-walked one-to-one per Oken 1982 (Am J Clin Oncol 5:649-655); the canonical name WHO_PS is chosen because the founding example (Leger 2004) names it as WHO performance status. Scope general because ordinal-linear PS is the dominant historical NONMEM covariate-model form for oncology popPK papers and is likely to recur in future extractions.

GEMOX (canonical for gemcitabine-then-oxaliplatin sequence indicator)

• Description: 1 = gemcitabine 30-min IV infusion administered first, immediately followed by oxaliplatin 120-min IV infusion on the same study day; 0 = otherwise (gemcitabine alone or oxaliplatin-then-gemcitabine). Time-fixed per cycle.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = gemcitabine monotherapy when paired with OXGEM = 0; the combination-vs-monotherapy reference is encoded jointly with OXGEM.
• Source aliases:
  • GEMOX – used in Jiang_2008_gemcitabine.R.
• Example models: Jiang_2008_gemcitabine.R (multiplicative factor 0.65 on apparent dFdU central volume V_C,dFdU/F when GEMOX = 1; Jiang 2008 page 330 covariate equation: 0.65^GEMOX).
• Notes: Paired with OXGEM as the second arm of a 3-level oxaliplatin-coadministration-sequence categorical decomposition (gem-alone / gem-then-ox / ox-then-gem). The two indicators are mutually exclusive: GEMOX + OXGEM <= 1. Follows the NIVO_1Q3W / NIVO_3Q2W decomposition pattern. Scope: specific because the sequence-specific effect is tied to Jiang’s gemcitabine + oxaliplatin study design; promote to general if a second paper retains the same pair of indicators.

OXGEM (canonical for oxaliplatin-then-gemcitabine sequence indicator)

• Description: 1 = oxaliplatin 120-min IV infusion administered first, immediately followed by gemcitabine 30-min IV infusion on the same study day; 0 = otherwise (gemcitabine alone or gemcitabine-then-oxaliplatin). Time-fixed per cycle.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = gemcitabine monotherapy when paired with GEMOX = 0.
• Source aliases:
  • OXGEM – used in Jiang_2008_gemcitabine.R.
• Example models: Jiang_2008_gemcitabine.R (multiplicative factor 0.54 on apparent dFdU central volume V_C,dFdU/F when OXGEM = 1; Jiang 2008 page 330 covariate equation: 0.54^OXGEM).
• Notes: Paired with GEMOX (see that entry for the joint encoding). The Jiang 2008 cohort distribution is gem-alone n=31, gem-then-ox n=38, ox-then-gem n=25; the larger V_C,dFdU/F reduction observed for ox-then-gem (factor 0.54) vs gem-then-ox (factor 0.65) is hypothesised by the authors to reflect order-dependent tissue-uptake effects of dFdU.

TUM_CELLS0 (canonical for subject-specific baseline tumour burden expressed as an absolute cell count)

• Description: Per-subject baseline number of malignant cells in the modelled compartment, in absolute cells (not mm, mm^3, or SLD). Used to initialise a mechanistic tumour-cell ODE state whose currency is cell count rather than a caliper- or imaging-derived size (tumor(0) <- TUM_CELLS0). In Minucci 2024 the value is the fitted initial malignant-B-cell burden obtained by trust-region optimisation against the individual CAR T-cell cellular-kinetic trajectory, because the IM19 phase I trial (Ying 2021) reported no direct longitudinal tumour-burden measurement.
• Units: cells (count)
• Type: continuous
• Scope: specific
• Reference category: n/a – used directly as the tumour-cell ODE initial condition.
• Source aliases:
  • N0_tumorCells – column in the Minucci 2024 Supplementary Material Table 1 (1).XLSX, sheet params-case-study-final; used in Minucci_2024_CART_qsp.R.
• Example models: Minucci_2024_CART_qsp.R (13 relapsed/refractory NHL patients; fitted values span 1.0e5 to 1.0e7 cells, with F0109 / F0110 / F0111 / F0123 pinned at the 1e5 lower optimisation bound and F0104 / F0106 / F0107 / F0118 / F0119 at the 1e7 upper bound).
• Notes: Deliberately distinct from the imaging/caliper tumour-burden family. TUMSZ and TUM_SLD carry measured sizes in mm; TUM_VOL carries a caliper volume in mm^3; IBASE carries an empirical-Bayes fitted baseline SLD from an upstream tumour-size model. TUM_CELLS0 is the cell-count analogue, appropriate for mechanistic / QSP models that track individual malignant cells and their receptor burden, where no length or volume conversion is defined by the source. Same fitted-input class as KG / KD0 / KD1 / IBASE (subject-specific values carried over from a prior fit rather than measured at baseline), hence scope: specific – a downstream user cannot populate this column from routine clinical data without re-running the source optimisation. Note that several Minucci 2024 values sit exactly on the optimiser’s bounds, so they are identifiability-limited rather than precisely estimated; treat the column as a per-subject model input, not as a clinical measurement.

NDIV (canonical for the fitted number of divisions per activated cell in an expansion-driven cell-therapy model)

• Description: Per-subject number of cell divisions that an activated (antigen-stimulated) therapeutic cell undergoes before differentiating into the effector phenotype. Drives the expansion amplification factor 2^NDIV applied to the activated-cell-to-effector flux. Non-integer values are expected and meaningful: the parameter is fitted on a continuous scale and represents a population-average division count, not a per-cell integer.
• Units: divisions (dimensionless count, continuous-valued)
• Type: continuous
• Scope: specific
• Reference category: n/a – multiplicative expansion parameter.
• Source aliases:
  • ndiv – column in the Minucci 2024 Supplementary Material Table 1 (1).XLSX, sheet params-case-study-final (the sheet’s units cell reads # cells, which is a labelling error: the description column and the paper’s Methods 2.3 both define it as a division count); used in Minucci_2024_CART_qsp.R.
• Example models: Minucci_2024_CART_qsp.R (fitted per-patient values 15.88 to 27.70 divisions across the 13 IM19 patients).
• Notes: Scope: specific because the value is the output of a per-subject fit to that subject’s own cellular-kinetic trajectory, not an independently measurable patient characteristic. Minucci 2024 global sensitivity analysis identifies NDIV as the single most influential parameter for CAR T-cell Cmax, contributing more than 80 percent of the variance – so the column is load-bearing and must not be defaulted. Although the paper classifies it as a drug-product (manufacturing) property, it is carried as a per-subject covariate because CAR T-cell products are manufactured from each patient’s own cells. Promote to general only if a second cell-therapy extraction retains an identically-defined division-count input.

FMEM (canonical for the fitted effector-to-memory conversion fraction in a cell-therapy model)

• Description: Per-subject fraction of effector therapeutic cells that convert to the long-lived memory phenotype at the point of effector-cell loss; the remaining 1 - FMEM truly die. Governs the long-term persistence phase of cellular kinetics.
• Units: fraction (0 to 1)
• Type: continuous
• Scope: specific
• Reference category: n/a – multiplicative branching fraction on the effector-loss flux.
• Source aliases:
  • fmem – column in the Minucci 2024 Supplementary Material Table 1 (1).XLSX, sheet params-case-study-final; used in Minucci_2024_CART_qsp.R.
• Example models: Minucci_2024_CART_qsp.R (fitted per-patient values spanning nine orders of magnitude, 2.91e-10 to 0.1, across the 13 IM19 patients).
• Notes: Scope: specific because the value is a per-subject fit output rather than a measured characteristic. Three of the 13 Minucci 2024 patients (F0110, F0111, F0123) sit exactly on the 0.1 upper optimisation bound and three more (F0119, F0125, F0126) are numerically indistinguishable from zero, so the column is identifiability-limited at both ends; the paper interprets the high-FMEM patients as those with no clear contraction phase in their cellular kinetics. Distinct from a residual-error or variance term: this is a structural branching fraction supplied per subject as data.

FCD8TDP (canonical for the CD8-positive fraction of an infused cell-therapy drug product)

• Description: Fraction of the infused therapeutic cell dose that is CD8-positive; the remaining 1 - FCD8TDP is CD4-positive. Characterises the composition of the administered product rather than the patient, and is used to split a single total cell dose between the CD8 and CD4 arms of a phenotype-resolved cellular-kinetic model.
• Units: fraction (0 to 1)
• Type: continuous
• Scope: specific
• Reference category: n/a – dose-splitting fraction.
• Source aliases:
  • fCD8Tdp – column in the Minucci 2024 Supplementary Material Table 1 (1).XLSX, sheet params-case-study-final, derived from the per-patient CD4:CD8 ratio of the infused IM19 product reported in Ying 2021; used in Minucci_2024_CART_qsp.R.
• Example models: Minucci_2024_CART_qsp.R (per-patient values 0.290 to 0.943 across the 13 IM19 patients; drives amt_CD8 = FCD8TDP * WT * dose_per_kg and amt_CD4 = (1 - FCD8TDP) * WT * dose_per_kg in the event table, and is exposed in model() as the CD8_dose_frac output).
• Notes: A drug-product characterisation covariate, not a patient covariate – the closest existing analogues are the FORM_<drug>_<formulation> formulation indicators, but those are categorical product selectors whereas this is a continuous composition fraction. Scope: specific because autologous cell-therapy product composition is measured per manufacturing run and has no analogue in small-molecule or monoclonal-antibody popPK. Note that Minucci 2024 assumes 100 percent product viability (fViable = 1), so FCD8TDP alone determines the CD8/CD4 dose split; a source reporting a viability below 1 would need that factor carried separately rather than folded into this column.

Laboratory / disease-activity

ALBR (canonical for serum albumin normalized to the laboratory’s upper limit of normal)

• Description: Serum albumin normalized to the laboratory’s upper limit of normal (albumin_observed / ULN_albumin).
• Units: (unitless ratio)
• Type: continuous
• Scope: specific
• Reference category: n/a – used as a power term (ALBR / <ref>)^exponent. Reference 0.78 used in Xu 2019 (corresponds to a median serum albumin of 38 g/L at a typical ULN of ~48.7 g/L).
• Source aliases: none.
• Example models: Xu_2019_sarilumab.R.
• Notes: Xu 2019 normalizes to each site’s ULN so that values across multiple labs with different reference ranges can be pooled. Scoped specific because the ULN-normalization convention is tied to the Xu 2019 analysis plan; future papers using the same ratio should either add themselves to the example_models list or promote this entry to general.

Inflammatory-bowel-disease disease-activity covariates

SCORE_CALPRO (canonical for fecal calprotectin)

• Description: Fecal calprotectin, a gut-inflammation biomarker (baseline or time-fixed per subject unless a paper explicitly uses a time-varying value).
• Units: mg/kg stool (equivalent to ug/g). Document per-model via covariateData[[SCORE_CALPRO]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (SCORE_CALPRO / ref)^exponent. Reference 700 mg/kg used in Rosario 2015 (overall population median).
• Source aliases:
  • CALPRO – prior canonical name (pre-2026-06-19 SCORE_ family standardization).
• Example models: Rosario_2015_vedolizumab.R (reference 700 mg/kg; exponent +0.0310 on linear clearance CLL).
• Notes: Common IBD severity biomarker (inflammation of the gut epithelium). Assays typically report in ug/g stool; 1 ug/g = 1 mg/kg. Document per-model whether baseline-only or time-varying in covariateData[[SCORE_CALPRO]]$notes.

SCORE_CDAI (canonical for Crohn’s Disease Activity Index)

• Description: Crohn’s Disease Activity Index composite score. Higher values indicate more active disease; <150 remission, 150-219 mild, 220-450 moderate, >450 severe. Defined only for patients with a CD diagnosis; set to the reference value (or gate via the indicator) for UC patients.
• Units: (score, 0-600)
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (SCORE_CDAI / ref)^exponent. Reference 300 used in Rosario 2015 (typical moderate-CD score).
• Source aliases:
  • CDAI – prior canonical name (pre-2026-06-19 SCORE_ family standardization).
• Example models: Rosario_2015_vedolizumab.R (reference 300; exponent -0.0515 on CLL gated by IBD_CD so the effect applies only to CD patients).
• Notes: Mutually exclusive with SCORE_PMAYO in pooled UC+CD populations: each patient has exactly one disease-activity score (SCORE_CDAI for CD, partial Mayo for UC). Gate via the IBD_CD indicator when pooling.

SCORE_PMAYO (canonical for partial Mayo score)

• Description: Partial Mayo score for ulcerative colitis (sum of stool-frequency, rectal-bleeding, and physician-global subscores, range 0-9). Higher values indicate more active disease. Defined only for patients with a UC diagnosis; gate via the IBD_CD indicator when pooling UC+CD.
• Units: (score, 0-9)
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (SCORE_PMAYO / ref)^exponent. Reference 6 used in Rosario 2015 (typical moderate-UC score).
• Source aliases:
  • PMAYO – prior canonical name (pre-2026-06-19 SCORE_ family standardization).
• Example models: Rosario_2015_vedolizumab.R (reference 6; exponent +0.0408 on CLL gated by (1 - IBD_CD) so the effect applies only to UC patients).
• Notes: The partial Mayo score excludes the endoscopy subscore (the full Mayo score is 0-12). Mutually exclusive with SCORE_CDAI in pooled UC+CD populations.

SCORE_MAYO_E (canonical for baseline Mayo endoscopic subscore)

• Description: Mayo endoscopic subscore at baseline for ulcerative colitis, integer 0-3 (0 = normal / inactive disease, 1 = mild, 2 = moderate, 3 = severe). The endoscopic subscore is one of the four components of the full Mayo score (0-12); the partial Mayo score (SCORE_PMAYO) excludes it. Time-fixed per subject.
• Units: (score, 0-3)
• Type: categorical
• Scope: general
• Reference category: depends on per-model encoding – papers that include the full 0-3 range typically reference Mayo 0 or 1 (mild), while papers restricted to moderate-to-severe UC (the typical biologic-induction-therapy population) reference Mayo 2 or 3. Document the per-model reference category in covariateData[[SCORE_MAYO_E]]$reference_category.
• Source aliases:
  • MAYO_E – prior canonical name (pre-2026-06-19 standardization audit).
  • MPRE – used in Faelens_2021_infliximab.R (NONMEM column for “Mayo endoscopic score pre-induction”). The Faelens 2021 dataset additionally codes a “missing” sentinel MPRE = -99; treat this as out-of-domain when applying the model and document per-model.
• Example models: Faelens_2021_infliximab.R (categorical effect on KE: separate typical KE for Mayo 1 / Mayo 2 / Mayo 3; reference category Mayo 2).
• Notes: Distinct from the full Mayo score (0-12) and the partial Mayo score (SCORE_PMAYO, 0-9). The endoscopic subscore alone is the core inclusion criterion in many UC induction-therapy popPK datasets (typically Mayo 2 or 3 = moderate-to-severe disease). Mutually compatible with SCORE_PMAYO in datasets that report both.

SCORE_SLEDAI (canonical for Systemic Lupus Erythematosus Disease Activity Index)

• Description: Systemic Lupus Erythematosus Disease Activity Index score, a weighted composite of 24 clinical and laboratory features scoring 1-8 points each and summed to a total of 0-105. Higher values indicate more active SLE disease. Time-fixed at baseline in most SLE trials but can be time-varying when measured at follow-up. In model-based meta-analyses of SLE, the per-arm mean baseline SLEDAI (rather than an individual score) is the modelled covariate.
• Units: (score, 0-105)
• Type: continuous
• Scope: general
• Reference category: n/a – used centered on a per-paper reference value (10.5 in Goteti 2024, the across-arm mean baseline SLEDAI across the 81 study arms). Enters via (1 + beta * (SCORE_SLEDAI - reference)) on latent disease activity.
• Source aliases:
  • SLEDAI – printed name in Goteti 2024 Table 2 / Table 3 / equations; same orientation, no transformation.
  • SLEDAI (mean) – Goteti 2024 covariate-summary column header.
• Example models: Goteti_2024_SLE_mbma.R (per-arm mean baseline SLEDAI; centered at 10.5; multiplicative shifts of +0.208 per unit on latent mu_ijk and +0.255 per unit on latent delta_ijk).
• Notes: SLEDAI has several closely related scoring variants (SLEDAI-2K, SELENA-SLEDAI); the numeric range and interpretation are similar and papers use SLEDAI as an umbrella label. When a paper uses a specific variant, document in covariateData[[SCORE_SLEDAI]]$notes. Distinct from disease-specific composite outcomes like BICLA / SRI / LLDAS / CLASI (which are treatment-response composites, not disease-activity scores). Follows the SCORE_* family pattern (SCORE_EASI, SCORE_MGADL, SCORE_CDAI, etc.); canonical name is SCORE_SLEDAI without a B or BL prefix to match the AGE / WT / ALB pattern where baseline vs time-varying status is recorded in notes rather than the column name. Ratified canonically on 2026-07-24 alongside the Goteti 2024 SLE MBMA extraction.

ENDO_ULCER (canonical for endoscopically active luminal disease at baseline)

• Description: 1 = mucosal ulcerations confirmed at baseline ileocolonoscopy / endoscopy (endoscopically active luminal disease), 0 = no mucosal ulcerations at baseline. Time-fixed (assessed at study entry).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no mucosal ulcerations at baseline).
• Source aliases: none known.
• Example models: Aguiar_2021_ustekinumab.R (Aguiar 2021 Table 3; covariate on baseline fecal calprotectin FC0: 213 mg/kg with ulcers vs 102 mg/kg without).
• Notes: IBD-specific structural-disease-activity indicator, distinct from the symptom-driven SCORE_CDAI / SCORE_PMAYO and the biomarker-driven SCORE_CALPRO / CRP. Useful as a covariate on baseline biomarkers (FC, CRP) and as a stratifier for simulations of biochemical remission.

Inflammatory-bowel-disease diagnosis

IBD_CD (canonical for Crohn’s disease indicator)

• Description: 1 = Crohn’s disease (CD) diagnosis, 0 = ulcerative colitis (UC) diagnosis. Used to gate pooled UC+CD population models where some parameters or covariate effects differ by diagnosis.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (ulcerative colitis).
• Source aliases:
  • DIAGNOSIS, DX (categorical "UC"/"CD") – derive IBD_CD = as.integer(DX == "CD").
• Example models: Rosario_2015_vedolizumab.R (two typical-CLL switch between UC and CD; gates SCORE_PMAYO and SCORE_CDAI effects; multiplicative +1% effect on Vc).
• Notes: Rosario 2015 models separate typical CLL for UC vs CD and gates the partial-Mayo (UC-only) and SCORE_CDAI (CD-only) disease-activity covariates via this indicator.

Concomitant / prior medication

CONMED_AD (canonical for concomitant Alzheimer’s-symptomatic medication)

• Description: 1 = subject is on concomitant Alzheimer’s-symptomatic medication (typically a cholinesterase inhibitor and/or memantine) at the observation, 0 = not on such medication. Time-fixed in the Conrado 2014 source dataset (one indicator per subject) and treated as such here; future models that need a time-varying form may register a successor canonical or document time-varying use in covariateData[[CONMED_AD]]$notes.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 1 (on concomitant Alzheimer’s-symptomatic medication) in the Conrado 2014 model – the source paper labels CONMED_AD = 1 as the “most common” category and centres the slope-effect coefficient on this group, so the multiplicative factor on disease-progression slope is 1 for CONMED_AD = 1 and 1 + e_sl_conmed_ad_off for CONMED_AD = 0. The non-standard “most-common-as-reference” convention is preserved from the source for traceability; future Alzheimer’s models that adopt the more common “off-treatment as reference” convention should register a successor canonical (CONMED_AD_TREATED) rather than overloading this one.
• Source aliases:
  • COMED2 – used in Conrado_2014_alzheimer.R (DDMORE Foundation Model Repository entry DDMODEL00000290). The 2 suffix in the source distinguishes this binary from upstream COMED and PRIMCOMED columns (free-text concomitant-medication entries) in the same NONMEM input dataset; the binary COMED2 flag is what enters the model.
• Example models: Conrado_2014_alzheimer.R (multiplicative factor on the typical-value disease-progression slope: slope_factor = 1 + (1 - CONMED_AD) * e_sl_conmed_ad_off with e_sl_conmed_ad_off = -0.302, i.e. ~30% smaller progression slope for the off-treatment reference cohort).
• Notes: The source .mod does not specify the symptomatic-medication class beyond the binary flag; the Conrado 2014 publication context (CAMD ADAS-Cog disease-progression dataset, 2014) makes cholinesterase-inhibitor / memantine the dominant interpretation. Treating CONMED_AD = 1 as the reference category is unusual relative to the rest of the CONMED_* family (CONMED_PARA, CONMED_NSAID, CONMED_AZA, etc.) which all use 0 = not-on as reference; the inversion is preserved here only because the source paper’s coefficient was estimated with the “most common = 1” convention. Ratified canonically on 2026-05-06 alongside the Conrado 2014 DDMORE extraction.

CONMED_AED (canonical for any concomitant antiepileptic drug coadministration)

• Description: 1 = subject is on at least one concomitant antiepileptic drug (AED) other than the modelled AED at the PK observation, 0 = on the modelled AED as monotherapy. Time-varying when concurrent AEDs cycle on / off across study occasions, time-fixed when the source paper analyses chronic-maintenance cohorts whose concurrent therapy is unchanged across the analysis window.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (modelled-AED monotherapy).
• Source aliases:
  • CO_AED – used in Yukawa_1990_phenytoin.R (paper’s CO indicator inverted: source CO = 1 if PHT alone, theta_co otherwise; canonical CONMED_AED = 1 - CO_indicator so 0 is the PHT-monotherapy reference).
• Example models: Yukawa_1990_phenytoin.R (multiplicative ^CONMED_AED factor on Vmax (1.08) and Km (1.32) for chronic phenytoin patients on at least one of phenobarbital, carbamazepine, valproate, primidone, clonazepam, sultiame, ethotoin, ethosuximide, acetazolamide, or diazepam), Yukawa_2002_clonazepam_pediatric.R (paired with CONMED_AED_GE2 to recover the paper’s 3-tier drug-interaction factor on clonazepam CL/F).
• Notes: Generic concomitant-AED indicator covering the heterogeneous mix of older AEDs (PB, CBZ, VPA, primidone, clonazepam, etc.) studied alongside the modelled drug; the per-paper list of qualifying AEDs must be documented in covariateData[[CONMED_AED]]$notes. Distinct from drug-specific concomitant-AED indicators (e.g., a future CONMED_PB for concomitant phenobarbital alone) which would warrant separate canonicals when a paper distinguishes effects by AED class. When a paper distinguishes AED-count tiers (monotherapy vs +1 AED vs +>=2 AEDs) rather than the pooled mono-vs-anything contrast, pair CONMED_AED with the sibling [[CONMED_AED_GE2]] binary; the >=2-AEDs tier is CONMED_AED = 1 AND CONMED_AED_GE2 = 1, and data assemblers must enforce that CONMED_AED_GE2 = 1 implies CONMED_AED = 1. Follows the CONMED_* family pattern (CONMED_AZA, CONMED_NSAID, etc.). Ratified canonically on 2026-05-10 alongside the Yukawa 1990 phenytoin extraction.

CONMED_AED_GE2 (canonical for >=2 concomitant antiepileptic drugs)

• Description: 1 = subject is on at least two concomitant antiepileptic drugs (AEDs) other than the modelled AED at the PK observation, 0 = on at most one concomitant AED (i.e., modelled-AED monotherapy or modelled AED + one concomitant AED). Sibling binary to [[CONMED_AED]] for AED-count-tier covariate models; the two binaries together encode a 3-level tier (monotherapy / +1 / +>=2). Time-varying when concurrent AEDs cycle on / off across study occasions, time-fixed when the source paper analyses chronic-maintenance cohorts whose concurrent therapy is unchanged across the analysis window.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (zero or one concomitant AED). The >=2-tier reference (CONMED_AED_GE2 = 0 with CONMED_AED = 1) is “1 concomitant AED”; the absolute reference (CONMED_AED_GE2 = 0 with CONMED_AED = 0) is “monotherapy”.
• Source aliases:
  • CONMED_AED_GE2 – used in Yukawa_2002_clonazepam_pediatric.R (paper’s Polytherapy (B) bucket per Table I and Table II groupings: any record with >=2 AEDs alongside clonazepam).
• Example models: Yukawa_2002_clonazepam_pediatric.R (paired with [[CONMED_AED]] inside model() to recover the paper’s 3-tier drug-interaction factor: monotherapy DIF = 1, +1 AED DIF = 1.18, +>=2 AEDs DIF = 2.12 * TBW^(-0.119)).
• Notes: Specific scope; the binary is only meaningful in papers that distinguish a >=2-AEDs tier from a +1-AED tier. Promote to general scope when a second AED-count-tier paper ratifies the name. Mutual-consistency constraint: CONMED_AED_GE2 = 1 MUST imply CONMED_AED = 1. A record asserting CONMED_AED_GE2 = 1 and CONMED_AED = 0 is malformed and the model() body will silently treat it as the >=2 tier (since the indicator decomposition uses CONMED_AED * (1 - CONMED_AED_GE2) for the +1 tier and CONMED_AED_GE2 for the >=2 tier); data assemblers should validate the pair before passing to rxSolve(). Distinct from drug-specific CONMED_<drug> indicators (CONMED_CBZ, CONMED_VPA, CONMED_PB) which capture a specific AED rather than a count tier; when a future paper distinguishes BOTH the count AND the specific drugs, the two families coexist. Ratified canonically on 2026-06-21 alongside the Yukawa 2002 clonazepam pediatric extraction.

CONMED_ABX (canonical for any-other-antibiotic composite coadministration indicator)

• Description: 1 = subject is receiving at least one concomitant systemic antibiotic other than the antibiotic being modelled at the PK observation, 0 = no other concomitant antibiotic. A composite pooled umbrella indicator for anti-infective popPK analyses in which the source paper records only whether additional antibacterial therapy was co-administered and never names the individual agents, so no per-INN indicator can be assigned. Time-varying in principle (co-therapy starts and stops during an admission); time-fixed when the source paper captures the flag once at treatment start.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant antibiotic other than the modelled drug).
• Source aliases:
  • COMED – used in Zieck_2023_ceftazidime.R (Supplementary File S1 $INPUT column; enters $PK as FLAG1 via IF(COMED.EQ.1)FLAG1=1). Note that COMED is a generic NONMEM column name meaning “co-medication”; map it to CONMED_ABX only when the source paper defines it specifically as other-antibiotic use, and to the appropriate per-INN or per-class canonical otherwise.
  • Concomitant other antibiotic use – Zieck 2023 Table 2 row label.
• Example models: Zieck_2023_ceftazidime.R (power-of-coefficient effect on CL: CL_typ * 1.56^CONMED_ABX, i.e. +56% clearance when another antibiotic is co-administered; 27/40 patients exposed).
• Notes: Distinct from the named-agent indicators CONMED_MER (meropenem), CONMED_GEN (gentamicin), CONMED_CIP (ciprofloxacin) and CONMED_FUSIDIC (fusidic acid), which model a specific co-administered antibiotic. Use CONMED_ABX only when the source paper itself pools all other antibacterials under a single unnamed binary; use the per-INN indicators when the paper identifies and estimates effects for specific agents. Structurally parallel to [[CONMED_IMMUNOMOD]], the register’s other “source paper does not separate the individual agents” composite. An ABX = 1 flag is frequently confounded with infection severity and with the breadth of empiric coverage, so a positive clearance effect should be read as an empirical marker of a sicker / more heavily co-treated subset rather than as a mechanistic drug-drug interaction – Zieck 2023’s Discussion explicitly makes this caveat about its own 1.56 estimate (“we could not find a physiological explanation for this association … it cannot be ruled out that the identification of this association is based on coincidence”). Ratified canonically on 2026-07-28 alongside the Zieck 2023 ceftazidime extraction (sidecar oare_PMC10044023 request-001 q1, operator answered A).

CONMED_ABI (canonical for concomitant abiraterone coadministration indicator)

• Description: 1 = subject is coadministered abiraterone acetate (oral CYP17A1 inhibitor used in metastatic castration-resistant prostate cancer; also a clinically relevant CYP2D6 / CYP2C8 inhibitor) at the PK observation, 0 = no abiraterone. Per-subject in Yoshida 2021 because abiraterone coadministration was a study-design factor (every patient in study GO27983 received 1000 mg abiraterone QD with prednisone / prednisolone 5 mg BID; six of 21 patients in JO29655 received abiraterone in stage 2; the other three studies enrolled no abiraterone arms).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no concomitant abiraterone).
• Source aliases:
  • ABIRATER – used in Yoshida_2021_ipatasertib.R (Yoshida 2021 NONMEM control stream variable name; same per-subject 0/1 orientation).
• Example models: Yoshida_2021_ipatasertib.R (linear-additive effects: -18.5% on apparent ipatasertib parent CL/F when abiraterone is present (Table 3 theta_CLI,Abi = -0.185); on the apparent M1 bioavailability the abiraterone effect is +61.5% but ONLY at multiple-dose state (Table 4 theta_FM1,Abi = 0.615 applied as (1 + 0.615 * MULTI_DOSE * CONMED_ABI) so that the same single-dose effect was zero per the source NONMEM control stream)).
• Notes: Specific scope until a second model legitimately ratifies abiraterone as a covariate; promote to general once that happens. Distinct from CONMED_AZOLE (CYP3A4 inhibitor used for fungal indications) and from CONMED_STEROID (the prednisone / prednisolone component that is always co-administered with abiraterone in mCRPC); when a model explicitly separates abiraterone vs the steroid backbone, use both covariates. Yoshida 2021 also reports that the precise mechanism of the abiraterone-on-ipatasertib effect is unknown (the standard CYP3A4 / CYP2C8 / CYP2D6 routes were ruled out by in-vitro and DDI studies), so the indicator captures the empirical exposure shift rather than a defined enzyme-inhibition mechanism. Ratified canonically on 2026-05-30 alongside the Yoshida 2021 ipatasertib extraction.

CONMED_ALTEPLASE (canonical for concomitant intravenous alteplase (r-tPA) coadministration indicator)

• Description: 1 = subject received concomitant intravenous alteplase (recombinant tissue plasminogen activator, r-tPA; intravenous thrombolysis, IVT) before or concurrent with the index PK measurement window, 0 = no concomitant alteplase. Time-fixed at the index event in stroke / reperfusion contexts where IVT is administered in the emergency window prior to endovascular thrombectomy. Distinct from the broader / class-level “any thrombolytic” indicator (which would also include tenecteplase, reteplase, urokinase, streptokinase); the canonical name is drug-specific because the published popPK literature ties the covariate to alteplase pharmacokinetics directly rather than to the broader tissue-plasminogen-activator class.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant alteplase / IVT).
• Source aliases:
  • IVT – intravenous thrombolysis indicator; used in Vos_2025_iminobiotin.R (Vos 2025 Supplemental Table S8 uses “IVT” interchangeably with “alteplase” because the Dutch standard-of-care thrombolytic for LVO stroke in 2022-2023 was alteplase exclusively; the same column form IVT = 0/1 would translate to CONMED_ALTEPLASE in any nlmixr2lib reuse).
• Example models: Vos_2025_iminobiotin.R (multiplicative effect on 2-iminobiotin clearance: cl *= (1 + e_alteplase_cl * CONMED_ALTEPLASE) with e_alteplase_cl = 0.6469, i.e. concomitant alteplase increases 2-IB CL by ~65% from 9.29 L/h to 15.3 L/h in adult LVO-stroke patients; founding example).
• Notes: General scope because alteplase is the dominant thrombolytic agent in acute ischemic stroke worldwide (with tenecteplase emerging as the principal alternative); future popPK models that combine concomitant tenecteplase as a covariate should register a sibling CONMED_TENECTEPLASE canonical rather than reusing this one (the two agents differ in plasmin-generation kinetics, fibrin specificity, and elimination half-life and so should not be pooled). The Vos 2025 mechanism for the +65% CL effect on 2-iminobiotin is not firmly established: the paper’s Discussion explicitly rules out plasmin-mediated cleavage (2-IB is a small molecule, not a peptide like nerinetide), so the indicator captures an empirical exposure shift rather than a defined biochemical mechanism. Ratified canonically on 2026-06-28 alongside the Vos 2025 2-iminobiotin extraction (sidecar Q3 = B).

CONMED_AMIO (canonical for concomitant amiodarone coadministration indicator)

• Description: 1 = subject is coadministered amiodarone (Class III antiarrhythmic; CYP3A4 / CYP2C9 / P-gp inhibitor) during the study, 0 = no concomitant amiodarone. Time-varying when amiodarone start / stop events are captured; the Xia 2024 source treats amiodarone as time-fixed at the analysis baseline.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant amiodarone).
• Source aliases:
  • CM1 – used in Xia_2024_warfarin.R (Xia 2024 Figure 1 / Table 2 covariate-screening variable labelling: CM1 = 1 indicates combined amiodarone, 0 no combination).
• Example models: Xia_2024_warfarin.R (piecewise multiplicative effect on warfarin EC50: ec50 *= (1 + e_amio_ec50 * CONMED_AMIO) with e_amio_ec50 = -0.602, i.e. amiodarone reduces EC50 by ~60% in the Han Chinese cohort).
• Notes: Amiodarone is the canonical CYP2C9 / CYP3A4 inhibitor that potentiates warfarin’s anticoagulant effect in clinical practice; the Xia 2024 cohort prevalence was 25.7% (Table 1). The per-paper definition (any amiodarone use vs current loading-dose use vs steady-state use) should be documented in covariateData[[CONMED_AMIO]]$notes. Ratified canonically on 2026-05-16 alongside the Xia 2024 warfarin extraction.

CONMED_AMINO (canonical for concomitant aminosalicylate therapy)

• Description: 1 = on concomitant aminosalicylate (5-aminosalicylic acid / mesalamine / mesalazine / olsalazine / sulfasalazine etc.) therapy at the PK observation, 0 = not.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant aminosalicylate).
• Source aliases: AMINO – used in Rosario_2015_vedolizumab.R.
• Example models: Rosario_2015_vedolizumab.R (power-form on CLL: CLL * 1.02^CONMED_AMINO).
• Notes: Covers the full aminosalicylate class (5-ASA is the single active moiety shared by most agents); use CONMED_AMINO rather than CONMED_5ASA unless the source paper explicitly restricts the indicator to 5-ASA monotherapy.

CONMED_AVD (canonical for brentuximab vedotin + AVD (adriamycin/doxorubicin, vinblastine, dacarbazine) combination indicator)

• Description: 1 = subject is receiving brentuximab vedotin in combination with the AVD chemotherapy backbone (adriamycin a.k.a. doxorubicin, vinblastine, dacarbazine) for newly diagnosed advanced-stage Hodgkin lymphoma; 0 = otherwise (single-agent brentuximab vedotin). Encodes the A+AVD frontline regimen as a study-design covariate on ADC clearance.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (single-agent brentuximab vedotin – no AVD coadministration).
• Source aliases:
  • DOX – used in Zhou_2025_brentuximab.R (the NONMEM dataset uses the doxorubicin-administration flag as the AVD-coadministration indicator because doxorubicin is given on the same days as the other AVD agents in this regimen).
• Example models: Zhou_2025_brentuximab.R (power-form effect on ADC clearance: CL * 2.12^CONMED_AVD – ADC clearance is ~2.1-fold higher under A+AVD vs single-agent BV).
• Notes: Distinct from CONMED_CHEMO (which is nivolumab + platinum-based chemotherapy). The A+AVD regimen is the standard chemotherapy backbone for frontline classical Hodgkin lymphoma; promote to general scope if a second BV paper reports the same A+AVD covariate effect with a comparable encoding.

CONMED_AZA (canonical for concomitant azathioprine)

• Description: 1 = on concomitant azathioprine at the PK observation, 0 = not on azathioprine.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant azathioprine).
• Source aliases: AZA – used in Rosario_2015_vedolizumab.R.
• Example models: Rosario_2015_vedolizumab.R (power-form on CLL: CLL * 0.998^CONMED_AZA; effect ~= null).
• Notes: Thiopurine immunomodulator used as maintenance therapy in IBD. Standard convention is baseline-use-only, but time-varying use is permitted; document per-model.

CONMED_AZOLE (canonical for concomitant azole antifungal therapy (CYP3A4/P-gp inhibitor))

• Description: 1 = patient coadministered an azole antifungal (itraconazole, voriconazole, fluconazole, ketoconazole, posaconazole, isavuconazole, or another systemic azole) during the observation interval, 0 = no concomitant azole antifungal. Time-varying per subject because azole exposure starts and stops during the observation period.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant azole antifungal).
• Source aliases:
  • AZOLE – used in Kirubakaran_2022_tacrolimus.R.
• Example models: Kirubakaran_2022_tacrolimus.R (state-dependent typical CL/F: 21.1 L/h without azole and 4.2 L/h with azole, an 80% reduction; also a state-dependent BSV magnitude on CL/F: 61% CV without azole vs 89.5% CV with azole).
• Notes: Azoles are mechanism-based CYP3A4 and P-glycoprotein inhibitors with different inhibitor potencies (itraconazole > voriconazole > fluconazole). The per-model covariateData[[CONMED_AZOLE]]$notes must document (1) which azoles are pooled into the indicator, (2) any post-cessation lag (Kirubakaran 2022 carries CONMED_AZOLE = 1 for one week after azole discontinuation to allow tacrolimus apparent clearance to stabilize given itraconazole’s long half-life), and (3) whether the indicator is a baseline-only proxy or a true time-varying flag.

CONMED_BLEOMYCIN (canonical for concomitant bleomycin coadministration indicator)

• Description: 1 = subject is receiving bleomycin as a component of the concomitant chemotherapy regimen during the observation interval, 0 = no concomitant bleomycin. Bleomycin is a glycopeptide antitumour antibiotic used in germ-cell-tumour, Hodgkin-lymphoma and squamous-cell regimens; roughly 65% of a dose is eliminated renally, so it is co-eliminated with (and potentially competes with or perturbs the renal handling of) other renally cleared cytotoxics such as methotrexate. Time-varying in principle because bleomycin dosing is scheduled on specific protocol days relative to the index drug.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no concomitant bleomycin).
• Source aliases:
  • BLM – used in Zhao_2025_methotrexate.R (Eq. 5 writes the term as e^BLM with BLM = 0.08 when combined with BLM, otherwise = 0; the register splits that into a 0/1 indicator times an estimated coefficient).
• Example models: Zhao_2025_methotrexate.R (exponential effect on MTX clearance, exp(0.08 * CONMED_BLEOMYCIN) = 1.083-fold; bleomycin is given on day 2 of a regimen whose methotrexate + vincristine dose falls on day 1).
• Notes: Specific scope until a second model reuses the indicator. The direction found by Zhao 2025 – bleomycin accelerates methotrexate clearance – is not mechanistically established; the paper states plainly that “the influence of BLM on MTX remains unexplored” and lists enzyme induction, transporter upregulation, oxidative-stress modulation and detoxifying-enzyme activation as candidate explanations. A per-model covariateData[[CONMED_BLEOMYCIN]]$notes should record the timing of bleomycin relative to the index drug, because a covariate flagged at the subject level can conflate a genuine interaction with a regimen-cohort difference when the two drugs are not concurrent in plasma.

CONMED_CBZ (canonical for concomitant carbamazepine coadministration indicator)

• Description: 1 = subject is taking carbamazepine (CBZ) as a concomitant antiepileptic drug at the PK observation, 0 = no concomitant carbamazepine. Carbamazepine is a strong CYP3A4 / UGT / P-gp inducer that increases the apparent clearance of co-administered drugs. Time-varying when carbamazepine starts / stops within the observation window; time-fixed when the source paper analyses chronic-maintenance cohorts whose AED therapy is stable across the analysis window.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant carbamazepine).
• Source aliases:
  • CBZ – used in Schoemaker_2017_brivaracetam.R (paper covariate CBZ for carbamazepine coadministration), Hashimoto_1994_zonisamide.R (paper covariate CBZ for carbamazepine coadministration on zonisamide Vmax) and Lee_2024_topiramate.R (Lee 2024 Table S1 theta4, additive enzyme-induction term on topiramate CL/F).
• Example models: Schoemaker_2017_brivaracetam.R (multiplicative effect on apparent oral clearance: cl *= (1 + 0.479 * CONMED_CBZ); +47.9% relative to no-CBZ reference, corresponding to ~32% lower brivaracetam exposure, Schoemaker 2017 Table 1), Hashimoto_1994_zonisamide.R (multiplicative power-form effect on Vmax of Michaelis-Menten zonisamide elimination: vmax *= 1.13^CONMED_CBZ; +13% relative to no-CBZ reference, Hashimoto 1994 Table II theta2), Lee_2024_topiramate.R (additive effect in absolute units: cl <- (exp(lcl) + e_cbz_cl * CONMED_CBZ + ...) * ... with e_cbz_cl = 0.703 L/h on a 1.45 L/h monotherapy intercept, i.e. +48% relative to no-CBZ reference, Lee 2024 Table S1 / Discussion).
• Notes: Drug-specific CONMED_* indicator anticipated in the [[CONMED_AED]] notes; used when a paper estimates a separate CBZ-induction effect distinct from the pooled EIAED / AED class effect. Distinct from the broader [[CONMED_EIAED]] (any enzyme-inducing AED) and [[CONMED_AED]] (any concomitant AED). When a paper distinguishes individual AEDs separately (Schoemaker 2017: PB, CBZ, VPA each carry their own coefficient; Lee 2024: PHT, CBZ, OXC, PB each carry their own coefficient), use the drug-specific canonicals [[CONMED_PB]], CONMED_CBZ, [[CONMED_OXC]], [[CONMED_PHT]], [[CONMED_VPA]] rather than collapsing into the class-level indicator. The functional form of the induction effect is per-paper and not a property of this column: it is multiplicative / fractional in Schoemaker 2017, a power form in Hashimoto 1994, and additive in absolute L/h in Lee 2024 – each model’s model() body is authoritative, so do not assume a fractional coefficient when reading a CONMED_CBZ effect size across models. Hashimoto 1994 (zonisamide) tested phenytoin and valproate coadministration on zonisamide PK but found no significant effect (page 325, “data not shown”) and retained only CBZ in the final model, consistent with the AED-specific decomposition rationale.

CONMED_COBICISTAT (canonical for concomitant cobicistat (CYP3A4 inhibitor / PK-booster) coadministration indicator)

• Description: 1 = subject is receiving concomitant cobicistat (COBI), a pharmacokinetic enhancer with no intrinsic antiviral activity used to boost co-administered antiretrovirals; 0 = no cobicistat. Cobicistat is a potent mechanism-based CYP3A4 inhibitor and also inhibits P-glycoprotein, BCRP, OATP1B1/1B3 and the renal transporters MATE1 and OCT2, so the indicator flags reduced CYP3A4-mediated clearance and/or increased bioavailability of the boosted co-administered drug. Time-fixed in fixed-dose-combination cohorts; time-varying when boosting starts / stops within the observation window.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no cobicistat coadministration).
• Source aliases:
  • Cobicistat – used in Thoueille_2023_tenofovir_reduced.R and its sibling models (paper covariate row “theta_Cobicistat”, Thoueille 2023 Table 2 / Table S2 / Table S3).
  • COBI, c – the standard antiretroviral-literature abbreviations; c appears as the trailing boosting token in fixed-dose-combination names (e.g., “elvitegravir/c”, “darunavir/c”).
• Example models: Thoueille_2023_tenofovir_reduced.R, Thoueille_2023_tenofovir_full.R (additive fractional effect on tenofovir alafenamide relative bioavailability: f(depot) <- exp(lfdepot + etalfdepot) * (1 + e_cobi_fdepot * CONMED_COBICISTAT), with e_cobi_fdepot = 1.11 in the reduced model and 1.15 in the full model, i.e. F rises from 1 to ~2.1-2.2).
• Notes: Follows the antiretroviral three/four-letter abbreviation convention already used by [[CONMED_RTV]] (ritonavir), [[CONMED_EFV]] (efavirenz), [[CONMED_NVP]] (nevirapine) and [[CONMED_LPV]] (lopinavir). Cobicistat and ritonavir are the two clinically used PK boosters and both are potent CYP3A4 inhibitors, but they are not interchangeable columns: ritonavir has intrinsic protease-inhibitor activity and a broader induction / inhibition profile (including CYP2D6 and several UGTs), so a paper that boosts with cobicistat must use this canonical rather than overloading [[CONMED_RTV]]. Per-model covariateData[[CONMED_COBICISTAT]]$notes must state whether the indicator is confounded with dose level: in Thoueille 2023 every cobicistat-treated subject received tenofovir alafenamide 10 mg and every other subject 25 mg (cobicistat was forced into the model specifically to absorb that dose difference into relative bioavailability), so the coefficient there is a combined dose-normalization-plus-boosting effect rather than a pure drug-drug-interaction effect. Papers that test cobicistat as a P-glycoprotein perpetrator alongside a separate P-gp-inhibitor class flag should keep the two columns distinct – Thoueille 2023 deliberately excluded cobicistat from its P-gp-inhibitor covariate analysis to avoid double-counting.

CONMED_CSA (canonical for concomitant cyclosporine (CsA) coadministration indicator)

• Description: 1 = subject is receiving cyclosporine (CsA) as the concomitant calcineurin inhibitor at the PK observation; 0 = no concomitant cyclosporine (in mycophenolate mofetil studies the typical alternative comparator is tacrolimus). Cyclosporine inhibits the MRP2 (ABCC2) biliary efflux transporter, which decreases biliary excretion of MPAG into the gut lumen and therefore decreases enterohepatic recirculation (EHC) of mycophenolic acid; cyclosporine also displaces mycophenolic acid from plasma protein binding sites. Time-varying when cyclosporine starts / stops within the observation window; time-fixed when the source paper analyses subjects randomized to one CNI regimen for the entire study window (e.g., de Winter 2009).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant cyclosporine; in MPA studies the reference is typically a tacrolimus-based regimen).
• Source aliases:
  • CsA – used in de Winter 2009 (binary indicator on the MPAG-to-gallbladder transport rate constant k57 to encode cyclosporine inhibition of MRP2-mediated biliary efflux); the canonical column is CONMED_CSA with the same value semantics.
• Example models: deWinter_2009_mycophenolic_acid.R (multiplicative power-form effect on the fMPAG-to-gallbladder rate constant k57: k57 <- exp(lk57) * e_csa_k57^CONMED_CSA with e_csa_k57 = 0.002, so cyclosporine cotreatment reduces k57 from 0.0796 1/h to 0.000159 1/h, suppressing EHC by ~99.8% relative to the tacrolimus reference; Table 2 / Eq. 9 of de Winter 2009).
• Notes: Ratified canonically on 2026-05-21 alongside the de Winter 2009 mycophenolic acid extraction. Distinct from a generic concomitant-immunosuppressant indicator: this canonical is specifically cyclosporine vs another CNI / no CNI. In renal-transplant popPK studies that randomise patients between cyclosporine and tacrolimus immunosuppression (the de Winter 2009 design), CONMED_CSA = 1 and CONMED_TAC (if registered later for tacrolimus-as-perpetrator effects) would be mutually exclusive at the patient level. Cyclosporine and tacrolimus differ in their effect on MPA pharmacokinetics: cyclosporine inhibits MRP2 (decreasing EHC of MPAG -> MPA) while tacrolimus does not, so co-administered MPA exposure is typically lower under cyclosporine than under tacrolimus for the same MMF dose. Data assemblers can derive CONMED_CSA = as.integer(cni_drug == "cyclosporine") from a raw CNI_DRUG text column.

CONMED_CSA_DOSE (canonical for total daily dose of co-administered ciclosporin (cyclosporine A))

• Description: Total daily dose of concomitant ciclosporin (cyclosporine A) at the current observation time. The continuous-dose companion to the binary CONMED_CSA: use CONMED_CSA when the source paper models cyclosporine cotreatment as a yes/no contrast (typically against a tacrolimus comparator arm) and CONMED_CSA_DOSE when it models a graded dose-response. In mycophenolic acid (MPA) popPK, ciclosporin inhibits the MRP2 (ABCC2) biliary efflux transporter, reducing biliary excretion of MPAG into the gut lumen and therefore suppressing enterohepatic recirculation of MPA; a higher ciclosporin dose consequently raises apparent MPA clearance.
• Units: mg/day
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as a centered power term (CONMED_CSA_DOSE / ref)^exponent. Reference value observed: 450 mg/day (van Hest 2005 as transcribed in the Maizaud 2025 S1 File [MAIN] block, pow(CIC/450, 0.31); the mrgsolve simulation default for the column is 300 mg/day). 0 is not a usable value in the power form – unlike CONMED_NAL_DOSE / CONMED_BUP_DOSE, whose Emax form vanishes at 0, a centered power term is singular at zero. A model needing to represent ciclosporin-free subjects must pair this column with the binary CONMED_CSA and gate the term.
• Source aliases:
  • CIC – Maizaud 2025 S1 File mrgsolve [PARAM] @annotated @covariates column name for the van Hest 2005 model (“Ciclosporine daily dose (mg)”).
  • CSA_DOSE / CYA_DOSE – common NONMEM column-name forms.
• Example models: vanHest_2005_mycophenolic_acid.R (centered power effect (CONMED_CSA_DOSE / 450)^0.31 on MPA apparent clearance CL/F; every subject in the van Hest 2005 cohort received ciclosporin, so the companion binary CONMED_CSA is 1 throughout and is not carried separately).
• Notes: Well-formed member of the CONMED_<drug>_DOSE daily-dose family (siblings CONMED_NAL_DOSE, CONMED_BUP_DOSE, CONMED_ATV_DOSE, CONMED_ATORVASTATIN_DOSE); per that family’s Notes, the CONMED_<drug>_DOSE shape replaces the earlier DOSE_<drug> / DOSE_<drug>_<unit> names. Specific scope because the exponent is tied to the van Hest 2005 MPA analysis; promote to general if a second paper ratifies a graded ciclosporin-dose effect in an independent programme. Distinct from CONMED_CSA, the binary cotreatment indicator, which remains the right canonical for cyclosporine-vs-tacrolimus contrasts such as deWinter_2009_mycophenolic_acid.R. Ratified canonically on 2026-08-22 alongside the Maizaud 2025 missed-mycophenolate-dose extraction.

CONMED_CHEMO (canonical for anti-PD-(L)1 mAb + chemotherapy combination indicator)

• Description: 1 = subject is receiving an anti-PD-(L)1 monoclonal antibody in combination with platinum-based chemotherapy (gemcitabine + cisplatin, pemetrexed + cisplatin, paclitaxel + carboplatin, or platinum-doublet); 0 = otherwise. Encodes chemotherapy coadministration as a study-design covariate on the antibody’s CL.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no chemotherapy coadministration – monotherapy or, where applicable, a non-chemotherapy combination such as anti-PD-1 + anti-CTLA-4).
• Source aliases:
  • CHEMO – used in Zhang_2019_nivolumab.R.
  • MONOTR – used in Kuchimanchi_2024_dostarlimab.R (the paper’s structural-equation indicator for monotherapy; the canonical column carries the inverse value, i.e. CONMED_CHEMO = 1 - MONOTR, so the canonical column is 1 when the patient is on combo-chemotherapy).
• Example models: Zhang_2019_nivolumab.R (exponential effect on baseline CL: exp(-0.104) ~= 0.90 fold, i.e. ~9.7% lower CL relative to monotherapy), Kuchimanchi_2024_dostarlimab.R (multiplicative effect on baseline CL: 1 - 0.0779 = 0.922, i.e. 7.79% lower CL on dostarlimab + carboplatin/paclitaxel relative to dostarlimab monotherapy).
• Notes: Promoted from specific to general scope on 2026-04-27 after the Kuchimanchi 2024 dostarlimab + carboplatin/paclitaxel analysis ratified the same pooling convention (any chemotherapy backbone collapsed into a single binary indicator). The two papers use different functional forms for the effect on CL – Zhang 2019 uses exp(theta * CONMED_CHEMO) (exponential) and Kuchimanchi 2024 uses (1 + theta * CONMED_CHEMO) (multiplicative); these are different parameterisations of the same underlying study-design indicator and the canonical column meaning is unchanged. Document the per-model functional form in covariateData[[CONMED_CHEMO]]$notes.

CONMED_DECITABINE (canonical for concomitant decitabine (hypomethylating-agent) chemotherapy backbone indicator)

• Description: 1 = subject is receiving decitabine as the hypomethylating-agent (HMA) chemotherapy backbone during the study window; 0 = subject is on a different chemotherapy backbone (e.g. low-dose cytarabine) or no HMA at all. Used as a treatment-arm indicator in head-to-head trials that compare a hedgehog-pathway inhibitor (or other targeted agent) layered on a decitabine backbone vs an alternative backbone in older adults with AML or high-risk MDS who are ineligible for intensive chemotherapy.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (subject is not on a decitabine backbone; typically paired with CONMED_GLASDEGIB = 1 in the only on-disk source (Lin 2020), where the decitabine arm carries both indicators = 1 and the LDAC arms have CONMED_DECITABINE = 0).
• Source aliases:
  • Derived from the protocol-defined treatment arm in Lin_2020_glasdegib_decitabine.R (BRIGHT AML 1003 Phase 1b Arm B: glasdegib + decitabine, n = 7).
• Example models: Lin_2020_glasdegib_decitabine.R (paired with CONMED_GLASDEGIB to identify the three trial arms; multiplicative hazard reduction 1 - 0.618 * (CONMED_GLASDEGIB * CONMED_DECITABINE) on the exponential overall-survival hazard).
• Notes: Specific scope because the only on-disk source is Lin 2020 BRIGHT AML 1003 Phase 1b Arm B (5 AML + 2 MDS, n = 7). The reported CI of the glasdegib + decitabine hazard reduction is wide (-95.0% to -28.6%) due to the small sample. Ratified canonically on 2026-06-24 alongside the Lin 2020 BRIGHT AML 1003 overall-survival extraction. Auto-approved member of the CONMED_<INN> family.

CONMED_DIURETIC (canonical for concomitant diuretic indicator; class composition is paper-specific)

• Description: 1 = subject is coadministered a diuretic during the study window (or, for per-time-point datasets, at the current time), 0 = no concomitant diuretic. Used in popPK / popPK-PD analyses of renally cleared substrates because diuretic-driven volume contraction and shifts in renal tubular handling can change apparent clearance, and because thiazide and loop diuretics are anti-uricosuric (raise serum urate) and so directly modify urate-related PD endpoints. The class membership pooled into the indicator is paper-specific and MUST be enumerated in covariateData[[CONMED_DIURETIC]]$notes per model – the observed range runs from any-class (loop + thiazide + potassium-sparing + osmotic + carbonic-anhydrase, Kleiber 2017) through thiazide + loop + spironolactone (Stocker 2012) to thiazide + loop only, deliberately excluding potassium-sparing agents (Wright 2016). Composite class indicator following the CONMED_STEROID / CONMED_AED / CONMED_AZOLE pattern (pooled class indicator) rather than the CONMED_<INN> per-drug pattern (CONMED_SPIRON, CONMED_FUROSEMIDE, …).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant diuretic of the paper-specific class set). Effect is typically encoded multiplicatively as Theta^CONMED_DIURETIC (Kleiber 2017 Eq 6 / Wright 2016 power form) or as a linear deviation 1 + e_conmed_diuretic_<param> * CONMED_DIURETIC (Stocker 2012 form).
• Source aliases:
  • DIURETIC – used in Kleiber_2017_clonidine.R (paper’s pooled indicator combining furosemide intermittent + furosemide infusion + spironolactone + bumetanide; source column DIURETIC in the NONMEM dataset).
  • DIUR – used in Stocker_2012_oxypurinol.R (Stocker 2012 dataset column; pools thiazide + loop + spironolactone; n = 72 of 155 gouty patients).
  • diuretic – used in Wright_2016_allopurinol.R and Wright_2013_allopurinol.R (Wright narrative term; pools thiazide + loop diuretics only, excludes spironolactone / amiloride; n = 44 of 133 gouty patients in Wright 2016).
• Example models: Kleiber_2017_clonidine.R (any-class; multiplicative effect on CL: diur_cl = 0.659^CONMED_DIURETIC; per Table 3 / Table 4 CL is reduced by 34.1% when any diuretic is active), Stocker_2012_oxypurinol.R (thiazide + loop + potassium-sparing; multiplicative linear-deviation effect on apparent CL/Fm: cl *= (1 + (-0.294) * CONMED_DIURETIC), i.e. -29.4% apparent oxypurinol clearance; Stocker 2012 Table 3 theta7), Wright_2016_allopurinol.R (thiazide + loop only, no potassium-sparing; multiplicative power-form effect on both CL/F_oxy and baseline urate U0: cl *= 0.740^CONMED_DIURETIC (-26%) and rbase *= 1.14^CONMED_DIURETIC (+14%); Wright 2016 Table 3 thetadiuretic and thetaE0_diuretic), Wright_2013_allopurinol.R (fractional effect 0.61 on renal CL_oxy via e_conmed_diuretic_cl_oxy_renal^CONMED_DIURETIC; Wright 2013 Table 2).
• Notes: Composite indicator with paper-specific class membership. Users simulating across models that share this column but differ in class membership MUST populate it according to each paper’s own definition – Wright 2016 deliberately excludes potassium-sparing diuretics because they tend to be uricosuric (lower serum urate) and therefore have an opposite-direction PD effect from the anti-uricosuric thiazide / loop agents, whereas Stocker 2012 and Kleiber 2017 pool them in. Distinct from CONMED_SPIRON (spironolactone-only per-drug indicator, used in Yukawa_1996_digoxin.R), a member of the CONMED_<INN> family; the two canonicals coexist when a paper distinguishes specific drugs. When a paper requires class-resolved encoding (separate thiazide / loop / K-sparing indicators), register sibling canonicals (e.g. CONMED_THIAZIDE, CONMED_LOOP_DIUR) rather than overloading this one. Ratified canonically alongside the Kleiber 2017 clonidine extraction; the former CONMED_DIUR canonical (ratified 2026-06-20 with Stocker 2012, extended 2026-06-30 with Wright 2016) was merged into this entry on 2026-07-26 per operator directive – the two names denoted the same concept and the split was an artifact of independent extractions. CONMED_DIUR is retired: do not reintroduce it.

CONMED_DOXORUBICIN (canonical for concomitant doxorubicin (anthracycline) chemotherapy backbone indicator)

• Description: 1 = subject is receiving (or has received during the relevant observation window) doxorubicin as the anthracycline component of an adjuvant or neoadjuvant chemotherapy regimen; 0 = subject is receiving a different anthracycline (or no anthracycline at all). Used in cardiac-biomarker PD models to differentiate doxorubicin-driven myocardial damage from epirubicin-driven damage (doxorubicin has approximately twice the per-mg cardiotoxic effect of epirubicin at clinically equivalent oncologic exposures).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (subject is not on doxorubicin). When CONMED_DOXORUBICIN and CONMED_EPIRUBICIN are used together as a pair of complementary indicators (as in de Vries Schultink 2018), both being 0 means no anthracycline.
• Source aliases:
  • ANTH_TYPE = 'doxorubicin' – used in deVriesSchultink_2018_anthracycline_troponinT.R (the source paper records anthracycline as a categorical with levels {‘doxorubicin’, ‘epirubicin’}; the canonical register splits this into a pair of binary indicators following the CONMED_<drug> precedent rather than the categorical ANTH_TYPE).
• Example models: deVriesSchultink_2018_anthracycline_troponinT.R (used jointly with CONMED_EPIRUBICIN to switch the K-PD SLOPE parameter for troponin T between doxorubicin (reference) and epirubicin; epirubicin = 0.524-fold of doxorubicin effect, per de Vries Schultink 2018 Table 2).
• Notes: Specific scope until a second cardiotoxicity or anthracycline-effect model legitimately reuses this indicator. Distinct from CONMED_PLDH (PEGylated liposomal doxorubicin) and from CONMED_AVD (the doxorubicin component of the AVD backbone in Hodgkin lymphoma); use those canonicals for product-formulation or regimen-backbone semantics. When the source dataset’s anthracycline column is categorical (ANTH_TYPE), derive the canonical indicators as CONMED_DOXORUBICIN = (ANTH_TYPE == 'doxorubicin'), CONMED_EPIRUBICIN = (ANTH_TYPE == 'epirubicin').

CONMED_EPIRUBICIN (canonical for concomitant epirubicin (anthracycline) chemotherapy backbone indicator)

• Description: 1 = subject is receiving (or has received during the relevant observation window) epirubicin as the anthracycline component of an adjuvant or neoadjuvant chemotherapy regimen; 0 = subject is receiving a different anthracycline (or no anthracycline at all). The pairing of CONMED_EPIRUBICIN with CONMED_DOXORUBICIN captures the cardiotoxicity-relevant choice within the anthracycline class; epirubicin is less cardiotoxic per mg than doxorubicin (lifetime cumulative dose threshold approximately 950 mg/m^2 vs 550 mg/m^2 for doxorubicin).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (subject is not on epirubicin). When CONMED_DOXORUBICIN and CONMED_EPIRUBICIN are used together (as in de Vries Schultink 2018), both being 0 means no anthracycline.
• Source aliases:
  • ANTH_TYPE = 'epirubicin' – used in deVriesSchultink_2018_anthracycline_troponinT.R. See the matching entry for CONMED_DOXORUBICIN.
• Example models: deVriesSchultink_2018_anthracycline_troponinT.R (used jointly with CONMED_DOXORUBICIN to switch the K-PD SLOPE parameter; epirubicin = 0.524-fold of the doxorubicin reference effect on troponin T).
• Notes: Specific scope until a second cardiotoxicity or anthracycline-effect model legitimately reuses this indicator. The two indicators (CONMED_DOXORUBICIN and CONMED_EPIRUBICIN) are not mutually exclusive in principle (a subject receiving both anthracyclines could have both set to 1), but in practice the de Vries Schultink 2018 cohort assigned exactly one anthracycline per subject.

CONMED_ECULIZUMAB (canonical for concurrent eculizumab (complement C5 inhibitor) coadministration indicator)

• Description: 1 = subject is receiving eculizumab concurrently with the modelled drug at the current time; 0 = the subject is not on eculizumab at the current time. Time-varying within a subject: it turns on and off as the co-treatment period starts and stops, which is what distinguishes it from the baseline-status sibling CONMED_ECULIZUMAB_BL.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no concurrent eculizumab; monotherapy with the modelled drug at the current time).
• Source aliases:
  • ECU – Crass 2024 ESM Table 1 LDH control stream $INPUT column.
  • C5I, ECULI, ECZ – plausible alternative NONMEM $INPUT forms; document the per-model source column in covariateData[[CONMED_ECULIZUMAB]]$source_name.
• Example models: Crass_2024_pegcetacoplan_ldh.R (additive shift of +0.783 on the logit maximal fractional LDH suppression, applied only within the baseline-eculizumab stratum per the source guard IF (BECU.EQ.1) TVEMAX = THETA(4) + THETA(8)*ECU; lifts the maximal LDH suppression from inverse-logit(-1.39) = 20.0% during pegcetacoplan monotherapy to inverse-logit(-0.607) = 35.3% during eculizumab + pegcetacoplan dual therapy, matching the 35.0% quoted in Crass 2024 Sect. 3.4.2. Non-zero only during the PEGASUS run-in and transition dual-therapy windows, Crass 2024 Table 1 footnote a).
• Notes: Eculizumab is a humanised monoclonal antibody against complement component C5 and the long-standing standard of care in paroxysmal nocturnal hemoglobinuria and atypical haemolytic uraemic syndrome; because it blocks terminal complement it profoundly suppresses intravascular haemolysis markers (notably serum lactate dehydrogenase), so it is a load-bearing covariate for any PD model whose endpoint is a haemolysis biomarker. Well-formed member of the auto-approved CONMED_<INN> family. Distinct from the disease-state canonical DIS_PNH, which records the indication rather than the comedication, and from CONMED_ECULIZUMAB_BL, which is the time-fixed baseline status. Ratified canonically alongside the Crass 2024 pegcetacoplan extraction.

CONMED_ECULIZUMAB_BL (canonical for eculizumab (complement C5 inhibitor) treatment status at baseline)

• Description: 1 = subject was receiving eculizumab at study entry (complement C5-inhibitor experienced); 0 = subject was eculizumab-naive at study entry. Time-fixed per subject – the value is frozen at baseline and does not change even if eculizumab is later withdrawn or co-administered, which is what distinguishes it from the time-varying sibling CONMED_ECULIZUMAB.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (eculizumab-naive at baseline).
• Source aliases:
  • BECU – Crass 2024 ESM Table 1 Hb and LDH control stream $INPUT column (“baseline eculizumab”).
  • PRIORC5I, BC5I, ECUBL – plausible alternative NONMEM $INPUT forms.
• Example models: Crass_2024_pegcetacoplan_ldh.R (selects BOTH the baseline-LDH stratum and the maximal-suppression stratum: eculizumab-naive patients have typical baseline exp(7.56) = 1920 U/L with 91.7% maximal suppression, and baseline-eculizumab patients exp(5.52) = 249 U/L with 20.0% maximal suppression, because terminal-complement blockade has already suppressed intravascular haemolysis; encoded as an indicator-weighted mixture of the two stratum parameters rather than an additive shift, because the source control stream uses IF(BECU.EQ.1) TVBLDH = THETA(2) full-replacement switches), Crass_2024_pegcetacoplan_hemoglobin.R (fractional effect on the maximal hemoglobin response emax * (1 + e_conmed_eculizumab_bl_emax * CONMED_ECULIZUMAB_BL) with the coefficient entered as (0 FIX) in the source $THETA block, which is why Crass 2024 Table 2 reports the same 51% Emax for both strata; retained with fixed(0) so the structural form and the fixed status stay visible).
• Notes: The _BL suffix follows the register’s established baseline-value convention (HGB_BL, TRAST_BL, PLAQUE_BL, INS_BL, …) applied to a member of the auto-approved CONMED_<INN> family. Register the pair whenever a source distinguishes prior/baseline C5-inhibitor exposure from concurrent co-treatment; conflating them loses a real modelling distinction (Crass 2024 estimates both, and they act on different parameters with different signs of practical consequence). Scope: specific because the reference category is defined by the source trial’s enrolment criteria. Ratified canonically alongside the Crass 2024 pegcetacoplan extraction.

CONMED_EFV (canonical for concomitant efavirenz indicator)

• Description: 1 = subject is receiving efavirenz (EFV)-based antiretroviral therapy as the third agent in a combination ART regimen; 0 = subject is on the comparator regimen specified by the source paper (typically a protease-inhibitor-based regimen such as standard lopinavir/ritonavir 4:1). Efavirenz is a CYP3A and UGT inducer, so the indicator is used to flag PXR-mediated induction of metabolic clearance for co-administered antiretrovirals.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-EFV reference regimen, paper-defined; e.g., LPV/r 4:1 in Tikiso 2021).
• Source aliases:
  • EFV – used in Tikiso_2021_abacavir.R (the dataset’s paper-defined indicator, 1 = on EFV-based ART, 0 = on standard LPV/r 4:1).
  • efavirenz – used in Jaiswal_2025_dordaviprone.R (Jaiswal 2025 Figure 3 forest plot arm, efavirenz 600 mg once daily as the index moderate CYP3A4 inducer; relative CYP3A4 activity 2.08 back-solved from the published dordaviprone AUC ratio of 0.349). Here efavirenz is a probe perpetrator in a DDI simulation rather than a component of an ART regimen, so the reference is simply “no efavirenz”.
• Example models: Tikiso_2021_abacavir.R (multiplicative effect on apparent oral clearance: cl *= (1 + 0.120 * CONMED_EFV); +12.0% relative to the LPV/r 4:1 reference in HIV-infected children on combination ART), Svensson_2013_bedaquiline.R (multiplicative factor on apparent CL_BDQ and CL_M2: cl_eff = cl_base * 2.07^CONMED_EFV and on apparent CL_M3: cl_eff = cl_base * 1.12^CONMED_EFV; healthy-volunteer DDI study with no-co-medication reference and 1-week onset lag from start of 600 mg once-nightly EFV), Hoglund_2015_lumefantrine.R (multiplicative linear-deviation effect on lumefantrine apparent CL/F: cl *= (1 + 0.726 * CONMED_EFV); +72.6% relative to the no-ART reference in HIV-infected Ugandan adults, Hoglund 2015 Table 2 attributing the effect to CYP3A4 induction), Hoglund_2015_artemether.R (multiplicative linear-deviation effect on artemether relative bioavailability F: fdepot_typ *= (1 + (-0.715) * CONMED_EFV); -71.5% relative to the no-ART reference, Hoglund 2015 Table 3), Kay_2020_lumefantrine.R (TWO independent linear-deviation effects, on lumefantrine apparent CL/F: cl *= (1 + 0.982 * CONMED_EFV) (+98.2%, CYP3A4 induction) and on the first-order absorption rate constant: ka *= (1 + 0.484 * CONMED_EFV) (+48.4%); relative to the no-ART reference in HIV-infected Ugandan children with malaria (ASTMH 2020 poster 2167 Table 1)), Kay_2022_lumefantrine.R (TWO independent linear-deviation effects: on apparent oral lumefantrine CL/F cl *= (1 + 0.982 * CONMED_EFV) (+98.2 %, CYP3A4 induction) and on the first-order absorption rate constant ka *= (1 + 0.484 * CONMED_EFV) (+48.4 %); HIV-uninfected children are the reference (CONMED_EFV = 0) in a pediatric Ugandan malaria cohort on six-dose Coartem Dispersible, Kay 2022 Table 2 theta_7 and theta_10), Bukkems_2021_raltegravir.R (linear additive effect on typical bioavailability F: f_typ *= (1 + e_efv_fdepot * CONMED_EFV) with e_efv_fdepot = -0.167; efavirenz-associated raltegravir F is 17% lower than the no-efavirenz reference, consistent with efavirenz-mediated UGT1A1 induction of raltegravir glucuronidation, Bukkems 2021 Table 2 ‘Factor change in F efavirenz co-administration’), Adeojo_2024_levonorgestrel.R (TWO independent linear-deviation effects in a meta-analytic fit to pooled trial mean profiles: on systemic clearance cl *= (1 + 0.7235 * CONMED_EFV) (CL 5.86 -> 10.1 L/h, +72.4%, CYP3A4 induction) and on oral bioavailability fdepot *= (1 + (-0.3632) * CONMED_EFV) (F_oral 0.837 -> 0.533, -36.3%, additionally reflecting gut-wall CYP3A4); reference is no efavirenz and no antiretroviral therapy, and every efavirenz-exposed arm used efavirenz 600 mg orally once daily; coefficients derived from the two absolute values published in Adeojo 2024 Supplementary Table S2), Jaiswal_2025_dordaviprone.R (efavirenz 600 mg once daily as a probe moderate CYP3A4 inducer in a PBPK drug-drug-interaction simulation, with a no-efavirenz reference; the effect enters through a single relative-CYP3A4-activity term of 2.08 that scales gut and hepatic first-pass extraction and systemic clearance together, back-solved from the published dordaviprone AUC ratio of 0.349).
• Notes: Specific scope because the comparator regimen (LPV/r 4:1 in Tikiso 2021) is paper-defined; future ART population-PK models that test EFV-vs-other contrasts should extend the example list when the comparator matches, or register a finer-grained sibling indicator otherwise. The reference category is paper-defined and is NOT always another ART regimen: Hoglund 2015, Kay 2020/2022 and Adeojo 2024 use a no-efavirenz (in Adeojo 2024, no-ART) reference rather than an active comparator, so read each example’s stated reference before transferring a coefficient.

CONMED_EFV_MD (canonical for multiple-dose (steady-state) efavirenz indicator, single-dose efavirenz reference)

• Description: 1 = the record was collected while the subject was at steady state on chronic efavirenz (typically 600 mg once daily for at least ~2 weeks, long enough for CYP3A / CYP2B6 induction to be established); 0 = the record was collected after a single dose of efavirenz. Unlike the parent canonical CONMED_EFV, both levels are efavirenz-exposed – the contrast is induction-established vs induction-not-yet-established, not efavirenz vs no efavirenz. Use this indicator for the sequential single-dose-then-chronic DDI design in which a probe drug is administered twice, once alongside the first efavirenz dose and once at efavirenz steady state, so that the perpetrator’s own autoinduction is held constant between arms and only the induction state differs. Typically occasion-level (a subject contributes both levels), not subject-level.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (single-dose efavirenz).
• Source aliases:
  • EFV – used in Collins_2025_midazolam.R with reversed polarity: the source dataset codes EFV = 0 for the multiple-dose condition and EFV = 1 for the single-dose condition, so CONMED_EFV_MD = 1 - EFV. Collins 2025 Results states this explicitly (‘In the NONMEM control stream, EFV = 0 represents the multiple-dose efavirenz condition, and EFV = 1 represents the single-dose condition … The reference (baseline) state is thus the single-dose efavirenz condition’), and the deposited control stream (Supporting Information File S5) confirms it with L0 = 0 / IF(EFV.EQ.0) L0 = 1 ;multiple dose efavirenz.
• Example models: Collins_2025_midazolam.R (THREE independent effects from one indicator, all taken from the deposited final control stream: a linear-deviation effect on midazolam clearance cl *= (1 + 0.652 * CONMED_EFV_MD) (+65.2%, hepatic CYP3A induction), a linear-deviation effect on the first-order absorption rate constant ka *= (1 + 0.255 * CONMED_EFV_MD) (+25.5%), and a wholesale switch of oral bioavailability from a fixed 0.5 on the single-dose occasion to a logit-scale estimated expit(-0.511) = 0.375 on the multiple-dose occasion. The bioavailability term is the only parameter in that model carrying between-subject variability on one occasion but not the other, because the control stream places ETA(10) inside the multiple-dose branch).
• Notes: Registered as the finer-grained sibling that the CONMED_EFV Notes anticipate (‘future ART population-PK models that test EFV-vs-other contrasts should extend the example list when the comparator matches, or register a finer-grained sibling indicator otherwise’). Do not fold this into CONMED_EFV: a model that used CONMED_EFV = 0 for the single-dose arm would assert that arm was efavirenz-free, which is false and would make the coefficient non-transferable to any study with a genuine no-efavirenz reference. The distinction matters quantitatively – Collins 2025 Discussion notes that a single efavirenz dose may already raise midazolam clearance somewhat, so a coefficient estimated against a single-dose reference understates the full efavirenz effect relative to one estimated against a drug-free reference. Also distinct from MULTI_DOSE_PT, which flags the multiple-dose phase of the analyte in a patient study; here the analyte (the CYP3A probe) is given as a single dose on both occasions and it is the perpetrator whose dosing history changes. Future papers using the same sequential single-dose/steady-state perpetrator design with a different inducer should register a parallel CONMED_<INN>_MD sibling rather than overloading this entry. Ratified canonically on 2026-08-19 alongside the Collins 2025 midazolam extraction.

CONMED_EPI (canonical for concomitant epinephrine (adrenaline) coadministration indicator)

• Description: 1 = subject received epinephrine (INN: epinephrine; alternate INN adrenaline) coadministered with the parent drug during the study window; 0 = subject received the parent drug alone. Used to encode the local vasoconstrictor effect of epinephrine on intraperitoneal (or perineural) drug distribution: alpha-1 vasoconstriction of the peritoneal (or local) vessels reduces splanchnic blood flow and slows the transfer of the parent drug out of the injection compartment, while systemic beta-1 / beta-2 effects (positive inotropic / chronotropic action and peripheral vasodilatation) increase the apparent volume of distribution of the parent drug.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no coadministered epinephrine).
• Source aliases:
  • EPI – used in Royer_2011_cisplatin.R (Royer 2011 Table 2 covariate on IPCL and V).
• Example models: Royer_2011_cisplatin.R (intraperitoneal epinephrine in the cisplatin IP bath: multiplicative fractional coefficient -0.531 on intraperitoneal clearance IPCL (53.1% decrease) and +0.805 on central volume V (80.5% increase); the paper dichotomises epinephrine because plasma Pt concentrations were similar across the three epinephrine dose levels of 1, 2, and 3 mg/L in the IP bath).
• Notes: Specific scope until a second popPK / PD extraction with coadministered epinephrine reuses this indicator (e.g., a perineural local-anaesthetic + epinephrine PK model). Auto-approved member of the CONMED_<INN> family. Ratified canonically on 2026-07-09 alongside the Royer 2011 cisplatin extraction. The per-model covariateData[[CONMED_EPI]]$notes field should record the paper’s specific epinephrine dosing (route, concentration in the bath / mL, whether dichotomised or dose-response) since the mechanism differs across administration routes.

CONMED_EIAED (canonical for concomitant enzyme-inducing antiepileptic drug indicator)

• Description: 1 = subject is taking at least one enzyme-inducing antiepileptic drug (EIAED) such as carbamazepine, phenobarbital, or phenytoin during the study; 0 = no EIAED coadministration. EIAEDs induce hepatic metabolism (CYP3A4/UGT-mediated pathways) and increase the clearance of co-administered antiepileptic drugs and their active metabolites.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no EIAED coadministration).
• Source aliases:
  • MED (with the value convention inverted: source codes 1 = absence of EIAEDs and 0 = presence; canonical inverts this so 1 = presence and 0 = absence) – used in Rodrigues_2017_oxcarbazepine.R.
• Example models: Rodrigues_2017_oxcarbazepine.R (exponential effect on MHD apparent clearance: cl_mhd *= exp(e_eiaed_cl_mhd * (1 - CONMED_EIAED)); CL_MHD is 29.3% higher with EIAEDs vs without, encoded as +0.257 on the absence-indicator in the source paper).
• Notes: Per-model covariateData[[CONMED_EIAED]]$notes should list the specific EIAEDs counted as “EIAED = 1” since inclusion criteria vary across antiepileptic-drug studies. Rodrigues 2017 counts carbamazepine, phenobarbital, and phenytoin as EIAEDs; other AEDs in the dataset (vigabatrin, clobazam, valproic acid, clonazepam, lamotrigine, diazepam, ethosuccimide, progabide) are not. The source paper uses an “absence of EIAED” indicator (MED = 1 if no EIAED, MED = 0 if EIAED present); the canonical column inverts this so that the 1 group is “on EIAED”, matching the convention for other CONMED_* indicators.

CONMED_ERA (canonical for concomitant endothelin-receptor-antagonist monotherapy indicator in PAH)

• Description: 1 = patient is on a concomitant endothelin-receptor-antagonist (ERA) PAH therapy (e.g., bosentan, macitentan, ambrisentan) alone – i.e. on an ERA but NOT also on a phosphodiesterase type 5 inhibitor; 0 = otherwise. Time-fixed per subject for the PK/PD analysis (PAH comedication at baseline, stable dose required before selexipag start).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (PAH-comedication-naive OR on PDE5 inhibitor alone OR on both ERA+PDE5; mutually exclusive with CONMED_PDE5I and CONMED_ERA_PDE5I).
• Source aliases:
  • PAHCOMED category 1 (“ERA only”) – decomposed from the categorical PAHCOMED source column with levels {naive, ERA, PDE5, ERA+PDE5} in Krause_2017_selexipag.R.
• Example models: Krause_2017_selexipag.R (multiplicative effect on the ACT-333679 elimination rate constant: km *= (1 + 0.15 * CONMED_ERA); +15% relative to the PAH-comedication-naive reference, Krause 2017 Table 1).
• Notes: Used together with CONMED_PDE5I and CONMED_ERA_PDE5I to decompose a four-level PAH-comedication categorical (naive / ERA-only / PDE5-only / ERA-and-PDE5) into three orthogonal mutually-exclusive binary indicators, with the PAH-comedication-naive group as the reference (all three indicators = 0). The four-level categorical encoding preserves the source paper’s stratum-specific coefficient set (Krause 2017 reports a separate categorical b-coefficient for each non-reference stratum rather than independent class-level effects). Specific scope because the indicator’s semantics are tied to the GRIPHON study’s PAH-comedication taxonomy.

CONMED_ERA_PDE5I (canonical for concomitant ERA + PDE5-inhibitor combination indicator in PAH)

• Description: 1 = patient is on both a concomitant endothelin-receptor-antagonist (ERA) and a phosphodiesterase type 5 inhibitor (PDE5I) as PAH therapy; 0 = otherwise. Time-fixed per subject for the PK/PD analysis.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (PAH-comedication-naive OR on ERA alone OR on PDE5I alone; mutually exclusive with CONMED_ERA and CONMED_PDE5I).
• Source aliases:
  • PAHCOMED category 3 (“ERA and PDE5 inh.”) – decomposed from the categorical PAHCOMED source column with levels {naive, ERA, PDE5, ERA+PDE5} in Krause_2017_selexipag.R.
• Example models: Krause_2017_selexipag.R (multiplicative effect on the ACT-333679 elimination rate constant: km *= (1 + 0.37 * CONMED_ERA_PDE5I); +37% relative to the PAH-comedication-naive reference, Krause 2017 Table 1; the combined stratum has its own categorical coefficient distinct from the sum of the ERA-only and PDE5I-only effects).
• Notes: Used together with CONMED_ERA and CONMED_PDE5I to decompose a four-level PAH-comedication categorical (naive / ERA-only / PDE5-only / ERA-and-PDE5) into three orthogonal mutually-exclusive binary indicators. The standalone combined-stratum coefficient (rather than a product of class-level indicator effects) preserves Krause 2017’s full categorical-effect parameterisation. Specific scope because the indicator’s semantics are tied to the GRIPHON study’s PAH-comedication taxonomy.

CONMED_EZE (canonical for concomitant ezetimibe coadministration indicator)

• Description: 1 = patient is taking ezetimibe (with or without other lipid-lowering comedication), 0 = not on ezetimibe.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (not on ezetimibe).
• Source aliases:
  • Derived from an ezetimibe-identifier column in the source.
• Example models: Kuchimanchi_2018_evolocumab.R (multiplicative effect 1.20 on Vmax: Vmax * 1.20^CONMED_EZE; labeled “Statin + ezetimibe exponent” in Kuchimanchi 2018 Table 3 because ~99% of ezetimibe users in the dataset were also on a conmed_statin, so the effect effectively captures combination therapy), Kuchimanchi_2018_evolocumab_ldlc.R (same PK-layer effect plus a multiplicative exponent 0.768 on baseline LDL-C in the exposure-response layer, Table 4), Kakara_2014_atorvastatin.R, Kakara_2014_pitavastatin.R, Kakara_2014_rosuvastatin.R (additive +0.109 contribution to the indirect-response Imax inhibition fraction INH on the LDL-C synthesis rate Kin: INH = Imax * DOSE / (ID50 + DOSE) + 0.109 * CONMED_EZE; Kakara 2014 Table 2 INH_EZT), Jadhav_2023_bempedoicAcid.R, Jadhav_2023_bempedoicAcid_ldlc.R (cleanly separated concomitant-ezetimibe indicator: proportional shift CL/F * (1 + (-0.0934) * CONMED_EZE) in the PK layer, Jadhav 2023 Table 2, and Imax * (1 + 0.190 * CONMED_EZE) in the indirect-response LDL-C layer, Table 3; Jadhav 2023 also carries a separate prior-therapy column [[PRIOR_EZE]], so this indicator is concomitant use only and is not a statin-combination proxy).
• Notes: Scope: specific because Kuchimanchi 2018 interprets the ezetimibe indicator as a combination-therapy marker rather than a pure ezetimibe effect. Future popPK/PD models with cleaner ezetimibe separation should add themselves here or register a more specific canonical.

CONMED_FLUCLOXACILLIN (canonical for concomitant flucloxacillin coadministration indicator)

• Description: 1 = patient is coadministered flucloxacillin (typically 6-12 g daily intravenously, or oral equivalent) alongside the modelled drug during the observation interval; 0 = no concomitant flucloxacillin. Flucloxacillin is an isoxazolyl penicillin (beta-lactamase-stable) used against beta-lactamase-producing staphylococci and streptococci. It is a pregnane X receptor (PXR) agonist: flucloxacillin binds PXR, which co-activates a PXR / retinoid X receptor complex on the XREM / PXRE enhancer and upregulates CYP3A4, CYP2C9 and CYP2C19 over roughly 2 days to 2 weeks. It is also about 95 % albumin bound and has been investigated as an albumin-binding-displacement perpetrator. Time-varying per subject, because flucloxacillin therapy starts and stops within the observation window and the induction effect has a documented onset lag.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant flucloxacillin).
• Source aliases:
  • flucloxacillin – Abdullah-Koolmees 2024 prose and Table 5 heading; same orientation as the canonical.
• Example models: AbdullahKoolmees_2024_voriconazole_pbpk.R (gates a step reduction of all three voriconazole CYP Michaelis constants – CYP2C19 9.3 to 3.72 uM, CYP3A4 834.7 to 181.45 uM, CYP2C9 20 to 13.33 uM – from a model-parameterised onset time tind fixed at 24 h, representing PXR-mediated CYP induction; the resulting rise in intrinsic hepatic clearance reproduces the undetectable voriconazole trough observed in the index case), AbdullahKoolmees_2024_posaconazole_pbpk.R (declared and carried at its reference value only – posaconazole is 83 % excreted unchanged and its minor UGT1A4 route was assumed unaffected by PXR upregulation, so the covariate has no structural effect in that model and documents the paper’s explicit negative finding).
• Notes: Distinct from CONMED_ABX (any-other-antibiotic composite) and from CONMED_FUSIDIC (a different staphylococcal agent whose interaction mechanism is CYP3A4 inhibition and albumin displacement rather than PXR-mediated induction) – flucloxacillin’s documented perpetrator mechanism is enzyme induction, so the sign of its effect on a CYP substrate’s clearance is opposite. The per-model covariateData[[CONMED_FLUCLOXACILLIN]]$notes must document the onset lag applied, because the induction is not instantaneous: published case reports place the fall in azole concentrations 2-7 days after flucloxacillin is started, while mechanistic CYP3A4 upregulation is reported to take 2 days to 2 weeks. Where a model implements the induction as a step change, record the step time. Ratified canonically alongside the Abdullah-Koolmees 2024 voriconazole / posaconazole whole-body PBPK extraction, as a well-formed member of the auto-approved CONMED_<INN> family.

CONMED_FUSIDIC (canonical for concomitant fusidic acid coadministration indicator)

• Description: 1 = patient is coadministered oral fusidic acid (typically 500 mg three times daily) alongside the modelled drug during the observation interval; 0 = no concomitant fusidic acid. Fusidic acid is a bacteriostatic protein-synthesis-inhibitor antibiotic used against staphylococcal infections; it is extensively plasma-protein bound (about 97 % to albumin), is orally bioavailable, and has been implicated as a CYP3A4 inhibitor and albumin-binding perpetrator in multiple drug-drug-interaction reports.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no concomitant fusidic acid).
• Source aliases:
  • fusidic acid – used in Marsot_2017_rifampicin.R (Marsot 2017 Table 1 / Table 2 stratifies typical CL/F and V/F by fusidic acid coadministration; the canonical column CONMED_FUSIDIC collapses the two strata into a single binary indicator).
• Example models: Marsot_2017_rifampicin.R (log-scale multiplicative effects on CL/F and V/F: cl = exp(lcl + e_conmed_fusidic_cl * CONMED_FUSIDIC + etalcl) with e_conmed_fusidic_cl = log(5.1 / 13.7) recovers Table 2’s stratified typical values 13.7 L/h at CONMED_FUSIDIC = 0 and 5.1 L/h at CONMED_FUSIDIC = 1; the analogous encoding on V/F recovers 61.1 L vs 23.8 L).
• Notes: Specific scope because the only on-disk source is Marsot 2017 (staphylococcal osteoarticular infections; 10 / 62 patients coadministered fusidic acid at 500 mg three times daily). Auto-approved member of the CONMED_<INN> family. Ratified canonically on 2026-07-01 alongside the Marsot 2017 rifampicin extraction. The Marsot 2017 Discussion attributes the interaction to fusidic acid inhibition of CYP3A4 and to displacement of rifampicin from albumin binding, but notes that the mechanism remains uncertain; downstream models that use CONMED_FUSIDIC for a different modelled drug should document the mechanism hypothesis in covariateData[[CONMED_FUSIDIC]]$notes. When a future paper reports a time-varying fusidic acid coadministration schedule (start / stop within the observation window), the same canonical column can carry the time-varying indicator; the per-model notes should record whether the encoding is time-fixed or time-varying.

CONMED_GLASDEGIB (canonical for concomitant glasdegib (smoothened inhibitor) coadministration indicator)

• Description: 1 = subject is in a glasdegib-containing treatment arm (glasdegib added to a chemotherapy backbone such as low-dose cytarabine (LDAC) or decitabine); 0 = subject is in the chemotherapy-backbone-alone comparator arm (no glasdegib). Glasdegib is an oral smoothened (SMO) inhibitor of the Hedgehog signaling pathway, approved in the United States in combination with LDAC for older adults with newly diagnosed AML who are ineligible for intensive chemotherapy.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (LDAC alone or chemotherapy-backbone-alone comparator arm). In Lin_2020_glasdegib_treatment.R the reference is the LDAC alone arm (BRIGHT AML 1003 Phase 2). In Lin_2020_glasdegib_decitabine.R the reference is also LDAC alone, paired with CONMED_DECITABINE to identify the three-arm structure of the exploratory pooled analysis.
• Source aliases:
  • Derived from the protocol-defined treatment arm in Lin_2020_glasdegib_treatment.R and Lin_2020_glasdegib_decitabine.R (BRIGHT AML 1003, NCT01546038).
• Example models: Lin_2020_glasdegib_treatment.R (binary indicator on the exponential overall-survival hazard; the published equation uses the complementary indicator LDAC_alone = 1 - CONMED_GLASDEGIB so the model file derives ldac_alone <- 1 - CONMED_GLASDEGIB inside model() to preserve the paper’s parameter values verbatim), Lin_2020_glasdegib_decitabine.R (paired with CONMED_DECITABINE in the three-arm exploratory analysis).
• Notes: Specific scope because the only on-disk source is the BRIGHT AML 1003 trial (Lin 2020). Auto-approved member of the CONMED_<INN> family. Ratified canonically on 2026-06-24 alongside the Lin 2020 BRIGHT AML 1003 overall-survival extraction. The Lin 2020 treatment-response equation uses LDAC_alone as the binary covariate (1 = LDAC alone, 0 = glasdegib + LDAC) with parameter theta_ldac_alone = 1.376; the canonical-column form (1 = glasdegib + LDAC) preserves the published value via the in-model 1 - CONMED_GLASDEGIB mapping rather than refitting the parameter against a re-encoded covariate.

CONMED_GCSF (canonical for concomitant granulocyte colony-stimulating factor (G-CSF) treatment indicator)

• Description: 1 = subject received granulocyte colony-stimulating factor (G-CSF; filgrastim, pegfilgrastim, lenograstim, or another G-CSF product) as concomitant medication during the observation period; 0 = did not receive G-CSF. Used in chemotherapy-induced myelosuppression popPK/PD analyses either (a) as an exclusion filter, or (b) as a covariate on Friberg-style bone-marrow lifespan-model parameters (typically MTT, kprol, Slope, and/or Gamma) to capture the effect of prophylactic or reactive G-CSF support on neutrophil recovery kinetics.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant G-CSF treatment).
• Source aliases:
  • G-CSF – van Hasselt 2013 paper narrative and Table 2 / Table 3 header notation.
  • GCSF – common NONMEM column-name form used by Friberg-style myelosuppression implementations.
• Example models: vanHasselt_2013_eribulin.R (proportional / dichotomous effect on MTT with theta = 0.883 – G-CSF shortens MTT by ~12% – and on SLOPE with theta = 1.3 – G-CSF increases the linear drug-effect coefficient by ~30%. Encoded as mtt = ... * e_gcsf_mtt^CONMED_GCSF and slope = ... * e_gcsf_slope^CONMED_GCSF).
• Notes: Time-varying per subject in principle (G-CSF is given as a course over specific days after chemotherapy), but van Hasselt 2013 treats it as a subject-level indicator in the covariate model (subject received G-CSF at any point during the observation period). Per-model covariateData[[CONMED_GCSF]]$notes should record whether the paper’s encoding is “ever received G-CSF” (subject-level, time-fixed) or per-cycle time-varying. Distinct from CSF1 (colony-stimulating factor 1 / M-CSF concentration, a target-engagement biomarker in anti-CSF-1R mAb models) – the two concepts share the abbreviation “CSF” but are otherwise unrelated. Ratified canonically on 2026-07-10 alongside the van Hasselt 2013 eribulin-neutropenia extraction (operator decision in sidecar request 002).

CONMED_H2RA (canonical for concomitant H2-receptor-antagonist use)

• Description: 1 = patient on concomitant histamine H2-receptor-antagonist therapy (e.g., ranitidine, famotidine), 0 = no CONMED_H2RA use. Captures another class of gastric-pH-modifying co-medication that may reduce bioavailability of pH-sensitive orally administered drugs.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no CONMED_H2RA use).
• Source aliases:
  • H2 – used in Goel_2016_Sonidegib.R (Goel 2016 dataset; defined as “significant” CONMED_H2RA use, i.e. duration of CONMED_H2RA use >= 80% of the PK assessment phase).
• Example models: Goel_2016_Sonidegib.R (multiplicative effect on F: 0.996^CONMED_H2RA, no clinically meaningful effect; reported alongside CONMED_PPI for completeness).
• Notes: Per-model covariateData[[CONMED_H2RA]]$notes must document the operational definition (Goel 2016: >= 80% of PK assessment phase). Distinct from CONMED_PPI.

CONMED_ANTACID (canonical for concomitant antacid use)

• Description: 1 = patient on concomitant antacid therapy (typically over-the-counter aluminum-, magnesium-, or calcium-based salts such as calcium carbonate, magnesium carbonate, magnesium hydroxide, or aluminum hydroxide), 0 = no CONMED_ANTACID use. Captures the third class of gastric-pH-modifying co-medication alongside CONMED_PPI and CONMED_H2RA; primarily used to test the impact of transient gastric-pH elevation on the bioavailability of pH-sensitive orally administered drugs.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no CONMED_ANTACID use).
• Source aliases:
  • ANTACIDF – used in Kemal_2026_nemtabrutinib.R (Kemal 2026 NONMEM control stream in the supplement; time-varying regressor coded 1 = concomitant antacid at the observation, 0 otherwise).
• Example models: Kemal_2026_nemtabrutinib.R (multiplicative effect on F: (1 + -0.0360 * CONMED_ANTACID), i.e. 3.6% lower F under antacid coadministration; RSE 72.8%, 95% CI includes zero – retained in the full covariate model).
• Notes: Per-model covariateData[[CONMED_ANTACID]]$notes should document the operational definition (per-observation time-varying indicator vs subject-level ever-vs-never) and the class of antacids pooled into the = 1 category, since inclusion criteria vary by study. Distinct from CONMED_PPI (proton-pump inhibitors: irreversible H+/K+-ATPase inhibition, sustained gastric-pH elevation) and CONMED_H2RA (histamine H2 receptor antagonists: reversible histamine-mediated acid-secretion blockade). Antacids act by direct chemical neutralization of gastric acid rather than by inhibiting acid secretion, giving them a shorter and more localized pH-elevating effect than the other two classes.

CONMED_PHOSBINDER (canonical for concomitant phosphate-binding agent use)

• Description: 1 = patient on a concomitant phosphate-binding agent (oral agents that sequester dietary phosphate in the gastrointestinal lumen to lower serum phosphate – e.g. sevelamer, lanthanum carbonate, calcium acetate, calcium carbonate, sucroferric oxyhydroxide), 0 = no phosphate-binder use. Typically a subject-level ever-vs-never indicator. Two distinct clinical settings generate this covariate: management of hyperphosphatemia in chronic kidney disease, and management of the on-target hyperphosphatemia that is a class effect of fibroblast growth factor receptor (FGFR) inhibitors.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant phosphate-binder use).
• Source aliases:
  • BINDER – used in Gong_2023_pemigatinib.R (Gong 2023 NONMEM control stream in Appendix S1; IF(BINDER.EQ.0) CLBINDER = 1 / IF(BINDER.EQ.1) CLBINDER = (1 + THETA(7))). Gong 2023 Table 1 footnote b defines it as “phosphate-binding agents”.
• Example models: Gong_2023_pemigatinib.R (multiplicative effect on CL/F: (1 + -0.155 * CONMED_PHOSBINDER), i.e. 15.5% lower apparent clearance with phosphate-binder coadministration; %RSE 14.6, 95% CI -19.9% to -11.1%; the paper states the effect is statistically but not clinically significant, raising simulated Cmax,ss by roughly 6%).
• Notes: Per-model covariateData[[CONMED_PHOSBINDER]]$notes should document the operational definition (subject-level ever-vs-never vs per-record time-varying) and which agents were pooled into the = 1 category, since these vary by study. Distinct from CONMED_ANTACID even though some compounds appear in both classes (calcium carbonate, calcium acetate, aluminium and magnesium salts): the two canonicals encode different therapeutic intents and different mechanisms of action on the drug of interest. CONMED_ANTACID is a gastric-pH-modifying covariate tested alongside CONMED_PPI and CONMED_H2RA for effects on the absorption of pH-sensitive drugs; CONMED_PHOSBINDER is a luminal-chelation covariate whose relevant mechanism is binding of the co-administered drug in the gut, and which in FGFR-inhibitor programmes is also a marker of on-treatment hyperphosphatemia management. When a source paper reports both, register both. Ratified canonically alongside the Gong 2023 pemigatinib extraction.

CONMED_IFNB1A (canonical for concomitant interferon beta-1a coadministration indicator)

• Description: 1 = patient coadministered subcutaneous interferon beta-1a (Rebif or equivalent recombinant IFN beta-1a product) during the observation interval, 0 = no concomitant IFN beta-1a. Time-varying per subject because the source studies enrol both monotherapy and IFN beta-1a combination periods.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant IFN beta-1a).
• Source aliases:
  • IFNB1A – used in Savic_2017_cladribine.R.
• Example models: Savic_2017_cladribine.R (multiplicative effect on cladribine non-renal clearance: cl_nonrenal *= (1 + e_ifn_clnr * CONMED_IFNB1A) with e_ifn_clnr = 0.21, i.e. a 21% increase in non-renal CL when coadministered with IFN beta-1a).
• Notes: Captured at the dose-record level in Savic 2017 (multiple-dose study 26486 alternated between cladribine alone and cladribine + IFN beta-1a periods). The interaction mechanism is not definitively established in the source paper; Savic 2017 discusses that the observed effect could alternatively be modelled on bioavailability or interpreted as a period-effect / interoccasion variability artefact. Future cladribine + immunomodulator extractions should reuse this canonical when IFN beta-1a is the specific concomitant agent; register a sibling canonical (e.g. CONMED_IFNB1B, CONMED_IFNALPHA) if a different interferon species is intended.

CONMED_IFNALPHA (canonical for concomitant interferon alpha coadministration indicator)

• Description: 1 = patient coadministered systemic interferon alpha (any subtype; recombinant IFN-alpha-2a, IFN-alpha-2b, or pegylated IFN-alpha) during the observation interval, 0 = no concomitant IFN-alpha. Time-fixed when the source paper analyses cohorts whose IFN-alpha exposure is unchanged across the analysis window (per-trial protocol); time-varying when IFN-alpha cycling on/off is recorded at the observation level.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant IFN-alpha).
• Source aliases:
  • IFNa – used in Han_2016_bevacizumab.R (Han 2016 Table 3 categorical indicator on bevacizumab CL; the 102 IFN-alpha-coadministered patients were enrolled in renal cell carcinoma study BO17705 per Han 2016 Table 1 and Table 2 row ‘Concomitant treatment’).
• Example models: Han_2016_bevacizumab.R (multiplicative effect on bevacizumab CL: cl *= exp(e_ifna_lcl * CONMED_IFNALPHA) with e_ifna_lcl = log(0.844) = -0.170, i.e. CL is 15.6% lower when IFN-alpha is coadministered).
• Notes: Sibling canonical to CONMED_IFNB1A (interferon beta-1a) under the CONMED_<INN> family; the existing CONMED_IFNB1A entry explicitly anticipated this CONMED_IFNALPHA name for the alpha-interferon species. Per-paper alpha-subtype detail (IFN-alpha-2a vs -2b vs pegylated) is documented in covariateData[[CONMED_IFNALPHA]]$notes where available; pooled here because the source paper (Han 2016) does not separately enumerate subtypes. Distinct from CONMED_IFNB1A/CONMED_IFNB1B (beta-interferon species). Ratified canonically on 2026-06-25 alongside the Han 2016 bevacizumab extraction.

CONMED_IL6RI (canonical for concomitant interleukin-6 receptor inhibitor (class) coadministration indicator)

• Description: 1 = subject received a concomitant interleukin-6 receptor (IL-6R) inhibitor – tocilizumab, sarilumab, or satralizumab – pooled as a class, 0 = no IL-6R inhibitor. A class-level rather than per-INN indicator, because papers that test this covariate are generally testing the pharmacological consequence of blocking IL-6R signalling (reversal of IL-6-mediated suppression of CYP3A4 / CYP2C8 and downregulation of acute-phase proteins such as alpha-1-acid glycoprotein and CRP), not the identity of the antibody. Document which agents, at what doses, and whether the flag is baseline-only or time-varying in each model’s covariateData[[CONMED_IL6RI]]$notes.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant IL-6R inhibitor).
• Source aliases:
  • IL6INHIB – NONMEM control-stream column name used in Said_2025_imatinib.R (Said 2025 Data S1 $INPUT; KD = EXP(THETA(3) + THETA(7)*IL6INHIB + ETA(3)) and CLM = EXP(THETA(10) + IL6INHIB*THETA(13) + ETA(5))).
• Example models: Said_2025_imatinib.R (two retained log-additive effects in a joint imatinib / N-desmethyl-imatinib popPK model: the imatinib-AAG dissociation constant Kd is multiplied by 1.7 and apparent DM-imatinib clearance CLm/(Fm x F1) by 0.46 when an IL-6R inhibitor preceded imatinib; the class pools 8 mg/kg IV tocilizumab and 400 mg IV sarilumab given as single doses on ICU admission. The same covariate was tested on unbound imatinib clearance and NOT retained – Said 2025 Data S1 holds that coefficient at (0) FIX).
• Notes: Class-level scope follows the established CONMED_<CLASS> precedents in this family (CONMED_AZOLE, CONMED_ABX, CONMED_AED, CONMED_SULFONYLUREA, CONMED_ERA), which likewise pool mechanism-equivalent agents. If a future paper estimates separate tocilizumab and sarilumab effects, register per-INN siblings (CONMED_TOCILIZUMAB, CONMED_SARILUMAB) and keep this canonical for the pooled-class case; do not use both a per-INN indicator and this class indicator on the same parameter in one model. Distinct from IL6 (the measured serum interleukin-6 concentration, a continuous covariate) – note that IL-6R blockade typically raises measured total IL-6 because the antibody-receptor complex slows IL-6 clearance, so the two covariates can move in opposite directions and must not be treated as substitutes. Also distinct from DIS_CRITILL and DIS_COVID19, with which it is frequently collinear in ICU cohorts: in Said 2025 every IL-6R-inhibitor patient was a ventilated COVID-19 ICU patient, so the two effects are separable only because the pooled dataset also contains ICU COVID-19 patients who received no IL-6R inhibitor. General scope because IL-6R blockade is a mechanism-defined, widely-used intervention whose disease-drug-drug-interaction effect on CYP substrates and protein binding is expected to recur. Ratified canonically on 2026-08-21 alongside the Said 2025 imatinib extraction.

CONMED_IMMUNOMOD (canonical for any-immunomodulator (purine analogue or methotrexate) composite indicator)

• Description: 1 = subject is on at least one concomitant immunomodulator (any of: azathioprine (AZA), 6-mercaptopurine (6-MP), or methotrexate (MTX)) at the PK observation, 0 = no concomitant immunomodulator from this class. A composite pooled indicator used in inflammatory-bowel-disease popPK analyses where the source paper does not separate the individual thiopurine vs MTX effects on therapeutic-mAb (typically anti-TNF) clearance.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant immunomodulator from the AZA / 6-MP / MTX class).
• Source aliases:
  • IMM – used in Frymoyer_2017_infliximab.R (pooled purine-analogue-or-MTX indicator per Table 1 footnote of Frymoyer 2017: “Concomitant immunomodulation refers to purine-analogue or methotrexate.”).
• Example models: Frymoyer_2017_infliximab.R (power-of-coefficient effect on CL: CL_typ * 0.863^CONMED_IMMUNOMOD, i.e. -13.7% when on an immunomodulator).
• Notes: Distinct from the per-drug indicators CONMED_AZA, CONMED_MP, and CONMED_MTX, which model the effect of each immunomodulator separately. Use CONMED_IMMUNOMOD only when the source paper itself pools the three under a single binary; use the per-drug indicators when the source paper estimates separate effects. Future IBD popPK extractions that pool thiopurines + MTX into a single indicator should reuse this canonical and extend the example list. Ratified canonically on 2026-05-20 alongside the Frymoyer 2017 infliximab pediatric Crohn’s disease extraction.

CONMED_IPI_1Q6W (canonical for nivolumab + ipilimumab 1 mg/kg q6w combination indicator)

• Description: 1 = subject is receiving nivolumab in combination with ipilimumab 1 mg/kg every 6 weeks (continuous maintenance); 0 = otherwise.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (any non-1Q6W regimen – monotherapy, chemotherapy combination, or another ipilimumab schedule).
• Source aliases:
  • IPI1Q6W – used in Zhang_2019_nivolumab.R.
• Example models: Zhang_2019_nivolumab.R (exponential effect on baseline CL: exp(0.159) ~= 1.17 fold increase relative to monotherapy).
• Notes: Paired with CONMED_IPI_3Q3W. See the CONMED_IPI_3Q3W note for how the other ipilimumab schedules collapse into the reference group.

CONMED_IPI_3Q3W (canonical for nivolumab + ipilimumab 3 mg/kg q3w combination indicator)

• Description: 1 = subject is receiving nivolumab in combination with ipilimumab 3 mg/kg every 3 weeks (4-dose induction); 0 = otherwise. Encodes the high-intensity ipilimumab combination regimen as a study-design covariate on nivolumab CL.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (any non-3Q3W regimen – monotherapy, chemotherapy combination, or another ipilimumab schedule).
• Source aliases:
  • IPI3Q3W – used in Zhang_2019_nivolumab.R.
• Example models: Zhang_2019_nivolumab.R (exponential effect on baseline CL: exp(0.227) ~= 1.25 fold increase relative to monotherapy).
• Notes: Paired with CONMED_IPI_1Q6W; both indicators can coexist in one population, but a single subject has at most one set to 1 in the Zhang 2019 cohort. The remaining ipilimumab schedules (1 mg/kg q3w x 4 induction, 1 mg/kg q12w) had no statistically significant effect on nivolumab CL and were therefore folded into the reference (0) group along with monotherapy, leaving only IPI3Q3W and IPI1Q6W as named non-reference indicators.

CONMED_IPI_ANY (canonical for any-ipilimumab-coadministration indicator)

• Description: 1 = subject is receiving nivolumab in combination with any ipilimumab regimen (regardless of dose or schedule); 0 = nivolumab monotherapy or nivolumab + chemotherapy. Encodes the “is there ipilimumab in the regimen” question as a single binary covariate, distinct from the regimen-specific CONMED_IPI_3Q3W and CONMED_IPI_1Q6W indicators above.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no ipilimumab coadministration).
• Source aliases:
  • IPICO – used in Zhang_2019_nivolumab.R.
• Example models: Zhang_2019_nivolumab.R (additive effect on the time-varying-CL Emax parameter: Emax += -0.0668 when CONMED_IPI_ANY = 1).
• Notes: Logically the union of the regimen-specific indicators (CONMED_IPI_3Q3W, CONMED_IPI_1Q6W, plus the unmodeled 1 mg/kg q3wx4 and 1 mg/kg q12w schedules). Zhang 2019 uses it on the time-varying Emax (a different structural parameter from baseline CL), which is why it coexists with the regimen-specific indicators on baseline CL rather than substituting for them.

CONMED_ITRACONAZOLE (canonical for concomitant itraconazole (strong CYP3A4 / P-gp inhibitor) coadministration indicator)

• Description: 1 = subject coadministered itraconazole during the observation interval (typically 200 mg once-daily oral or higher), 0 = no concomitant itraconazole. Distinct from the broader [[CONMED_AZOLE]] indicator (any azole antifungal) and from [[CONMED_CYP3A4_INH]] (any CYP3A4 inhibitor) when a paper singles out itraconazole as an index victim / perpetrator; itraconazole is used clinically as a probe strong CYP3A4 + P-glycoprotein inhibitor in dedicated DDI studies. Per-subject time-varying when a DDI study spans on / off itraconazole periods; time-fixed on the on-treatment day of a fixed-sequence DDI arm.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant itraconazole).
• Source aliases:
  • INTR – used in vandenBerg_2021_uprifosbuvir_pbpk.R (van den Berg 2021 supplement input column INTR = 0 alone / 1 itraconazole, per PN001 Group F and PN010).
  • Itraconazole – used in Zhou_2025_tacrolimus.R (Zhou 2025 Table 1 counts 38 of 988 trough records on itraconazole; exponential coefficient e_itra_cl = -0.87 on tacrolimus CL/F, a 57.98% reduction – the largest of the three azoles in that cohort, consistent with itraconazole being the most lipophilic mould-active azole and the most potent CYP3A4 inhibitor after ketoconazole).
  • itraconazole – used in Jaiswal_2025_dordaviprone.R (Jaiswal 2025 Table 2 / Table S6, multiple-dose itraconazole with a single 125 mg dordaviprone dose; relative CYP3A4 activity 0.138 back-solved from the published AUC ratio of 4.62).
• Example models: vandenBerg_2021_uprifosbuvir_pbpk.R (multiplicative factors on eight gut / hepatic parameters per van den Berg 2021 Table 3: CL_int,G x 0.444, CL_int,H x 0.797, ALAG2 x 0.145, KA2 x 0.499, Ktr1 x 0.166, KM6g x 0.294, FrM6g x 0.174, KelM6g x 0.0290; the combined effect of these factors is a delay in uprifosbuvir Tmax and a large increase in uprifosbuvir plasma exposure without a corresponding rise in M5 / M6, consistent with itraconazole shunting uprifosbuvir away from gut CYP3A4 / P-gp-mediated first-pass metabolism toward hepatic conversion), Comisar_2025_rimegepant.R (two multiplicative fractional effects per Comisar 2025 Table 3: -0.743 on CL/F, a 74.3% clearance decrease giving a 3.9-fold rise in simulated steady-state AUCtau and supporting the label recommendation to avoid strong CYP3A4 inhibitors, and -0.351 on the transit rate constant ktr, a 35.1% slowing judged not clinically meaningful because ka is the slower of the two absorption steps; paired with the new sibling CONMED_FLUCONAZOLE (-0.427 on CL/F) so the model carries the strong and the moderate CYP3A4 probe inhibitor as separate covariates), Jaiswal_2025_dordaviprone.R (PBPK drug-interaction simulation; the effect enters through a single relative-CYP3A4-activity term of 0.138 that scales gut and hepatic first-pass extraction and systemic clearance together, back-solved from the published dordaviprone AUC ratio of 4.62).
• Notes: Auto-approved member of the CONMED_<INN> family (INN = itraconazole). Distinct from the class-level [[CONMED_AZOLE]] (Kirubakaran 2022 tacrolimus, which pools itraconazole with other systemic azoles as a common CYP3A4 / P-gp inhibitor class effect) because dedicated itraconazole DDI studies – particularly those where itraconazole is used as the strong probe inhibitor in a Phase 1 mechanistic study – carry substrate-specific factors on multiple gut and hepatic parameters that would be diluted by pooling with weaker azoles. Future dedicated itraconazole DDI popPK extractions should reuse this canonical and extend the example-models list. Promote to a drug-specific variant if a paper distinguishes multiple itraconazole regimens; keep as the class-neutral indicator otherwise.

CONMED_ERYTHROMYCIN (canonical for concomitant erythromycin (moderate CYP3A4 inhibitor) coadministration indicator)

• Description: 1 = subject coadministered erythromycin during the observation interval (typically 500 mg four times daily oral), 0 = no concomitant erythromycin. Erythromycin is the FDA index moderate CYP3A4 inhibitor and acts through mechanism-based (time-dependent) inactivation rather than reversible competition. Distinct from the class-level [[CONMED_CYP3A4_INH_MOD]] indicator: use this canonical when a paper singles erythromycin out by name as the index moderate inhibitor in a dedicated DDI simulation or study, and the class indicator when the paper pools several moderate inhibitors into one covariate.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant erythromycin).
• Source aliases:
  • erythromycin – used in Jaiswal_2025_dordaviprone.R (Jaiswal 2025 Figure 3 forest plot arm, erythromycin 500 mg four times a day).
• Example models: Jaiswal_2025_dordaviprone.R (multiplicative effect through a single relative-CYP3A4-activity term that scales gut and hepatic first-pass extraction and systemic clearance together; the activity 0.372 was back-solved from the published dordaviprone AUC ratio of 2.68).
• Notes: Auto-approved member of the CONMED_<INN> family (INN = erythromycin). Sibling of [[CONMED_FLUCONAZOLE]], [[CONMED_CIMETIDINE]] and [[CONMED_RIFAMPICIN]], registered alongside the Jaiswal 2025 dordaviprone extraction, which simulates one arm per named index CYP3A4 modulator rather than pooling by potency tier. Per-model notes should record the erythromycin regimen, because the magnitude of a mechanism-based inhibitor’s effect depends strongly on dose and duration.

CONMED_FLUCONAZOLE (canonical for concomitant fluconazole (moderate CYP3A4 inhibitor) coadministration indicator)

• Description: 1 = subject coadministered fluconazole during the observation interval (typically 200 mg once daily oral), 0 = no concomitant fluconazole. Fluconazole is an FDA index moderate CYP3A4 inhibitor and is also a moderate CYP2C9 / CYP2C19 inhibitor, so per-model notes should record which pathway the source paper attributes the interaction to. Distinct from the class-level [[CONMED_AZOLE]] (any azole antifungal) and from [[CONMED_CYP3A4_INH_MOD]] (any moderate CYP3A4 inhibitor) when a paper singles out fluconazole as the index moderate-inhibitor probe in a dedicated DDI arm. Per-subject time-varying when a DDI study spans on / off fluconazole periods; time-fixed on the on-treatment day of a fixed-sequence DDI arm.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant fluconazole).
• Source aliases:
  • Fluconazole use – used in Comisar_2025_rimegepant.R (Comisar 2025 Table 2 row label).
  • fluconazole – used in Jaiswal_2025_dordaviprone.R (Jaiswal 2025 Figure 3 forest plot arm, fluconazole 200 mg once daily).
• Example models: Comisar_2025_rimegepant.R (founding example; multiplicative fractional effect on apparent clearance only, cl * (1 + (-0.429) * CONMED_FLUCONAZOLE) per Comisar 2025 Table 2 ‘Fluconazole use on CL/F’, a 42.9% clearance reduction giving a 1.75-fold AUC increase. The paired strong-inhibitor arm in the same analysis carries CONMED_ITRACONAZOLE with a larger clearance effect (-0.744) plus an additional effect on the transit rate constant, which fluconazole does not have – the two azoles are deliberately NOT pooled), Jaiswal_2025_dordaviprone.R (relative CYP3A4 activity 0.410, back-solved from the published dordaviprone AUC ratio of 2.48).
• Notes: Auto-approved member of the CONMED_<INN> family (INN = fluconazole). Sibling of [[CONMED_ITRACONAZOLE]]: the two are registered separately, rather than under a shared azole or CYP3A4-inhibitor class canonical, because dedicated phase 1 DDI programmes commonly run a moderate (fluconazole) and a strong (itraconazole) probe in the same study and estimate distinct effect sizes – pooling them into one indicator would discard exactly the contrast the DDI arm was designed to measure. Use the class-level [[CONMED_AZOLE]] or [[CONMED_CYP3A4_INH_MOD]] only when the source itself pools. Also a sibling of [[CONMED_ERYTHROMYCIN]], [[CONMED_CIMETIDINE]] and [[CONMED_RIFAMPICIN]], added with the Jaiswal 2025 dordaviprone extraction: fluconazole and erythromycin are both index moderate inhibitors but produce measurably different victim exposures there (dordaviprone AUC ratios 2.48 and 2.68 respectively), a second argument for per-drug indicators over a pooled moderate-inhibitor covariate whenever the source reports each arm separately.

CONMED_CIMETIDINE (canonical for concomitant cimetidine (weak CYP3A4 inhibitor) coadministration indicator)

• Description: 1 = subject coadministered cimetidine during the observation interval (typically 400 mg twice daily oral), 0 = no concomitant cimetidine. Cimetidine is an FDA index weak CYP3A4 inhibitor and is additionally an H2-receptor antagonist and an inhibitor of renal organic-cation transport, so a cimetidine arm can perturb a victim drug through gastric pH and renal secretion as well as through CYP3A4; per-model notes must record which mechanism the source attributes the effect to. Distinct from the class-level [[CONMED_H2RA]] (any H2-receptor antagonist, used when the source is interested in the gastric-pH effect) and from the reserved CONMED_CYP3A4_INH_WEAK.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant cimetidine).
• Source aliases:
  • cimetidine – used in Jaiswal_2025_dordaviprone.R (Jaiswal 2025 Figure 3 forest plot arm, cimetidine 400 mg twice a day).
• Example models: Jaiswal_2025_dordaviprone.R (relative CYP3A4 activity 0.739, back-solved from the published dordaviprone AUC ratio of 1.42; the source attributes the effect entirely to weak CYP3A4 inhibition, since dordaviprone renal clearance is zero and the pH effect was separately shown to be null in the rabeprazole arm).
• Notes: Auto-approved member of the CONMED_<INN> family (INN = cimetidine). Registered alongside [[CONMED_ERYTHROMYCIN]] and [[CONMED_RIFAMPICIN]] with the Jaiswal 2025 dordaviprone extraction, which also extended the pre-existing [[CONMED_FLUCONAZOLE]] entry. When a future source uses cimetidine as a probe for renal organic-cation-transport inhibition rather than for CYP3A4, reuse this same column and disambiguate in per-model notes; do not register a mechanism-specific variant.

CONMED_RIFAMPICIN (canonical for concomitant rifampicin (strong CYP3A4 inducer) coadministration indicator)

• Description: 1 = subject coadministered rifampicin (rifampin) during the observation interval (typically 600 mg once daily oral), 0 = no concomitant rifampicin. Rifampicin is the FDA index strong CYP3A4 inducer and also induces CYP2C9 / CYP2C19 / UGT and P-glycoprotein, so per-model notes should record which pathways the source attributes the effect to. Because induction requires enzyme turnover, the indicator is time-varying with a multi-day onset and offset when a source models the induction time course explicitly; time-fixed in fixed-sequence DDI arms that dose to steady-state induction before the victim dose. Distinct from [[CONMED_RPT]] (rifapentine at full CYP3A4 induction) and from the class-level [[CONMED_CYP3A4_IND]].
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant rifampicin).
• Source aliases:
  • rifampicin / rifampin – both spellings appear in source manuscripts (INN is rifampicin, USAN is rifampin); standardize the column name to CONMED_RIFAMPICIN. Used in Jaiswal_2025_dordaviprone.R (Jaiswal 2025 Figure 3 forest plot arm, rifampicin 600 mg once daily).
• Example models: Jaiswal_2025_dordaviprone.R (relative CYP3A4 activity 3.13, back-solved from the published dordaviprone AUC ratio of 0.167; this is the one modulator whose held-out Cmax ratio the reduction reproduces poorly, over-predicting 0.328 as 0.404).
• Notes: Auto-approved member of the CONMED_<INN> family (INN = rifampicin). Registered alongside [[CONMED_ERYTHROMYCIN]] and [[CONMED_CIMETIDINE]] with the Jaiswal 2025 dordaviprone extraction, which also extended the pre-existing [[CONMED_FLUCONAZOLE]] entry. Sources that model the induction onset / washout time course should carry the covariate as time-varying rather than registering a separate steady-state-induction canonical.

CONMED_VORICONAZOLE (canonical for concomitant voriconazole (strong CYP3A inhibitor) coadministration indicator)

• Description: 1 = subject coadministered voriconazole during the observation interval (typically 200 mg twice daily oral); 0 = no concomitant voriconazole. Voriconazole is both a reversible and a time-dependent CYP3A inhibitor and is the dominant tacrolimus / calcineurin-inhibitor perpetrator in solid-organ transplant care, where antifungal prophylaxis and treatment are routine. Time-varying when the observation record spans on / off voriconazole periods (the usual case in transplant TDM datasets); time-fixed in a fixed-sequence DDI arm.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant voriconazole).
• Source aliases:
  • Voriconazole – used in Pei_2023_tacrolimus.R (Pei 2023 Supplement 2.1.1: “Categorical covariates, such as SEX (male = 1 and female = 2) and Voriconazole (co-administration = 1 and none = 0), were included in the analysis using indicator variables”).
  • Voriconazole – used in Zhou_2025_tacrolimus.R (Zhou 2025 Table 1 counts 741 of 988 trough records on voriconazole; the azole antifungal drug (AFD) covariate carries a separate coefficient per agent).
• Example models: Pei_2023_tacrolimus.R (exponential effect on apparent oral clearance: exp(e_vori_cl * CONMED_VORICONAZOLE) with e_vori_cl = -0.64, i.e. a 47% reduction in tacrolimus CL/F on voriconazole; Pei 2023 Table S4), Zhou_2025_tacrolimus.R (exponential effect on apparent oral clearance: exp(e_vori_cl * CONMED_VORICONAZOLE) with e_vori_cl = -0.48, i.e. a 38.21% reduction in tacrolimus CL/F in Chinese lung-transplant recipients; Zhou 2025 Table 2 final model, back-transformed factor 0.62 in Eq. 4 – closely reproducing the 36.2% reduction reported by Cai et al. in an independent lung-transplant cohort).
• Notes: Auto-approved member of the CONMED_<INN> family (INN = voriconazole). Distinct from the class-level [[CONMED_AZOLE]] (Kirubakaran 2022 tacrolimus, which pools voriconazole with the other systemic azoles) and from [[CONMED_CYP3A4_INH_STRONG]]: register the drug-specific indicator when a paper singles voriconazole out with its own coefficient, because the magnitude of voriconazole’s tacrolimus interaction is much larger than the pooled azole class effect (Pei 2023’s own PBPK arm predicts a 5.80-fold rise in tacrolimus AUC). Siblings [[CONMED_ITRACONAZOLE]] and [[CONMED_POSACONAZOLE]]; use all three together when a paper resolves a separate coefficient per mould-active azole (Zhou 2025 lung-transplant tacrolimus). When a source instead supplies the perpetrator’s measured concentration rather than an on/off flag, use [[CONC_VORI_NGML]]. Ratified 2026-08-05 alongside the Pei 2023 tacrolimus extraction.

CONMED_POSACONAZOLE (canonical for concomitant posaconazole (strong CYP3A4 / P-gp inhibitor) coadministration indicator)

• Description: 1 = subject coadministered posaconazole during the observation interval (oral suspension 200 mg three times daily, or delayed-release tablet 300 mg once daily after loading); 0 = no concomitant posaconazole. Posaconazole is a strong CYP3A4 inhibitor and, with voriconazole and itraconazole, one of the three mould-active azoles routinely used for antifungal prophylaxis and treatment in solid-organ transplant and haematology care. Time-varying when the observation record spans on / off posaconazole periods (the usual case in transplant TDM datasets); time-fixed in a fixed-sequence DDI arm.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant posaconazole).
• Source aliases:
  • Posaconazole – used in Zhou_2025_tacrolimus.R (Zhou 2025 Methods ‘General clinical data’ records azole antifungal drugs (AFDs) as a combined-medication category, and the final model estimates a separate AFD coefficient per agent; Table 1 counts 157 of 988 trough records on posaconazole).
• Example models: Zhou_2025_tacrolimus.R (exponential effect on apparent oral clearance: exp(e_posa_cl * CONMED_POSACONAZOLE) with e_posa_cl = -0.31, i.e. tacrolimus CL/F multiplied by 0.73 – Zhou 2025 reports the back-transformed factor as 0.74 in Eq. 4 and the reduction as 26.30% in the Discussion; Zhou 2025 Table 2 final model).
• Notes: Auto-approved member of the CONMED_<INN> family (INN = posaconazole). Sibling to [[CONMED_VORICONAZOLE]] and [[CONMED_ITRACONAZOLE]]; register the drug-specific indicator rather than the class-level [[CONMED_AZOLE]] whenever a paper resolves a separate coefficient per azole, because the three agents differ substantially in CYP3A4 inhibitory potency (Zhou 2025 Discussion, citing equimolar-potency ranking ketoconazole > itraconazole > voriconazole > fluconazole, and finding tacrolimus CL/F reductions of 57.98% for itraconazole, 38.21% for voriconazole and 26.30% for posaconazole in the same cohort). The three drug-specific indicators are mutually exclusive in the Zhou 2025 dataset (no recipient received two mould-active azoles concurrently), but the canonical does not assume exclusivity – a model that needs to forbid overlap should say so in covariateData[[CONMED_POSACONAZOLE]]$notes. Formulation matters: the oral suspension has markedly lower and more variable bioavailability than the delayed-release tablet, so the magnitude of the tacrolimus interaction is formulation-dependent (Zhou 2025 Discussion: ‘the patients enrolled in our study mainly used posaconazole as suspension, which shows smaller impact than delayed-release tablets’); record the formulation used in the per-model notes. Distinct from [[FORM_POSA_AB]] and [[STUDY_POSA_PHASE3]], which are covariates of posaconazole as the victim drug in the van Iersel 2018 posaconazole popPK analysis rather than indicators of posaconazole as a perpetrator. Ratified 2026-08-19 alongside the Zhou 2025 tacrolimus extraction.

CONMED_AMLODIPINE (canonical for concomitant amlodipine coadministration indicator)

• Description: 1 = subject is receiving amlodipine at the observation; 0 = not. Amlodipine is a dihydropyridine calcium-channel blocker and a weak CYP3A4 inhibitor, and is one of the most common antihypertensives in solid-organ transplant care, where post-transplant hypertension is near-universal. Time-varying when the record spans start / stop of amlodipine; time-fixed when the source paper records only a per-subject yes/no.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant amlodipine).
• Source aliases:
  • Amlodipine – used in Xiang_2025_tacrolimus_fpg.R (Xiang 2025 Table 1 co-medication row and Supplementary Table 3 covariate screen).
• Example models: Xiang_2025_tacrolimus_fpg.R (declared in covariatesDataExcluded: amlodipine reached the forward-inclusion threshold on baseline fasting plasma glucose, dOFV = -8.613, p < 0.01, but failed backward elimination at p < 0.001 and carries no published point estimate, so it is documented rather than used).
• Notes: Auto-approved member of the CONMED_<INN> family (INN = amlodipine). Distinct from a class-level calcium-channel-blocker indicator and from [[CONMED_CYP3A4_INH]] / [[CONMED_CYP3A4_INH_MOD]]: register the drug-specific indicator when a paper singles amlodipine out with its own screening step or coefficient. Amlodipine’s own CYP3A4 inhibition is weak relative to the azoles, so a significant amlodipine term in a tacrolimus or calcineurin-inhibitor model is as likely to be a marker of blood-pressure or graft-function status as a direct drug-drug interaction. Ratified 2026-08-18 alongside the Xiang 2025 tacrolimus PK/PD extraction.

CONMED_WUZHI (canonical for concomitant Wuzhi capsule (Schisandra sphenanthera extract) coadministration indicator)

• Description: 1 = subject is receiving the Wuzhi capsule at the observation; 0 = no concomitant Wuzhi capsule. Wuzhi capsule is a standardised ethanolic extract of Schisandra sphenanthera fruit (principal lignan schisantherin A, with deoxyschisandrin and schisandrin B) marketed in China and routinely co-prescribed with tacrolimus in solid-organ transplant care as a deliberate pharmacokinetic booster: its lignans inhibit CYP3A4/CYP3A5 and P-glycoprotein, raising tacrolimus exposure and allowing a lower tacrolimus dose. Time-varying when the observation record spans on / off Wuzhi periods; time-fixed when the source paper analyses a fixed co-medication assignment.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant Wuzhi capsule).
• Source aliases:
  • WZ – used in Xiang_2025_tacrolimus.R (Xiang 2025 Eq. 6: “WZ = 1 if WZ is present; otherwise = 0”).
• Example models: Xiang_2025_tacrolimus.R (exponential effect on apparent oral clearance: exp(e_conmed_wuzhi_cl * CONMED_WUZHI) with e_conmed_wuzhi_cl = -0.211, i.e. a 19% reduction in tacrolimus CL/F on Wuzhi capsule; Xiang 2025 Table 2 and Eq. 6), Xiang_2025_tacrolimus_fpg.R, Xiang_2025_tacrolimus_egfr.R (the same effect carried as a fixed PK parameter in the sequential PK/PD steps).
• Notes: Member of the CONMED_<agent> family; the token is the product name rather than an INN because Wuzhi capsule is a multi-constituent herbal preparation with no single INN. Distinct from the class-level [[CONMED_CYP3A4_INH]] and from [[CONMED_CYP3A4_INH_MOD]]: register the product-specific indicator when a paper singles the Wuzhi capsule out with its own coefficient, as the preparation is dosed and regulated as a discrete product in Chinese transplant practice rather than as a member of a pooled inhibitor class. Because Wuzhi capsule is prescribed in order to raise tacrolimus exposure, it is confounded with genotype in observational cohorts – Xiang 2025 notes it is “often prescribed for patients with CYP3A51/1 but rarely for patients with CYP3A53/3” – so a model carrying both this indicator and [[CYP3A5_EXPR]] should be simulated over the covariate combinations that actually occur in the source cohort. Ratified 2026-08-18 alongside the Xiang 2025 tacrolimus PK/PD extraction.

CONMED_LCM (canonical for lacosamide (monotherapy or coadministration) indicator)

• Description: 1 = subject is on lacosamide (LCM) at the observation, 0 = not on lacosamide. In an active-controlled monotherapy trial (SP0993) the indicator identifies the LCM arm (the CBZ-CR arm gets 0). In adjunctive-treatment cohorts the same canonical would flag concomitant lacosamide use. Time-varying is permitted when a study spans on / off transitions; time-fixed in monotherapy parallel-group trials.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (not on lacosamide; in monotherapy head-to-head trials this is the comparator arm, e.g., carbamazepine controlled-release in Lindauer 2017 SP0993).
• Source aliases:
  • TYPE – used in Lindauer_2017_lacosamide_dropout.R and Lindauer_2017_lacosamide_seizure.R (Lindauer 2017 SP0993 encoding: TYPE = 1 for the lacosamide arm, 0 for the CBZ-CR arm; same value semantics as the canonical CONMED_LCM column).
• Example models: Lindauer_2017_lacosamide_dropout.R (multiplicative log-hazard shift on the dropout hazard: hazard *= exp(-0.138 * CONMED_LCM), HR = 0.871 vs the CBZ-CR arm, Lindauer 2017 Table 2 Coeff_TYPE), Lindauer_2017_lacosamide_seizure.R (gates the drug-specific AUC covariate contributions – AUC_LCM centred deviation is active only when CONMED_LCM = 1, AUC_CBZ centred deviation active only when CONMED_LCM = 0 – and gates the AGE covariate effect on the first-seizure hazard which is LCM-only per Lindauer 2017 Section 3.4).
• Notes: Auto-approved member of the CONMED_<INN> family. Follows the CONMED_* concomitant-medication pattern (CONMED_CBZ, CONMED_EFV, CONMED_AZA, etc.). Semantically the indicator captures “is the subject taking lacosamide at this record”; in a monotherapy parallel-group trial that equates to arm membership, and in adjunctive or cross-over designs it captures the on / off status. Ratified canonically on 2026-07-03 alongside the Lindauer 2017 lacosamide time-to-seizure / dropout extraction.

CONMED_LPV (canonical for concomitant lopinavir co-administration indicator)

• Description: 1 = subject is receiving concomitant lopinavir as part of an antiretroviral regimen at the observation, 0 = not on lopinavir. Captures the LPV-ritonavir DDI on ritonavir apparent oral clearance: when LPV/r is co-administered, ritonavir CL/F is roughly 2.7-fold higher than when ritonavir is given without lopinavir (Kappelhoff 2005 attributes the increase to lopinavir-driven enzyme induction superimposed on ritonavir’s own CYP3A4 inhibition). Distinct from the joint rifampicin + super-boosted-LPV/r 4:4 indicator CONMED_RIF_LPVR4 and from the cholesterol-surrogate use in Archary_2018_lopinavir.R; this canonical is the binary “is the subject on lopinavir at all” flag, not a regimen-specific contrast.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant lopinavir).
• Source aliases:
  • LPV – used in Kappelhoff_2005_ritonavir.R (paper Results equation following Table 2: CL/F = 10.5 * 2.72^LPV).
• Example models: Kappelhoff_2005_ritonavir.R (multiplicative power-form effect on apparent oral ritonavir CL/F: cl = exp(lcl + etalcl) * e_lpv_cl^CONMED_LPV with e_lpv_cl = 2.72; 36 of 186 subjects on LPV/r in the source cohort), Hoglund_2015_lumefantrine.R (TWO independent linear-deviation effects: on lumefantrine apparent CL/F cl *= (1 + (-0.621) * CONMED_LPV) (-62.1 %, CYP3A4 inhibition by ritonavir) and on desbutyl-lumefantrine apparent CL/F cl_desbutlum *= (1 + 3.92 * CONMED_LPV) (+392 %; mechanism unknown, Hoglund 2015 Discussion); Table 2), Hoglund_2015_artemether.R (TWO independent linear-deviation effects: on artemether apparent CL/F cl *= (1 + 0.328 * CONMED_LPV) (+32.8 %) and on dihydroartemisinin apparent CL/F cl_dha *= (1 + 1.43 * CONMED_LPV) (+143 %; attributed to CYP2B6 / 2C induction by lopinavir, Hoglund 2015 Discussion); Table 3), Kay_2020_lumefantrine.R (TWO independent linear-deviation effects, on lumefantrine apparent CL/F: cl *= (1 + (-0.514) * CONMED_LPV) (-51.4 %, CYP3A4 inhibition by the ritonavir booster) and on the first-order absorption rate constant: ka *= (1 + (-0.212) * CONMED_LPV) (-21.2 %); relative to the no-ART reference in HIV-infected Ugandan children with malaria (ASTMH 2020 poster 2167 Table 1)).
• Notes: Follows the CONMED_* concomitant-medication pattern (CONMED_AZA, CONMED_RIF, CONMED_EFV, CONMED_CBZ, CONMED_AMIO, etc.). The Kappelhoff 2005 cohort tested concomitant saquinavir and indinavir on the same parameter via the same univariate procedure; neither was retained, so this canonical captures the only co-PI covariate kept in that paper’s final model. Time-fixed within an evaluated regimen in the source cohort (the dataset records baseline LPV/r vs non-LPV/r assignment); a time-varying form is permitted for cohorts with on / off regimen transitions and should be documented in covariateData[[CONMED_LPV]]$notes. Ratified canonically alongside the Kappelhoff 2005 ritonavir extraction.

CONMED_METFORMIN (canonical for concomitant metformin co-administration indicator)

• Description: 1 = subject is on concomitant metformin during the modelled treatment period, 0 = not. Time-fixed in source datasets where metformin is a study-design “add-on” arm (e.g., Retlich 2015 Study 4 add-on-to-metformin design); permits a time-varying form for cohorts with on/off metformin transitions, document per-model via covariateData[[CONMED_METFORMIN]]$notes.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant metformin).
• Source aliases: none known.
• Example models: Retlich_2015_linagliptin.R (1 = study 4 add-on-to-metformin cohort; multiplicative effect on linagliptin relative bioavailability F: +69% F for metformin co-administration vs the monotherapy reference; the effect is attributed to a metformin – linagliptin drug-drug interaction consistent with a separately published DDI study, Graefe-Mody 2009).
• Notes: Follows the CONMED_* concomitant-medication pattern (AZA / MP / MTX / AMINO / NSAID / PARA / AD / RITUX / AED / CHEMO / EIAED / EFV / AZOLE). Metformin is a widely-co-prescribed first-line T2DM oral antidiabetic; future T2DM-popPK / -DDI extractions should reuse this canonical. Ratified canonically alongside the Retlich 2015 linagliptin extraction.

CONMED_MP (canonical for concomitant 6-mercaptopurine)

• Description: 1 = on concomitant 6-mercaptopurine (6-MP), 0 = not.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant 6-MP).
• Source aliases: MP – used in Rosario_2015_vedolizumab.R.
• Example models: Rosario_2015_vedolizumab.R (power-form on CLL: CLL * 1.04^CONMED_MP).
• Notes: Second thiopurine immunomodulator used in IBD maintenance; typically mutually exclusive with CONMED_AZA for a given subject.

CONMED_SULFONYLUREA (canonical for concomitant sulfonylurea (class) co-administration indicator)

• Description: 1 = subject is on a concomitant sulfonylurea (class indicator pooling glyburide / glibenclamide / glipizide / glimepiride / gliclazide / tolbutamide / glipizide-ER / chlorpropamide and other oral insulin-secretagogue sulfonylureas), 0 = not. Time-fixed in source datasets where the sulfonylurea is a study-design background-therapy arm (e.g., the Baron 2016 metformin + sulfonylurea cohort); permits a time-varying form when a study tracks on / off sulfonylurea transitions.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant sulfonylurea).
• Source aliases:
  • SU – used in Baron_2016_empagliflozin.R (Baron 2016 Methods and Table S2 ‘background sulfonylurea’ cohort; multiplicative effects on the model-predicted baseline FPG and on Gmax in Table S3).
• Example models: Baron_2016_empagliflozin.R (multiplicative effect on BFPG: bfpg_su^CONMED_SULFONYLUREA = 1.01^...; on Gmax: gmax_su^CONMED_SULFONYLUREA = 1.27^... – sulfonylurea co-treatment increases empagliflozin’s maximal FPG-lowering effect, attributed by the authors to possible improvement in beta-cell function).
• Notes: Class-level pooling indicator analogous to CONMED_AZOLE, CONMED_NSAID, CONMED_STATIN, CONMED_AED, etc. Future T2DM popPK / PD extractions that need to distinguish individual sulfonylureas (e.g., for a DDI sub-analysis on a CYP2C9-metabolized member) should register a per-INN sibling canonical (CONMED_GLYBURIDE, CONMED_GLIPIZIDE, …) and keep CONMED_SULFONYLUREA for the class-pooled summary. Ratified canonically on 2026-06-24 alongside the Baron 2016 empagliflozin extraction.

CONMED_PIOGLITAZONE (canonical for concomitant pioglitazone co-administration indicator)

• Description: 1 = subject is on concomitant pioglitazone (thiazolidinedione (TZD) class oral antidiabetic), 0 = not. Time-fixed in source datasets where pioglitazone is a study-design background-therapy arm; permits a time-varying form when a study tracks on / off transitions.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant pioglitazone).
• Source aliases:
  • PIO – used in Baron_2016_empagliflozin.R (Baron 2016 Methods and Study 7 background pioglitazone cohort; multiplicative effects on the model-predicted baseline FPG and on Gmax in Table S3).
• Example models: Baron_2016_empagliflozin.R (multiplicative effect on BFPG: bfpg_pio^CONMED_PIOGLITAZONE = 0.999^...; on Gmax: gmax_pio^CONMED_PIOGLITAZONE = 1.02^... – both effects are not statistically significant in the source analysis but are retained per the full-covariate-modelling approach).
• Notes: Follows the per-INN CONMED_<INN> pattern (CONMED_METFORMIN, CONMED_LPV, CONMED_ATAZANAVIR, CONMED_RIF, CONMED_PROBENECID, etc.). Distinct from a putative future CONMED_TZD (class indicator) – the thiazolidinedione class additionally contains rosiglitazone (largely withdrawn) and lobeglitazone (Korean market); pool a TZD class indicator only if a future paper aggregates them. Ratified canonically on 2026-06-24 alongside the Baron 2016 empagliflozin extraction.

CONMED_MTX (canonical for concomitant methotrexate)

• Description: 1 = on concomitant methotrexate, 0 = not.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant methotrexate).
• Source aliases: MTX – used in Rosario_2015_vedolizumab.R.
• Example models: Rosario_2015_vedolizumab.R (power-form on CLL: CLL * 0.983^CONMED_MTX).
• Notes: Immunomodulator used especially in CD maintenance. Generic concomitant-MTX indicator that may also appear in non-IBD models; start as scope: general.

CONMED_NSAID (canonical for concomitant NSAID use)

• Description: 1 = on concomitant non-steroidal anti-inflammatory drug (NSAID) therapy at baseline, 0 = not.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant NSAID use; typical patient).
• Source aliases:
  • NSAID – used in Li_2019_abatacept.R.
• Example models: Li_2019_abatacept.R (exponential effect on CL: CL * exp(0.0640 * CONMED_NSAID); ~6.6% higher CL, not clinically relevant per Li 2019).
• Notes: Baseline-use-only in Li 2019; time-varying use is permitted, document per-model. Follows the CONMED_* concomitant-medication pattern established for IBD models (AZA / MP / MTX / AMINO).

CONMED_NVP (canonical for concomitant nevirapine indicator)

• Description: 1 = subject is receiving nevirapine (NVP) – typically as the non-nucleoside reverse-transcriptase inhibitor (NNRTI) third agent in an HIV combination ART regimen – coadministered with the modelled antiretroviral; 0 = subject is not on nevirapine. Nevirapine is a CYP3A inducer; the indicator is used to flag PXR-mediated induction of metabolic clearance for coadministered CYP3A-cleared antiretrovirals (e.g., the protease inhibitor lopinavir).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no nevirapine; paper-defined ART backbone excluding NVP).
• Source aliases:
  • N – in-equation indicator used by Jullien 2006 (final covariate submodel: CL/F = ... * 1.34^N with N = 1 if nevirapine combined with lopinavir).
  • NVP – standard 3-letter HIV abbreviation in NONMEM control streams from pediatric-ART cohorts; same orientation, no value transformation.
• Example models: Jullien_2006_lopinavir.R (exponential effect on CL/F: cl *= exp(log(1.34) * CONMED_NVP); +34% CL/F when nevirapine is coadministered), Hoglund_2015_lumefantrine.R (multiplicative linear-deviation effect on lumefantrine relative bioavailability F: fdepot_typ *= (1 + (-0.248) * CONMED_NVP); -24.8% relative to the no-ART reference in HIV-infected Ugandan adults, Hoglund 2015 Table 2), Hoglund_2015_artemether.R (two independent linear-deviation effects: on artemether relative bioavailability F fdepot_typ *= (1 + (-0.663) * CONMED_NVP) (-66.3 %) and on apparent dihydroartemisinin CL/F cl_dha *= (1 + (-0.445) * CONMED_NVP) (-44.5 %); Hoglund 2015 Table 3), Svensson_2014_bedaquiline_nvp.R (power-form effects on apparent CL of bedaquiline and its M2 metabolite at full induction: cl *= 0.915 ^ CONMED_NVP and cl_m2 *= 1.05 ^ CONMED_NVP; Svensson 2014 Supplementary Table S1b ‘EFF NVP on BDQ CL = 0.915’ and ‘EFF NVP on M2 CL = 1.05’), Kay_2022_lumefantrine.R (TWO independent linear-deviation effects: on apparent oral lumefantrine CL/F cl *= (1 + 0.0191 * CONMED_NVP) (+1.91 %; not statistically significant, 95% CI -0.324 to 0.362) and on the first-order absorption rate constant ka *= (1 + (-0.0589) * CONMED_NVP) (-5.89 %; not statistically significant, 95% CI -0.207 to 0.0891); HIV-uninfected children are the reference (CONMED_NVP = 0) in a pediatric Ugandan malaria cohort, Kay 2022 Table 2 theta_9 and theta_12), Kay_2020_lumefantrine.R (TWO independent linear-deviation effects, on lumefantrine apparent CL/F: cl *= (1 + 0.0191 * CONMED_NVP) (+1.9%) and on the first-order absorption rate constant: ka *= (1 + (-0.0589) * CONMED_NVP) (-5.9%); both retained in the final model despite 95% CIs spanning zero (ASTMH 2020 poster 2167 Table 1)).
• Notes: Scope promoted from specific to general on 2026-06-27 alongside the Svensson 2014 bedaquiline-NVP extraction (the fourth model ratifying the canonical: Jullien 2006, Hoglund 2015 lumefantrine, Hoglund 2015 artemether, Svensson 2014). Sister indicator to the registered CONMED_EFV (efavirenz) entry, applying to the second commonly co-administered NNRTI. Future ART popPK models that test an NVP-vs-comparator contrast should extend the example list when the comparator matches.

CONMED_PARA (canonical for concomitant paracetamol (acetaminophen) use)

• Description: 1 = on concomitant paracetamol (acetaminophen) at the observation, 0 = not.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant paracetamol).
• Source aliases:
  • PCM – used in Plan_2012_pain.R (DDMORE Foundation Model Repository entry DDMODEL00000194).
• Example models: Plan_2012_pain.R (additive shift on the placebo-effect logit of the typical pain-score lambda: phl = logit(TVLAM) + 0.364 * CONMED_PARA; mean pain score ~9% higher on the 0-10 Likert scale during paracetamol use).
• Notes: Distinct from CONMED_NSAID – paracetamol is not classed as an NSAID (no anti-inflammatory mechanism, distinct AE profile). Time-varying use is permitted; the daily Likert measurements in Plan 2012 carry PCM as a per-observation flag. Follows the CONMED_* concomitant-medication pattern established for IBD models (AZA / MP / MTX / AMINO) and CONMED_NSAID (Li 2019). Ratified canonically alongside the Plan 2012 DDMORE extraction.

CONMED_PACLITAXEL (canonical for concomitant paclitaxel coadministration indicator)

• Description: 1 = the modelled drug’s observation was collected while the patient was concurrently receiving paclitaxel as the co-administered antineoplastic, 0 = not on concomitant paclitaxel. A per-INN member of the CONMED_* family, used when a source paper resolves paclitaxel as its own co-administration stratum rather than pooling it into a generic chemotherapy-backbone indicator.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (not on concomitant paclitaxel). The substantive meaning of the zero level is set by the source paper’s stratification and must be documented per model – in Zuo_2024_apatinib.R the all-zero reference is “apatinib co-administered with an immune-checkpoint-inhibitor monoclonal antibody”, not “no co-medication”.
• Source aliases:
  • CM (category B) – used in Zuo_2024_apatinib.R (Zuo 2024 Table 1 footnote defines four mutually exclusive co-administration groups A/B/C/D; B is paclitaxel).
• Example models: Zuo_2024_apatinib.R (multiplicative effect on apparent clearance: CL/F * 0.58^CONMED_PACLITAXEL, i.e. 42% lower CL/F on concomitant paclitaxel relative to the anti-PD-1 mAb reference group; Zuo 2024 Table 2, RSE 9%, bootstrap 95% CI 0.52-0.67).
• Notes: Distinct from PRIOR_TAXANE, which records taxane exposure before study entry rather than concurrent co-administration, and from CONMED_CHEMO, which pools any chemotherapy backbone (including paclitaxel + carboplatin) into a single binary for anti-PD-(L)1 antibody popPK. Use CONMED_PACLITAXEL only when the source paper estimates a paclitaxel-specific effect separable from other cytotoxics; use CONMED_CHEMO when the paper itself collapses the backbone. Forms a mutually exclusive indicator set with [[CONMED_ANTINEO_OTHER]] and [[CONMED_ANTINEO_NONE]] in Zuo 2024; a subject in the reference stratum carries 0 on all three. The paclitaxel direction in Zuo 2024 is an empirical TDM association (paclitaxel and apatinib are both CYP3A4 substrates and the taxane co-treatment cohort is the sickest stratum), and the authors do not claim a mechanistic DDI.

CONMED_ANTINEO_OTHER (canonical for concomitant non-taxane cytotoxic antineoplastic composite indicator)

• Description: 1 = the modelled drug’s observation was collected while the patient was concurrently receiving a cytotoxic antineoplastic agent that the source paper pools into an unresolved “other” stratum rather than naming individually, 0 = not in that stratum. A composite umbrella indicator for oncology popPK analyses that resolve one or two co-administered agents by name and collapse the remainder; the exact class membership is paper-specific and must be recorded in covariateData[[CONMED_ANTINEO_OTHER]]$notes.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (not in the pooled other-cytotoxic stratum). As with the other members of the set, the substantive meaning of the zero level is set by the source paper’s stratification and is documented per model.
• Source aliases:
  • CM (category C) – used in Zuo_2024_apatinib.R, where the pooled stratum is platinum agents, capecitabine, and the tegafur/gimeracil/oteracil potassium combination (S-1).
• Example models: Zuo_2024_apatinib.R (multiplicative effect on apparent clearance: CL/F * 1.60^CONMED_ANTINEO_OTHER, i.e. 60% higher CL/F relative to the anti-PD-1 mAb reference group; Zuo 2024 Table 2, RSE 27%, bootstrap 95% CI 1.35-1.90).
• Notes: Structurally parallel to [[CONMED_ABX]], the register’s other “the source paper does not separate the individual agents” composite: use it only when the paper itself pools the agents, and use per-INN indicators (CONMED_PACLITAXEL, CONMED_DOXORUBICIN, CONMED_EPIRUBICIN, CONMED_DECITABINE, …) whenever the paper estimates a named-agent effect. Distinct from CONMED_CHEMO, which is specifically the anti-PD-(L)1 mAb + platinum-doublet study-design indicator with its own fixed class definition. Because the pooled membership varies between papers, a positive or negative effect on clearance should be read as an empirical marker of the co-treated subset rather than as a transferable drug-drug-interaction estimate. Forms a mutually exclusive indicator set with [[CONMED_PACLITAXEL]] and [[CONMED_ANTINEO_NONE]] in Zuo 2024.

CONMED_ANTINEO_NONE (canonical for antineoplastic-monotherapy indicator (no concomitant antineoplastic))

• Description: 1 = the modelled antineoplastic was given as monotherapy, with no concomitant antineoplastic agent at the observation; 0 = at least one concomitant antineoplastic agent was co-administered. Used when a source paper’s co-administration covariate is a multi-level categorical whose reference level is an active combination rather than monotherapy, so that “monotherapy” needs its own indicator column rather than being recovered as the all-zero baseline.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (some concomitant antineoplastic present). Note that this canonical deliberately does not assume monotherapy is the reference; that is the situation it exists to handle.
• Source aliases:
  • CM (category D) – used in Zuo_2024_apatinib.R (Zuo 2024 Table 1 footnote group D, “Monotherapy”).
• Example models: Zuo_2024_apatinib.R (multiplicative effect on apparent clearance: CL/F * 1.38^CONMED_ANTINEO_NONE, i.e. 38% higher CL/F relative to the anti-PD-1 mAb reference group; Zuo 2024 Table 2, RSE 38%, bootstrap 95% CI 1.14-1.65).
• Notes: Scoped to concomitant antineoplastic therapy only – a patient on apatinib monotherapy who is also taking, say, an antiemetic or an antihypertensive still carries CONMED_ANTINEO_NONE = 1. Distinct from CONMED_STATIN_MONO and CONMED_ERA / CONMED_PDE5I, which are monotherapy indicators for a co-medication class (the patient is on that class alone); this canonical says the modelled drug is unaccompanied. Where a paper does use monotherapy as its reference level, encode the combination strata instead (as Zhang_2019_nivolumab.R and Kuchimanchi_2024_dostarlimab.R do with CONMED_CHEMO) and do not introduce this column. Forms a mutually exclusive indicator set with [[CONMED_PACLITAXEL]] and [[CONMED_ANTINEO_OTHER]] in Zuo 2024; exactly one of the four Zuo strata applies to any observation, and the reference stratum (immune-checkpoint-inhibitor mAb co-administration) carries 0 on all three.

CONMED_PB (canonical for concomitant phenobarbital coadministration indicator)

• Description: 1 = subject is taking phenobarbital (PB) – including primidone, which is metabolised to phenobarbital and is conventionally pooled with PB – as a concomitant antiepileptic drug at the PK observation, 0 = no concomitant phenobarbital / primidone. Phenobarbital is a broad-spectrum CYP and UGT inducer that increases the apparent clearance of co-administered drugs. Time-varying when phenobarbital starts / stops within the observation window; time-fixed when the source paper analyses chronic-maintenance cohorts whose AED therapy is stable.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant phenobarbital or primidone).
• Source aliases:
  • PB – used in Schoemaker_2017_brivaracetam.R (paper covariate PB for phenobarbital or primidone coadministration; the source pools primidone with phenobarbital because primidone is metabolised to phenobarbital) and Lee_2024_topiramate.R (Lee 2024 Table S1 theta7, additive enzyme-induction term on topiramate CL/F; that paper does not state whether primidone was pooled with phenobarbital).
• Example models: Schoemaker_2017_brivaracetam.R (multiplicative effect on apparent oral clearance: cl *= (1 + 0.408 * CONMED_PB); +40.8% relative to no-PB reference, corresponding to ~29% lower brivaracetam exposure, Schoemaker 2017 Table 1), Lee_2024_topiramate.R (additive effect in absolute units: cl <- (exp(lcl) + e_pb_cl * CONMED_PB + ...) * ... with e_pb_cl = 0.376 L/h on a 1.45 L/h monotherapy intercept, i.e. +26% relative to no-PB reference and the smallest of that model’s four enzyme-inducing coefficients, Lee 2024 Table S1 / Discussion).
• Notes: Drug-specific CONMED_* indicator anticipated in the [[CONMED_AED]] notes; used when a paper estimates a separate phenobarbital-induction effect distinct from the pooled EIAED / AED class effect. Per-model covariateData[[CONMED_PB]]$notes should document whether primidone is pooled with phenobarbital (Schoemaker 2017 pools them because primidone is metabolised to phenobarbital; Lee 2024 is silent on the point). Distinct from the broader [[CONMED_EIAED]] (any enzyme-inducing AED) and [[CONMED_AED]] (any concomitant AED). When a paper distinguishes individual AEDs separately, use the drug-specific canonicals [[CONMED_CBZ]], CONMED_PB, [[CONMED_OXC]], [[CONMED_PHT]], [[CONMED_VPA]] rather than collapsing into the class-level indicator. Ratified canonically on 2026-05-20 alongside the Schoemaker 2017 brivaracetam paediatric extraction.

CONMED_PHT (canonical for concomitant phenytoin coadministration indicator)

• Description: 1 = subject is taking phenytoin (PHT) as a concomitant antiepileptic drug at the PK observation, 0 = no concomitant phenytoin. Phenytoin is a strong, broad-spectrum CYP (2C9, 2C19, 3A4) and UGT inducer and is generally the most potent enzyme-inducing antiseizure medication in routine use, so it typically carries the largest induction coefficient when a paper decomposes the enzyme-inducing class into individual drugs. Time-varying when phenytoin starts / stops within the observation window; time-fixed when the source paper analyses chronic-maintenance cohorts whose antiseizure therapy is stable across the analysis window.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant phenytoin).
• Source aliases:
  • PHT – used in Lee_2024_topiramate.R (Lee 2024 Table S1 theta6, additive enzyme-induction term on topiramate CL/F).
• Example models: Lee_2024_topiramate.R (additive effect on apparent oral clearance in absolute units: cl <- (exp(lcl) + e_pht_cl * CONMED_PHT + ...) * ... with e_pht_cl = 1.02 L/h on a 1.45 L/h monotherapy intercept, i.e. +70% relative to no-PHT reference, Lee 2024 Table S1 / Discussion; the largest of the four enzyme-inducing coefficients in that model).
• Notes: Drug-specific CONMED_* indicator anticipated in the [[CONMED_AED]] notes; used when a paper estimates a separate phenytoin-induction effect distinct from the pooled EIAED / AED class effect. Distinct from the broader [[CONMED_EIAED]] (any enzyme-inducing AED) and [[CONMED_AED]] (any concomitant AED). When a paper distinguishes individual AEDs separately, use the drug-specific canonicals [[CONMED_CBZ]], [[CONMED_PB]], [[CONMED_OXC]], CONMED_PHT and [[CONMED_VPA]] rather than collapsing into the class-level indicator. Distinct from [[DOSE_PHT_MGKGD]], which carries the phenytoin daily dose per kg as a continuous covariate rather than a coadministration indicator – use this binary canonical when the source paper models only the presence of phenytoin, and DOSE_PHT_MGKGD when it models the phenytoin dose level. Hashimoto 1994 (zonisamide) tested phenytoin coadministration but found no significant effect and retained only CBZ in the final model, so that model carries no CONMED_PHT column. Ratified canonically on 2026-08-03 alongside the Lee 2024 topiramate extraction.

CONMED_OXC (canonical for concomitant oxcarbazepine coadministration indicator)

• Description: 1 = subject is taking oxcarbazepine (OXC) as a concomitant antiepileptic drug at the PK observation, 0 = no concomitant oxcarbazepine. Oxcarbazepine is the 10-keto analogue of carbamazepine and, via its active monohydroxy metabolite, is a weaker and more selective inducer than carbamazepine itself (principally CYP3A4 / UGT, without the potent autoinduction and broad CYP induction of CBZ), so it typically carries a smaller induction coefficient than [[CONMED_CBZ]] when a paper decomposes the enzyme-inducing class into individual drugs. Time-varying when oxcarbazepine starts / stops within the observation window; time-fixed when the source paper analyses chronic-maintenance cohorts whose antiseizure therapy is stable across the analysis window.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant oxcarbazepine).
• Source aliases:
  • OXC – used in Lee_2024_topiramate.R (Lee 2024 Table S1 theta5, additive enzyme-induction term on topiramate CL/F).
• Example models: Lee_2024_topiramate.R (additive effect on apparent oral clearance in absolute units: cl <- (exp(lcl) + e_oxc_cl * CONMED_OXC + ...) * ... with e_oxc_cl = 0.419 L/h on a 1.45 L/h monotherapy intercept, i.e. +29% relative to no-OXC reference, Lee 2024 Table S1 / Discussion).
• Notes: Drug-specific CONMED_* indicator anticipated in the [[CONMED_AED]] notes; used when a paper estimates a separate oxcarbazepine-induction effect distinct from the pooled EIAED / AED class effect. Must not be collapsed into [[CONMED_CBZ]] despite the close structural relationship between the two drugs: oxcarbazepine is a materially weaker inducer, and Lee 2024 estimates the two coefficients separately (OXC 0.419 L/h vs CBZ 0.703 L/h, a 1.7-fold difference), so overloading the carbamazepine column would misattribute the induction magnitude. Distinct from the broader [[CONMED_EIAED]] (any enzyme-inducing AED) and [[CONMED_AED]] (any concomitant AED). When a paper distinguishes individual AEDs separately, use the drug-specific canonicals [[CONMED_CBZ]], [[CONMED_PB]], CONMED_OXC, [[CONMED_PHT]] and [[CONMED_VPA]]. Ratified canonically on 2026-08-03 alongside the Lee 2024 topiramate extraction.

CONMED_PLDH (canonical for concomitant PEGylated liposomal doxorubicin combination indicator)

• Description: 1 = subject is receiving the modelled drug in combination with PEGylated liposomal doxorubicin (PLDH; Doxil / Caelyx), 0 = otherwise (no concomitant PLDH). Encodes the PLDH co-administration regimen as a study-design covariate on clearance.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no concomitant PEGylated liposomal doxorubicin).
• Source aliases:
  • PLDH – used in Schmitt_2018_vinflunine.R (Schmitt 2018 NONMEM dataset; 1 = vinflunine + PEGylated liposomal doxorubicin combination cohort).
• Example models: Schmitt_2018_vinflunine.R (power-form effect on vinflunine clearance: cl *= 0.865^CONMED_PLDH – vinflunine apparent CL is reduced to 86.5% of single-agent value when coadministered with PLDH, Schmitt 2018 Table 2 and explicit CL formula on p.1607).
• Notes: PEGylated liposomal doxorubicin (PLDH) is a long-circulating doxorubicin formulation used in oncology combination regimens. The Schmitt 2018 vinflunine analysis is the first paper in this register to use PLDH as a popPK combination covariate; promote to general scope when a second paper ratifies the same encoding. Distinct from CONMED_AVD (brentuximab vedotin + AVD chemotherapy backbone, which includes free doxorubicin rather than the PEGylated liposomal formulation) and from PRIOR_ANTHRACYCLINE_DOSE (cumulative prior anthracycline exposure, not concomitant). Ratified canonically on 2026-05-25 alongside the Schmitt 2018 vinflunine extraction.

CONMED_PDE5I (canonical for concomitant PDE5-inhibitor monotherapy indicator in PAH)

• Description: 1 = patient is on a concomitant phosphodiesterase type 5 inhibitor (PDE5I) PAH therapy (e.g., sildenafil, tadalafil) alone – i.e. on a PDE5 inhibitor but NOT also on an endothelin-receptor antagonist; 0 = otherwise. Time-fixed per subject for the PK/PD analysis (PAH comedication at baseline, stable dose required before selexipag start).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (PAH-comedication-naive OR on ERA alone OR on both ERA+PDE5; mutually exclusive with CONMED_ERA and CONMED_ERA_PDE5I).
• Source aliases:
  • PAHCOMED category 2 (“PDE5 inh.”) – decomposed from the categorical PAHCOMED source column with levels {naive, ERA, PDE5, ERA+PDE5} in Krause_2017_selexipag.R.
• Example models: Krause_2017_selexipag.R (multiplicative effect on the ACT-333679 elimination rate constant: km *= (1 + 0.07 * CONMED_PDE5I); +7% relative to the PAH-comedication-naive reference, Krause 2017 Table 1. The PDE5-only coefficient is statistically not significant (p = 0.19) but retained in the final model so the four-level PAH-comedication categorical is preserved end-to-end).
• Notes: Used together with CONMED_ERA and CONMED_ERA_PDE5I to decompose a four-level PAH-comedication categorical (naive / ERA-only / PDE5-only / ERA-and-PDE5) into three orthogonal mutually-exclusive binary indicators. Specific scope because the indicator’s semantics are tied to the GRIPHON study’s PAH-comedication taxonomy.

CONMED_PPI (canonical for concomitant proton-pump inhibitor use)

• Description: 1 = patient on concomitant proton-pump inhibitor (CONMED_PPI) therapy, 0 = no CONMED_PPI use. Captures gastric-pH-elevating co-medication that can reduce the bioavailability of solubility-limited (typically weakly basic) orally administered drugs.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no CONMED_PPI use).
• Source aliases:
  • CONMED_PPI – used in Goel_2016_Sonidegib.R (Goel 2016 dataset; defined as “significant” CONMED_PPI use, i.e. duration of CONMED_PPI use >= 80% of the PK assessment phase).
  • PPI – source-paper column name; used in Shu_2024_posaconazole.R (Shu 2024 dataset; subject-level ever-versus-never indicator, 11 of 62 HSCT patients, with no minimum-duration threshold stated).
• Example models: Goel_2016_Sonidegib.R (multiplicative effect on F: 0.696^CONMED_PPI, ~30% lower bioavailability under CONMED_PPI coadministration), Shu_2024_posaconazole.R (multiplicative effect on apparent volume: 3.832335^CONMED_PPI on V/F for posaconazole oral suspension; mechanistically a bioavailability effect that surfaces on V/F because the dataset is oral-only, so F is not separately identifiable – the same physiology as the Goel 2016 effect on F, relocated by the parameterisation).
• Notes: Per-model covariateData[[CONMED_PPI]]$notes must document the operational definition (e.g., Goel 2016 requires CONMED_PPI use covering >= 80% of the PK assessment window; other studies may use a simpler ever-vs-never indicator or a per-record time-varying flag). Distinct from CONMED_H2RA (H2-receptor antagonist) which acts on gastric pH via a different mechanism. On an apparent-parameter (CL/F, V/F) model fitted to oral-only data the PPI effect can legitimately appear on V/F or CL/F rather than on F; document which parameter carries it, because the sign convention flips (reduced F raises both apparent parameters).

CONMED_PROBENECID (canonical for concomitant probenecid co-administration indicator)

• Description: 1 = subject coadministered probenecid (organic-anion transport inhibitor; multidrug resistance protein (MRP) family modulator with documented activity at the blood-brain barrier, blood-CSF barrier, and renal tubular secretion sites), 0 = no concomitant probenecid. Time-varying per subject because probenecid exposure can start and stop within the observation period.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no probenecid coadministration).
• Source aliases:
  • Paper-specific day-2 treatment-arm indicator – used in Xie_2000_m3g_rat.R (the paper distinguishes a probenecid-treatment arm from a control arm by experimental day; the canonical column is 1 during the day-2 probenecid co-infusion and 0 otherwise, including on day 1 of the probenecid arm before the probenecid loading dose).
• Example models: Xie_2000_m3g_rat.R (multiplicative exponential effect on the unbound BBB influx clearance CL_u,in: cluin *= exp(e_conmed_probenecid_cluin * CONMED_PROBENECID) with e_conmed_probenecid_cluin = log(0.17 / 0.11) = 0.4353, i.e. a 1.55-fold increase in CL_u,in into rat brain ECF under probenecid co-administration; CL_u,out and the intercompartmental brain clearance Q_br are not statistically affected by probenecid in the paper’s model selection and so are not paired with this canonical), Landersdorfer_2009_gemifloxacin.R (binary treatment-arm covariate that captures the static / non-mechanism-specific probenecid effects on gemifloxacin absorption rate (Ka 0.839 -> 0.897 1/h; +6.9%), absorption lag (Tlag 0.223 -> 0.129 h; -42%), and non-renal clearance (CL_NR 25.2 -> 21.0 L/h; -16.7%); the mechanism-specific competitive inhibition of renal tubular secretion is encoded separately via the time-varying CP_PRB_MGL covariate), Hamren_2008_tesaglitazar.R (multiplicative effect on parent CLrt and metabolite Vmax under probenecid co-administration: (1 - 0.75 * CONMED_PROBENECID) reduces both by 75% during the three-dose probenecid window in four healthy-control subjects; the shared estimate is encoded as two canonical-named parameters e_probenecid_cl_renal and e_probenecid_vmax_gluc to keep each effect’s naming canonical), Stocker_2012_oxypurinol.R (multiplicative linear-deviation effect on apparent CL/Fm: cl *= (1 + 0.383 * CONMED_PROBENECID), i.e. +38.3% apparent oxypurinol clearance with concomitant probenecid in adults with gout, consistent with the paper’s mechanism narrative of probenecid inhibiting renal tubular reabsorption of oxypurinol; Stocker 2012 Table 3 theta8), Ujihira_2025_glycochenodeoxycholicAcidSulfate.R (multiplicative effect on the hepatobiliary clearance arm of the endogenous OATP1B3 / OAT3 biomarker GCDCA-S: cl_nonren *= (1 + e_prob_clh * CONMED_PROBENECID) with e_prob_clh = 1/1.7 - 1 = -0.4118, encoding the paper’s estimated fold reduction X = 1.7 attributed to probenecid’s weak OATP1B3 inhibition; the concentration-dependent OAT3 inhibition of the renal arm is encoded separately through the coupled probenecid PK states and Ki,u,OAT3 rather than through this indicator).
• Notes: Scope: specific because the only on-disk source is a preclinical microdialysis rat BBB-transport paper (Xie 2000) and the column meaning is intrinsically tied to the day-2 probenecid co-infusion design rather than a general clinical-coadministration indicator. Per-model covariateData[[CONMED_PROBENECID]]$notes must document the dose / regimen of probenecid used (Xie 2000: 70 umol/kg IV loading dose plus 70 umol/kg/h constant infusion in male Sprague-Dawley rats), the parameter the indicator modifies, and any per-subject vs per-record time-varying convention.

CONMED_QPRL_ORAL (canonical for concomitant oral quinapril co-administration indicator)

• Description: 1 = subject coadministered oral quinapril (angiotensin-converting enzyme inhibitor; competitive substrate of the intestinal H+/oligopeptide carrier PEPT1 used by orally absorbed beta-lactams and a substrate of the renal anionic transport system shared by many beta-lactams), 0 = no oral quinapril coadministration. Captures the specific oral-quinapril coadministration condition under which the Padoin 1998 cephalexin / quinapril drug-drug interaction (DDI) on intestinal absorption and renal tubular secretion of cephalexin is observed. Time-fixed per subject in the on-disk source (parallel-group design); time-varying use in future per-occasion DDI studies is permitted.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no oral quinapril coadministration; reference includes cephalexin alone, cephalexin with intra-arterial quinapril, and quinapril-naive subjects).
• Source aliases:
  • Paper-specific group indicator – used in Padoin_1998_cephalexin_rat.R (the paper’s final model specification is Ka_j = Ka2 and CL_j = CL2 when oral quinapril is coadministered with oral cephalexin; the canonical column is 1 for group 5 (cephalexin GT + quinapril GT) and 0 for groups 1, 2, 3, and 4).
• Example models: Padoin_1998_cephalexin_rat.R (multiplicative exponential effect on both absorption rate Ka and elimination clearance CL: ka <- exp(lka + e_conmed_qprl_oral_ka * CONMED_QPRL_ORAL + etalka) with e_conmed_qprl_oral_ka = log(0.177 / 0.249) = -0.3413 (~29% lower Ka under oral quinapril coadministration, paper Table 4); cl <- exp(lcl + e_conmed_qprl_oral_cl * CONMED_QPRL_ORAL + etalcl) with e_conmed_qprl_oral_cl = log(0.640 / 0.810) = -0.2356 (~21% lower CL under oral quinapril coadministration, paper Table 4). The CL effect was found significant only in the oral-cephalexin + oral-quinapril group (paper found no DDI on CL when cephalexin was given intra-arterially, attributed to higher cephalexin renal concentrations outcompeting quinapril at the carrier)…………
• Notes: Scope: specific because the only on-disk source is a single preclinical rat popPK paper (Padoin 1998) and the column meaning is intrinsically tied to the oral cephalexin + oral quinapril DDI design rather than a general quinapril-coadministration indicator. Per-model covariateData[[CONMED_QPRL_ORAL]]$notes must document the dose / regimen of quinapril used (Padoin 1998: 0.8 mg/kg single oral dose via gastric tube, 15 min before cephalexin, in male Wistar rats), the parameters the indicator modifies, and any per-subject vs per-record convention. The paper’s full specification distinguishes intra-arterial vs oral quinapril (no DDI on cephalexin CL was observed for either intra-arterial cephalexin with intra-arterial quinapril or intra-arterial cephalexin with oral quinapril); the canonical CONMED_QPRL_ORAL = 0 collapses all three non-DDI conditions (no quinapril, intra-arterial quinapril, oral quinapril paired with intra-arterial cephalexin) into the reference category because the model predicts the same CL and Ka values for each. Future popPK extractions of beta-lactam / ACE-inhibitor DDIs that test oral quinapril should reuse this canonical; ACE-inhibitor coadministration with a different perpetrator drug (e.g., enalapril, lisinopril) should register a separate canonical with the perpetrator’s INN in the name.

CONMED_RIF (canonical for concomitant rifampicin co-administration (acute or chronic))

• Description: 1 = subject is within a paper-defined rifampicin co-administration window for the modeled effect (either acute single-dose use as an OATP1B inhibitor or perfusion-style perpetrator, or chronic daily-dose use after the post-induction equilibrium lag is reached); 0 = subject is not on rifampicin or is in the inactive pre-effect window. Time-varying per subject. The semantics of “effect active” is paper-specific: for chronic-induction substrate popPK (Svensson 2014 bedaquiline), the indicator switches on after several days of daily 600 mg rifampicin dosing to reach the post-induction CYP3A4 equilibrium; for acute single-dose DDI popPK (Barnett 2018 coproporphyrin I / rosuvastatin), the indicator switches on at the rifampicin dose time and stays on for the within-occasion sampling window, with no induction lag because the mechanism of interest is competitive OATP1B inhibition rather than enzyme induction.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no rifampicin co-administration or pre-effect lag).
• Source aliases:
  • RIF – used in Svensson_2014_bedaquiline.R (paper-defined indicator switching on at day 3 of 600 mg daily rifampicin co-administration; the 3-day lag was selected by NONMEM objective-function search over 1-8 day candidates and chosen as the best fit per the paper’s Methods). Also used in Barnett_2018_coproporphyrin_I.R and Barnett_2018_rosuvastatin.R as the binary period-level covariate that captures Barnett 2018 Table 1’s reductions of Vcpi (CPI) and V1 / V2 / Q (RSV) during the rifampicin phase of the three-occasion crossover study. Also used in Gatti_1996_dapsone.R (Gatti 1996 used the source-paper symbol R; the indicator switches on for the 7 of 53 patients on rifampin co-administration for at least 2 weeks before blood sampling, so the chronic CYP3A4-induction effect was at equilibrium).
• Example models: Svensson_2014_bedaquiline.R (multiplicative factor on apparent CL_BDQ and CL_M2: cl_eff = cl_base * 4.78^CONMED_RIF; 4.78-fold induction of apparent clearance at chronic full induction), Barnett_2018_coproporphyrin_I.R (multiplicative factor on Vcpi: vc <- exp(lvc + etalvc) * (1 + e_rif_vc * CONMED_RIF) with e_rif_vc = -0.4841 capturing the Vcpi 6.59 L -> 3.4 L reduction in the acute rifampicin phase), Barnett_2018_rosuvastatin.R (multiplicative factors on V1, Q, and V2 with the same encoding form, capturing V1 430 -> 2.98 L, Q 45.3 -> 5.03 L/h, V2 865 -> 128 L reductions during the acute rifampicin phase), Gatti_1996_dapsone.R (multiplicative factor SHARED between apparent CL/F and V/F: cl = exp(lcl + etalcl) * (1 + e_rif_cl_vc * CONMED_RIF), vc = exp(lvc) * (1 + e_rif_cl_vc * CONMED_RIF) with e_rif_cl_vc = 0.696; 69.6% increase in both CL/F and V/F driven primarily by a first-pass / bioavailability effect, with the shared theta enforced by likelihood-ratio test against a non-shared parameterisation), Zhang_2012_lopinavir_ritonavir.R (multiplicative factors on LPV CL/F (+71.0%), RTV CL/F (+36.0%), LPV F (-20.0%), and RTV F (-45.0% at the 100 mg reference RTV dose) captured by the same (1 + e_rif_* * CONMED_RIF) encoding; the CONMED_RIF flag also gates the secondary dose-dependent boost on RTV F ((1 + e_dose_f_rtv * (DOSE - 100))) so the dose-effect is identifiable only within the rifampicin-coadministered arm), Zhang_2013_lopinavir_ritonavir.R (per-population multiplicative shifts on apparent CL of LPV adults +58% / children +48% and RTV adults +34% / children +22%, and per-population log-additive shifts on relative bioavailability of LPV and RTV with separate adult-only and child-only RIF effects), Comisar_2025_zavegepant.R (fractional reduction of true systemic clearance: (1 + e_conmed_rif_cl * CONMED_RIF) with e_conmed_rif_cl = -0.411, i.e. -41.1% CL per Comisar 2025 Table 3. This is the sign-inverted case relative to the other example models: rifampicin decreases rather than increases zavegepant clearance, which Comisar 2025 attributes to OATP1B3 and NTCP transporter inhibition outweighing CYP3A induction for a compound whose primary route is hepatobiliary. Encoders should therefore not assume a positive induction coefficient for this covariate), Choules_2024_enfortumab.R, Choules_2024_brentuximab.R (multiplicative factor on the MMAE payload clearance of an antibody-drug conjugate: cl_mmae *= e_rif_cl_mmae^CONMED_RIF with e_rif_cl_mmae = 1/0.47 = 2.12766, i.e. a 2.13-fold increase in MMAE clearance reproducing the published MMAE AUC geometric mean ratio of 0.47 in Choules 2024 Table 5; the indicator is a step at full induction after 7 days of 600 mg daily rifampin, and it lumps the CYP3A4-induction and P-gp-induction components of the interaction into one clearance multiplier).
• Notes: Distinct from CONMED_RIF_LPVR4 (which is a compound indicator for rifampicin + super-boosted LPV/r 4:4 used by Tikiso 2021 pediatric abacavir popPK and carries a non-CL effect). The acute-vs-chronic semantic distinction is per-paper – document the exact window (rifampicin-dose time and effect duration; lag days to full induction) in covariateData[[CONMED_RIF]]$notes so downstream simulations can construct the correct event-table indicator. Future popPK extractions that hold rifampicin co-administration as a binary indicator should reuse this canonical; if a future model needs a paper-specific time-decaying induction trajectory rather than a step indicator, register a separate canonical (CONMED_RIF_INDUCTION_TIME). Specific scope because the effect-window definition is paper-specific. Ratified canonically on 2026-05-21 alongside the Svensson 2014 bedaquiline chronic-induction extraction; broadened on 2026-05-26 to cover acute OATP1B-inhibition use alongside the Barnett 2018 CPI / RSV extractions; example list extended on 2026-05-31 to include the Gatti 1996 dapsone chronic-induction shared-CL/V-effect case.

CONMED_RPT (canonical for concomitant rifapentine co-administration at full CYP3A4 induction)

• Description: 1 = subject is on concomitant rifapentine (typically daily 600 mg oral rifapentine) and has reached the post-induction equilibrium state on the perpetrator drug’s enzyme-induced expression; 0 = subject is not on rifapentine or is still inside the pre-induction lag window.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no rifapentine co-administration or pre-induction lag).
• Source aliases:
  • RPT – used in Svensson_2014_bedaquiline.R (paper-defined indicator switching on at day 3 of 600 mg daily rifapentine co-administration; same lag as the sibling CONMED_RIF arm of the same paper).
• Example models: Svensson_2014_bedaquiline.R (multiplicative factor on apparent CL_BDQ and CL_M2: cl_eff = cl_base * 3.96^CONMED_RPT; 3.96-fold induction of apparent clearance at full induction).
• Notes: Sibling canonical to CONMED_RIF. Rifapentine and rifampicin are both CYP3A4-inducing rifamycins with similar mechanisms; the per-paper magnitude differs (rifapentine often slightly less induction than rifampicin per the on-disk Svensson 2014 fit). Like CONMED_RIF, this is a step indicator at full induction with the paper-specific lag documented in covariateData[[CONMED_RPT]]$notes. Specific scope until a second model ratifies the canonical; ratified on 2026-05-21 alongside the Svensson 2014 extraction.

CONMED_RIF_LPVR4 (canonical for concomitant rifampicin-based antitubercular treatment with super-boosted lopinavir/ritonavir 4:4 indicator)

• Description: 1 = subject is receiving rifampicin-based antitubercular treatment together with super-boosted lopinavir/ritonavir 4:4 (extra ritonavir added to standard 4:1 LPV/r to counter rifampicin-driven LPV induction); 0 = subject is on the comparator regimen specified by the source paper (typically standard LPV/r 4:1 without rifampicin in Tikiso 2021). Used to flag the combined induction effect of rifampicin (PXR-mediated UGT induction) plus extra ritonavir on co-administered antiretroviral PK.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-RIF + standard LPV/r 4:1 reference).
• Source aliases:
  • RIF – used in Tikiso_2021_abacavir.R (the dataset’s paper-defined indicator, 1 = on rifampicin-based TB treatment with super-boosted LPV/r 4:4, 0 = standard LPV/r 4:1 or EFV).
• Example models: Tikiso_2021_abacavir.R (multiplicative effect on bioavailability: f_depot *= (1 + (-0.294) * CONMED_RIF_LPVR4); -29.4% relative to the LPV/r 4:1 reference).
• Notes: Specific scope because the joint rifampicin + super-boosted-LPV/r contrast is paper-defined; the underlying induction is plausibly rifampicin-driven (the paper’s discussion notes that LPV concentrations were similar with vs without super-boosting, weakening a separate ritonavir contribution), but the canonical column captures the joint indicator the paper modeled. Future TB/HIV co-treatment models that test the same regimen contrast should extend the example list rather than register a new canonical.

CONMED_RITUX (canonical for concomitant rituximab combination therapy)

• Description: 1 = on concomitant rituximab combination therapy (with or without backbone chemotherapy), 0 = not on rituximab.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant rituximab; single-agent or non-rituximab combination).
• Source aliases:
  • RITUX – used in Wu_2024_inotuzumab.R.
• Example models: Wu_2024_inotuzumab.R (additive fractional change on CL1: CL1 * (1 + (-0.132) * CONMED_RITUX) ~= 13% lower CL1 with concomitant rituximab).
• Notes: Wu 2024 Table 3 footnote b explicitly flips the reference category vs. the predecessor Garrett 2019 adult model: in Garrett 2019 the reference was “with rituximab” (RITUX = 0 meant on-rituximab), whereas in Wu 2024 the reference is “without rituximab” (RITUX = 0 means no concomitant rituximab). Future models that pool an analogous rituximab-combination cohort with a single-agent reference should use this canonical with the Wu 2024 sign convention; if a paper retains the Garrett 2019 reverse-coded convention, document the value transformation in covariateData[[CONMED_RITUX]]$notes (CONMED_RITUX = 1 - source$RITUX) rather than registering a second canonical. Ratified canonically on 2026-04-26.

CONMED_RTV (canonical for concomitant ritonavir (CYP3A4 inhibitor / PK-booster) coadministration indicator)

• Description: 1 = subject is receiving concomitant ritonavir (RTV), typically at low “booster” doses (100 mg twice daily) as a pharmacokinetic enhancer of co-administered HIV protease inhibitors or other CYP3A4-metabolised antiretrovirals; 0 = no ritonavir. Ritonavir is a potent CYP3A4 inhibitor and P-glycoprotein modulator, so the indicator flags reduced CYP3A4-mediated clearance (and potential bioavailability changes) of the perpetrator-sensitive co-administered drug.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant ritonavir).
• Source aliases:
  • PI / PIs – used in Csajka_2004_indinavir.R (the dataset’s binary protease-inhibitor / ritonavir-presence indicator; all 177 RTV-positive patients in Csajka 2004 received ritonavir specifically, so the column captures concomitant ritonavir).
• Example models: Csajka_2004_indinavir.R (multiplicative effect on apparent oral clearance: cl *= (1 + e_rtv_cl * CONMED_RTV) with e_rtv_cl = -0.63, i.e. ritonavir reduces indinavir CL/F by ~63% relative to the no-RTV reference; Csajka 2004 Table 3), Colombo_2006_atazanavir.R (linear-deviation effect on apparent oral clearance: cl = exp(lcl) * (1 + e_rtv_cl * CONMED_RTV) with e_rtv_cl = -0.46; a 46% reduction in atazanavir CL/F when RTV is coadministered).
• Notes: Follows the CONMED_* concomitant-medication pattern (AED / AMIO / AZA / AZOLE / CBZ / EFV / etc.). Use CONMED_RTV for the binary “is ritonavir co-administered” question; for continuous ritonavir exposure as a covariate use the separate AUC_RTV canonical (e.g. Dickinson_2009_atazanavir.R). The two canonicals are not synonyms – AUC_RTV carries dose-response information that a binary CONMED_RTV flag intentionally collapses. Distinct from CONMED_RIF_LPVR4, which is a joint rifampicin + super-boosted-LPV/r indicator. Ratified canonically on 2026-05-30 alongside the Csajka 2004 indinavir extraction.

CONMED_SGLT2I (canonical for concomitant SGLT2 inhibitor coadministration indicator)

• Description: 1 = subject coadministered a sodium-glucose cotransporter 2 inhibitor (canagliflozin, dapagliflozin, empagliflozin, ertugliflozin, or another systemic SGLT2 inhibitor) during the study, 0 = no concomitant SGLT2 inhibitor. The per-model notes field should document the per-study cumulative-exposure threshold if one is applied (e.g., FIDELIO-DKD used “long-term use” = “SGLT2 inhibitor present for at least 50% of the on-finerenone treatment period”). Distinct from the CINH plasma SGLT-inhibitor concentration regressor used in renal-glucose-reabsorption QSP models – that canonical carries continuous time-varying plasma exposure of the SGLT2 inhibitor itself, whereas CONMED_SGLT2I is a binary drug-drug-interaction indicator for the SGLT2i as a coadministered drug.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant SGLT2 inhibitor coadministration).
• Source aliases:
  • SGLT / SGLT_INH0 / GLYCSI_CATN = 2 – used in vandenBerg_2021_finerenone.R (the FIDELIO-DKD popPK NONMEM control stream’s three-level SGLT categorical with 2 flagging long-term SGLT2i use).
• Example models: vandenBerg_2021_finerenone.R (multiplicative effect 1.10^CONMED_SGLT2I on apparent CL/F AND on relative bioavailability F1 inversely; long-term SGLT2 inhibitor coadministration in the FIDELIO-DKD analysis reduces steady-state finerenone AUC by ~17.1% per Table 2 / Figure 3 of the source paper).
• Notes: Follows the CONMED_<concept> concomitant-medication pattern. When a model needs the on-treatment change in SGLT2 inhibitor use rather than its absolute level, pair this column with CONMED_SGLT2I_BASE. SGLT2 inhibitors are a class of anti-diabetic agents with cardio-renal protective effects; concomitant use is increasingly common in chronic kidney disease and type 2 diabetes populations, where they overlap therapeutically with mineralocorticoid receptor antagonists (e.g., finerenone). The mechanism by which SGLT2i coadministration modestly alters finerenone PK is hypothesised by van den Berg 2021 to relate to SGLT2i-mediated partial reversal of uremic-toxin-induced CYP3A4 inhibition (Discussion paragraph 4.1 ‘The small effect of SGLT2i use on clearance might also be attributable to this phenomenon, as at least pre-clinical data indicate that SGLT2i use may partly reverse this pathophysiology’). Distinct from DIS_DIAB (diabetes-mellitus comorbidity) and from CINH (continuous plasma SGLT2-inhibitor concentration regressor).

CONMED_SGLT2I_BASE (canonical for SGLT2 inhibitor use at treatment start)

• Description: 1 = the subject was already using a sodium-glucose cotransporter 2 inhibitor at the start of study treatment (randomisation), 0 = not using one at that time. Time-fixed per subject. Exists so that a model carrying the TIME-VARYING CONMED_SGLT2I flag can apply a drug-drug-interaction effect to the on-treatment CHANGE in SGLT2 inhibitor use, theta * (CONMED_SGLT2I - CONMED_SGLT2I_BASE), rather than to its absolute level. That centring is required whenever the model’s estimated baseline state already embeds the SGLT2 inhibitor’s effect for subjects who were on one at randomisation – applying the raw flag would double-count it.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (SGLT2-inhibitor-naive at treatment start).
• Source aliases:
  • SGLTSTART – Goulooze 2022 convention in both the UACR and eGFR control streams.
• Example models: Goulooze_2022_finerenone_uacr.R, Goulooze_2022_finerenone_egfr.R (both apply the acute SGLT2 inhibitor effects as theta * (CONMED_SGLT2I - CONMED_SGLT2I_BASE); the eGFR model applies its CHRONIC-slope SGLT2i effect to the raw CONMED_SGLT2I flag instead, so a model may legitimately use both forms).
• Notes: Member of the _BASE baseline-suffix family alongside CRCL_BASE; the _BASE suffix marks the time-fixed treatment-start value of a column that is otherwise time-varying. Ratified canonically alongside the Goulooze 2022 finerenone UACR / eGFR extraction. Do not use this column as a substitute for CONMED_SGLT2I when a model only needs the time-fixed baseline status and never the time course – use CONMED_SGLT2I with a note that it is time-fixed in that analysis.

CONMED_SPART (canonical for spartalizumab (PDR001, anti-PD-1) coadministration indicator)

• Description: 1 = the analyzed therapeutic mAb is coadministered with spartalizumab (PDR001, anti-PD-1 IgG4), 0 = no spartalizumab coadministration. Time-fixed per subject in source analyses to date.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no spartalizumab coadministration – monotherapy or combination with non-spartalizumab agents such as a hypomethylating agent).
• Source aliases:
  • HASPDR – used in Xu_2023_MBG453.R (Monolix supplement Appendix S2; the source describes the column as “this patient HAS received PDR001 [spartalizumab, anti PD-1 mAb]”).
• Example models: Xu_2023_MBG453.R (exponential effect on CL: exp(0.0194 * CONMED_SPART); not statistically significant in the full covariate model but retained because Xu 2023 used the full-covariate-model approach).
• Notes: Parallels COMBO_NIVO (ipilimumab + nivolumab) and COMBO_DURVA (durvalumab combinations) but for spartalizumab. Promote to general scope if a second paper reports a spartalizumab-coadministration covariate with comparable encoding.

CONMED_SPIRON (canonical for concomitant spironolactone coadministration indicator)

• Description: 1 = subject is coadministered spironolactone (aldosterone-receptor antagonist; potassium-sparing diuretic; renal P-glycoprotein / OATP inhibitor at the digoxin tubular-secretion site) during the study, 0 = no concomitant spironolactone. Spironolactone is widely combined with digoxin in CHF therapy and inhibits digoxin renal tubular excretion, raising digoxin serum concentration.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no concomitant spironolactone).
• Source aliases:
  • SPI – used in Zhou_2010_digoxin.R (Zhou 2010 Table 1 and Table 7 column label).
• Example models: Zhou_2010_digoxin.R (multiplicative linear-deviation form on Cl/F: cl *= (1 - 0.412 * CONMED_SPIRON), i.e. ~41% lower Cl/F with concomitant spironolactone in the older Chinese CHF cohort; Zhou 2010 Table 7), MarquesMinana_2010_vancomycin.R (multiplicative linear-deviation form on weight-normalised V_d: vc *= (1 - 0.344 * CONMED_SPIRON), i.e. ~34% smaller V_d with concomitant spironolactone in the 70-neonate Marques-Minana 2010 cohort; Table 3 theta4).
• Notes: 32 of 119 subjects (27%) in Zhou 2010 were coadministered spironolactone. The clinical rationale (Zhou 2010 Discussion) is that spironolactone inhibits the renal-tubular secretion of digoxin via competition for the renal P-glycoprotein transporter, raising digoxin steady-state concentration. Promote to general scope if a second popPK paper reports a spironolactone-coadministration covariate with comparable encoding. Ratified canonically on 2026-05-21 alongside the Zhou 2010 digoxin extraction.

CONMED_STATIN (canonical for concomitant conmed_statin (HMG-CoA reductase inhibitor) therapy)

• Description: 1 = patient coadministered a conmed_statin (HMG-CoA reductase inhibitor) during the study, 0 = no conmed_statin coadministration.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no conmed_statin coadministration).
• Source aliases: none known.
• Example models: Martinez_2019_alirocumab.R (additive effect on linear clearance CLL: CLL = TVCLL + COV1*(WT-82.9) + COV2*CONMED_STATIN; +0.00644 L/h when conmed_statin is coadministered).
• Notes: Per-model covariateData[[CONMED_STATIN]]$notes must document which statins and dose thresholds are included in the “CONMED_STATIN = 1” category, since inclusion criteria vary by study. Martinez 2019 codes CONMED_STATIN = 1 for coadministration of rosuvastatin (< 20 mg/day), atorvastatin (< 40 mg/day), or simvastatin (any dose); other conmed_statin regimens are coded as 0.

CONMED_STATIN_MONO (canonical for concomitant statin-monotherapy indicator)

• Description: 1 = patient is on a conmed_statin and no other lipid-lowering comedication (conmed_statin monotherapy), 0 = not on conmed_statin monotherapy (either on no lipid-lowering therapy or on a multi-drug lipid-lowering combination such as conmed_statin + ezetimibe).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (not on conmed_statin monotherapy).
• Source aliases:
  • Derived from a conmed_statin-identifier column in the source (any of atorvastatin, rosuvastatin, simvastatin, lovastatin, pravastatin, pitavastatin, fluvastatin) with an AND over “no other lipid-lowering comedication”.
• Example models: Kuchimanchi_2018_evolocumab.R (multiplicative effect 1.13 on Vmax: Vmax * 1.13^CONMED_STATIN_MONO), Budha_2015_rg7652.R (time-varying within-subject multiplicative log-effect on the indirect-response Ksyn anchor for LDL-C: Ksyn = Kdeg * LDLC * exp(-0.648 * CONMED_STATIN_MONO(t)); same parameter dynamically reproduces both the ~45% lower pre-dose baseline LDL-C in atorvastatin-pretreated cohorts and the LDL rebound after statin cessation, Budha 2015 Table II ‘Statin on screening LDLc’ = -0.648), Kuchimanchi_2018_evolocumab_ldlc.R (same PK-layer effect plus an exponent 0.797 on baseline LDL-C and 0.937 on Emax in the exposure-response layer, Table 4).
• Notes: Scope: specific because both registered example models narrowly define the statin covariate as monotherapy only – Kuchimanchi 2018 (“patients on a conmed_statin only and no other comedication”); Budha 2015 (atorvastatin 40 mg daily as the sole concomitant lipid-lowering therapy in the Phase 1 statin cohorts). Mutually compatible with CONMED_EZE: a subject on conmed_statin+ezetimibe has CONMED_STATIN_MONO = 0 and CONMED_EZE = 1; a subject on conmed_statin alone has CONMED_STATIN_MONO = 1 and CONMED_EZE = 0; a subject on no lipid-lowering therapy has both 0. Per-model temporal grain (time-fixed per subject vs time-varying within subject) is documented in covariateData[[CONMED_STATIN_MONO]]$notes per model. Future popPK/PD models that adopt a broader “any conmed_statin” definition should register a separate CONMED_STATIN or CONMED_STATIN canonical rather than reusing this name.

CONMED_STATIN_LI, CONMED_STATIN_MI, CONMED_STATIN_HI (canonical for concomitant statin-intensity stratum indicators)

• Description: Three mutually exclusive binary indicators identifying the therapeutic-intensity stratum of a patient’s concomitant statin regimen, following the low- / moderate- / high-intensity classification of the 2018 ACC/AHA cholesterol guideline (Grundy et al., J Am Coll Cardiol 2019;73:e285-e350). CONMED_STATIN_LI = 1 for a low-intensity regimen (roughly < 30% expected LDL-C lowering; e.g. simvastatin 10 mg, pravastatin 10-20 mg, lovastatin 20 mg, fluvastatin 20-40 mg, pitavastatin 1 mg). CONMED_STATIN_MI = 1 for a moderate-intensity regimen (~30-49% expected lowering; e.g. atorvastatin 10-20 mg, rosuvastatin 5-10 mg, simvastatin 20-40 mg, pravastatin 40-80 mg, lovastatin 40 mg, fluvastatin 40 mg BID or XL 80 mg, pitavastatin 2-4 mg). CONMED_STATIN_HI = 1 for a high-intensity regimen (>= 50% expected lowering; e.g. atorvastatin 40-80 mg, rosuvastatin 20-40 mg). A patient not on any concomitant statin has all three set to 0.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 for all three simultaneously = no concomitant statin therapy. At most one of the three may be 1 for a given patient/record; a model that references more than one of them multiplies the corresponding effects, so a data set that sets two to 1 is malformed.
• Source aliases:
  • Low-intensity statin, Moderate-intensity statin, High-intensity statin – Jadhav 2023 Table 3 covariate row names.
  • STATINT / STATIN_INTENSITY (a single ordinal 0/1/2/3 column) – decompose into the three binaries rather than carrying the ordinal, per the register’s decomposed-indicator convention.
• Example models: Jadhav_2023_bempedoicAcid_ldlc.R (proportional shifts on the indirect-response Imax of -0.238 / -0.302 / -0.424 and on baseline LDL-C of -0.159 / -0.268 / -0.293 for low / moderate / high intensity; Jadhav 2023 Table 3).
• Notes: Distinct from CONMED_STATIN (any concomitant statin, undifferentiated by intensity) and from CONMED_STATIN_MONO (statin monotherapy, i.e. a statin with no other lipid-lowering comedication) – the intensity strata say nothing about whether other lipid-modifying therapies are co-administered, and the monotherapy flag says nothing about dose intensity, so the three families are orthogonal and may coexist in one model. Also distinct from the CONMED_<INN> binaries (CONMED_ATORVASTATIN, CONMED_SIMVASTATIN, …), which identify the specific molecule irrespective of dose, and from the CONMED_<drug>_DOSE continuous family. The intensity classification is dose- and molecule-dependent, so a source paper’s mapping from regimen to stratum must be recorded per model in covariateData[[CONMED_STATIN_MI]]$notes when it departs from the ACC/AHA table. Ratified canonically on 2026-07-27 alongside the Jadhav 2023 bempedoic acid extraction (sidecar request 001, operator answer A).

CONMED_ATORVASTATIN (canonical for concomitant atorvastatin coadministration indicator)

• Description: 1 = patient is coadministered atorvastatin at the observation / dose record, 0 = no concomitant atorvastatin. Molecule-specific binary companion to the class-level CONMED_STATIN flag and the intensity strata.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant atorvastatin).
• Source aliases:
  • Atorvastatin – Jadhav 2023 Table 2 covariate row name.
  • ATORVA, ATOR – common dataset abbreviations. The three-letter form ATV is NOT an acceptable alias: it is reserved for the atazanavir indicator CONMED_ATAZANAVIR (see the CONMED_<drug>_DOSE notes on that collision), which is why this entry spells out the full INN.
• Example models: Jadhav_2023_bempedoicAcid.R, Jadhav_2023_bempedoicAcid_ldlc.R (proportional shift on relative oral bioavailability F1 * (1 + 0.142 * CONMED_ATORVASTATIN), i.e. 14.2% higher relative bioavailability of bempedoic acid with concomitant atorvastatin relative to the F1 = 1 no-atorvastatin anchor; Jadhav 2023 Table 2 and footnote b).
• Notes: Distinct from CONMED_ATORVASTATIN_DOSE (continuous mg/day), which should be used instead when a model scales an effect with the atorvastatin dose rather than switching on its presence. Ratified canonically on 2026-07-27 alongside the Jadhav 2023 bempedoic acid extraction, under the auto-approved CONMED_<INN> family.

CONMED_SIMVASTATIN (canonical for concomitant simvastatin coadministration indicator)

• Description: 1 = patient is coadministered simvastatin at the observation / dose record, 0 = no concomitant simvastatin. Molecule-specific binary companion to the class-level CONMED_STATIN flag and the intensity strata.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant simvastatin).
• Source aliases:
  • Simvastatin – Jadhav 2023 Table 2 covariate row name.
  • SIMVA, SMV – common dataset abbreviations.
• Example models: Jadhav_2023_bempedoicAcid.R, Jadhav_2023_bempedoicAcid_ldlc.R (proportional shift on apparent central volume Vc/F * (1 + (-0.154) * CONMED_SIMVASTATIN), i.e. 15.4% lower Vc/F with concomitant simvastatin; Jadhav 2023 Table 2).
• Notes: Distinct from CONMED_SMV_DOSE (continuous mg/day). Ratified canonically on 2026-07-27 alongside the Jadhav 2023 bempedoic acid extraction, under the auto-approved CONMED_<INN> family.

CONMED_STEROID (canonical for systemic corticosteroid administration indicator)

• Description: 1 = patient is on systemic corticosteroid therapy at the observation (or, depending on the paper’s encoding, during the time interval the observation summarises), 0 = no systemic corticosteroid administration. Supports two temporal grains depending on the source paper: (i) time-fixed per subject, capturing baseline / chronic concurrent corticosteroid use in diseases where background steroid use is standard of care (SLE, severe asthma); (ii) time-varying per record, capturing acute corticosteroid pulses for relapse / flare treatment in diseases where steroids are administered as a per-event course (multiple sclerosis acute relapse, autoimmune flares).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no systemic corticosteroid administration in the current record / interval).
• Source aliases:
  • BSTEROID – used in Narwal_2013_sifalimumab.R and Zheng_2016_sifalimumab.R (time-fixed baseline-use form).
  • STEROID – used in VelezdeMendizabal_2013_multipleSclerosis.R (time-varying per-monthly-record acute-administration form).
• Example models: Narwal_2013_sifalimumab.R (time-fixed multiplicative on CL: CL * (1 + 0.195 * CONMED_STEROID)), Zheng_2016_sifalimumab.R (time-fixed multiplicative on CL (1 + 0.11 * CONMED_STEROID) and on V1 (1 - 0.09 * CONMED_STEROID) in the SLE phase IIb cohort, which was ~85% conmed_steroid-treated at baseline), VelezdeMendizabal_2013_multipleSclerosis.R (time-varying per-monthly-record switch of the first-order Markov coefficient from theta_pdv to theta_pdv_s when a corticosteroid course was given for a clinical MS relapse that month).
• Notes: Distinct from PRICORT, which is strictly a prior (pre-study) indicator. CONMED_STEROID covers both concurrent chronic corticosteroid use at / from study baseline and per-record acute corticosteroid pulses; the per-model covariateData[[CONMED_STEROID]]$notes field documents the temporal grain (time-fixed vs time-varying) the source paper used. When a future paper needs CONMED_STEROID and PRICORT jointly, both can coexist on the same subject. The name STEROID_BL was used as an alias in earlier register drafts and is retired; use CONMED_STEROID for all future models.

CONMED_STEROID_SPARING (canonical for steroid-sparing immunosuppression-protocol indicator)

• Description: 1 = subject was assigned a steroid-sparing immunosuppressive protocol (corticosteroids administered for a short period – typically <= 7-14 days post-transplant – as opposed to a continuous-steroid regimen); 0 = subject was on a continuous-corticosteroid regimen. Time-fixed per subject (the protocol is assigned at the transplantation date and does not change during the analysis window). Operationally a centre-level attribute in source datasets where the protocol is determined by the transplanting centre (Passey 2011) or a per-subject protocol decision in other datasets.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (continuous-corticosteroid regimen; non-sparing protocol).
• Source aliases:
  • steroid sparing centre – used in Passey_2011_tacrolimus.R (Passey 2011 Methods: “Centres were designated as using a steroid sparing immunosuppressive regimen if they administered steroids for <= 7 days post transplant”).
• Example models: Passey_2011_tacrolimus.R (power-of-binary-indicator multiplicative factor on apparent oral clearance: e_steroid_spare_cl ^ CONMED_STEROID_SPARING with e_steroid_spare_cl = 0.70; steroid-sparing patients have 30% lower apparent oral tacrolimus CL/F than continuous-steroid patients; Passey 2011 Discussion attributes the effect to reduced CYP3A induction in the absence of ongoing corticosteroid therapy).
• Notes: Companion to CONMED_STEROID (which captures concurrent / baseline corticosteroid USE; CONMED_STEROID_SPARING captures the protocol-level decision to MINIMIZE corticosteroid use). The two coexist in the same dataset when needed: a steroid-sparing patient still has CONMED_STEROID = 1 during days 1-7 post-transplant (the short-duration administration window) and CONMED_STEROID = 0 thereafter. The Passey 2011 binary indicator collapses both phases into a single time-invariant per-subject attribute via the centre-level assignment; document the per-model temporal interpretation in covariateData[[CONMED_STEROID_SPARING]]$notes. Distinct from PRICORT (pre-study corticosteroid history) and from HCT_COND_RIC (reduced-intensity conditioning regimen for HSC transplantation, which is a different protocol axis). Future models that distinguish the specific duration of steroid administration (e.g. 7 days vs 14 days vs 30 days) should register companion canonicals rather than overloading CONMED_STEROID_SPARING. Ratified canonically on 2026-05-20 alongside the Passey 2011 tacrolimus extraction.

CONMED_UGT_INH (canonical for concomitant UGT-inhibitor coadministration indicator (pooled))

• Description: 1 = subject is receiving at least one drug the source paper classifies as a UGT (uridine 5’-diphospho-glucuronosyltransferase) inhibitor at the PK observation, 0 = not on any such drug. Class-level pool paralleling CONMED_EIAED (pooled enzyme-inducing antiepileptic drugs); used when a paper aggregates multiple mechanism-related UGT-inhibiting comedications into a single indicator because per-drug sample sizes are too small to identify separate effects. Time-varying in principle; time-fixed for chronic-maintenance cohorts.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant UGT inhibitor).
• Source aliases:
  • Inh / Inhibitors – used in Milosheska_2016_lamotrigine.R (paper Table 4 footnote *Valproic acid or sertraline (Inh); the paper pools valproic acid (n = 13) and sertraline (n = 2) because both were hypothesised to act via UGT2B7 competitive inhibition and the per-drug sample sizes were too small to estimate distinct effects).
• Example models: Milosheska_2016_lamotrigine.R (multiplicative effect on parent lamotrigine apparent clearance: cl *= (1 - 0.579 * CONMED_UGT_INH); carriers have -57.9% lower CL relative to the no-inhibitor reference, Milosheska 2016 Table 4 row Co-treatment with inhibitors).
• Notes: The per-paper list of drugs counted as UGT inhibitors MUST be documented in covariateData[[CONMED_UGT_INH]]$notes because the pooling criterion varies across studies (Milosheska 2016 pools valproic acid + sertraline; a future paper might pool valproic acid + probenecid + sertraline + fluvoxamine). Distinct from the drug-specific canonical CONMED_VPA (concomitant valproate alone). When a paper reports enough per-drug data to identify separate effects, use the drug-specific canonicals (CONMED_VPA, CONMED_SERTRALINE, …) rather than collapsing into CONMED_UGT_INH. Analogous to CONMED_EIAED (pooled enzyme-inducing AEDs) which serves the same role for the inducer side. Ratified canonically on 2026-06-20 alongside the Milosheska 2016 lamotrigine extraction.

CONMED_VPA (canonical for concomitant valproate (valproic acid) coadministration indicator)

• Description: 1 = subject is taking valproate (valproic acid, sodium valproate, divalproex) as a concomitant antiepileptic drug at the PK observation, 0 = no concomitant valproate. Valproate is a broad-spectrum AED that inhibits UGT and CYP2C9, and chronic use is associated with weight gain. Time-varying when valproate starts / stops within the observation window; time-fixed when the source paper analyses chronic-maintenance cohorts whose AED therapy is stable.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant valproate).
• Source aliases:
  • VPA – used in Schoemaker_2017_brivaracetam.R (paper covariate VPA for valproate coadministration).
• Example models: Schoemaker_2017_brivaracetam.R (multiplicative effect on apparent oral clearance: cl *= (1 - 0.101 * CONMED_VPA); -10.1% relative to no-VPA reference, corresponding to ~11% higher brivaracetam exposure, Schoemaker 2017 Table 1).
• Notes: Drug-specific CONMED_* indicator anticipated in the [[CONMED_AED]] notes. Schoemaker 2017 retained the VPA effect in the final model even though it did not formally meet the SCM inclusion criteria (forward p < 0.01) because quantifying its contribution was considered informative; the authors note the apparent VPA-induced exposure rise may be confounded with VPA-driven weight / fat gain in chronic users. Distinct from the broader [[CONMED_AED]] (any concomitant AED). When a paper distinguishes individual AEDs separately, use the drug-specific canonicals [[CONMED_CBZ]], [[CONMED_PB]], CONMED_VPA rather than collapsing into the class-level indicator. Ratified canonically on 2026-05-20 alongside the Schoemaker 2017 brivaracetam paediatric extraction.

CONMED_VERTEPORFIN (canonical for concomitant verteporfin photodynamic therapy (PDT) coadministration indicator)

• Description: 1 = subject received one or more concomitant verteporfin photodynamic-therapy (PDT) procedures during the ranibizumab treatment period; 0 = no concomitant PDT. Time-fixed per subject in Xu 2013 (All covariates adopted their values measured at baseline). Verteporfin (Visudyne) is a benzoporphyrin-derivative photosensitizer activated by non-thermal 689 nm laser to induce localized choroidal vessel occlusion in neovascular age-related macular degeneration; the PDT procedure is administered 7 days before ranibizumab in the FOCUS combination protocol.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant verteporfin PDT).
• Source aliases:
  • PDT – used in Xu_2013_ranibizumab.R (Xu 2013 Fig 1 lists “concomitant PDT therapy” as a screened covariate; “Specifically in this case, PDT is verteporfin for injection.”).
• Example models: Xu_2013_ranibizumab.R (multiplicative reduction of vitreous elimination rate Ka: ka *= (1 - e_conmed_verteporfin_ka * CONMED_VERTEPORFIN) with e_conmed_verteporfin_ka = 0.353, i.e. 35.3% lower Ka on subjects with concomitant PDT; Xu 2013 Table 3, theta6 = 0.353 stored as a positive fractional-reduction magnitude under the header ‘Covariate multiplier for Ka’).
• Notes: Member of the CONMED_<INN> binary concomitant-medication family. Verteporfin PDT is the specific PDT modality in the Xu 2013 AMD analysis, so the INN-specific canonical name is used rather than a generic CONMED_PDT. Xu 2013 attributes the slower vitreous elimination in PDT-treated subjects to vessel occlusion and soft tissue scarring reducing choroidal drainage of ranibizumab from the vitreous humor. Ratified canonically alongside the Xu 2013 ranibizumab extraction (2026-07-09).

CONMED_SILDENAFIL (canonical for concomitant sildenafil coadministration indicator)

• Description: Binary indicator for concomitant sildenafil coadministration. 1 = sildenafil was administered during this experimental occasion (in the Bender 2009 rat study, a 2 mg/kg bolus followed by a 6 h steady-state infusion), 0 = saline (no sildenafil). Per-occasion (not per-subject) in a crossover design.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (saline / no concomitant sildenafil).
• Source aliases:
  • SLDB – used in Bender_2009_pregabalin_rat_binary.R (Bender 2009 binary sildenafil-presence indicator).
• Example models: Bender_2009_pregabalin_rat_binary.R (proportional reduction in pregabalin CL when sildenafil is coadministered: cl *= (1 - e_sild_cl * CONMED_SILDENAFIL) with e_sild_cl = 0.302, i.e. 30.2% lower CL on the sildenafil occasion, Bender 2009 Table IV).
• Notes: Specific scope because the sildenafil-on-pregabalin-CL drug-drug-interaction effect is paper-specific (Bender 2009 used sildenafil as a PDE-5-inhibitor probe). Member of the CONMED_<drug> binary concomitant-presence family. Per-occasion in the Bender 2009 crossover (each rat received saline on one occasion and sildenafil on the other, separated by a >= 3-day washout). Companion to CONMED_SILDENAFIL_NMETAB_CC, the continuous N-desmethyl-sildenafil-metabolite-concentration covariate used in the saturable-inhibition variant of the same model. Ratified canonically alongside the Bender 2009 pregabalin rat extraction.

CONMED_SILDENAFIL_NMETAB_CC (canonical for concomitant sildenafil N-desmethyl metabolite concentration)

• Description: Time-varying plasma concentration of the active N-desmethyl (N-methyl) metabolite of sildenafil (ng/mL), used as the saturable-inhibition driver on the clearance of a co-administered drug. Time-varying covariate column supplied by the user; set to 0 to recover the no-sildenafil typical-clearance prediction.
• Units: ng/mL
• Type: continuous
• Scope: specific
• Reference category: n/a – enters a saturable (Michaelis-Menten-style) inhibition factor on CL; set to 0 for the no-sildenafil case. Reference values observed: in Bender 2009 the metabolite reached Cmax ~ 2,100 ng/mL at 4-7 h post bolus and was roughly steady-state during the 6 h sildenafil infusion.
• Source aliases:
  • SLDM – used in Bender_2009_pregabalin_rat_smetab.R (Bender 2009 measured N-methyl-sildenafil-metabolite plasma concentration; paper notation [SLDM]).
• Example models: Bender_2009_pregabalin_rat_smetab.R (saturable inhibition of pregabalin CL: cl *= (1 - CONMED_SILDENAFIL_NMETAB_CC / (e_sldm_cl + CONMED_SILDENAFIL_NMETAB_CC)) with the IC50 e_sldm_cl = 1350 ng/mL, Bender 2009 Table IV).
• Notes: Specific scope because the metabolite-driven saturable-inhibition effect on pregabalin CL is paper-specific. Member of the CONMED_<drug>_CC time-varying-concentration family (the _CC suffix marks a dynamic concentration covariate, as distinct from the CONMED_<drug> binary-presence indicator). Continuous-covariate counterpart to CONMED_SILDENAFIL (the binary-presence variant of the same Bender 2009 drug-drug-interaction analysis). Ratified canonically alongside the Bender 2009 pregabalin rat extraction.

CONMED_NAL_DOSE, CONMED_BUP_DOSE (canonical for daily dose of co-administered naltrexone / bupropion)

• Description: Time-varying daily dose of the named drug at the current observation time (suffix = INN lowercase abbreviation: nal naltrexone, bup bupropion). 0 = the named drug is not part of the regimen at this time; positive value = current total daily dose. Drives the combined dose- and time-dependent Emax drug effect in the naltrexone/bupropion fixed-dose-combination PD model.
• Units: mg/day
• Type: continuous
• Scope: specific
• Reference category: 0 (the drug is not given; the drug-effect term contributes 0).
• Source aliases:
  • DOSE_NAL_MGD (NAL, Sharma 2018 Eq. 4 daily naltrexone dose in mg) – earlier DOSE_<drug>_<unit> form used in Sharma_2018_naltrexone_bupropion.R before the CONMED_<drug>_DOSE rename. Maps to CONMED_NAL_DOSE.
  • DOSE_BUP_MGD (BUP, Sharma 2018 Eq. 4 daily bupropion dose in mg) – earlier DOSE_<drug>_<unit> form used in Sharma_2018_naltrexone_bupropion.R before the rename. Maps to CONMED_BUP_DOSE.
• Example models: Sharma_2018_naltrexone_bupropion.R (each daily-dose covariate enters the combined dose- and time-dependent Emax drug effect on body-weight kout: drug_term = CONMED_NAL_DOSE / (ed50nal + CONMED_NAL_DOSE) + CONMED_BUP_DOSE / (ed50bup + CONMED_BUP_DOSE), Sharma 2018 Eq. 4).
• Notes: Specific scope because the dose-effect amplitudes are tied to the Sharma 2018 naltrexone/bupropion DTPD body-weight analysis. Members of the CONMED_<drug>_DOSE daily-dose family (siblings CONMED_ATV_DOSE, CONMED_INH_DOSE, etc.); the CONMED_<drug>_DOSE shape replaces the earlier DOSE_<drug> / DOSE_<drug>_<unit> names (which conflated a covariate with a dose-amount column) per the naming audit. Companion to DIS_DIAB (the diabetes covariate in the same model; pre-2026-06-19 canonical name T2DM). Ratified canonically alongside the Sharma 2018 naltrexone/bupropion extraction.

PRICORT (canonical for prior corticosteroid use indicator)

• Description: 1 = patient received systemic corticosteroid treatment prior to study entry, 0 = no prior corticosteroid use. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no prior corticosteroid use).
• Source aliases: none.
• Example models: Ma_2020_sarilumab_das28crp.R (multiplicative on DAS28-CRP Kout: Kout * theta^PRICORT), Ma_2020_sarilumab_anc.R (power-form on Emax: Emax * 0.819^PRICORT).
• Notes: Ma 2020 applies it as a multiplicative effect of the form param * theta^PRICORT in both DAS28-CRP and ANC PD models. Generally applicable clinical-history indicator.

PRIOR_ANTHRACYCLINE_DOSE (canonical for prior cumulative anthracycline dose)

• Description: Cumulative dose of anthracycline chemotherapy received by the subject prior to the first dose analysed in the current popPK / popPK-PD model, expressed in doxorubicin-equivalent body-surface-area-normalised mg/m^2. Time-fixed per subject for the analysis window (the running cumulative anthracycline dose at the first observed dose).
• Units: mg/m^2 (doxorubicin-equivalent)
• Type: continuous
• Scope: specific
• Reference category: n/a – typically used as a linear shift ((1 + theta * (PRIOR_ANTHRACYCLINE_DOSE - ref))) on a baseline parameter (e.g., baseline cardiac troponin I before the next anthracycline cycle). Reference values observed: 90 mg/m^2 (Kunarajah 2017, cohort median).
• Source aliases:
  • PCAMT – Kunarajah 2017 NM-TRAN convention (“Prior Cumulative Anthracyclines aMounT”; doxorubicin-equivalent mg/m^2).
• Example models: Kunarajah_2017_doxorubicin.R (linear shift on baseline cardiac troponin I: bl_cTnI * (1 + 0.00308 * (PRIOR_ANTHRACYCLINE_DOSE - 90)) – ~0.31% increase in baseline cTnI per 1 mg/m^2 of prior cumulative anthracycline exposure).
• Notes: Distinct from PRIOR_ANTICANCER (a binary modality indicator, 1 = any prior anticancer therapy) – PRIOR_ANTHRACYCLINE_DOSE carries the actual cumulative dose, restricted to the anthracycline drug class (doxorubicin, daunorubicin, epirubicin, idarubicin), and is the column needed when the source paper’s effect is dose-response in the prior-exposure regime rather than presence / absence. When a paper records anthracycline exposure as anthracycline-class-by-class doses and the model effect aggregates them, sum to a single doxorubicin-equivalent value before populating this column (use the published bone-marrow / cardiotoxicity isoeffective conversion factors). When a paper distinguishes the type of anthracycline (e.g., doxorubicin vs daunorubicin separately), register parallel canonicals (PRIOR_DOXORUBICIN_DOSE, PRIOR_DAUNORUBICIN_DOSE) rather than overloading this name. Scope: specific because the column meaning is intrinsically tied to anthracycline-class chemotherapy exposure; promote to general if a second paper ratifies the same definition.

PRIOR_ANTICANCER (canonical for prior anticancer therapy of any modality)

• Description: 1 = subject received any prior anticancer therapy (cytotoxic chemotherapy, radiotherapy, hormonal therapy, targeted therapy, immunotherapy, or surgical debulking with adjuvant intent) before the start of the analyzed treatment, 0 = treatment-naive. Broader than LINE_1L, which is specifically a treatment-line indicator restricted to systemic drug therapy lines. PRIOR_ANTICANCER captures the full clinical concept of prior cancer treatment exposure as used in cytotoxic-chemotherapy myelosuppression analyses, where any prior anticancer modality may have depleted the bone-marrow proliferating pool and therefore affects baseline ANC.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (treatment-naive; no prior anticancer therapy of any modality).
• Source aliases:
  • PC (Kloft 2006 / Netterberg 2017 NM-TRAN convention for “previous anticancer therapy”; values 0 = naive, 1 = had prior anticancer therapy) – used in Netterberg_2017_docetaxel.R.
• Example models: Netterberg_2017_docetaxel.R (multiplicative effect on baseline ANC: BACOV *= (1 + theta * PRIOR_ANTICANCER) with theta = -0.147; prior-anticancer patients have ~14.7% lower baseline ANC than treatment-naive patients).
• Notes: Distinct from LINE_1L (which is the inverse semantics for systemic-drug therapy lines only) and PRIOR_TNF / PRIOR_BIO (which are modality-specific to anti-TNF / biologic exposure in inflammatory-disease cohorts). Use PRIOR_ANTICANCER when the source paper’s covariate counts any anticancer modality (including radiotherapy and surgery) as prior exposure. When a future paper restricts the indicator to cytotoxic chemotherapy alone, use LINE_1L (with values inverted: paper’s PRIOR_CHEMO = 1 - LINE_1L). When a paper distinguishes prior chemotherapy from prior radiotherapy, register a parallel PRIOR_RADIATION canonical.

PRIOR_RADIATION (canonical for prior radiotherapy exposure indicator)

• Description: 1 = subject received any prior radiotherapy (external-beam, brachytherapy, or radionuclide-targeted) before the start of the analyzed treatment; 0 = radiotherapy-naive. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (radiotherapy-naive).
• Source aliases:
  • “prior radiotherapy (yes/no)” (Lu 2017 narrative; coded as a binary 0/1 in the source NONMEM dataset) – used in Lu_2017_polatuzumab_neuropathy.R.
• Example models: Lu_2017_polatuzumab_neuropathy.R (multiplicative effect on the grade >= 2 peripheral neuropathy hazard via exp(theta_priorRadioTx * PRIOR_RADIATION) with theta_priorRadioTx = -7.94e-3, SE 0.319 – effectively no detectable effect given the large SE).
• Notes: Sibling of PRIOR_ANTICANCER (the broader any-modality indicator) and PRIOR_VINCA / PRIOR_PLATIN (chemotherapy-class-specific siblings). Use this canonical when the source paper distinguishes radiotherapy as a separate covariate from prior chemotherapy / hormonal / immunotherapy exposure. Pre-authorized as a parallel-sibling canonical in the PRIOR_ANTICANCER notes. Ratified canonically on 2026-06-24 alongside the Lu 2017 polatuzumab vedotin TTE extraction.

PRIOR_VINCA (canonical for prior vinca-alkaloid chemotherapy indicator)

• Description: 1 = subject received any prior vinca-alkaloid chemotherapy (vincristine, vinblastine, vinorelbine, vindesine) before the start of the analyzed treatment; 0 = vinca-naive. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (vinca-naive).
• Source aliases:
  • “prior vinca alkaloids treatment (yes/no)” (Lu 2017 narrative; coded as a binary 0/1 in the source NONMEM dataset) – used in Lu_2017_polatuzumab_neuropathy.R.
• Example models: Lu_2017_polatuzumab_neuropathy.R (multiplicative effect on the grade >= 2 peripheral neuropathy hazard via exp(theta_priorVinca * PRIOR_VINCA) with theta_priorVinca = -0.102, SE 0.469 – not detectable given the large SE; the paper hypothesized prior vinca-alkaloid exposure could sensitize patients to antimicrotubule-induced PN but did not find a clinically meaningful effect).
• Notes: Vinca alkaloids are microtubule-destabilising agents (the natural-product parent class of the vc-MMAE / vc-MMAF ADC payloads); prior exposure is a clinically meaningful pretreatment indicator in any analysis where antimicrotubule-toxicity history affects subsequent dosing decisions or AE risk. Sibling of PRIOR_ANTICANCER (broader any-modality indicator), PRIOR_RADIATION, PRIOR_PLATIN, and PRIOR_TAXANE (parallel chemotherapy-class siblings). Ratified canonically on 2026-06-24 alongside the Lu 2017 polatuzumab vedotin TTE extraction.

PRIOR_PLATIN (canonical for prior platinum-based chemotherapy indicator)

• Description: 1 = subject received any prior platinum-based chemotherapy (cisplatin, carboplatin, oxaliplatin, nedaplatin, satraplatin) before the start of the analyzed treatment; 0 = platinum-naive. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (platinum-naive).
• Source aliases:
  • “prior platinum-based treatment (yes/no)” (Lu 2017 narrative; coded as a binary 0/1 in the source NONMEM dataset) – used in Lu_2017_polatuzumab_neuropathy.R.
• Example models: Lu_2017_polatuzumab_neuropathy.R (multiplicative effect on the grade >= 2 peripheral neuropathy hazard via exp(theta_Platin * PRIOR_PLATIN) with theta_Platin = 0.159, SE 0.345 – not detectable given the large SE; the paper hypothesized prior platinum exposure could sensitize patients to subsequent antimicrotubule-induced PN given that platinum agents themselves cause sensory PN, but did not find a clinically meaningful effect).
• Notes: Platinum agents are DNA-crosslinking cytotoxics with peripheral-neuropathy as a well-known adverse-event profile (especially for cisplatin and oxaliplatin); prior exposure is therefore a defensible pretreatment indicator in any subsequent-PN-risk analysis. Sibling of PRIOR_ANTICANCER (broader any-modality indicator), PRIOR_RADIATION, PRIOR_VINCA, and PRIOR_TAXANE (parallel chemotherapy-class siblings). Distinct from AUC_CARBO (the continuous per-cycle carboplatin-AUC drug-exposure covariate, which is time-varying and used as a direct drug-exposure driver in tumor-dynamics models, e.g. Zecchin_2016_survival.R). Ratified canonically on 2026-06-24 alongside the Lu 2017 polatuzumab vedotin TTE extraction.

PRIOR_BIO (canonical for prior biologic exposure)

• Description: 1 = subject previously treated with any biologic (broader than PRIOR_TNF: includes anti-TNF agents plus anti-integrin, anti-IL-12/23, anti-IL-17, anti-IL-23, anti-IL-6, etc.), 0 = biologic-naive.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (biologic-naive).
• Source aliases:
  • bio-naive (Aguiar 2021 source-paper variable, with the indicator inverted: paper’s bio-naive = 1 - PRIOR_BIO). Effect coefficients in the source paper apply to bio-naive; in model() derive bio_naive <- 1 - PRIOR_BIO to preserve the paper’s reported coefficient.
• Example models: Aguiar_2021_ustekinumab.R (Aguiar 2021 Table 2 footnote a; multiplicative effect on CL: factor (1 - 0.227 * (1 - PRIOR_BIO)), so bio-naive patients have ~23% lower CL than previously-exposed patients).
• Notes: Distinct from PRIOR_TNF (a strict subset). Use PRIOR_BIO when the source paper’s covariate counts any biologic as prior exposure (anti-TNF, anti-integrin, anti-IL-12/23, anti-IL-17, anti-IL-23, anti-IL-6, etc.); use PRIOR_TNF when the source paper specifically tested anti-TNF exposure. When the source paper uses the inverted “bio-naive” indicator (1 = naive), document the inversion in covariateData[[PRIOR_BIO]]$notes and apply 1 - PRIOR_BIO in model() so the canonical column stores 1 = previously exposed.

PRIOR_TNF (canonical for prior anti-TNF biologic exposure indicator)

• Description: 1 = subject previously treated with an anti-TNF (tumor necrosis factor) inhibitor, 0 = TNF-naive.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (TNF-naive).
• Source aliases:
  • PRIORTNF (all caps, no underscore) – acceptable alternative spelling.
• Example models: Moein_2022_etrolizumab.R (multiplicative fractional effect on CL, +4.9%).
• Notes: Use when the source paper reports a binary “prior anti-TNF inhibitor” covariate on any PK parameter. Generally applicable across RA/PsA/IBD/axSpA biologic PK models.

PRIOR_IPI (canonical for prior ipilimumab treatment indicator)

• Description: 1 = subject previously treated with ipilimumab (anti-CTLA-4 monoclonal antibody) before the start of the current anti-PD-1 / anti-PD-L1 (or other) checkpoint-inhibitor regimen, 0 = ipilimumab-naive.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (ipilimumab-naive). Document per-paper how subjects with missing IPI status are pooled (Ahamadi 2017 pools “missing” with naive in the final coefficient; some papers may report a separate “missing” effect).
• Source aliases:
  • IPI (Ahamadi 2017; categorical with levels IPI-naive, IPI-treated, missing) – decompose into PRIOR_IPI = as.integer(IPI == "IPI-treated") and treat the missing category like naive unless the source paper retains a separate “missing” coefficient.
• Example models: Ahamadi_2017_pembrolizumab.R (proportional changes on CL of +14.0% and on Vc of +7.36% for IPI-treated relative to IPI-naive; “missing” 26.4% of cohort is pooled with naive in the canonical encoding because Table 3 reports only the naive-vs-treated coefficient).
• Notes: Distinct from PRIOR_ANTICANCER (any modality), PRIOR_BIO (any biologic), PRIOR_TNF (anti-TNF biologic). Use PRIOR_IPI when the source paper specifically tested prior ipilimumab exposure as a covariate; this is a common covariate in advanced-melanoma popPK analyses where ipilimumab was the standard-of-care immune-checkpoint inhibitor preceding PD-1 / PD-L1 entrants. Ratified canonically on 2026-05-17 alongside the Ahamadi 2017 pembrolizumab extraction.

PRIOR_STATIN (canonical for prior (pre-study) statin therapy indicator)

• Description: 1 = patient was on established statin (HMG-CoA reductase inhibitor) therapy before entering the study, 0 = statin-naive at study entry. Time-fixed per subject. Captures the pharmacological history that shapes the observed baseline lipid panel and the residual room for further LDL-C lowering, independent of whether a statin continues to be taken during the study.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (statin-naive at study entry).
• Source aliases:
  • Statin prior therapy – Jadhav 2023 Table 3 covariate row name.
  • PRIORSTAT, STATINPR – common dataset abbreviations.
• Example models: Jadhav_2023_bempedoicAcid_ldlc.R (proportional shifts on the indirect-response Imax of -0.373 and on baseline LDL-C of -0.296; Jadhav 2023 Table 3).
• Notes: Deliberately distinct from the concomitant-use family (CONMED_STATIN, CONMED_STATIN_MONO, CONMED_STATIN_LI / _MI / _HI): Jadhav 2023 screened “prior established LMTs” and “concomitant medication (low-, moderate-, or high-intensity statin or ezetimibe)” as separate covariate sets and retained both in the final model, so the two must be carried as separate columns. Statin-intolerance cohorts commonly have PRIOR_STATIN = 1 with all concomitant-statin indicators 0. Sibling of PRIOR_EZE. Ratified canonically on 2026-07-27 alongside the Jadhav 2023 bempedoic acid extraction (sidecar request 001, operator answer A).

PRIOR_EZE (canonical for prior (pre-study) ezetimibe therapy indicator)

• Description: 1 = patient was on established ezetimibe therapy before entering the study, 0 = ezetimibe-naive at study entry. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (ezetimibe-naive at study entry).
• Source aliases:
  • Ezetimibe prior therapy – Jadhav 2023 Table 3 covariate row name.
  • PRIOREZE – common dataset abbreviation.
• Example models: Jadhav_2023_bempedoicAcid_ldlc.R (proportional shift on baseline LDL-C of -0.0596; Jadhav 2023 Table 3).
• Notes: Distinct from CONMED_EZE (concomitant ezetimibe during the study). Jadhav 2023 Online Resource 5 reports a “Prior Treatment (Ezetimibe : No Ezetimibe)” contrast alongside a separate “Concomitant Treatment (Ezetimibe : No Ezetimibe)” contrast, confirming that the analysis dataset carried both columns and that the two effects act on different model parameters (prior use on baseline LDL-C; concomitant use on Imax and on bempedoic acid CL/F). Sibling of PRIOR_STATIN. Ratified canonically on 2026-07-27 alongside the Jadhav 2023 bempedoic acid extraction (sidecar request 001, operator answer A).

CONMED_NNRTI_IND (canonical for concomitant enzyme-inducing NNRTI indicator)

• Description: 1 = subject is coadministered an enzyme-inducing non-nucleoside reverse transcriptase inhibitor (efavirenz or nevirapine) at the observation, 0 = no concomitant enzyme-inducing NNRTI. Both efavirenz and nevirapine are CYP3A4 inducers; this pooled indicator collapses their effects on metabolite elimination of co-administered antiretrovirals into a single binary covariate.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no enzyme-inducing NNRTI; subject on NRTI-only or PI-based backbone).
• Source aliases:
  • NNI – used in Hirt_2006_nelfinavir.R (paper’s pooled “nonnucleosidic inhibitor” indicator: 1 if EFV or NVP, 0 otherwise; pooling justified by the paper’s finding that the two drugs’ inducer effects on M8 elimination were not significantly different and the two were never administered simultaneously in the cohort).
• Example models: Hirt_2006_nelfinavir.R (additive-form multiplicative effect on M8 apparent elimination rate kel_m8: kel_m8 *= (1 + 0.91 * CONMED_NNRTI_IND); KM0 is ~1.9-fold higher under EFV or NVP co-administration, consistent with CYP3A4 induction of M8 elimination).
• Notes: Pooling the two named NNRTIs follows the precedent of CONMED_EIAED (any enzyme-inducing AED) and CONMED_AZOLE (any azole antifungal). Distinct from CONMED_EFV (efavirenz alone, founded by Tikiso 2021): the pooled CONMED_NNRTI_IND indicator is used when the source paper did not separately identify EFV vs NVP effects. A future paper that needs to distinguish them should use CONMED_EFV (and a future sibling CONMED_NVP) rather than this pooled indicator. The “inducer” qualifier excludes non-inducing NNRTIs (rilpivirine, doravirine, etravirine); record the per-paper list of pooled drugs in covariateData[[CONMED_NNRTI_IND]]$notes. Specific scope until a second model ratifies the convention; at that point promote to general. Ratified canonically on 2026-06-09 alongside the Hirt 2006 nelfinavir extraction.

CONMED_PSEUDOEPHEDRINE (canonical for concomitant pseudoephedrine coadministration indicator)

• Description: 1 = subject is taking concomitant pseudoephedrine at the observation, 0 = not. Pseudoephedrine is a sympathomimetic decongestant that is renally excreted via active tubular secretion in addition to glomerular filtration, and competes with other tubularly-secreted drugs for proximal-tubule transporters. Time-varying when pseudoephedrine starts / stops within the observation window; in Van Wart 2004 the indicator is set per PK sample (PSEU_jk, sample-level).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant pseudoephedrine).
• Source aliases:
  • PSEU – used in VanWart_2004_garenoxacin.R (paper covariate PSEU_jk, sample-level indicator that the kth PK sample from the jth patient was collected during concomitant pseudoephedrine administration).
• Example models: VanWart_2004_garenoxacin.R (multiplicative effect on the structural CL/F covariate term: cl_structural *= (1 - 0.144 * CONMED_PSEUDOEPHEDRINE); -14.4% relative to the no-pseudoephedrine reference, Van Wart 2004 Table 4).
• Notes: Drug-specific CONMED_* indicator capturing competition for proximal-tubule active secretion; distinct from CYP-based and UGT-based induction or inhibition mechanisms encoded elsewhere. Renal clearance of pseudoephedrine itself is approximately 511-532 mL/min in a 70 kg adult (Van Wart 2004 Discussion), well above the glomerular filtration estimate (~120 mL/min), confirming active tubular secretion as the dominant elimination route. Other drugs that share this active-tubular-secretion route (levofloxacin, ciprofloxacin, gatifloxacin, gemifloxacin, garenoxacin) are candidates for the same indicator in future models. Distinct from CONMED_PROBENECID (anionic OAT inhibitor, mechanistically a perpetrator on anion-secreted substrates) – pseudoephedrine is the cationic / mixed-substrate competitor rather than a transporter inhibitor.

CONMED_ATAZANAVIR (canonical for concomitant atazanavir coadministration indicator)

• Description: 1 = subject is receiving atazanavir (ATV; an HIV-1 protease inhibitor) as a co-medication during the PK observation period, 0 = no concomitant atazanavir. Atazanavir is a UGT1A1 inhibitor (and weak CYP3A4 inhibitor) so the indicator is used to flag UGT1A1-mediated inhibition of co-administered antiretrovirals whose elimination depends on glucuronidation. Time-fixed when the source paper analyses cohorts whose ART regimen is stable across the analysis window; time-varying when atazanavir is added or stopped during the observation period.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no concomitant atazanavir).
• Source aliases:
  • ATV – used in ArabAlameddine_2012_raltegravir.R (paper Table 2 covariate symbol for the atazanavir coadministration indicator; same orientation as the canonical, 1 = on atazanavir).
• Example models: ArabAlameddine_2012_raltegravir.R (multiplicative additive shift on relative bioavailability in HIV-positive individuals: F_HIV+ *= (1 + e_atazanavir_fdepot * CONMED_ATAZANAVIR) with e_atazanavir_fdepot = 0.39; atazanavir-coadministered patients have ~39% higher relative bioavailability than non-atazanavir reference, consistent with ATV-mediated UGT1A1 inhibition of raltegravir glucuronidation), vonHentig_2009_saquinavir.R (power-of-binary multiplier on saquinavir CL/F: cl *= e_atazanavir_cl^CONMED_ATAZANAVIR with e_atazanavir_cl = 0.703, i.e. saquinavir CL/F is reduced to 70.3% when atazanavir 300 mg QD is coadministered, attributed to ATV-mediated CYP3A inhibition; von Hentig 2009 Table 2 full-model column), Bukkems_2021_raltegravir.R (linear additive effect on apparent oral clearance: cl *= (1 + e_atazanavir_cl * CONMED_ATAZANAVIR) with e_atazanavir_cl = -0.17; atazanavir-coadministered raltegravir CL is 17% lower than the no-atazanavir reference, consistent with ATV-mediated UGT1A1 inhibition of raltegravir glucuronidation, Bukkems 2021 Table 2 ‘Factor change in CL with atazanavir’).
• Notes: Specific scope because the indicator is paper-defined as the presence of atazanavir within an HIV ART regimen; the comparator (no-atazanavir) pools all other backbones (PI without ATV, NNRTI-based, integrase-only, etc.). The full INN spelling ATAZANAVIR is used rather than the 3-letter abbreviation ATV because ATV collides with the legacy atorvastatin slot in the CONMED_<drug>_DOSE family (now spelled out as CONMED_ATORVASTATIN_DOSE); both surfaces use the full INN to keep the CONMED_ namespace unambiguous. Distinct from AUC_RTV (a continuous ritonavir AUC0-24 covariate used in Dickinson_2009_atazanavir.R to scale atazanavir CL/F via the booster’s UGT1A1 inhibition) and from CONMED_EFV (efavirenz, a CYP3A and UGT inducer that drives the opposite direction of effect on raltegravir bioavailability). Ratified canonically on 2026-06-09 alongside the ArabAlameddine 2012 raltegravir extraction; the full-INN-spelling decision was operator-confirmed via sidecar 001.

CONMED_INOTROPE (canonical for concomitant inotrope / vasoactive coadministration indicator)

• Description: 1 = subject is receiving at least one inotropic / vasoactive agent (dopamine, dobutamine, epinephrine, norepinephrine, milrinone, vasopressin, or equivalent) at the time of the observation; 0 = not on any such agent. Time-varying in principle; typically captured per-occasion for popPK observations in critical-care / neonatal-ICU studies. Used as a hemodynamic / renal-perfusion surrogate when the pharmacology mechanism is reduced GFR due to hemodynamic instability rather than a direct CYP / transporter DDI.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (not on any inotrope). Effect form in Zhao 2014 is a direct multiplicative selection on CL: F_inotrope = theta8 = 0.708 when CONMED_INOTROPE == 1 and 1 otherwise; encoded in nlmixr2 as f_inotrope <- e_inotrope_cl ^ CONMED_INOTROPE with e_inotrope_cl = 0.708.
• Source aliases:
  • inotropic agents / inotropes – used in Zhao_2014_ciprofloxacin.R (Zhao 2014 Table 2 ‘Comedication’ row and Table 4 F_inotrope notation); same orientation as the canonical, no value transformation.
• Example models: Zhao_2014_ciprofloxacin.R (multiplicative selection on CL: 0.708 ^ CONMED_INOTROPE – 29.2% CL reduction in patients on inotropes).
• Notes: Specific scope pending corroboration by a second model that ratifies the same semantics. The per-paper drug list pooled into the indicator should be documented in covariateData[[CONMED_INOTROPE]]$notes for each model (Zhao 2014 does not enumerate the exact agents; the cohort is critical-care neonates so the practical mix is dominated by dopamine / dobutamine / epinephrine / norepinephrine / milrinone). Distinct from extracorporeal-support modalities (ECMO_PUMP_SPEED, etc.) – inotropes are a pharmacological intervention rather than a mechanical-support modality. Distinct from heart rate (HR) – inotropes may cause both reduced GFR and altered HR but the indicator captures the therapy itself rather than its hemodynamic consequence. If a future paper retains a finer-grained taxonomy (epinephrine-only vs dopamine-only vs vasopressor-only), register CONMED_EPI / CONMED_DOBU / CONMED_VASOPRESS as siblings rather than overload CONMED_INOTROPE with conflicting semantics. Ratified canonically on 2026-06-09 alongside the Zhao 2014 ciprofloxacin extraction.

CONMED_STANOZOLOL (canonical for concomitant stanozolol coadministration indicator)

• Description: 1 = subject is coadministered the anabolic-androgenic steroid stanozolol at the observation, 0 = not. Stanozolol is a synthetic 17-alpha-alkylated androgen used in aplastic anemia to stimulate erythropoiesis; it inhibits CYP3A4-mediated metabolism of ciclosporin (and several other CYP3A4 substrates). In Ni 2013 stanozolol coadministration was time-fixed per subject (78 of 102 children remained on stanozolol throughout the TDM observation window).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no stanozolol coadministration).
• Source aliases:
  • stanozolol indicator (no specific NONMEM column name reported in Ni 2013) – used in Ni_2013_ciclosporin.R.
• Example models: Ni_2013_ciclosporin.R (multiplicative power-of-binary effect on CL/F: cl *= e_conmed_stanozolol_cl ^ CONMED_STANOZOLOL with e_conmed_stanozolol_cl = 0.83, i.e. ~17% lower CL/F when stanozolol is coadministered relative to the no-stanozolol reference; Ni 2013 Table 2 ‘F comedication’).
• Notes: Anabolic-androgenic 17-alpha-alkylated steroid; mechanistically distinct from glucocorticoid corticosteroids (do not reuse [[CONMED_STEROID]]). Ni 2013 tested both prednisone and stanozolol in forward covariate selection and retained only stanozolol after backward elimination – the clinically relevant mechanism is CYP3A4 inhibition (Ni 2013 Discussion paragraph 5). A future popPK paper that tests stanozolol or related 17-alpha-alkylated anabolic androgens (oxandrolone, oxymetholone, danazol) as covariates would either ratify this canonical (promoting to general scope) or warrant a family-level canonical (e.g. CONMED_ANDROGEN) – defer that decision until a second paper appears. Ratified canonically on 2026-06-07 alongside the Ni 2013 ciclosporin paediatric aplastic-anemia extraction (sidecar request-001, response Option A).

CONMED_BUDESONIDE (canonical for inhaled-budesonide treatment-arm indicator)

• Description: 1 = subject is in the inhaled-budesonide treatment arm; 0 = subject is in the placebo arm or in an alternative-controller arm (e.g., nedocromil). Per-subject time-fixed binary indicator carrying the head-to-head randomized-arm assignment in an inhaled-corticosteroid trial where pulmonary function (FEV1) is modelled as a longitudinal disease-progression endpoint and the inhaled-corticosteroid effect modifies the typical-value FEV1 prediction. Distinct from a PK-driven exposure effect: the model treats the indicator as an additive on/off term and does NOT carry a budesonide PK compartment.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (placebo or alternative-controller arm; the complement composition is defined per-model in covariateData[[CONMED_BUDESONIDE]]$notes).
• Source aliases:
  • derived per subject from the CAMP-trial assigned arm – used in Wu_2014_FEV1_asthma.R (the CAMP-trial randomized-arm assignment with three levels {placebo, nedocromil, budesonide}; CONMED_BUDESONIDE = as.integer(arm == "budesonide"); nedocromil is pooled into the reference because the paper reports no significant nedocromil effect on FEV1).
• Example models: Wu_2014_FEV1_asthma.R (additive shift on the linear-scale FEV1 typical-value: FEV1 = exp(theta_age*AGE + theta_ht*HT - theta_int) + e_conmed_bud_fev1 * CONMED_BUDESONIDE with e_conmed_bud_fev1 = +0.103 L (paper Table 2) – budesonide-arm subjects have ~0.10 L higher FEV1 relative to the placebo+nedocromil reference, with an IIV SD of 0.129 L on the drug-effect term itself; the high shrinkage 67.3% reported in the paper reflects that only ~30% of the cohort carries the indicator).
• Notes: Sibling of the existing head-to-head drug-arm CONMED_* family and follows the CONMED_<INN> convention (full INN spelled out, matching CONMED_METFORMIN, CONMED_PROBENECID, CONMED_SILDENAFIL, CONMED_SPIRON). The contrast is between an active treatment arm and a reference arm in a randomized trial, encoded as a per-subject time-fixed binary. Specific scope because the budesonide-vs-{placebo, nedocromil} head-to-head is tied to the CAMP-trial design; future inhaled-corticosteroid-vs-placebo popPD models can extend this entry’s example_models list, while a contrast between a different ICS (e.g., fluticasone, mometasone) and budesonide should register a sibling canonical rather than reusing CONMED_BUDESONIDE. Distinct from TRT (Novakovic 2017 categorical 3-level cladribine-arm covariate) and TRT_PHASE (active-treatment-phase indicator); CONMED_BUDESONIDE is a per-subject randomized-arm indicator rather than a time-varying treatment-phase flag. Ratified canonically on 2026-06-12 alongside the Wu 2014 FEV1-asthma extraction.

CONMED_NNRTI (canonical for concomitant NNRTI (any non-nucleoside reverse transcriptase inhibitor) co-administration indicator)

• Description: 1 = subject is on at least one concomitant non-nucleoside reverse transcriptase inhibitor (NNRTI) – typically efavirenz, nevirapine, etravirine, rilpivirine, doravirine, or delavirdine – at the PK observation; 0 = no concomitant NNRTI. A pooled class indicator used in popPK analyses of CYP3A4 substrates (such as HIV protease inhibitors) where the source paper does not separate the individual NNRTI effects because the included NNRTIs are both PXR-mediated CYP3A4 inducers with a similar net effect on CYP3A4 substrate clearance.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant NNRTI).
• Source aliases:
  • NNRTI – used in Kappelhoff_2005_indinavir.R (Kappelhoff 2005 Table 2 footnote: “NNRTI is 1 when efavirenz or nevirapine are administered and 0 when they are not”). Kappelhoff 2005 specifically pooled efavirenz and nevirapine because “inclusion of separate effects for each drug did not improve goodness-of-fit.”
• Example models: Kappelhoff_2005_indinavir.R (multiplicative power-form effect on CL/F: cl *= 1.41^CONMED_NNRTI; concomitant NNRTI use increases indinavir apparent CL by 41%).
• Notes: Distinct from the per-drug indicator CONMED_EFV (efavirenz only) – use CONMED_EFV when the source paper estimates the efavirenz effect alone and CONMED_NNRTI when the source paper pools the class. Per-model covariateData[[CONMED_NNRTI]]$notes should list which specific NNRTIs are counted as “1” in the dataset (Kappelhoff 2005: efavirenz and nevirapine; future extractions including rilpivirine / doravirine / etravirine / delavirdine should document each one). Ratified canonically on 2026-06-10 alongside the Kappelhoff 2005 indinavir extraction.

CONMED_RTV_AUC_12h (canonical for ritonavir AUC over the 0-12 h q12h dosing interval (BID ritonavir regimens))

• Description: Per-subject (time-fixed within an evaluated regimen) ritonavir AUC over a 12-hour dosing interval (q12h, BID ritonavir), used as a co-medication exposure covariate driving boosted-protease-inhibitor (lopinavir, atazanavir, darunavir, etc.) clearance in popPK models that account for ritonavir’s CYP3A4-inhibition effect on a BID regimen. In Crommentuyn 2005 the value is computed per-subject as DOSE_RTV / CL_RTV using individual Bayesian CL_RTV estimates from the Kappelhoff et al. 2005 ritonavir popPK model (Crommentuyn 2005 reference [19], Br J Clin Pharmacol 59:174-82), and feeds the lopinavir CL/F inverse-saturable form cl = exp(lcl) * (auc50 / (auc50 + CONMED_RTV_AUC_12h)) * IND with auc50 = 2.26 mg*h/L (Crommentuyn 2005 Methods Equations 1-2; Table 2).
• Units: mg*h/L (document per-model via covariateData[[CONMED_RTV_AUC_12h]]$units if a different exposure unit is reported).
• Type: continuous
• Scope: specific
• Reference category: n/a – enters via the inverse-saturable form auc50 / (auc50 + CONMED_RTV_AUC_12h). Reference value observed: 3.58 mg*h/L (Crommentuyn 2005 cohort median across 122 HIV-1-infected adults on BID LPV/r; range 0.85-18.77).
• Source aliases:
  • AUC12h – printed name in Crommentuyn 2005 (Methods Equations 1-2; Results page 6; Table 2 row 3). The paper writes the subscript 12h to denote the BID dosing-interval AUC; the column name is registered with the 12h token in the column name to keep the q12h-BID convention visible without relying on subscripts.
• Example models: Crommentuyn_2005_lopinavir.R (lopinavir CL/F inverse-saturable dependence on CONMED_RTV_AUC_12h: cl = exp(lcl) * (auc50 / (auc50 + CONMED_RTV_AUC_12h)) * IND with auc50 = 2.26 mg*h/L and IND = 1 + 0.39 * CONMED_NNRTI), vonHentig_2009_saquinavir.R (saquinavir CL/F centred power dependence on CONMED_RTV_AUC_12h: cl = exp(lcl) * e_atazanavir_cl^CONMED_ATAZANAVIR * (CONMED_RTV_AUC_12h / 6.70355)^e_rtv_auc_12h_cl with e_rtv_auc_12h_cl = -0.403; centred at the cohort median 6.70355 mg*h/L so saquinavir CL/F equals the typical 60.4 L/h at the median ritonavir exposure when atazanavir is absent).
• Notes: Specific scope – column meaning is tied to ritonavir as the booster drug AND to the q12h-BID dosing-interval AUC convention. Sibling of AUC_RTV (which is the q24h once-daily form, Dickinson 2009 cohort median 7.52 mgh/L). The two canonicals capture chemically-identical ritonavir AUC but at different dosing-interval scopes; never overload them. For simulation users without observed per-subject ritonavir AUC, the Crommentuyn 2005 cohort median 3.58 mgh/L reproduces typical-value behaviour (the value at which the inverse-saturable form gives CL/F = 5.73 L/h in the lopinavir + ritonavir steady-state simulation, matching the paper’s reported per-cohort-median CL/F). Ratified canonically on 2026-06-10 alongside the Crommentuyn 2005 lopinavir extraction.

CONMED_SEVO (canonical for concomitant sevoflurane volatile-anaesthesia maintenance indicator)

• Description: 1 = subject received sevoflurane as the volatile anaesthetic for maintenance of general anaesthesia during the perioperative observation window (typically a propofol IV induction followed by sevoflurane gas maintenance), 0 = no sevoflurane maintenance (e.g., propofol-only TIVA, isoflurane, desflurane, or another agent). Time-fixed per subject in perioperative-PK / NMB-reversal models where sevoflurane is administered as a continuous maintenance gas from induction until reversal of neuromuscular blockade. Distinct from ETSEVO (continuous end-tidal sevoflurane concentration in vol %), which is the quantitative driver used in emergence-from-anaesthesia models such as Shin 2014; CONMED_SEVO is the per-subject on/off flag.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no sevoflurane; typically propofol-only TIVA or another volatile agent).
• Source aliases:
  • SEV – used in Kleijn_2011_sugammadex_rocuronium.R (Kleijn 2011 Table 4 covariate-effect rows “No sevoflurane: keoSEV = 1” and “Sevoflurane: keoSEV = 1 + theta”).
• Example models: Kleijn_2011_sugammadex_rocuronium.R (multiplicative effect on the rocuronium effect-compartment equilibration rate keo *= (1 + e_conmed_sevo_keo * CONMED_SEVO) with e_conmed_sevo_keo = -0.567 and on the rocuronium NMB EC50 ec50 *= (1 + e_conmed_sevo_ec50 * CONMED_SEVO) with e_conmed_sevo_ec50 = -0.395; sevoflurane simultaneously slows the central-to-effect-compartment equilibration and lowers the rocuronium concentration required for half-maximal neuromuscular blockade, reproducing the well-known sevoflurane potentiation of aminosteroid NMBAs).
• Notes: Distinct from ETSEVO (end-tidal sevoflurane volume percent, a continuous PD driver). Use CONMED_SEVO when the source paper encodes sevoflurane maintenance as a per-subject on/off indicator without a continuous end-tidal trajectory; use ETSEVO when the per-record end-tidal concentration is available and the model uses it directly. Follows the CONMED_* family pattern (CONMED_VPA, CONMED_RIF, CONMED_AMIO). Future perioperative models that use a similar binary indicator for isoflurane (CONMED_ISO) or desflurane (CONMED_DES) should register parallel canonicals rather than overloading this name. Ratified canonically alongside the Kleijn 2011 sugammadex-rocuronium extraction.

CONMED_DOPA (canonical for concomitant dopamine (inotrope) coadministration indicator)

• Description: 1 = subject is receiving dopamine (an inotropic agent used in critically ill neonates and infants for haemodynamic support) at the PK observation; 0 = no concomitant dopamine. Treated as time-varying per observation in the source paper (each PK record carries the dopamine flag corresponding to the dosing window).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no concomitant dopamine).
• Source aliases:
  • DOPA – used in Fuchs_2014_gentamicin.R (paper covariate name in Table 2; same orientation as the canonical, 1 = on dopamine, 0 = otherwise).
• Example models: Fuchs_2014_gentamicin.R (multiplicative linear effect on CL: 1 + (-0.120) * CONMED_DOPA, i.e. 12% lower CL on dopamine-coadministered neonates relative to no-dopamine reference; Fuchs 2014 Table 2 theta_CL_DOPA = -0.120, SE 22%).
• Notes: Specific scope until a second model legitimately reuses the indicator. Fuchs 2014 Discussion notes that the effect “might principally reflect cardiovascular instability in critically ill newborns” – so the dopamine flag is partly a marker of disease severity rather than a clean drug-drug-interaction effect. The mechanism on glomerular filtration in neonates remains debated (Fuchs 2014 Discussion citations [57-59]).

CONMED_PROPOFOL_CC (canonical for measured plasma concentration of co-administered propofol)

• Description: Time-varying measured plasma concentration of propofol administered concomitantly as the intravenous general-anaesthetic agent, supplied as a covariate column rather than computed from a coupled propofol PK model. Used as the perpetrator exposure in models where propofol modifies the disposition of another drug – Li 2024 found that measured propofol concentration explains the reduced norepinephrine clearance seen under general anaesthesia better than the accompanying fall in cardiac output does, consistent with propofol’s inhibition of the norepinephrine transporter. Set to 0 whenever no propofol is on board (awake phase, before induction, after full washout), which collapses the exponential effect term to 1.
• Units: ug/mL (document per-model via covariateData[[CONMED_PROPOFOL_CC]]$units if a different exposure unit is reported).
• Type: continuous
• Scope: specific
• Reference category: 0 (no propofol administered). Reference values observed: median measured propofol concentration during Eleveld-model target-controlled maintenance anaesthesia was 3.53 ug/mL in Li 2024’s healthy-volunteer cohort; a typical maintenance target is 3 ug/mL and a high target 6 ug/mL.
• Source aliases:
  • CPROP (Li 2024 Table 3 covariate-effect row “CPROP~CL” and Results 3.2; per-record measured propofol plasma concentration in ug/mL, same orientation as the canonical, no value transformation).
• Example models: Li_2024_norepinephrine.R (exponential effect on norepinephrine clearance, cl *= exp((e_conmed_propofol_cc_cl + etae_conmed_propofol_cc_cl) / 100 * CONMED_PROPOFOL_CC) with e_conmed_propofol_cc_cl = -3.57 per 100 ug/mL; the covariate replaced a binary awake-versus-anaesthesia session factor in the final model).
• Notes: Follows the registered CONMED_<drug>_CC family (CONMED_RIF_CC, CONMED_INH_CC, CONMED_MER_CC, …) for the time-varying concentration of a co-administered named drug, with the full INN spelled out as in CONMED_SILDENAFIL_NMETAB_CC because a PROP abbreviation would collide with propranolol and propranolol-like INNs. Distinct from CEFFECT, which carries the propofol effect-site concentration acting as the PD driver of propofol’s own anaesthetic effect (Koo_2012_propofol.R); CONMED_PROPOFOL_CC is a measured plasma concentration acting as a perpetrator on a different drug’s PK. Also distinct from the binary CONMED_SEVO / volatile-agent indicators, which carry no magnitude information.

CONMED_DIUR (canonical for concomitant diuretic indicator (composite or class-restricted))

• Description: 1 = subject is coadministered a diuretic during the study window, 0 = no concomitant diuretic. Used in popPK / popPK-PD analyses of renally cleared substrates because diuretic-driven volume contraction and shifts in renal tubular handling can change apparent clearance, and because thiazide and loop diuretics are anti-uricosuric (raise serum urate) and so directly modify urate-related PD endpoints. The class membership of the diuretics pooled into the indicator is paper-specific and must be documented in covariateData[[CONMED_DIUR]]$notes per model. Two patterns are observed:
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant diuretic of the paper-specific class set).
• Source aliases:
  • DIUR – used in Stocker_2012_oxypurinol.R (Stocker 2012 dataset column; pools thiazide + loop + spironolactone; n = 72 of 155 gouty patients).
  • diuretic – used in Wright_2016_allopurinol.R (Wright 2016 narrative term; pools thiazide + loop diuretics only, excludes spironolactone / amiloride; n = 44 of 133 gouty patients).
• Example models: Stocker_2012_oxypurinol.R (multi-class composite: thiazide + loop + potassium-sparing; multiplicative linear-deviation effect on apparent CL/Fm: cl *= (1 + (-0.294) * CONMED_DIUR), i.e. -29.4% apparent oxypurinol clearance with any concomitant diuretic in adults with gout; Stocker 2012 Table 3 theta7), Wright_2016_allopurinol.R (thiazide + loop only, no potassium-sparing; multiplicative power-form effect on both CL/F_oxy and baseline urate U0: cl *= 0.740^CONMED_DIUR (-26%) and rbase *= 1.14^CONMED_DIUR (+14%); Wright 2016 Table 3 thetadiuretic and thetaE0_diuretic), Wright_2013_allopurinol.R (merged from a duplicate register entry, 2026-07-25 dedup).
• Notes: Composite indicator with paper-specific class membership. When the source paper pools thiazide + loop + potassium-sparing into a single column (e.g. Stocker 2012), CONMED_DIUR is the canonical and the per-model covariateData[[CONMED_DIUR]]$notes must enumerate the pooled classes. When the source paper pools a narrower set – typically thiazide + loop only, because potassium-sparing diuretics tend to be uricosuric (lower serum urate) and have an opposite-direction PD effect (e.g. Wright 2016) – the same CONMED_DIUR canonical is reused with the narrower definition documented in per-model notes. Users simulating across models that share the canonical column name but differ in class membership must populate the column according to the paper’s own definition. Distinct from CONMED_SPIRON (spironolactone-only, used in Zhou 2010 digoxin popPK to encode P-glycoprotein inhibition at the renal tubular secretion site); the two canonicals can coexist when a future paper distinguishes specific drug classes. When a paper requires class-resolved encoding (separate thiazide / loop / K-sparing indicators), register sibling canonicals (e.g. CONMED_THIAZIDE, CONMED_LOOP_DIUR) rather than overloading CONMED_DIUR. Ratified canonically on 2026-06-20 alongside the Stocker 2012 oxypurinol extraction; class-restricted definition extended on 2026-06-30 alongside the Wright 2016 allopurinol extraction.

CONMED_KETOCONAZOLE (canonical for concomitant ketoconazole (combined strong CYP3A4 and P-glycoprotein inhibitor) coadministration indicator)

• Description: 1 = subject coadministered ketoconazole during the observation interval (typically 400 mg once-daily oral, the standard index-inhibitor regimen), 0 = no concomitant ketoconazole. Ketoconazole is the classical probe combined strong CYP3A4 + P-glycoprotein inhibitor and is the perpetrator arm of many dedicated DDI studies and DDI-prediction PBPK analyses. Per-subject time-varying when a DDI study spans on / off ketoconazole periods; time-fixed on the on-treatment day of a fixed-sequence DDI arm.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant ketoconazole).
• Source aliases:
  • No source column name; Choules 2024 encodes the perpetrator arm as a separate simulation rather than as a dataset column, so the indicator is constructed by the model user.
• Example models: Choules_2024_enfortumab.R, Choules_2024_brentuximab.R (multiplicative factor on MMAE clearance: cl_mmae *= e_keto_cl_mmae^CONMED_KETOCONAZOLE with e_keto_cl_mmae = 1/1.38 = 0.72464 for enfortumab vedotin and 1/1.37 = 0.72993 for brentuximab vedotin, i.e. a 27-28% reduction in MMAE clearance reproducing the published MMAE AUC geometric mean ratios of 1.38 and 1.37 in Choules 2024 Table 5).
• Notes: Auto-approved member of the CONMED_<INN> family (INN = ketoconazole). Distinct from the class-level [[CONMED_AZOLE]] (which pools ketoconazole with the other systemic azoles as a common inhibitor class effect) and from [[CONMED_CYP3A4_INH_STRONG]]: register the drug-specific indicator when a paper singles ketoconazole out with its own coefficient, which is the usual case because ketoconazole is chosen precisely for the combined CYP3A4 + P-gp mechanism rather than for CYP3A4 inhibition alone, so its effect size is not interchangeable with a CYP3A4-only strong inhibitor. Sibling to [[CONMED_ITRACONAZOLE]] and [[CONMED_VORICONAZOLE]]. When a model lumps the CYP3A4 and P-gp components of the interaction into a single clearance multiplier (as the Choules 2024 reductions do), say so in covariateData[[CONMED_KETOCONAZOLE]]$notes so a downstream user does not attribute the whole effect to CYP3A4. Frequently paired with a [[CONMED_RIF]] inducer arm in the same model.

Rheumatoid-arthritis disease-activity covariates

RHEUMATOID_FACTOR (canonical for serum rheumatoid factor concentration)

• Description: Serum rheumatoid factor (an autoantibody, predominantly IgM, directed against the Fc portion of IgG) concentration. Baseline value typical; document time-varying use in per-model notes.
• Units: U/mL or IU/mL (interchangeable in the clinical-PK literature). Document per-model via covariateData[[RHEUMATOID_FACTOR]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling on a log-transformed value: (log(RHEUMATOID_FACTOR) / log(ref))^exponent (or, equivalently, the source-paper form (LRF / log(ref))^exponent where LRF = log(RHEUMATOID_FACTOR)). Reference value observed: 110 U/mL (Frey 2010, corresponding to LRF = 4.7 in the paper’s final-model equation).
• Source aliases:
  • LRF – log-transformed RF (natural log of the value in U/mL); Frey 2010 fits the covariate on the log scale and reports the reference as LRF = 4.7 (i.e., log(110) ~= 4.7). The canonical column carries the raw RF concentration in U/mL; the log transform is applied inside model().
  • RF – universal NONMEM/clinical-PK abbreviation; rejected as the canonical name on 2026-04-28 because the bare two-letter abbreviation is uncommon in published popPK papers and could be confused with other shortenings.
• Example models: Frey_2010_tocilizumab.R (U/mL, reference 110 U/mL == LRF = 4.7; small positive exponent +0.1 on linear CL applied to log(RHEUMATOID_FACTOR)).
• Notes: RF concentrations span several orders of magnitude across the rheumatoid-arthritis population (Frey 2010 observed range 15-11,800 U/mL across the four phase-III studies; reference paper: Frey 2010 Table I), motivating the log transform before power scaling. The mechanistic rationale (Frey 2010 Discussion, p764) is that RF – being an anti-IgG autoantibody – could in principle bind the Fc region of the therapeutic IgG monoclonal antibody and accelerate clearance, but the observed CL effect was small (-4.9% to +6.5% across the observed RF range) and the paper acknowledges that high RF concentrations may also reduce the assay’s ability to detect the drug, leading to an apparent CL increase. Ratified canonically on 2026-04-28 alongside the Frey 2010 extraction.

BLPHYVAS (canonical for baseline physician’s global assessment VAS)

• Description: Baseline Physician’s Global Assessment of Disease Activity, 100-mm visual analogue scale (0 = no disease activity, 100 = maximum). Time-fixed per subject.
• Units: mm (0-100 VAS)
• Type: continuous
• Scope: general
• Reference category: n/a – used as a power term (BLPHYVAS / <ref>)^exponent. Reference 66 used in Ma 2020.
• Source aliases: none.
• Example models: Ma_2020_sarilumab_das28crp.R.
• Notes: One of the components of the DAS28 composite score; in Ma 2020 it appears as a baseline covariate on the DAS28-CRP disease-activity BASE rather than on the score itself. Applicable to any rheumatology model where baseline physician-assessed disease activity is used as a PK/PD covariate.

BLHAQ (canonical for baseline HAQ-DI score)

• Description: Baseline Health Assessment Questionnaire Disability Index (HAQ-DI; 0 = no disability, 3 = maximum disability). Time-fixed per subject.
• Units: unitless (0-3 composite score)
• Type: continuous
• Scope: general
• Reference category: n/a – used as a power term (BLHAQ / <ref>)^exponent. Reference 1.75 used in Ma 2020.
• Source aliases: none.
• Example models: Ma_2020_sarilumab_das28crp.R.
• Notes: Patient-reported disability score frequently used as a baseline covariate in rheumatoid-arthritis PK/PD analyses.

PAIN (canonical for patient-reported global pain visual analogue score)

• Description: Patient-reported global pain on a 100-mm visual analogue scale (PAIN; 0 = no pain, 100 = worst imaginable pain). Distinct from BLPHYVAS (the physician’s global assessment of disease activity). Both baseline and time-varying usages are covered; document per-model in covariateData[[PAIN]]$notes whether the column is baseline-only.
• Units: mm (0-100 VAS).
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (PAIN / ref)^exponent. Reference value observed: 60 in Frey 2013 (approximate dataset median across OPTION/TOWARD).
• Source aliases: none known (the PAIN column name is used directly in the Frey 2013 NONMEM control stream).
• Example models: Frey_2013_tocilizumab.R (baseline; power effect on the indirect-response BASE parameter with reference 60 and exponent 0.062), Mercier_2014_tramadol_tapentadol_mbma.R (arm-level baseline pain intensity on the 0-10 normalized scale used across the pooled MBMA trials – see per-model covariateData[[PAIN]]$notes for the 0-10 vs 0-100 scale convention).
• Notes: PAIN can take the value 0 in real cohorts (a patient who reports no pain at the assessment), which makes the bare power form (PAIN/60)^exp return 0. Frey 2013 documents an explicit 0.010 floor on PAIN in its Table 2 covariate range; the model file applies the same floor inside model() so simulation under PAIN = 0 returns a finite BASE rather than collapsing to zero. Canonical name follows the EOS / SCORE_EASI convention (no BL prefix); baseline-vs-time-varying status is per-model. The canonical scale is 0-100 mm VAS; MBMA extractions that pool multiple heterogeneous pain scales (e.g., Mercier 2014) may carry PAIN on a 0-10 normalized scale – document the scale per-model in covariateData[[PAIN]]$notes so downstream users know to rescale before merging with a 0-100 mm cohort.

SWOL_28JOINT (canonical for 28-joint swollen joint count)

• Description: Swollen joint count on the 28-joint (DAS28) scale (integer 0-28; component of the DAS28 composite). Baseline value is typical; document time-varying use in per-model notes.
• Units: count (0-28)
• Type: continuous
• Scope: general
• Reference category: n/a – used as a shifted power term ((SWOL_28JOINT + 1)/(<ref> + 1))^exponent to avoid the zero-count edge case. Reference value observed: 16 in Li 2019 (approximate dataset median of the popPK cohort); 12 in Williams 2016 (median of DAS28cfb PK/PD dataset).
• Source aliases:
  • SWOL – used in Li_2019_abatacept.R (Li 2019 Methods abbreviation).
  • SJ28 – used in Williams_2016_rituximab_das28cfb.R (Williams 2016 Supplemental Methods g_i covariate function).
• Example models: Li_2019_abatacept.R (power effect on CL with exponent 0.0965; not clinically relevant per Li 2019), Williams_2016_rituximab_das28cfb.R (additive log-scale effect (SWOL_28JOINT - 12) on each of PMAX, kp, and Emax in the DAS28 change-from-baseline model).
• Notes: The _28JOINT suffix distinguishes this from the 66-joint swollen count (SWOL_66JOINT) used in the extended ACR joint set. Canonical name drops the BL prefix to match the SCORE_EASI / AGE / WT / ALB convention where baseline-vs-time-varying status is documented in covariateData notes rather than the column name.

TEND_28JOINT (canonical for 28-joint tender joint count)

• Description: Tender joint count on the 28-joint (DAS28) scale (integer 0-28; component of the DAS28 composite). Baseline value is typical; document time-varying use in per-model notes.
• Units: count (0-28)
• Type: continuous
• Scope: general
• Reference category: n/a – used with additive shift (TEND_28JOINT - <ref>) in log-scale coefficient models or shifted power ((TEND_28JOINT + 1)/(<ref> + 1))^exponent to avoid the zero-count edge case. Reference value observed: 16 in Williams 2016 (median of DAS28cfb PK/PD dataset).
• Source aliases:
  • TJ28 – used in Williams_2016_rituximab_das28cfb.R (Williams 2016 Supplemental Methods g_i covariate function).
• Example models: Williams_2016_rituximab_das28cfb.R (additive log-scale effect (TEND_28JOINT - 16) on each of PMAX, kp, and Emax in the DAS28 change-from-baseline model).
• Notes: Companion to SWOL_28JOINT — the tender-count analog of the swollen count on the 28-joint DAS28 subscale. Distinct from TEND_68JOINT which uses the 68-joint extended count. Ratified canonically on 2026-07-09 alongside the Williams 2016 rituximab-biosimilar DAS28cfb extraction.

SWOL_66JOINT (canonical for 66-joint swollen joint count)

• Description: Swollen joint count on the 66-joint extended (ACR) scale (integer 0-66; component of the ACR responder-rate criteria alongside TEND_68JOINT). Baseline value is typical; document time-varying use in per-model notes.
• Units: count (0-66)
• Type: continuous
• Scope: general
• Reference category: n/a – used with additive shift (SWOL_66JOINT - <ref>) in log-scale coefficient models. Reference value observed: 16 in Williams 2016 (median of the ACR dataset).
• Source aliases:
  • SJ66 – used in Williams_2016_rituximab_acr.R (Williams 2016 Supplemental Methods g_i covariate function).
• Example models: Williams_2016_rituximab_acr.R (additive log-scale effect (SWOL_66JOINT - 16) on PMAX and onset half-life in the cumulative-probit ACR responder-rate model).
• Notes: Register-anticipated: the existing SWOL_28JOINT entry’s Notes section explicitly reserves SWOL_66JOINT for the extended-joint scale used by ACR. Companion to TEND_68JOINT. Ratified canonically on 2026-07-09 alongside the Williams 2016 rituximab-biosimilar ACR extraction.

TEND_68JOINT (canonical for 68-joint tender joint count)

• Description: Tender joint count on the 68-joint extended (ACR) scale (integer 0-68; component of the ACR responder-rate criteria alongside SWOL_66JOINT). Baseline value is typical; document time-varying use in per-model notes.
• Units: count (0-68)
• Type: continuous
• Scope: general
• Reference category: n/a – used with additive shift (TEND_68JOINT - <ref>) in log-scale coefficient models. Reference value observed: 24 in Williams 2016 (median of the ACR dataset).
• Source aliases:
  • TJ68 – used in Williams_2016_rituximab_acr.R (Williams 2016 Supplemental Methods g_i covariate function).
• Example models: Williams_2016_rituximab_acr.R (additive log-scale effect (TEND_68JOINT - 24) on PMAX and onset half-life in the cumulative-probit ACR responder-rate model).
• Notes: Companion to SWOL_66JOINT. Distinct from TEND_28JOINT (28-joint DAS28 subscale). Ratified canonically on 2026-07-09 alongside the Williams 2016 rituximab-biosimilar ACR extraction.

PGA_PT (canonical for baseline patient’s global assessment of arthritis (VAS))

• Description: Patient’s own overall rating of arthritis disease activity on a 100-mm visual analogue scale (0 = no disease activity, 100 = maximum). Distinct from BLPHYVAS (the physician’s global assessment) and from PAIN (the patient’s pain-specific rating). Time-fixed per subject in the known example; document time-varying use in per-model notes.
• Units: mm (0-100 VAS)
• Type: continuous
• Scope: general
• Reference category: n/a – used with additive shift (PGA_PT - <ref>) in log-scale coefficient models or as a power term (PGA_PT / <ref>)^exponent. Reference value observed: 70 in Williams 2016 (median of both the DAS28cfb and ACR datasets).
• Source aliases:
  • PGA – used in Williams_2016_rituximab_das28cfb.R and Williams_2016_rituximab_acr.R (Williams 2016 Supplemental Methods g_i covariate function; the paper writes “PGA” for the patient’s global assessment).
• Example models: Williams_2016_rituximab_das28cfb.R (additive log-scale effect on each of PMAX, kp, and Emax), Williams_2016_rituximab_acr.R (additive log-scale effect on PMAX and onset half-life in the cumulative-probit ACR model).
• Notes: The _PT suffix disambiguates the patient’s global assessment from the physician’s global assessment (BLPHYVAS) — the paper-verbatim “PGA” abbreviation is ambiguous between the two in cross-literature usage. Ratified canonically on 2026-07-09 alongside the Williams 2016 rituximab-biosimilar extraction (the paper reports both PGA and PhGA separately and estimates independent covariate effects on each).

Pharmacogenetics

CYP2D6 (canonical for CYP2D6 individual metabolic-activity score)

• Description: Continuous individual-level CYP2D6 metabolic-activity score. The intent is a single canonical column for any CYP2D6 phenotype proxy that the source paper reports as a continuous number (probe-substrate model-based individual clearance, copy-number-corrected expression score, activity-score sum from *allele genotypes, etc.); the per-model covariateData[[CYP2D6]]$units, description, and notes document which proxy is in force and the population-median reference value used inside the model. Time-invariant in all known examples (germline genotype or one-time probe-substrate measurement).
• Units: Paper-specific – document per-model (e.g., ng/L in Ter Heine 2014 where the value is the dextromethorphan-probe model-based individual CYP2D6 clearance).
• Type: continuous
• Scope: general
• Reference category: n/a (continuous). Models center on a population median (e.g., 1560 ng/L in Ter Heine 2014); document the reference value per-model in covariateData[[CYP2D6]]$notes.
• Source aliases:
  • CYP2D6 – used directly in TerHeine_2014_tamoxifen.R.
  • AS (CYP2D6 activity score) – Ashraf 2024 (paper Section 3.2 and Table 1 footnote a). The CPIC consensus sum of per-allele activity values for the subject’s diplotype: 1 per normal-function allele (1, 2), 0.5 per decreased-function allele (9, 17, 29, 41), 0.25 per 10 allele, 0 per no-function allele (3, 4, 5, 6, 40), with duplicated normal-function alleles scoring 2.25, 3 or 4 depending on the partner allele. Observed values 0, 0.25, 0.5, 0.75, 1, 1.25, 1.5, 2, 2.25, 3, 4. No value transformation is needed – the activity score is used directly as the column value.
• Example models: TerHeine_2014_tamoxifen.R (power-law effect on the tamoxifen -> endoxifen formation clearance: (CYP2D6 / 1560)^e_CYP2D6_cl_endx), Ashraf_2024_codeine.R (exponential effect on the codeine -> morphine metabolic fraction referenced to activity score 2: f = exp(lfm_mor + eta + e_cyp2d6_fm_mor * (CYP2D6 - 2)) followed by the paper’s f / (1 + f) rescaling; e_cyp2d6_fm_mor = 1.00).
• Notes: The activity-score-sum encoding anticipated by the original TODO below is now in use (Ashraf_2024_codeine.R), confirming that the single continuous canonical spans both a probe-substrate clearance (Ter Heine 2014, ng/L) and a genotype-derived CPIC activity score (Ashraf 2024, unitless 0-4); the per-model units and notes fields carry which proxy is in force, so no separate CYP2D6_ACTSCORE canonical is needed. Papers that report only the categorical PM / IM / NM / UM grouping still use the binary CYP2D6_PM / CYP2D6_EM indicators below rather than this column – Ashraf 2024 fitted both parameterisations and retained the continuous activity score because it explained more of the between-subject variability in the codeine-to-morphine metabolic ratio (33% unexplained vs 45% for the phenotype classes), so a paper that offers both should prefer the continuous column. A CYP2D6_PHENO_GROUP categorical companion remains unregistered and should be proposed when a future model genuinely needs the four-level grouping as a model term. The companion binary CYP2D6_PM entry below covers source papers that report only a poor-metabolizer-versus-not-poor-metabolizer dichotomy.

CYP2D6_PM (canonical for CYP2D6 poor-metabolizer phenotype indicator)

• Description: 1 = subject is a CYP2D6 poor metabolizer (genotype encoding no functional enzyme activity; e.g., homozygous 4/4, 5/5, or any compound combination of nonfunctional alleles); 0 = subject is an extensive, intermediate, or ultrarapid metabolizer (i.e., carrying at least one functional or reduced-function but not null allele). Time-fixed per subject (germline genotype-derived phenotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (extensive, intermediate, or ultrarapid metabolizer; carries at least one functional or reduced-function CYP2D6 allele).
• Source aliases:
  • 2D6PM – used in Knights_2015_aripiprazole.R (Knights 2015 Eq. 2 binary 2D6PM = 1 if CYP2D6 poor metabolizer, = 0 otherwise; in the supplement NONMEM control stream the dataset column CYP2D6EM is recoded as PM = (CYP2D6EM == 0) – i.e., the source dataset’s CYP2D6EM extensive-metabolizer indicator is value-inverted to produce the model’s PM indicator).
  • P1 (PM) / “poor metabolizer” – Sherwin 2012 (paper Table 2 mixture-model subpopulation P1; Table 1 phenotype assignment for genotyped subjects). Sherwin 2012 uses CYP2D6_PM paired with CYP2D6_EM to encode all three PM / IM / EM phenotypes; IM is the implicit reference (both indicators = 0).
• Example models: Knights_2015_aripiprazole.R (proportional-shift effect on apparent oral clearance: CL/F = TVCL * (1 + e_2d6pm_cl * CYP2D6_PM) with e_2d6pm_cl = -0.478; CYP2D6 poor metabolizers have 47.8% lower apparent oral clearance than non-poor-metabolizers; Knights 2015 Equation 2 and Figure 1B), Sherwin_2012_risperidone.R (phenotype-indicator covariate gating subpopulation-specific apparent oral clearance and metabolite-formation fraction in a one-compartment risperidone + (+/-)-9-hydroxyrisperidone mixture model: PM CL/F = 9.38 L/h, IM CL/F = 29.2 L/h, EM CL/F = 37.4 L/h at the 70 kg allometric reference; KF (fraction metabolized) PM = 0.16, EM = 0.13, IM = 1 fixed; Sherwin 2012 Table 2).
• Notes: Opposite-orientation companion to the CYP2C9_EM precedent (which encodes the extensive-metabolizer-equals-1 phenotype) – here the 1 indicates the poor-metabolizer end of the phenotype spectrum because the Knights 2015 source paper explicitly defines its binary as 2D6PM (PM = 1) and reports the coefficient sign relative to that orientation; preserving the PM-equals-1 orientation reproduces the paper’s reported coefficient sign and typical-value parameters directly. Distinct from the continuous CYP2D6 activity-score canonical above: use CYP2D6_PM when the source paper reports a categorical phenotype (PM / IM / EM); use CYP2D6 when the source reports a probe-derived continuous activity number. For three-level PM / IM / EM phenotype encoding (Sherwin 2012 mixture model), pair CYP2D6_PM with the companion CYP2D6_EM below following the SLCO1B1_HAP15_HET / SLCO1B1_HAP15_HOM two-binary-indicator pattern with IM as the implicit reference (both indicators = 0); this is consistent with the CYP2C9_EM register entry’s anticipated CYP2C9_PM companion. The Knights 2015 source dataset records CYP2D6EM (extensive-metabolizer indicator, 1 = EM, 0 = not-EM); the model’s PM indicator is derived by value inversion (CYP2D6_PM = 1 - CYP2D6EM after handling the supplement’s -99 missing-value sentinel as CYP2D6_PM = 0). Ratified canonically on 2026-05-21 alongside the Knights 2015 aripiprazole extraction; scope retained as general on 2026-05-24 alongside the Sherwin 2012 risperidone extraction (second model using the same indicator-binary encoding, demonstrating the canonical generalizes beyond the Knights 2015 PM-vs-non-PM dichotomy into the three-level PM / IM / EM paired-indicator pattern with CYP2D6_EM).

CYP2D6_EM (canonical for CYP2D6 extensive-metabolizer phenotype indicator)

• Description: 1 = subject is a CYP2D6 extensive metabolizer (genotype encoding full or near-full enzyme activity; e.g., 1/1 wild-type homozygote, or one functional allele paired with another functional allele); 0 = subject is an intermediate, poor, or ultrarapid metabolizer. Time-fixed per subject (germline genotype-derived phenotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (intermediate, poor, or ultrarapid metabolizer). When CYP2D6_EM is paired with CYP2D6_PM (Sherwin 2012 three-level encoding), the reference category specifically corresponds to the intermediate-metabolizer (IM) stratum: both CYP2D6_PM = 0 and CYP2D6_EM = 0 together indicate an IM subject.
• Source aliases:
  • P2 (EM) / “extensive metabolizer” – Sherwin 2012 (paper Table 2 mixture-model subpopulation P2; Table 1 phenotype assignment for genotyped subjects).
• Example models: Sherwin_2012_risperidone.R (phenotype-indicator covariate gating subpopulation-specific apparent oral clearance and metabolite-formation fraction in a one-compartment risperidone + (+/-)-9-hydroxyrisperidone mixture model; paired with CYP2D6_PM with IM as the implicit reference; Sherwin 2012 Table 2).
• Notes: Companion to the CYP2D6_PM canonical above; the paired-indicator pattern follows SLCO1B1_HAP15_HET / SLCO1B1_HAP15_HOM (two binary indicators encoding a three-level categorical with an implicit reference). When a source paper reports only a binary PM-vs-non-PM dichotomy (e.g., Knights 2015), CYP2D6_PM alone is sufficient and CYP2D6_EM is omitted from the model’s covariateData. When a source distinguishes all three PM / IM / EM phenotypes (Sherwin 2012), include both indicators with IM as the implicit reference. UM (ultrarapid metabolizer) – when present in a source cohort – must be handled by registering an additional paired CYP2D6_UM indicator on the same pattern; this is not yet needed for any existing model and is deferred until a UM-aware paper is extracted. Distinct from the continuous CYP2D6 activity-score canonical above and from CYP2D6_PM: use CYP2D6_EM when the source separately identifies the EM stratum (vs lumping IM + EM into a single non-PM group). Ratified canonically on 2026-05-24 alongside the Sherwin 2012 risperidone extraction.

CYP2D6_IM (canonical for CYP2D6 intermediate-metabolizer phenotype indicator)

• Description: 1 = subject is a CYP2D6 intermediate metabolizer (genotype encoding reduced but non-zero enzyme activity; e.g. one non-functional allele paired with one reduced-function allele, or two reduced-function alleles); 0 = subject is an extensive, ultrarapid, or poor metabolizer. Time-fixed per subject (germline genotype-derived phenotype). Designed to be paired with CYP2D6_PM so that both indicators = 0 selects the POOLED extensive-plus-ultrarapid reference group.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0. When paired with CYP2D6_PM (the Nguyen 2025 two-indicator encoding), CYP2D6_PM = 0 and CYP2D6_IM = 0 together indicate an extensive OR ultrarapid metabolizer – the pooled reference against which both proportional shifts are reported.
• Source aliases:
  • IM – used in Nguyen_2025_valbenazine.R (Nguyen 2025 Supplemental Table S2 equation (5) binary IM; Table 1 theta 18 row label “Intermediate metabolizer on CLM”).
• Example models: Nguyen_2025_valbenazine.R (proportional-shift effect on the [+]-alpha-HTBZ metabolite clearance: CLM = 31.0 * (1 + e_cyp2d6_pm_cl_htbz * CYP2D6_PM) * (1 + e_cyp2d6_im_cl_htbz * CYP2D6_IM) with e_cyp2d6_im_cl_htbz = -0.281; intermediate metabolizers have 28.1% lower metabolite clearance and a 1.39-fold higher steady-state metabolite AUC than the pooled extensive-or-ultrarapid reference; Nguyen 2025 Table 1 and Supplemental Table S2).
• Notes: Third member of the CYP2D6_<phenotype> paired-binary-indicator family (CYP2D6_PM, CYP2D6_EM), following the pattern those entries document and the CYP2D6_EM entry explicitly anticipates for additional strata (“UM … must be handled by registering an additional paired CYP2D6_UM indicator on the same pattern”). Required rather than optional for the Nguyen 2025 encoding: reusing CYP2D6_EM inverted would be WRONG, because CYP2D6_EM = 0 also captures ultrarapid metabolizers and would therefore apply the intermediate-metabolizer clearance reduction to them, whereas the source paper pools ultrarapid WITH extensive in the reference group. Which pairing to use depends on how the source stratifies: CYP2D6_PM + CYP2D6_EM when IM is the implicit reference (Sherwin 2012), CYP2D6_PM + CYP2D6_IM when EM + UM is the pooled reference (Nguyen 2025). A source that resolves all four phenotypes will additionally need CYP2D6_UM (still unregistered; no model requires it yet). Note the assignment convention Nguyen 2025 Table 1 footnote a records: subjects whose genotype was inconclusive between intermediate and extensive were assigned to the INTERMEDIATE category, so CYP2D6_IM = 1 for those subjects. Distinct from the continuous CYP2D6 activity-score canonical, which is the right choice when a source reports a probe-derived continuous activity number rather than a categorical phenotype. Registered alongside the Nguyen 2025 valbenazine extraction.

CYP2D6_STAR10_HET (**canonical for CYP2D6*10 (rs1065852) heterozygote indicator**)

• Description: Binary genotype indicator for the CYP2D610 heterozygote group at rs1065852 (the c.100C>T transition in exon 1, P34S substitution). 1 = subject carries exactly one CYP2D610 (T) allele (genotype C/T); 0 = otherwise (the union of C/C wild-type homozygotes and T/T homozygous 10 carriers; the paired indicator CYP2D6_STAR10_HOM flags the T/T homozygous-mutant group). Time-fixed per subject (germline genotype). Distinct from the broader CYP2D6_PM / CYP2D6_EM phenotype canonicals above: CYP2D6_PM / CYP2D6_EM summarise the overall metabolic phenotype across all assayed CYP2D6 alleles, while CYP2D6_STAR10_HET / CYP2D6_STAR10_HOM resolve just the rs1065852 SNP and are the right canonical when the source paper genotypes only the 10 variant.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (C/C wild-type at rs1065852, when paired with CYP2D6_STAR10_HOM = 0). The reference group is the C/C wild-type stratum; CYP2D6_STAR10_HOM flags the T/T homozygous-mutant stratum.
• Source aliases:
  • CYP2D6*10 C/T – used in Pei_2016_iloperidone.R (Pei 2016 Results ‘Population pharmacokinetic (PPK) models’ encodes CYP2D6*10 C/C, C/T, T/T as integers 1, 2, 3; the C/T = 2 stratum maps to CYP2D6_STAR10_HET = 1).
• Example models: Pei_2016_iloperidone.R (multiplicative effect on the iloperidone -> M2 (P-95) formation rate constant K24: K24_CT = 0.693 * K24_typical relative to the C/C wild-type reference; Pei 2016 Table 3 and Results ‘Population pharmacokinetic (PPK) models’ equation (K24)_i = theta * 0.00649 * exp(eta_i) with theta = 0.693 for C/T).
• Notes: Follows the CYP3A5_STAR1_HET / CYP3A5_STAR1_HOM and SLCO1B1_HAP15_HET / SLCO1B1_HAP15_HOM precedents (paired binary indicators for a three-level germline genotype with an implicit wild-type reference). The _STAR10_ token names the variant-allele orientation (the *10 allele is the reduced-function variant of CYP2D6 that causes lower CYP2D6 metabolic activity); the C/C wild-type stratum is the implicit reference because the source-paper coefficient signs are reported relative to that reference. Pei 2016 pooled C/C and C/T as the reference for the K23 (M1 formation) effect (only T/T was distinguished on K23), so a model using these two canonicals will route the K23 effect through CYP2D6_STAR10_HOM alone while routing the K24 effect through both CYP2D6_STAR10_HET and CYP2D6_STAR10_HOM – this asymmetric per-parameter use is normal and is what the paired-indicator pattern is designed to express. Ratified canonically on 2026-06-03 alongside the Pei 2016 iloperidone extraction.

CYP2D6_STAR10_HOM (**canonical for CYP2D6*10 (rs1065852) homozygous-mutant indicator**)

• Description: Binary genotype indicator for the CYP2D610 homozygous-mutant group at rs1065852. 1 = subject carries two CYP2D610 (T) alleles (genotype T/T); 0 = otherwise (the union of C/C wild-type homozygotes and C/T heterozygotes; the paired indicator CYP2D6_STAR10_HET flags the C/T heterozygous group). Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (C/C wild-type at rs1065852, when paired with CYP2D6_STAR10_HET = 0). The reference group is the C/C wild-type stratum; CYP2D6_STAR10_HET flags the C/T heterozygous stratum.
• Source aliases:
  • CYP2D6*10 T/T – used in Pei_2016_iloperidone.R (Pei 2016 Results ‘Population pharmacokinetic (PPK) models’ encodes CYP2D6*10 C/C, C/T, T/T as integers 1, 2, 3; the T/T = 3 stratum maps to CYP2D6_STAR10_HOM = 1).
• Example models: Pei_2016_iloperidone.R (multiplicative effects on two iloperidone metabolite-formation rate constants: K23 (M1 formation) is multiplied by 1.34 in T/T subjects relative to the C/C + C/T pooled reference, and K24 (M2 formation) is multiplied by 0.492 in T/T subjects relative to the C/C wild-type reference; Pei 2016 Table 3 and Results ‘Population pharmacokinetic (PPK) models’ equations (K23)_i = theta * 0.00451 * exp(eta_i) with theta = 1.34 for T/T and (K24)_i = theta * 0.00649 * exp(eta_i) with theta = 0.492 for T/T).
• Notes: Paired with CYP2D6_STAR10_HET (see that entry’s Notes for the three-level decomposition rationale, the variant-orientation convention, and the asymmetric K23-vs-K24 use the paired indicators support in the Pei 2016 model). Allele frequencies for the CYP2D6*10 (rs1065852) variant in Chinese populations are 48-70% per the source paper’s Introduction; the Pei 2016 cohort (n = 70 Chinese schizophrenia patients) observed C/C 15.7%, C/T 60.0%, T/T 24.3%. Ratified canonically on 2026-06-03 alongside the Pei 2016 iloperidone extraction.

CYP3A4 (canonical for CYP3A4 / CYP3A4-and-CYP3A5 individual metabolic-activity score)

• Description: Continuous individual-level CYP3A4 (or combined CYP3A4 + CYP3A5) metabolic-activity score. Same intent and documentation policy as CYP2D6 above. Some sources measure CYP3A4 alone via probe substrate; others (Ter Heine 2014) report a combined CYP3A4/5 activity because the chosen probe (dextromethorphan N-demethylation) cannot distinguish CYP3A4 from CYP3A5. The per-model notes field documents whether the value is CYP3A4-only or the CYP3A4 + CYP3A5 combined score.
• Units: Paper-specific – document per-model (e.g., ng/L in Ter Heine 2014 where the value is the dextromethorphan-probe model-based individual CYP3A4/5 clearance).
• Type: continuous
• Scope: general
• Reference category: n/a (continuous). Models center on a population median (e.g., 44.7 ng/L in Ter Heine 2014); document the reference value per-model.
• Source aliases:
  • CYP3A4 – used directly in TerHeine_2014_tamoxifen.R (the column carries combined CYP3A4 + CYP3A5 activity per the source paper).
  • CYP3A4/5 – long form sometimes used in source manuscripts; standardize the column name to CYP3A4 and document the combined-isoform semantics in per-model notes.
• Example models: TerHeine_2014_tamoxifen.R (power-law effect on the tamoxifen -> endoxifen formation clearance: (CYP3A4 / 44.7)^e_CYP3A4_cl_endx).
• Notes: TODO – register the rest of the canonical drug-metabolizing-CYP set prospectively (CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2E1, CYP3A5) using the same continuous-individual-activity-score pattern, so future popPK models that report CYP-probe-derived covariates can drop straight into the existing convention rather than re-deliberating the encoding each time. Coordinate with the consolidation TODO on CYP2D6 so categorical-vs-continuous encoding is handled uniformly across all CYPs.

CYP3A5_EXPR (canonical for CYP3A5 expresser status)

• Description: 1 = subject carries at least one functional CYP3A51 allele (genotype 1/1 or 1/3, equivalent to one or two A alleles at rs776746); 0 = homozygous CYP3A53/*3 (G/G at rs776746) – i.e., a nonexpresser. Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (CYP3A53/3 nonexpresser).
• Source aliases:
  • X – used in Bergmann_2014_tacrolimus.R (Bergmann 2014 Table 2 footnote: X = 1 for 1/1 and 1/3; X = 0 for 3/3).
  • CYP3A5 expresser – used in Storset_2014_tacrolimus.R (Storset 2014 Table 2 final theory-based model; *1/*1 and *1/*3 pooled as expressers because Storset 2014 had only n = 3 1/1 subjects).
• Example models: Bergmann_2014_tacrolimus.R (multiplicative effect on tacrolimus CL/F: theta_CYP3A5 ^ CYP3A5_EXPR, with theta_CYP3A5 = 1.60; expressers have 60% higher apparent oral clearance than nonexpressers), Storset_2014_tacrolimus.R (multiplicative effects on apparent plasma clearance: cl *= 1.30^CYP3A5_EXPR; and on oral bioavailability: fdepot *= 0.82^CYP3A5_EXPR; Storset 2014 Table 2 final theory-based model).
• Notes: Distinct from the SNP-pattern canonical SNP_<GENE>_<RSID> (which encodes “mutant allele presence” – 1 = at least one variant allele). For CYP3A5 the 3 allele (rs776746 G) is the variant that abolishes function, so a literal “mutant-allele-presence” indicator (1 = any G allele) would group 1/3 heterozygotes with the 3/3 nonexpressers, which is the opposite of the clinically meaningful expresser-vs-nonexpresser dichotomy used by every CYP3A5-aware popPK model. The CYP3A5_EXPR canonical preserves the expresser-equals-1 orientation directly. Future CYP3A5 papers using a 3/3 indicator (rather than 1 carrier) should still record their values under CYP3A5_EXPR and document the value inversion in notes (CYP3A5_EXPR = 1 - source_indicator); registering a parallel CYP3A5_NONEXPR is discouraged. The canonical name follows the <gene>_<phenotype> rather than the <gene>_<rsid> pattern because the column captures derived metabolic phenotype rather than raw genotype. Distinct from CYP3A4 (continuous individual-activity score for CYP3A4 / CYP3A4 + CYP3A5 combined): the binary CYP3A5_EXPR is the right fit for source papers that report only the rs776746 genotype, while the continuous CYP3A4 is for sources that report a probe-substrate-derived activity number. In solid-organ-transplant popPK / pharmacogenetics studies that genotype both the recipient and the donated graft separately (e.g., Moes 2016 liver-transplant tacrolimus), the recipient genotype goes into CYP3A5_EXPR and the donor genotype into the sibling canonical CYP3A5_EXPR_DONOR below. Ratified canonically on 2026-05-08 alongside the Bergmann 2014 extraction.

CYP3A5_EXPR_DONOR (canonical for transplanted-graft donor CYP3A5 expresser status)

• Description: 1 = transplanted graft (typically liver, kidney, or other CYP3A5-expressing organ) was donated by a donor carrying at least one functional CYP3A51 allele (genotype 1/1 or 1/3 at rs776746); 0 = donor is homozygous CYP3A53/*3 (nonexpresser). Time-fixed per recipient (germline genotype of the donor at the time of transplantation). Sibling canonical to the recipient-genotype indicator CYP3A5_EXPR.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (donor CYP3A53/3 nonexpresser graft).
• Source aliases:
  • Donor CYP3A5*3 – used in Moes_2016_tacrolimus.R (Moes 2016 Methods; donor and recipient genotypes pooled with the four-level combination indicator C1 / C2 / C3 / C4 defined in Methods, where CYP3A5_EXPR_DONOR = 1 corresponds to graft from a *1-carrying donor).
  • CYP3A5 donor – used in Ji_2018_tacrolimus.R (Ji 2018 derives the donor-side genotype from the four-level combinational CYP3A5 group: REDE/RNDE -> 1; REDN/RNDN -> 0).
• Example models: Moes_2016_tacrolimus.R (categorical donor + recipient CYP3A5 combination effect on apparent oral CL/F: reference C1 = both nonexpressers; C2 = recipient *1 carrier + donor nonexpresser = +33%; C3 = recipient nonexpresser + donor *1 carrier = +33%; C4 = both *1 carriers = +71%; Moes 2016 Table 4 final model – the C2 / C3 / C4 levels are reconstructed inside model() from the two binary inputs CYP3A5_EXPR and CYP3A5_EXPR_DONOR), Ji_2018_tacrolimus.R (combinational categorical effect on tacrolimus CL/F that depends on both recipient and donor CYP3A5 status: multiplier 2.314 when the recipient is an expresser and the donor is an expresser (REDE), 1.523 when the recipient is an expresser and the donor is a nonexpresser (REDN), and 1.0 otherwise (RNDE / RNDN reference, which Ji 2018 merged because the two estimated effects were similar)).
• Notes: Genotyped only in transplant studies where donor DNA is recoverable (Moes 2016 obtained donor DNA from spleen / liver biopsies). When the same paper reports both recipient and donor genotypes separately, encode them as two binary inputs (CYP3A5_EXPR for the recipient, CYP3A5_EXPR_DONOR for the donor) rather than as a single four-level combination column, so the underlying recipient / donor biology is explicit in the dataset. Models that fit a categorical four-level combination effect (paper-Moes-style C1 / C2 / C3 / C4) reconstruct the levels inside model() from the two binary inputs, so the source-paper’s per-level coefficients remain the estimated quantities. The intestinal-CYP3A5 contribution (recipient genotype) and the hepatic-CYP3A5 contribution (donor genotype) act on different anatomic compartments of tacrolimus’s first-pass metabolism, which is why both donor and recipient genotypes are independently informative in liver-transplant tacrolimus PK. Ratified canonically on 2026-05-20 alongside the Moes 2016 tacrolimus extraction.

CYP3A5_STAR1_HET (canonical for CYP3A51/3 heterozygote indicator)

• Description: Binary genotype indicator for the CYP3A51/3 heterozygote group. 1 = subject carries exactly one functional CYP3A51 allele at rs776746 (genotype 1/3); 0 = otherwise (the union of 3/3 nonexpressers and 1/*1 homozygotes; the paired indicator CYP3A5_STAR1_HOM flags the homozygous-expresser group). Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (CYP3A53/3 nonexpresser, when paired with CYP3A5_STAR1_HOM = 0). The reference group is the homozygous 3/3 nonexpresser stratum; CYP3A5_STAR1_HOM flags the homozygous-expresser stratum.
• Source aliases:
  • CYP3A5*1/*3 – used directly in Passey_2011_tacrolimus.R (Passey 2011 Table 3 reports a separate multiplicative factor of 1.70 for the heterozygote stratum vs the 3/3 reference).
• Example models: Passey_2011_tacrolimus.R (power-of-binary-indicator multiplicative factor on apparent oral clearance: e_cyp3a5_het_cl ^ CYP3A5_STAR1_HET with e_cyp3a5_het_cl = 1.70; 1/3 heterozygotes have ~70% higher apparent oral CL/F than 3/3 nonexpressers; paired with CYP3A5_STAR1_HOM and used jointly), Pei_2023_tacrolimus.R (exponential form, heart transplant), Zhou_2025_tacrolimus.R (exponential form exp(e_cyp3a5_het_cl * CYP3A5_STAR1_HET) with e_cyp3a5_het_cl = 0.63, i.e. a 1.87-fold higher CL/F in 1/3 heterozygotes; the first lung-transplant cohort to resolve all three CYP3A5 strata – Zhou 2025 Discussion notes that the only prior lung-transplant estimate (Cai et al.) distinguished 1/3 from 3/3 at only 1.30-fold and did not resolve 1/1 at all).
• Notes: Follows the SLCO1B1_HAP15_HET / SLCO1B1_HAP15_HOM precedent (paired binary indicators for a three-level genotype) rather than overloading CYP3A5_EXPR (which pools 1/1 and 1/3 into a single expresser indicator and is the right canonical when a source paper does the same pooling). Use the paired CYP3A5_STAR1_HET + CYP3A5_STAR1_HOM binaries when the source paper assigns a distinct typical-value covariate effect to each of the three CYP3A5 genotype strata (3/3, 1/3, 1/1). Passey 2011 motivated the three-level decomposition because the cohort (n = 681) had enough 1/1 homozygotes (72, 11%) to identify a distinct typical-value factor for that stratum, whereas earlier popPK papers (Bergmann 2014, Storset 2014) had too few 1/1 subjects (or the equivalent expresser-pooled treatment) to distinguish 1/1 from 1/3. The _STAR1_ token (rather than _STAR3_) names the functional-allele-presence orientation consistent with the parent CYP3A5_EXPR canonical’s expresser-equals-1 convention. Ratified canonically on 2026-05-20 alongside the Passey 2011 tacrolimus extraction.

CYP3A5_STAR1_HOM (canonical for CYP3A51/1 homozygote indicator)

• Description: Binary genotype indicator for the CYP3A51/1 homozygote group. 1 = subject carries two functional CYP3A51 alleles at rs776746 (genotype 1/1); 0 = otherwise (the union of 3/3 nonexpressers and 1/*3 heterozygotes; the paired indicator CYP3A5_STAR1_HET flags the heterozygous group). Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (CYP3A53/3 nonexpresser, when paired with CYP3A5_STAR1_HET = 0). The reference group is the homozygous 3/3 nonexpresser stratum; CYP3A5_STAR1_HET flags the heterozygous-expresser stratum.
• Source aliases:
  • CYP3A5*1/*1 – used directly in Passey_2011_tacrolimus.R (Passey 2011 Table 3 reports a separate multiplicative factor of 2.00 for the homozygote-expresser stratum vs the 3/3 reference).
• Example models: Passey_2011_tacrolimus.R (power-of-binary-indicator multiplicative factor on apparent oral clearance: e_cyp3a5_hom_cl ^ CYP3A5_STAR1_HOM with e_cyp3a5_hom_cl = 2.00; 1/1 homozygotes have 100% higher apparent oral CL/F than 3/3 nonexpressers; paired with CYP3A5_STAR1_HET and used jointly), Pei_2023_tacrolimus.R (exponential form, heart transplant), Zhou_2025_tacrolimus.R (exponential form exp(e_cyp3a5_hom_cl * CYP3A5_STAR1_HOM) with e_cyp3a5_hom_cl = 1.00, i.e. a 2.72-fold higher CL/F in 1/1 homozygotes; identified from only 13 of 142 lung-transplant recipients (9.15%), so the estimate is the least precisely determined of the paper’s covariate effects at RSE 9.23% on the log scale).
• Notes: Paired with CYP3A5_STAR1_HET (see that entry’s Notes for the three-level decomposition rationale). Ratified canonically on 2026-05-20 alongside the Passey 2011 tacrolimus extraction.

CYP2C9_EM (canonical for CYP2C9 extensive-metabolizer phenotype indicator)

• Description: 1 = subject is a CYP2C9 extensive metabolizer (genotype 1/1; wild-type homozygote with full enzyme activity); 0 = subject is a CYP2C9 intermediate or poor metabolizer (heterozygous or homozygous carrier of any reduced-function allele such as 2, 3, *13) OR has unknown phenotype (when paired with CYP2C9_PM_IM). Time-fixed per subject (germline genotype-derived phenotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (intermediate or poor metabolizer, reduced-function-allele carrier; OR unknown phenotype when paired with CYP2C9_PM_IM).
• Source aliases:
  • CYP2C9 phenotype – Jeong 2022 (paper Table 1 phenotype classification: *1/*1 = EM; *1/*3, *1/*13 = IM).
• Example models: Jeong_2022_torsemide.R (linear-deviation effect on apparent clearance and inter-compartmental clearance: CL/F = tvCL/F * (1 + 0.510 * CYP2C9_EM) and Q/F = tvQ/F * (1 + 0.365 * CYP2C9_EM); CYP2C9 extensive metabolizers have 51% higher apparent clearance and 36.5% higher apparent inter-compartmental clearance than intermediate metabolizers; Jeong 2022 Table 4 final Pop-PK model), Kleideiter_2017_cebranopadol.R (additive log shifts on CL: e_cyp2c9em_lcl = log(82.4 / 74.3) = 0.1037 and e_cyp2c9pmim_lcl = log(58.7 / 74.3) = -0.2353; reference category is unknown phenotype with both indicators = 0, the most common stratum; paired with CYP2C9_PM_IM), Kleideiter_2018_cebranopadol.R (multiplicative effect on CL applied as e_em_cl^CYP2C9_EM with e_em_cl = 82.4 / 74.3 = 1.109; EM subjects have about +11% CL vs the model’s CYP2C9 reference, which in this paper is the ‘unknown phenotype’ pool rather than IM/PM – only 38.3% of the analysis cohort had a known CYP2C9 phenotype, so the 0-level here pools unknown subjects together with PIM subjects whose effect is carried separately by the sibling CYP2C9_PIM canonical; Kleideiter 2018 Table 13).
• Notes: Follows the CYP3A5_EXPR precedent above (functional-allele-carrier = 1) rather than the SNP_<GENE>_<RSID> mutant-presence pattern, because (a) clinical CYP2C9 phenotype is reported as EM / IM / PM and the canonical name should mirror the clinically meaningful axis, and (b) Jeong 2022 chose IM as the model reference, so the EM-equals-1 orientation preserves the paper’s reported coefficient signs and typical-value parameters directly. The canonical name pools reduced-function alleles (2, 3, *13, etc.) into the 0 category because the population-PK literature typically does not separately resolve them; future papers that distinguish IM from PM should propose a paired companion (CYP2C9_PM) so the three-level EM / IM / PM phenotype can be encoded with two binary indicators on the SLCO1B1_HAP15_HET / SLCO1B1_HAP15_HOM pattern. Distinct from CYP2D6 / CYP3A4 (continuous-activity-score canonicals): use CYP2C9_EM when the source paper reports a discrete phenotype label; use a continuous CYP2C9 canonical (not yet ratified; see TODO on CYP3A4) when a future paper reports a probe-derived activity number. Ratified canonically on 2026-05-17 alongside the Jeong 2022 torsemide extraction.

CYP2C9_PM_IM (canonical for pooled CYP2C9 poor-or-intermediate-metabolizer phenotype indicator)

• Description: 1 = subject is a CYP2C9 poor metabolizer OR intermediate metabolizer (i.e., heterozygous or homozygous carrier of a reduced-function allele such as 2, 3, *13, pooled because the source paper did not distinguish PM from IM); 0 = subject is an extensive metabolizer OR has an unknown / unassayed CYP2C9 phenotype. Time-fixed per subject (germline genotype-derived phenotype where known). The companion canonical CYP2C9_EM carries the 1 = EM indicator; subjects with unknown CYP2C9 status appear with both CYP2C9_PM_IM = 0 and CYP2C9_EM = 0.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (extensive metabolizer OR unknown phenotype; the per-paper reference cohort that 0 represents is paper-defined). When paired with CYP2C9_EM, both indicators = 0 indicates the unknown-phenotype stratum and CYP2C9_EM = 1 with CYP2C9_PM_IM = 0 indicates the EM stratum.
• Source aliases:
  • CYP2C9 – Kleideiter 2017 (paper Table 13 row “CYP2C9 poor and intermediate metabolizers 58.7 L/h”).
  • CYP2C9_PIM – used in Kleideiter_2018_cebranopadol.R (the erratum-corrected re-extraction; same pooled poor-or-intermediate-metabolizer semantics, abbreviated PIM).
• Example models: Kleideiter_2017_cebranopadol.R (additive log shift on CL: e_cyp2c9pmim_lcl = log(58.7 / 74.3) = -0.2353; reduced apparent clearance vs the unknown-phenotype reference; paired with CYP2C9_EM to form the three-level stratification), Kleideiter_2018_cebranopadol.R (multiplicative CL ratio 58.7 / 74.3 = 0.790 applied as ratio^CYP2C9_PIM; paired with CYP2C9_EM, both 0 = the unknown-phenotype reference, Kleideiter 2018 Table 13).
• Notes: Pairs with CYP2C9_EM for the three-level “unknown / EM / PM-IM” stratification used in the Kleideiter cebranopadol model, where the unknown-phenotype reference (both indicators = 0) is the most common category (only 38.3% of the analysis cohort had a known CYP2C9 phenotype). PM and IM are pooled because the source covariate analysis does not separately resolve them; downstream papers that distinguish PM from IM should register a paired CYP2C9_PM canonical and split this group, encoding the three-level EM / IM / PM phenotype with two binary indicators on the SLCO1B1_HAP15_HET / SLCO1B1_HAP15_HOM pattern. Distinct from the CYP2C9_EM canonical’s Jeong-2022 use (where 0 = IM/PM, no unknown subjects, and the 0-level directly carries the PM/IM phenotype): in the Kleideiter cohort the 0-level of both CYP2C9_EM and CYP2C9_PM_IM pools all subjects not assigned that specific phenotype label (including the ‘unknown’ fraction), so per-model covariateData notes must document the reference complement. Ratified canonically on 2026-05-25 alongside the Kleideiter 2017 cebranopadol extraction.

CYP2C9_IM_AS15 (canonical for CYP2C9 intermediate-metabolizer, CPIC activity score 1.5, phenotype indicator)

• Description: 1 = subject is a CYP2C9 intermediate metabolizer whose CPIC genotype-predicted activity score (AS) is 1.5 – i.e. one normal-function allele plus one decreased-function allele, canonically *1/*2; 0 = any other CYP2C9 phenotype. Time-fixed per subject (germline genotype-derived phenotype). Pairs with CYP2C9_PM_IM_AS10 as two mutually-exclusive binary indicators; both 0 identifies the extensive-metabolizer reference stratum (AS 2.0, *1/*1 and *1/*9).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0. When paired with CYP2C9_PM_IM_AS10, both indicators = 0 is the extensive-metabolizer (AS 2.0) reference stratum, CYP2C9_IM_AS15 = 1 is the AS-1.5 stratum, and CYP2C9_PM_IM_AS10 = 1 is the AS-1.0 stratum.
• Source aliases:
  • IM with AS of 1.5 – Chen 2024 (paper Table 2 phenotype classification row; Table 4 parameter row CL/F_IM_1.5).
• Example models: Chen_2024_noscapine.R (additive log shift on apparent clearance: e_cyp2c9im_as15_lcl = log(531 / 958) = -0.5901; Chen 2024 Table 4 estimates a free typical value per stratum – CL/F_EM = 958 L/h, CL/F_IM_1.5 = 531 L/h, CL/F_PM_IM_1.0 = 343 L/h – so the extensive-metabolizer value is carried by lcl and the other two strata enter as log shifts, the Kleideiter_2017_cebranopadol.R pattern).
• Notes: Ratified 2026-08-06 alongside the Chen 2024 noscapine extraction, discharging the TODO recorded in the CYP2C9_EM Notes (“future papers that distinguish IM from PM should propose a paired companion … so the three-level EM / IM / PM phenotype can be encoded with two binary indicators”). This pair resolves CYP2C9 phenotype on the activity-score axis rather than the bare PM / IM label, because that is the axis on which the source model stratifies and because the two axes do not coincide: CPIC assigns AS 1.0 to both *1/*3 (an intermediate metabolizer) and *3/*3 (a poor metabolizer), which is why the AS-1.0 stratum is named PM_IM rather than PM. Distinct from the pooled CYP2C9_PM_IM canonical, whose 1-level is the union of the AS-1.5 and AS-1.0 strata; a paper that resolves the activity score should use this pair, and a paper that does not should use CYP2C9_EM / CYP2C9_PM_IM. Do not mix the two pairs in one model. A continuous activity-score covariate is not a substitute: in Chen 2024 the clearance ratios (531/958 = 0.554, 343/958 = 0.358) are not proportional to the activity-score ratios (0.75, 0.50), so discrete indicators are required to reproduce the published typical values. Extend to further strata as CYP2C9_<PHENOTYPE>_AS<score> (e.g. CYP2C9_PM_AS00) if a future cohort resolves them.

CYP2C9_PM_IM_AS10 (canonical for CYP2C9 poor-or-intermediate-metabolizer, CPIC activity score 1.0, phenotype indicator)

• Description: 1 = subject’s CYP2C9 CPIC genotype-predicted activity score (AS) is 1.0, pooling the intermediate metabolizers that reach AS 1.0 (*1/*3, *2/*2) with the poor metabolizers (*2/*3, *3/*3); 0 = any other CYP2C9 phenotype. Time-fixed per subject (germline genotype-derived phenotype). Pairs with CYP2C9_IM_AS15 as two mutually-exclusive binary indicators; both 0 identifies the extensive-metabolizer reference stratum (AS 2.0, *1/*1 and *1/*9).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0. When paired with CYP2C9_IM_AS15, both indicators = 0 is the extensive-metabolizer (AS 2.0) reference stratum.
• Source aliases:
  • PM & IM with AS of 1 – Chen 2024 (paper Table 2 phenotype classification row; Table 4 parameter row CL/F_PM_IM_1.0).
• Example models: Chen_2024_noscapine.R (additive log shift on apparent clearance: e_cyp2c9pmim_as10_lcl = log(343 / 958) = -1.0271; paired with CYP2C9_IM_AS15, both 0 = the extensive-metabolizer reference carried by lcl; Chen 2024 Table 4).
• Notes: The PM_IM element of the name records that this stratum deliberately pools poor metabolizers with the activity-score-1.0 intermediate metabolizers – Chen 2024 Sect. 3.1: “CYP2C91/3 and CYP2C92/2 as IMs with an activity score (AS) of 1, were classified together with the PMs into one group in this study because of the limited sample size of homozygous carriers”. A source paper that separately estimates PM and AS-1.0 IM effects should register a further split rather than reuse this canonical. This is a strict subset of the pooled CYP2C9_PM_IM canonical (which additionally absorbs the AS-1.5 *1/*2 subjects that CYP2C9_IM_AS15 carries here), so the two must not be used interchangeably: substituting CYP2C9_PM_IM for this column would silently mis-assign every *1/*2 subject (20.8% of the Chen 2024 cohort). Derivable from the per-allele count canonicals as CYP2C9_S3_COUNT >= 1 | CYP2C9_S2_COUNT == 2, but the indicators are preferred when the source paper declares a phenotype group and estimates a free typical value per group rather than an allele-dosage function. See the CYP2C9_IM_AS15 Notes for the full rationale. Ratified 2026-08-06 alongside the Chen 2024 noscapine extraction.

CYP2B6_IM (canonical for CYP2B6 intermediate-metabolizer phenotype indicator)

• Description: 1 = subject is a CYP2B6 intermediate metabolizer defined by the combined 516G>T (rs3745274) | 983T>C (rs28399499) SNP-vector phenotype assignment used in the African paediatric antiretroviral literature: 516GT | 983TT (heterozygous loss-of-function at 516) or 516GG | 983TC (heterozygous loss-of-function at 983); 0 = any other phenotype (extensive, slow, or ultra-slow metabolizer; see paired canonicals CYP2B6_SM and CYP2B6_USM). Time-fixed per subject (germline genotype-derived phenotype). EM (516GG | 983TT) is the reference category when all three of CYP2B6_IM, CYP2B6_SM, and CYP2B6_USM are 0.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (any non-IM phenotype: EM, SM, or USM).
• Source aliases:
  • metabolizer status IM / IM – Bienczak 2016 (paper Methods ‘Covariate effects’ paragraph 2, Results ‘Population pharmacokinetics’ paragraph 3, and Table 3 final estimates; categorical metabolizer indicator entered as multiplicative effect on intrinsic clearance CLint with EM reference).
• Example models: Bienczak_2016_nevirapine.R (multiplicative log-additive effect on CLint: cl_meta <- exp(e_cyp2b6_im_cl * CYP2B6_IM + e_cyp2b6_sm_cl * CYP2B6_SM + e_cyp2b6_usm_cl * CYP2B6_USM), with e_cyp2b6_im_cl = log(1 - 0.17) = -0.186, giving the 17% lower CLint reported in Bienczak 2016 Table 3 / Results ‘Population pharmacokinetics’ paragraph 3).
• Notes: Sibling indicators CYP2B6_SM and CYP2B6_USM together encode the four-level EM / IM / SM / USM phenotype with three binary columns (EM = all three zero); follows the dummy-coding pattern used elsewhere in the register for multi-level categoricals (e.g. RACE_*, HEPIMP_MILD / HEPIMP_SEV / HEPIMP_MODSEV). The IM grouping pools the two distinct genotypes (516GT | 983TT and 516GG | 983TC) into a single indicator because Bienczak 2016 Results ‘Population pharmacokinetics’ paragraph 3 reports that ‘Using six rather than four 516G>T | 983T>C SNP-vector metabolizer groups reduced OFV by only 5 points (df = 2, P = 0.08) and was therefore not used.’ Distinct from the continuous CYP3A4 activity-score canonical (which captures probe-substrate-derived activity rather than SNP-vector phenotype) and from CYP3A5_EXPR (binary expresser indicator built on a single rs776746 genotype). Ratified canonically on 2026-05-21 alongside the Bienczak 2016 nevirapine extraction.

CYP2B6_SM (canonical for CYP2B6 slow-metabolizer phenotype indicator)

• Description: 1 = subject is a CYP2B6 slow metabolizer defined by the combined 516G>T (rs3745274) | 983T>C (rs28399499) SNP-vector phenotype assignment used in the African paediatric antiretroviral literature: 516TT | 983TT (homozygous loss-of-function at 516) or 516GT | 983TC (compound heterozygous loss-of-function at both); 0 = any other phenotype (extensive, intermediate, or ultra-slow metabolizer; see paired canonicals CYP2B6_IM and CYP2B6_USM). Time-fixed per subject (germline genotype-derived phenotype). EM (516GG | 983TT) is the reference category when all three of CYP2B6_IM, CYP2B6_SM, and CYP2B6_USM are 0.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (any non-SM phenotype: EM, IM, or USM).
• Source aliases:
  • metabolizer status SM / SM – Bienczak 2016 (paper Methods ‘Covariate effects’ paragraph 2, Results ‘Population pharmacokinetics’ paragraph 3, and Table 3 final estimates; categorical metabolizer indicator entered as multiplicative effect on intrinsic clearance CLint with EM reference).
• Example models: Bienczak_2016_nevirapine.R (multiplicative log-additive effect on CLint: cl_meta <- exp(e_cyp2b6_im_cl * CYP2B6_IM + e_cyp2b6_sm_cl * CYP2B6_SM + e_cyp2b6_usm_cl * CYP2B6_USM), with e_cyp2b6_sm_cl = log(1 - 0.50) = -0.693, giving the 50% lower CLint reported in Bienczak 2016 Table 3 / Results ‘Population pharmacokinetics’ paragraph 3).
• Notes: Sibling indicators CYP2B6_IM and CYP2B6_USM together encode the four-level EM / IM / SM / USM phenotype with three binary columns (EM = all three zero). See CYP2B6_IM Notes for the SNP-vector pooling rationale and the link back to the dummy-coding pattern used elsewhere in the register. Ratified canonically on 2026-05-21 alongside the Bienczak 2016 nevirapine extraction.

CYP2B6_USM (canonical for CYP2B6 ultra-slow-metabolizer phenotype indicator)

• Description: 1 = subject is a CYP2B6 ultra-slow metabolizer defined by the combined 516G>T (rs3745274) | 983T>C (rs28399499) SNP-vector phenotype assignment used in the African paediatric antiretroviral literature: 983CC homozygosity (irrespective of 516G>T genotype, i.e. 516GG | 983CC in Bienczak 2016’s principal grouping); 0 = any other phenotype (extensive, intermediate, or slow metabolizer; see paired canonicals CYP2B6_IM and CYP2B6_SM). Time-fixed per subject (germline genotype-derived phenotype). EM (516GG | 983TT) is the reference category when all three of CYP2B6_IM, CYP2B6_SM, and CYP2B6_USM are 0.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (any non-USM phenotype: EM, IM, or SM).
• Source aliases:
  • metabolizer status USM / USM – Bienczak 2016 (paper Methods ‘Covariate effects’ paragraph 2, Results ‘Population pharmacokinetics’ paragraph 3, and Table 3 final estimates; categorical metabolizer indicator entered as multiplicative effect on intrinsic clearance CLint with EM reference).
• Example models: Bienczak_2016_nevirapine.R (multiplicative log-additive effect on CLint: cl_meta <- exp(e_cyp2b6_im_cl * CYP2B6_IM + e_cyp2b6_sm_cl * CYP2B6_SM + e_cyp2b6_usm_cl * CYP2B6_USM), with e_cyp2b6_usm_cl = log(1 - 0.68) = -1.139, giving the 68% lower CLint reported in Bienczak 2016 Table 3 / Results ‘Population pharmacokinetics’ paragraph 3).
• Notes: Sibling indicators CYP2B6_IM and CYP2B6_SM together encode the four-level EM / IM / SM / USM phenotype with three binary columns (EM = all three zero). Bienczak 2016 is the first study to quantify the USM phenotype on nevirapine clearance (cohort prevalence 0.6%; Table 2 row 4); the rs28399499 (983T>C) loss-of-function allele is essentially absent from European-ancestry populations but reaches appreciable frequency in sub-Saharan African cohorts, so models on European or East Asian populations may report only EM / IM / SM (with CYP2B6_USM identically zero across the dataset). See CYP2B6_IM Notes for the SNP-vector pooling rationale. Ratified canonically on 2026-05-21 alongside the Bienczak 2016 nevirapine extraction.

CYP2B6 (canonical for CYP2B6 individual metabolic-activity score)

• Description: Continuous individual-level CYP2B6 metabolic-activity score. Same intent and documentation policy as the CYP2D6, CYP3A4, and CYP2C19 continuous canonicals above. Typically derived from *-allele diplotype-to-phenotype conversion following CPIC / PharmVar guidance (poor metabolizer -> 0, intermediate -> 0.5, normal / extensive -> 1, rapid -> 1.5, ultra-rapid -> 2), but the per-model notes field documents which mapping the source paper used and the underlying diplotype-to-phenotype table when non-standard. Distinct from the categorical phenotype-indicator canonicals CYP2B6_IM / CYP2B6_SM / CYP2B6_USM (Bienczak 2016 African-paediatric SNP-vector encoding): use the continuous CYP2B6 when the source paper reports a numeric activity score, and the categorical indicators when the source reports discrete phenotype labels.
• Units: Paper-specific – document per-model. Aruldhas 2021 (methadone) uses a 5-level activity score {0, 0.5, 1, 1.5, 2} for PM / IM / NM / RM / UM derived from CPIC efavirenz guideline diplotype assignments (Supplemental Table S1 for the paper’s per-diplotype mapping).
• Type: continuous
• Scope: general
• Reference category: n/a (continuous). Models center on the normal-metabolizer reference (activity score 1); document the reference value per-model. Aruldhas 2021 uses the standard centered-additive form CLF = CLF_ref * (1 + Theta_CYP2B6 * (CYP2B6 - 1)) * ... with CLF_ref reported at activity score 1.
• Source aliases:
  • CYP2B6 activity score – Aruldhas 2021 (paper Methods ‘Covariate models’ and Table S1; used as a linear covariate on the fractional metabolite-formation clearance CLF for both R- and S-methadone).
• Example models: Aruldhas_2021_R_methadone.R, Aruldhas_2021_S_methadone.R (linear covariate on the fractional metabolite-formation clearance CLF: clf <- exp(lclf) * (1 + e_cyp2b6_clf * (CYP2B6 - 1)) * ... with e_cyp2b6_clf = 0.745 for R-methadone and 0.636 for S-methadone).
• Notes: Ratified canonically on 2026-07-24 alongside the Aruldhas 2021 methadone extraction. Fulfils the prospective-registration TODO recorded under CYP3A4 for the drug-metabolizing-CYP set (CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2E1, CYP3A5).

SNP_ORM1_RS17650 (canonical for ORM1 rs17650 active-allele count)

• Description: Continuous individual-level ORM1 rs17650 active-allele count: 0, 1, or 2 copies. The rs17650 SNP distinguishes the “F” (fast-migrating) and “S” (slow-migrating) allozymes of alpha-1 acid glycoprotein (AAG) encoded by ORM1. The variant F-phenotype allele binds methadone with lower affinity than the reference S phenotype (Aruldhas 2021 Discussion). Time-invariant (germline genotype). The paper uses the standard centered-additive form V2 = V2_ref * (1 + Theta_rs17650 * (n_active - 1)) with n_active = number of active alleles at rs17650.
• Units: (count, 0/1/2 alleles per subject)
• Type: continuous
• Scope: specific
• Reference category: n/a (continuous). Aruldhas 2021 centers the covariate at n_active = 1 (heterozygous), so V2_ref is the typical central-compartment volume in a heterozygous subject at the reference AAG concentration.
• Source aliases:
  • rs17650 / number of active alleles rs17650 – Aruldhas 2021 (paper Results ‘Covariate modeling on R methadone’ / ‘Covariate modeling on S methadone’ and Table 2).
• Example models: Aruldhas_2021_R_methadone.R, Aruldhas_2021_S_methadone.R (linear covariate on the central-compartment volume V2: vc <- vc_ref * (1 + e_snp_orm1_rs17650_vc * (SNP_ORM1_RS17650 - 1)) * (1 + e_aag_vc * (AAG - 94.76)); the effect is independent of and additive with the AAG-concentration effect on V2).
• Notes: Aruldhas 2021 reports that the addition of rs17650 to V2 improved the fit even after AAG concentration was already in the model (R-methadone dOFV = -6.5; S-methadone dOFV = -7.2), attributing this to differences in binding affinity between the F- and S- allozymes rather than to changes in AAG concentration. Rs1126801 (a companion SNP that further subdivides the F allele into F1 vs F2) was too rare in the Aruldhas 2021 study population (n = 2 subjects with the variant) to be included as a covariate. Scoped specific because rs17650 has only been reported as a covariate in the Aruldhas 2021 methadone analysis; future paediatric or adult popPK models of AAG-bound basic / lipophilic drugs may promote the scope to general. Ratified canonically on 2026-07-24 alongside the Aruldhas 2021 methadone extraction.

SNP_CYP3A4_RS2246709 (canonical for CYP3A4 rs2246709 active-allele count)

• Description: Continuous individual-level CYP3A4 rs2246709 active-allele count: 0, 1, or 2 copies. rs2246709 is an intronic CYP3A4 variant that Aruldhas 2021 identified as significantly associated with reduced R- and S-methadone fractional clearance to EDDP after backward elimination. Time-invariant (germline genotype). The paper uses the standard centered-additive form CLF = CLF_ref * (1 + Theta_rs2246709 * (n_active - 1)).
• Units: (count, 0/1/2 alleles per subject)
• Type: continuous
• Scope: specific
• Reference category: n/a (continuous). Aruldhas 2021 centers the covariate at n_active = 1 (heterozygous), so CLF_ref is the typical fractional metabolite-formation clearance in a subject with heterozygous rs2246709 and CYP2B6 activity score 1.
• Source aliases:
  • rs2246709 / number of active alleles rs2246709 – Aruldhas 2021 (paper Results ‘Covariate modeling on R methadone’ and Table 2).
• Example models: Aruldhas_2021_R_methadone.R, Aruldhas_2021_S_methadone.R (linear covariate on the fractional metabolite-formation clearance CLF: clf <- clf_ref * (1 + e_cyp2b6_clf * (CYP2B6 - 1)) * (1 + e_snp_cyp3a4_rs2246709_clf * (SNP_CYP3A4_RS2246709 - 1)); e_snp_cyp3a4_rs2246709_clf = 0.450 for R-methadone (RSE 33.9%) and 1.68 for S-methadone (RSE 57.8%, retained as the final S-methadone model in preference to the initial rs11882424-based model because rs11882424 is less commonly genotyped in clinical laboratories)).
• Notes: Note that the very-large S-methadone coefficient (1.68) with wide RSE (57.8%) implies that the paper’s model can produce a physically-invalid (negative) CLF for S-methadone at n_active = 0 (wild-type homozygous) subjects: 1 + 1.68 * (0 - 1) = -0.68. This is a limitation of the paper’s parameter estimates at the extreme of the covariate range, not of the extraction; the vignette Assumptions and deviations section documents that users simulating a wild-type-homozygous rs2246709 subject should either use the alternative rs11882424-based S-methadone model reported in the paper’s Table 2 discussion or accept the paper’s noted limitation. Distinct from the standard SNP_<GENE>_RS<rsid>_<allele>_COUNT naming convention because Aruldhas 2021 does not specify the wild-type vs variant allele letters at rs2246709 in the paper text; the column carries the paper’s “number of active alleles” (which the paper treats as a symmetric additive count without labelling which allele is which). Scoped specific because rs2246709 has only been reported as a covariate in the Aruldhas 2021 methadone analysis. Ratified canonically on 2026-07-24 alongside the Aruldhas 2021 methadone extraction.

CONMED_CYP3A4_INH (canonical for concomitant CYP3A4 inhibitor coadministration indicator)

• Description: 1 = subject coadministered any CYP3A4 inhibitor during the study, 0 = no concomitant CYP3A4 inhibitor. Distinct from the CYP3A4 continuous-activity-score canonical above: CONMED_CYP3A4_INH captures concomitant-medication exposure (a drug-drug-interaction indicator), not intrinsic enzyme activity. Use this canonical when the source paper enters CYP3A4-inhibitor coadministration into the popPK model as a binary indicator, regardless of which inhibitor strengths (strong / moderate / weak) the paper pools into the 1 category.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no CYP3A4 inhibitor coadministration).
• Source aliases:
  • CYP3A4_INH – prior canonical name (pre-2026-06-19 CONMED_ prefix standardization).
  • Other source-dataset column names typically: CYP3AI, CYP3A4I, CYP3AINH, or a free-text concomitant-medication indicator. Document the source-column name per-model in covariateData[[CONMED_CYP3A4_INH]]$source_name.
• Example models: Yassen_2025_asundexian.R (proportional-shift effect on CL/F: (1 + e_cyp3a4_inh_cl * CONMED_CYP3A4_INH) with e_cyp3a4_inh_cl = -0.0531; the asundexian dataset pools weak + moderate CYP3A4 inhibitors into the CONMED_CYP3A4_INH = 1 category because strong inhibitors were a Phase II exclusion criterion).
• Notes: Per-model covariateData[[CONMED_CYP3A4_INH]]$notes must document which inhibitor strengths (strong / moderate / weak) and which specific drug examples are pooled into the CONMED_CYP3A4_INH = 1 category, since inclusion criteria vary by study. Future models that need stratified encoding (separate strong / moderate / weak indicators) should register companion canonicals (e.g. CONMED_CYP3A4_INH_STRONG, CONMED_CYP3A4_INH_MOD, CONMED_CYP3A4_INH_WEAK) rather than overloading CONMED_CYP3A4_INH. The complementary CYP3A4-inducer indicator follows the same pattern as CONMED_CYP3A4_IND. Ratified canonically on 2026-05-08 alongside the Yassen 2025 asundexian extraction. Renamed from CYP3A4_INH to CONMED_CYP3A4_INH on 2026-06-19 per the canonical-register standardization audit (operator decision: the indicator captures a concomitant medication, so it belongs in the CONMED_<concept> family alongside CONMED_PROBENECID, CONMED_AZOLE, etc.).

CONMED_CYP3A4_INH_HI (canonical for concomitant CYP3A4 inhibitor (strong/moderate/weak) coadministration with high (>=50%) cumulative exposure during the on-treatment period)

• Description: 1 = subject coadministered any CYP3A4 inhibitor classified as strong, moderate, or weak (FDA/EMA classification) for at least 50% of the on-study treatment period; 0 = otherwise. Companion canonical to CONMED_CYP3A4_INH that stratifies by cumulative exposure intensity (rather than by inhibitor strength). Distinct from CONMED_CYP3A4_INH_LO which captures the complementary “lower-exposure / unclassified” category. The two indicators are mutually exclusive per subject (a subject is either in CONMED_CYP3A4_INH_HI = 1, CONMED_CYP3A4_INH_LO = 1, or both = 0); when both are 0 the subject was either never on a CYP3A4 inhibitor or was on one only briefly.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no high-cumulative-exposure CYP3A4 inhibitor coadministration; subject may still have CONMED_CYP3A4_INH_LO = 1).
• Source aliases:
  • CYPINH = 4 (strong, >=50%) or CYPINH = 6 (moderate, >=50%) or CYPINH = 8 (weak, >=50%) in the FIDELIO-DKD popPK NONMEM control stream’s 9-level CYPINH categorical variable; used in vandenBerg_2021_finerenone.R.
• Example models: vandenBerg_2021_finerenone.R (multiplicative effect 0.951^CONMED_CYP3A4_INH_HI on CL/F AND on F1 inversely, per paper’s NONMEM control stream).
• Notes: Companion canonical to the broader CONMED_CYP3A4_INH (any inhibitor coadministration, no exposure-intensity stratification). The pooled strong/moderate/weak grouping at the >=50% exposure threshold matches the FIDELIO-DKD analysis’s covariate-engineering choice (the study did not separate strong from moderate from weak inhibitors but did separate high-cumulative-exposure from lower-exposure). The accompanying inhibitor-strength-stratified canonicals CONMED_CYP3A4_INH_STRONG / CONMED_CYP3A4_INH_MOD / CONMED_CYP3A4_INH_WEAK mentioned in the CONMED_CYP3A4_INH Notes follow a different stratification axis (by strength, ignoring cumulative duration) and would coexist with the HI / LO duration-based pair without conflict.

CONMED_CYP3A4_INH_LO (canonical for concomitant CYP3A4 inhibitor coadministration in ‘other’ lower-exposure or unclassified category)

• Description: 1 = subject coadministered a CYP3A4 inhibitor in any of the following sub-categories: (a) unclassified inhibitor (strength not assignable to the strong / moderate / weak FDA/EMA tiers) at any duration, OR (b) strong / moderate / weak inhibitor present for LESS THAN 50% of the on-study treatment period; 0 = otherwise. Companion canonical to CONMED_CYP3A4_INH_HI that captures all CYP3A4-inhibitor exposure other than the high-cumulative-exposure-and-known-strength category. The two indicators are mutually exclusive per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no lower-exposure / unclassified CYP3A4 inhibitor coadministration; subject may still have CONMED_CYP3A4_INH_HI = 1).
• Source aliases:
  • CYPINH IN (1, 2, 3, 5, 7) in the FIDELIO-DKD popPK NONMEM control stream’s 9-level CYPINH categorical variable – corresponding to unclassified <50% (1), unclassified >=50% (2), strong <50% (3), moderate <50% (5), weak <50% (7). Used in vandenBerg_2021_finerenone.R.
• Example models: vandenBerg_2021_finerenone.R (multiplicative effect 0.996^CONMED_CYP3A4_INH_LO on CL/F AND on F1 inversely, per paper’s NONMEM control stream).
• Notes: Companion to CONMED_CYP3A4_INH_HI. The unclassified-inhibitor category pools any concomitant medication the source dataset’s medication-classification logic could not assign to a strong / moderate / weak tier (typically due to ambiguous drug-coding); these subjects would otherwise be lost to analysis if the encoding required a strength tier. Combining unclassified + <50%-exposure into a single “lower-impact” indicator was the FIDELIO-DKD analysis’s choice; the effect size (0.996) is much smaller than the high-exposure category (0.951).

CONMED_PGP_INH (canonical for concomitant P-glycoprotein inhibitor coadministration indicator)

• Description: 1 = subject coadministered a P-glycoprotein (P-gp / MDR1 / ABCB1) efflux-transporter inhibitor during the study, 0 = no concomitant P-gp inhibitor. P-gp inhibition raises the systemic exposure of P-gp substrates by reducing intestinal efflux (increasing oral bioavailability) and by reducing biliary and renal-tubular secretion (reducing apparent clearance). Use this canonical when the source paper enters P-gp-inhibitor coadministration into the popPK model as a binary indicator, regardless of which inhibitor potencies (potent / strong / moderate / weak) the paper pools into the 1 category.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no P-glycoprotein-inhibitor coadministration).
• Source aliases:
  • DDICYPSH – used in Mitra_2026_ziftomenib.R (Kura Oncology KOMET-001 + KO-MEN-003 NONMEM control-stream column; strong CYP3A4 inhibitor coadministration during a ziftomenib dose record; dominant driver in the R/R AML cohort is prophylactic antifungal azole use).
• Example models: Mitra_2026_ziftomenib.R (multiplicative effects: 0.459x on parent ziftomenib CL/F, 0.195x on KO-739 CL, 0.449x on KO-516 CL when CONMED_CYP3A4_INH_STRONG = 1; encoded as additive shifts on the log(CL) scale per the paper’s NONMEM PK block), Kemal_2026_nemtabrutinib.R (multiplicative effect on CL/F: (1 + -0.0119 * CONMED_CYP3A4_INH_STRONG), i.e. 1.2% lower CL/F under strong CYP3A4 inhibition; RSE 774%, 95% CI includes zero – retained in the full covariate model), Wada_2023_sparsentan.R (log-additive effect on CL/F: exp(-1.069 * CONMED_CYP3A4_INH_STRONG), a 66% reduction in apparent clearance and a 191.3% increase in steady-state AUC – the largest covariate effect in the model and the only one for which the paper suggests a dose adjustment may be warranted; paired with the sibling new canonical CONMED_CYP3A4_INH_MOD at -0.273), Thoueille_2023_tenofovir_full.R, Thoueille_2023_tenofovir_alafenamide.R (multiplicative fractional effect on apparent tenofovir clearance: (1 + e_conmed_pgp_inh_cl * CONMED_PGP_INH) with e_conmed_pgp_inh_cl = -0.121 and -0.116 respectively, i.e. ~12% lower CL/F, an effect the paper reports as independent of cobicistat coadministration).
• Notes: Companion canonical to CONMED_CYP3A4_INH (which pools any inhibitor strength into a single 0/1 indicator) and to CONMED_CYP3A4_INH_HI / CONMED_CYP3A4_INH_LO (which stratify by cumulative exposure duration rather than by inhibitor-strength category). Registered per the CONMED_CYP3A4_INH Notes explicit guidance: “Future models that need stratified encoding (separate strong / moderate / weak indicators) should register companion canonicals (e.g. CONMED_CYP3A4_INH_STRONG, CONMED_CYP3A4_INH_MOD, CONMED_CYP3A4_INH_WEAK) rather than overloading CONMED_CYP3A4_INH.” Ratified canonically on 2026-07-24 alongside the Mitra 2026 ziftomenib extraction. Future extractions that need the sibling moderate- and weak-strength indicators should register CONMED_CYP3A4_INH_MOD and CONMED_CYP3A4_INH_WEAK following the same pattern.

CONMED_CYP3A4_INH_MOD (canonical for concomitant moderate CYP3A4 inhibitor coadministration indicator)

• Description: 1 = subject / dose-record with concomitant coadministration of a moderate CYP3A4 inhibitor (FDA/EMA classification of “moderate”: AUC increase >= 2-fold and < 5-fold for a sensitive CYP3A4 substrate; representative agents include cyclosporine, erythromycin, diltiazem, verapamil, fluconazole, and grapefruit juice), 0 = no moderate CYP3A4 inhibitor coadministration during the observation window (whether no CYP3A4 inhibitor at all, or a weak or strong inhibitor only). Time-varying per record. Strength-stratified companion to the broader CONMED_CYP3A4_INH and sibling to CONMED_CYP3A4_INH_STRONG; distinct from CONMED_CYP3A4_INH_HI / CONMED_CYP3A4_INH_LO, which stratify by cumulative exposure duration rather than by inhibitor-strength tier.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no moderate CYP3A4 inhibitor coadministration; a subject on a weak or strong inhibitor, or on none, falls here when using this indicator alone). When paired with CONMED_CYP3A4_INH_STRONG, both = 0 selects the pooled “no moderate or strong inhibitor” reference.
• Source aliases:
  • CYP3A4 inhibitor three- or four-level categorical column (none / weak / moderate / strong) – the moderate level maps to CONMED_CYP3A4_INH_MOD = 1. Used in Wada_2023_sparsentan.R, where the source dataset’s CYP3A4 inhibitor column has levels none / weak / moderate / strong and the paper estimates separate coefficients for the moderate and strong levels only, leaving weak pooled into the reference.
• Example models: Wada_2023_sparsentan.R (log-additive effect on CL/F: exp(-0.273 * CONMED_CYP3A4_INH_MOD), i.e. 24% lower CL/F under moderate CYP3A4 inhibition, which the paper translates to a 31.4% increase in steady-state AUC and a 16.0% increase in Cmax; paired with CONMED_CYP3A4_INH_STRONG at -1.069; cohort exposure 10.5% moderate).
• Notes: Registered per the explicit forward guidance in the CONMED_CYP3A4_INH and CONMED_CYP3A4_INH_STRONG register entries (“Future extractions that need the sibling moderate- and weak-strength indicators should register CONMED_CYP3A4_INH_MOD and CONMED_CYP3A4_INH_WEAK following the same pattern”). Per-model covariateData[[CONMED_CYP3A4_INH_MOD]]$notes must document which specific agents the source paper classified as moderate and what happens to the weak stratum (pooled into the reference, or carried in a separate CONMED_CYP3A4_INH_WEAK indicator). The remaining sibling CONMED_CYP3A4_INH_WEAK is still reserved and unregistered. Ratified canonically alongside the Wada 2023 sparsentan extraction.

CONMED_CYP3A4_IND (canonical for concomitant CYP3A4 inducer coadministration indicator)

• Description: 1 = subject coadministered any CYP3A4 inducer during the study, 0 = no concomitant CYP3A4 inducer. Sibling indicator to CONMED_CYP3A4_INH; both capture concomitant-medication exposure (a drug-drug-interaction indicator), not intrinsic enzyme activity (distinct from the CYP3A4 continuous-activity-score canonical above). Use this canonical when the source paper enters CYP3A4-inducer coadministration into the popPK model as a binary indicator, regardless of which inducer strengths (strong / moderate / weak) the paper pools into the 1 category.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no CYP3A4 inducer coadministration).
• Source aliases:
  • CYP3A4_IND – prior canonical name (pre-2026-06-19 CONMED_ prefix standardization).
  • Other source-dataset column names typically: CYP3AIND, CYP3A4IND, INDU, INDUCER, or a free-text concomitant-medication indicator. Document the source-column name per-model in covariateData[[CONMED_CYP3A4_IND]]$source_name.
• Example models: Gupta_2016_lenvatinib.R (multiplicative power-form effect on CL/F: 1.30^CONMED_CYP3A4_IND with e_cyp3a4_ind_cl = log(1.30) ~ 0.262; the Gupta dataset pools any concomitant CYP3A4 inducer reported in the per-subject medication log into the CONMED_CYP3A4_IND = 1 category, with n = 19 (2.4%) of the 779-subject pooled cohort flagged positive).
• Notes: Per-model covariateData[[CONMED_CYP3A4_IND]]$notes must document which inducer strengths (strong / moderate / weak) and which specific drug examples are pooled into the CONMED_CYP3A4_IND = 1 category, since inclusion criteria vary by study. Future models that need stratified encoding (separate strong / moderate / weak indicators) should register companion canonicals (e.g. CONMED_CYP3A4_IND_STRONG, CONMED_CYP3A4_IND_MOD, CONMED_CYP3A4_IND_WEAK) rather than overloading CONMED_CYP3A4_IND. Sibling canonical to CONMED_CYP3A4_INH. Ratified canonically alongside the Gupta 2016 lenvatinib extraction. Renamed from CYP3A4_IND to CONMED_CYP3A4_IND on 2026-06-19 per the canonical-register standardization audit (operator decision: the indicator captures a concomitant medication, so it belongs in the CONMED_<concept> family).

CONMED_CYP3A4_IND_MOD (canonical for concomitant moderate CYP3A4 inducer coadministration indicator)

• Description: 1 = subject coadministered a moderate-strength CYP3A4 inducer (FDA / EMA classification) at the observation, 0 = no concomitant moderate CYP3A4 inducer. Strength-stratified companion to CONMED_CYP3A4_IND (pooled any-inducer indicator) and to the sibling canonicals CONMED_CYP3A4_IND_STRONG / CONMED_CYP3A4_IND_WEAK (reserved for future extractions using the same stratification convention). Use this canonical when the source paper enters moderate-strength CYP3A4 inducers as an isolated indicator rather than pooling them with strong or weak inducers.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant moderate CYP3A4 inducer; subjects on strong or weak inducers, or on no inducer, all fall here when using this indicator alone).
• Source aliases:
  • C3A4INDM – used in Kemal_2026_nemtabrutinib.R (Kemal 2026 NONMEM control stream; time-varying regressor 1 = moderate CYP3A4 inducer at the observation, 0 otherwise. In the Kemal 2026 cohort no subjects received a strong inducer, so the source dataset enters only the moderate-inducer stratum into the model).
• Example models: Kemal_2026_nemtabrutinib.R (multiplicative effect on CL/F: (1 + 0.0220 * CONMED_CYP3A4_IND_MOD), i.e. 2.2% higher CL/F under moderate CYP3A4 induction; RSE 458%, 95% CI includes zero – retained in the full covariate model).
• Notes: Companion to the pooled CONMED_CYP3A4_IND (any inducer, no strength stratification). Per-model covariateData[[CONMED_CYP3A4_IND_MOD]]$notes should document which specific inducers the source paper classified as moderate (typically per FDA guidance: efavirenz, bosentan, etravirine, phenobarbital, modafinil, dabrafenib) and the fraction of the cohort exposed. Sibling canonicals CONMED_CYP3A4_IND_STRONG and CONMED_CYP3A4_IND_WEAK are reserved for parallel future extractions and are named in the CONMED_CYP3A4_IND register entry.

APOE4_COUNT (canonical for APOE-epsilon4 allele count)

• Description: Continuous individual-level APOE-epsilon4 allele count: 0 = non-carrier, 1 = heterozygous (one epsilon4 allele), 2 = homozygous (two epsilon4 alleles). Time-invariant (germline genotype). Models in the Alzheimer’s-disease-progression literature treat the 0 / 1 / 2 count as a continuous effect on baseline cognitive score and / or disease-progression slope, with the population-mean carrier-allele count used as the centring value (e.g., 0.72 in the Conrado 2014 CAMD cohort).
• Units: (count, 0 / 1 / 2 alleles per subject; population-mean centring value documented per-model)
• Type: continuous
• Scope: general
• Reference category: n/a (continuous). Models centre on the dataset / population mean APOE-epsilon4 count; document the centring value per-model in covariateData[[APOE4_COUNT]]$notes.
• Source aliases:
  • APOE4C – used directly in Conrado_2014_alzheimer.R. The “C” suffix in the source distinguishes the cleaned continuous APOE-epsilon4 count column (0 / 1 / 2 with unknown recoded to the population mean) from the upstream raw APOE4 column (0 = non-carrier, 1 = heterozygous, 2 = homozygous, 3 = unknown).
• Example models: Conrado_2014_alzheimer.R (centring 0.72; multiplicative effect on baseline ADAS-Cog and on disease-progression slope: factor = 1 + e * (APOE4_COUNT - 0.72) with e_blapoe4 = 0.0372 on baseline and e_slapoe4 = 0.195 on slope).
• Notes: APOE-epsilon4 carrier status is the strongest established genetic risk factor for late-onset Alzheimer’s disease; the allele-count form (rather than a binary carrier indicator) is preferred when the source paper distinguishes heterozygous from homozygous carriers. Future models that report only a binary carrier indicator (any-epsilon4 vs none) should register a separate canonical (APOE4_CARRIER) rather than overloading APOE4_COUNT. The unknown category (often recorded as APOE4 = 3 in CDISC datasets) is conventionally recoded by the source paper to the population-mean count to avoid dropping subjects; document the recoding rule used per-model. Ratified canonically on 2026-05-06 alongside the Conrado 2014 DDMORE extraction.

NAT2_SLOW (canonical for NAT2 slow-acetylator phenotype indicator)

• Description: 1 = subject is an NAT2 (arylamine N-acetyltransferase 2) slow acetylator, defined by carrying two reduced-function NAT2 SNP alleles (homozygous variant for one or more of the canonical slow-acetylator SNPs rs1801279, rs1801280, rs1799930, rs1799931, OR heterozygous for two or more of those SNPs); 0 = subject is an intermediate or rapid acetylator (heterozygous for at most one of the canonical SNPs, or wild-type homozygous for all). Time-fixed per subject (germline genotype-derived phenotype). The intermediate and rapid (fast) phenotypes are pooled into the 0 category because the Horita 2018 source paper found no significant differences in t1/2, CL/F, or AUC0-8 between rapid and intermediate genotypes and combined them as the “nonslow” group; this pooling is the standard convention in the NAT2-aware antituberculosis-isoniazid popPK literature.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (nonslow: intermediate or rapid acetylator pooled). Reference category in the source-paper IIV model is the SLOW group; the canonical-orientation convention here is NAT2_SLOW = 1 for slow, consistent with the _SLOW naming, and the source-paper-reported coefficient signs map back to typical-value parameters via (1 - NAT2_SLOW) selection inside model() where needed.
• Source aliases:
  • NAT2 (categorical with values "slow" / "intermediate" / "rapid" or 0 / 1 / 2): derive NAT2_SLOW = as.integer(NAT2 == "slow") (or as.integer(NAT2 == 0) depending on the source’s level coding); the intermediate and rapid levels collapse to NAT2_SLOW = 0.
  • NAT2_SS (slow-vs-not-slow indicator already in source datasets) – same orientation as the canonical, no transformation.
  • ACETYL_SLOW (slow-acetylator indicator) – same orientation as the canonical, no transformation.
• Example models: Horita_2018_isoniazid.R (selects between two typical-value clearances via lcl_slow * NAT2_SLOW + lcl_nonslow * (1 - NAT2_SLOW) and pairs each typical value with its own IIV variance; reproduces the source paper’s separate CL/F slow = 4.44 L/h and CL/F nonslow = 8.08 L/h typical-value estimates with separate omegas 0.105 and 0.230 respectively).
• Notes: The NAT2 (rs1208 / rs1041983 / rs1801279 / rs1801280 / rs1799929 / rs1799930 / rs1799931 / rs1208) gene encodes the cytosolic arylamine N-acetyltransferase 2 enzyme responsible for the major isoniazid metabolic pathway (acetylation to acetyl-isoniazid); slow acetylators have substantially reduced isoniazid clearance, higher Cmax, and higher AUC than intermediate or rapid acetylators, with documented impact on both efficacy (treatment failure in rapid acetylators given standard doses) and toxicity (hepatotoxicity in slow acetylators given high doses). The slow / intermediate / rapid trimodal phenotype is conventionally collapsed to slow vs nonslow in popPK models when the cohort lacks enough rapid acetylators to identify a distinct rapid typical value, OR when the rapid and intermediate phenotypes are statistically indistinguishable in the data (Horita 2018 cohort: 51 slow / 50 intermediate / 12 fast). Future papers that distinguish rapid from intermediate (separately from slow) should register a paired companion canonical (NAT2_RAPID) so the three-level phenotype can be encoded with two binary indicators on the SLCO1B1_HAP15_HET / SLCO1B1_HAP15_HOM and CYP3A5_STAR1_HET / CYP3A5_STAR1_HOM patterns. The _SLOW orientation (slow = 1) follows the clinically meaningful axis (slow acetylators are the at-risk group for isoniazid hepatotoxicity and the higher-AUC group for treatment outcomes), paralleling the CYP2D6_PM = 1 orientation for the poor-metabolizer end of the CYP2D6 phenotype spectrum. Distinct from any genotype-string column (which carries the raw allele information); NAT2_SLOW captures the derived metabolic phenotype only. Ratified canonically on 2026-05-26 alongside the Horita 2018 isoniazid extraction.

FCGR3A_VV (canonical for FCGR3A 158 V/V homozygote indicator)

• Description: 1 = subject is homozygous for valine at amino-acid position 158 of the FcgammaRIIIa receptor (V/V), encoded by the rs396991 polymorphism in the FCGR3A gene; 0 = otherwise (heterozygote V/F or homozygote F/F pooled). The dominant V/V vs (V/F + F/F) grouping is the encoding used in the Aguiar 2021 source paper after testing dominant and recessive groupings during covariate model building.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 = V/F heterozygote or F/F homozygote (combined).
• Source aliases:
  • FCGR3A (genotype string, e.g., "V/V" / "V/F" / "F/F"): derive FCGR3A_VV = as.integer(FCGR3A == "V/V").
  • rs396991 (raw allele coding, often "AA" / "AC" / "CC" or "GG" / "GT" / "TT" depending on assay strand): map V allele -> 1, F allele -> 0 with the assay-specific allele convention; derive FCGR3A_VV = as.integer(genotype is V-homozygous).
• Example models: Aguiar_2021_ustekinumab.R (Aguiar 2021 Table 2 footnote; logit-scale effect on subcutaneous bioavailability F: 88.8% in V/V vs 71.0% in V/F + F/F).
• Notes: rs396991 (FCGR3A 158V>F) is a well-studied pharmacogenetic polymorphism affecting FcgammaRIIIa-IgG affinity and has been associated with response to several IgG monoclonal antibodies (rituximab, infliximab, ustekinumab). The V allele is the higher-affinity variant. Document the assay-strand allele convention used in the source paper in covariateData[[FCGR3A_VV]]$notes. Future models that use a recessive (F/F vs V/* combined) or codominant (additive 0/1/2) coding should register a separate canonical (e.g., FCGR3A_FF, FCGR3A_VCT for V-allele count) rather than overloading FCGR3A_VV.

DSBAL_TT (canonical for DsbA-L (GSTK1) rs1917760 T/T genotype indicator)

• Description: Indicator for the DsbA-L (GSTK1) rs1917760 -1308G>T T/T genotype; 1 = subject carries the T/T genotype, 0 = subject carries the G/G or G/T genotype (the pooled reference). Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (G/G or G/T, pooled).
• Source aliases:
  • DsbAL (three-level column 0 = G/G, 1 = G/T, 2 = T/T) – used in Oniki_2018_bmi.R; derive DSBAL_TT = as.integer(DsbAL == 2). G/G and G/T are pooled because the T/T state is the functional minor-allele genotype most strongly associated with elevated BMI.
• Example models: Oniki_2018_bmi.R (additive +1.5 kg/m^2 shift on the typical BMI for T/T carriers vs the pooled G/G-or-G/T reference, Oniki 2018 Eq. 1).
• Notes: General scope because the genotype is a stable germline marker; the GSTK1 (DsbA-L) rs1917760 polymorphism has been associated with adiposity / metabolic phenotypes. Pools the G/G homozygous and G/T heterozygous strata into the reference following the dominant-for-the-minor-allele encoding used by Oniki 2018; future extractions that resolve a separate G/T heterozygote effect should register a paired DSBAL_GT indicator on the SLCO1B1_HAP15_HET / SLCO1B1_HAP15_HOM precedent. Ratified canonically alongside the Oniki 2018 BMI extraction.

PNPLA3_CG (canonical for PNPLA3 rs738409 C/G heterozygote indicator)

• Description: Indicator for the PNPLA3 rs738409 c.444C>G (I148M) C/G heterozygote genotype; 1 = subject carries the C/G genotype, 0 = subject does not carry the C/G genotype. Paired with PNPLA3_GG to encode the three-level rs738409 genotype with two binary indicators (C/C is the reference when both are 0). Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (C/C wild-type, when PNPLA3_GG is also 0).
• Source aliases:
  • PNPLA3 (three-level column 0 = C/C, 1 = C/G, 2 = G/G) – used in Oniki_2018_nafld_risk.R; derive PNPLA3_CG = as.integer(PNPLA3 == 1).
• Example models: Oniki_2018_nafld_risk.R (multiplicative factor 0.761 on the (BMI50 - 17) half-saturation offset of the logit-of-NAFLD sigmoid for C/G heterozygotes vs the C/C reference, Oniki 2018 Eq. 4 / Figure 2c; closer to 1 than the G/G factor, consistent with an additive allele-dose effect).
• Notes: General scope because the genotype is a stable germline marker; PNPLA3 rs738409 (I148M) is the best-established common genetic risk variant for non-alcoholic fatty liver disease (NAFLD). Paired with PNPLA3_GG (homozygote) following the SLCO1B1_HAP15_HET / SLCO1B1_HAP15_HOM paired-binary precedent for a three-level genotype where each stratum carries a distinct typical-value effect. Ratified canonically alongside the Oniki 2018 NAFLD-risk extraction.

PNPLA3_GG (canonical for PNPLA3 rs738409 G/G homozygote indicator)

• Description: Indicator for the PNPLA3 rs738409 c.444C>G (I148M) G/G homozygote genotype; 1 = subject carries the G/G genotype, 0 = subject does not carry the G/G genotype. Paired with PNPLA3_CG to encode the three-level rs738409 genotype with two binary indicators (C/C is the reference when both are 0). Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (C/C wild-type, when PNPLA3_CG is also 0).
• Source aliases:
  • PNPLA3 (three-level column 0 = C/C, 1 = C/G, 2 = G/G) – used in Oniki_2018_nafld_risk.R; derive PNPLA3_GG = as.integer(PNPLA3 == 2).
• Example models: Oniki_2018_nafld_risk.R (multiplicative factor 0.592 on the (BMI50 - 17) half-saturation offset of the logit-of-NAFLD sigmoid for G/G homozygotes vs the C/C reference, Oniki 2018 Eq. 4 / Figure 2c).
• Notes: General scope. Companion homozygote indicator to PNPLA3_CG; see PNPLA3_CG notes for the joint three-level usage and reference category. Ratified canonically alongside the Oniki 2018 NAFLD-risk extraction.

UGT2B7_211GG (**canonical for UGT2B7 211G>T (rs7438135 / UGT2B7*2 Ala71Ser) homozygous G/G genotype indicator**)

• Description: 1 = subject is homozygous for the wild-type (ancestral) guanine at nucleotide 211 of the UGT2B7 gene, corresponding to alanine at amino-acid position 71 (Ala71/Ala71) in the substrate-binding N-terminal half of the UGT2B7 enzyme; 0 = otherwise (211GT heterozygote or 211TT homozygote). Encoded by the UGT2B7 211G>T single-nucleotide polymorphism (rs7438135, also referred to as the UGT2B7*2 variant). Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (any non-G/G genotype: 211GT or 211TT).
• Source aliases:
  • UGT2B7 211GG / UGT2B7-211GG / 211GG – used in Yu_2017_mycophenolic_acid.R (paper Table 5 column headers; one of three binary group-membership indicators reconstructed from the paper’s ordinal column “UGT2B7 genotype”).
• Example models: Yu_2017_mycophenolic_acid.R (one of three UGT2B7-211 binary indicators that reconstruct the paper’s ordinal column UGT2B7 genotype in {GT=1, GG=2, TT=3}; the model() block computes ugt2b7_211_code = UGT2B7_211GT * 1 + UGT2B7_211GG * 2 + UGT2B7_211TT * 3 and applies V1/F = 14.7 + 7.72 * ugt2b7_211_code per Yu 2017 Table 3 / page 1574 final-model formula).
• Notes: rs7438135 (UGT2B7 c.211G>T, p.Ala71Ser) is a coding-region SNP in the UGT2B7 N-terminal substrate-binding half; the T (Ser71) allele is the variant. Allele frequencies vary by ethnicity (the T allele is most common in Asian populations in Yu 2017’s Chinese renal-transplant cohort: 51/101 GT + 8/101 TT = 59/101 PK evaluations carrying at least one T allele). Future papers that use a dominant (any-G vs T/T) or recessive (G/G vs any-T) grouping should still register the same three binary canonicals UGT2B7_211GG / UGT2B7_211GT / UGT2B7_211TT in the input data so that downstream simulations can reconstruct the paper’s chosen grouping. Distinct from UGT1A9 promoter / 5’-UTR variants (Yu 2017 also genotyped UGT1A9*22 but did not find it significant; no UGT1A9 canonical is being proposed here). Ratified canonically on 2026-06-03 alongside the Yu 2017 mycophenolic acid extraction.

UGT2B7_211GT (**canonical for UGT2B7 211G>T (rs7438135 / UGT2B7*2 Ala71Ser) heterozygous G/T genotype indicator**)

• Description: 1 = subject is heterozygous at nucleotide 211 of the UGT2B7 gene, carrying one wild-type G allele (Ala71) and one variant T allele (Ser71); 0 = otherwise (211GG homozygote or 211TT homozygote). Encoded by the UGT2B7 211G>T single-nucleotide polymorphism (rs7438135, also referred to as the UGT2B7*2 variant). Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (any non-G/T genotype: 211GG or 211TT).
• Source aliases:
  • UGT2B7 211GT / UGT2B7-211GT / 211GT – used in Yu_2017_mycophenolic_acid.R (paper Table 5).
• Example models: Yu_2017_mycophenolic_acid.R (one of three UGT2B7-211 binary indicators; see UGT2B7_211GG notes for the paper’s ordinal-code reconstruction).
• Notes: Companion canonical to UGT2B7_211GG and UGT2B7_211TT; see UGT2B7_211GG notes for variant biology and allele-frequency context. Ratified canonically on 2026-06-03 alongside the Yu 2017 mycophenolic acid extraction.

UGT2B7_211TT (**canonical for UGT2B7 211G>T (rs7438135 / UGT2B7*2 Ala71Ser) homozygous T/T genotype indicator**)

• Description: 1 = subject is homozygous for the variant thymine at nucleotide 211 of the UGT2B7 gene, corresponding to serine at amino-acid position 71 (Ser71/Ser71); 0 = otherwise (211GG homozygote or 211GT heterozygote). Encoded by the UGT2B7 211G>T single-nucleotide polymorphism (rs7438135, UGT2B7*2 variant). Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (any non-T/T genotype: 211GG or 211GT).
• Source aliases:
  • UGT2B7 211TT / UGT2B7-211TT / 211TT – used in Yu_2017_mycophenolic_acid.R (paper Table 5).
• Example models: Yu_2017_mycophenolic_acid.R (one of three UGT2B7-211 binary indicators; see UGT2B7_211GG notes for the paper’s ordinal-code reconstruction).
• Notes: Companion canonical to UGT2B7_211GG and UGT2B7_211GT; see UGT2B7_211GG notes for variant biology and allele-frequency context. Ratified canonically on 2026-06-03 alongside the Yu 2017 mycophenolic acid extraction.

UGT2B7_M161CC (canonical for UGT2B7 -161C>T (rs7668258) homozygous C/C genotype indicator)

• Description: 1 = subject is homozygous for the ancestral cytosine at nucleotide -161 of the UGT2B7 gene promoter (position numbered relative to the transcription start site; the leading M in the canonical name stands for minus); 0 = otherwise (M161CT heterozygote or M161TT homozygote). Encoded by the UGT2B7 -161C>T single-nucleotide polymorphism (rs7668258), a promoter-region variant in strong linkage disequilibrium with UGT2B7*2 (211G>T) in most populations. Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (any non-C/C genotype: M161CT or M161TT).
• Source aliases:
  • UGT2B7 -161CC / UGT2B7 -161 C/C / -161CC – used in Milosheska_2016_lamotrigine.R (paper Table 4 covariate equation reference stratum, listed as CC in the paper’s Table 2 genotype-frequency column; the canonical prefixes M for minus because R identifiers cannot carry a leading hyphen).
• Example models: Milosheska_2016_lamotrigine.R (reference stratum for the three-indicator UGT2B7 -161C>T genotype categorical on parent lamotrigine apparent clearance; the two paired non-reference indicators are UGT2B7_M161CT and UGT2B7_M161TT).
• Notes: Distinct from the coding-region UGT2B7*2 variant registered as UGT2B7_211GG / GT / TT (rs7438135, Ala71Ser). The -161C>T promoter SNP is in strong linkage disequilibrium with 211G>T in European populations; carrying both position-level canonicals lets downstream models distinguish papers that genotype only one position from those that genotype both. Position notation follows the paper’s convention (c.-161C>T) with the leading minus rewritten as the letter M for R-identifier compatibility, paralleling the ATC-style naming of promoter SNPs already registered under SNP_VEGFA_RS1570360 (VEGFA -1154G>A) and SNP_VEGFA_RS699947 (VEGFA -2578C>A). Ratified canonically on 2026-06-20 alongside the Milosheska 2016 lamotrigine extraction.

UGT2B7_M161CT (canonical for UGT2B7 -161C>T (rs7668258) heterozygous C/T genotype indicator)

• Description: 1 = subject is heterozygous at nucleotide -161 of the UGT2B7 gene promoter, carrying one wild-type C allele and one variant T allele; 0 = otherwise (M161CC homozygote or M161TT homozygote). Encoded by the UGT2B7 -161C>T single-nucleotide polymorphism (rs7668258). Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (any non-C/T genotype: M161CC or M161TT). The reference used in effect estimation is the M161CC homozygous wild-type stratum, encoded when all three UGT2B7_M161* indicators equal 0.
• Source aliases:
  • UGT2B7 -161CT / UGT2B7 -161 C/T / -161CT – used in Milosheska_2016_lamotrigine.R (paper Table 4 covariate equation UGT2B7 -161CT vs CC row).
• Example models: Milosheska_2016_lamotrigine.R (multiplicative effect on parent apparent clearance: cl *= (1 - 0.0358 * UGT2B7_M161CT); heterozygotes have -3.6% lower CL relative to the CC homozygous wild-type reference, Milosheska 2016 Table 4 row UGT2B7 -161C>T genotype CT vs CC).
• Notes: Companion canonical to UGT2B7_M161CC and UGT2B7_M161TT. See UGT2B7_M161CC for variant biology and the M-for-minus naming rationale. Ratified canonically on 2026-06-20 alongside the Milosheska 2016 lamotrigine extraction.

UGT2B7_M161TT (canonical for UGT2B7 -161C>T (rs7668258) homozygous T/T genotype indicator)

• Description: 1 = subject is homozygous for the variant thymine at nucleotide -161 of the UGT2B7 gene promoter; 0 = otherwise (M161CC homozygote or M161CT heterozygote). Encoded by the UGT2B7 -161C>T single-nucleotide polymorphism (rs7668258). Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (any non-T/T genotype: M161CC or M161CT). The reference used in effect estimation is the M161CC homozygous wild-type stratum, encoded when all three UGT2B7_M161* indicators equal 0.
• Source aliases:
  • UGT2B7 -161TT / UGT2B7 -161 T/T / -161TT – used in Milosheska_2016_lamotrigine.R (paper Table 4 covariate equation UGT2B7 -161TT vs CC row).
• Example models: Milosheska_2016_lamotrigine.R (multiplicative effect on parent apparent clearance: cl *= (1 - 0.204 * UGT2B7_M161TT); homozygous variant carriers have -20.4% lower CL relative to the CC homozygous wild-type reference, Milosheska 2016 Table 4 row UGT2B7 -161C>T genotype TT vs CC).
• Notes: Companion canonical to UGT2B7_M161CC and UGT2B7_M161CT. Reduced UGT2B7 transcriptional activity attributable to the -161 T allele is one of the biological interpretations offered by Milosheska 2016 Discussion paragraph 5 for the lower lamotrigine glucuronidation observed in TT carriers. Ratified canonically on 2026-06-20 alongside the Milosheska 2016 lamotrigine extraction.

UGT2B7_372AA (canonical for UGT2B7 372A>G (rs28365063) homozygous A/A genotype indicator)

• Description: 1 = subject is homozygous for the ancestral adenine at nucleotide 372 of the UGT2B7 coding sequence (synonymous variant, His124His); 0 = otherwise (372AG heterozygote or 372GG homozygote). Encoded by the UGT2B7 372A>G single-nucleotide polymorphism (rs28365063). Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (any non-A/A genotype: 372AG or 372GG).
• Source aliases:
  • UGT2B7 372AA / UGT2B7 372 A/A / 372AA – used in Milosheska_2016_lamotrigine.R (paper Table 4 covariate equation reference stratum, listed as AA in the paper’s Table 2 genotype-frequency column).
• Example models: Milosheska_2016_lamotrigine.R (reference stratum for the three-indicator UGT2B7 372A>G genotype categorical on parent lamotrigine apparent clearance; the two paired non-reference indicators are UGT2B7_372AG and UGT2B7_372GG).
• Notes: Distinct from the -161C>T promoter variant (UGT2B7_M161*) and the 211G>T coding variant (UGT2B7_211*). The 372A>G variant is a synonymous coding change but Milosheska 2016 identify a strong effect on parent lamotrigine apparent clearance (GG homozygotes have +117% higher CL vs AA reference), attributed in the Discussion to linkage with a functionally consequential 3’ UTR variant. Following the position-based UGT2B7_<position><genotype> register precedent from UGT2B7_211GG / GT / TT. Ratified canonically on 2026-06-20 alongside the Milosheska 2016 lamotrigine extraction.

UGT2B7_372AG (canonical for UGT2B7 372A>G (rs28365063) heterozygous A/G genotype indicator)

• Description: 1 = subject is heterozygous at nucleotide 372 of the UGT2B7 coding sequence, carrying one wild-type A allele and one variant G allele; 0 = otherwise (372AA homozygote or 372GG homozygote). Encoded by the UGT2B7 372A>G single-nucleotide polymorphism (rs28365063). Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (any non-A/G genotype: 372AA or 372GG). The reference used in effect estimation is the 372AA homozygous wild-type stratum, encoded when all three UGT2B7_372* indicators equal 0.
• Source aliases:
  • UGT2B7 372AG / UGT2B7 372 A/G / 372AG – used in Milosheska_2016_lamotrigine.R (paper Table 4 covariate equation UGT2B7 372AG vs AA row).
• Example models: Milosheska_2016_lamotrigine.R (multiplicative effect on parent apparent clearance: cl *= (1 + 0.194 * UGT2B7_372AG); heterozygotes have +19.4% higher CL relative to the AA homozygous wild-type reference, Milosheska 2016 Table 4 row UGT2B7 372 A > G genotype AG vs AA).
• Notes: Companion canonical to UGT2B7_372AA and UGT2B7_372GG. See UGT2B7_372AA for variant biology. Ratified canonically on 2026-06-20 alongside the Milosheska 2016 lamotrigine extraction.

UGT2B7_372GG (canonical for UGT2B7 372A>G (rs28365063) homozygous G/G genotype indicator)

• Description: 1 = subject is homozygous for the variant guanine at nucleotide 372 of the UGT2B7 coding sequence; 0 = otherwise (372AA homozygote or 372AG heterozygote). Encoded by the UGT2B7 372A>G single-nucleotide polymorphism (rs28365063). Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (any non-G/G genotype: 372AA or 372AG). The reference used in effect estimation is the 372AA homozygous wild-type stratum, encoded when all three UGT2B7_372* indicators equal 0.
• Source aliases:
  • UGT2B7 372GG / UGT2B7 372 G/G / 372GG – used in Milosheska_2016_lamotrigine.R (paper Table 4 covariate equation UGT2B7 372GG vs AA row).
• Example models: Milosheska_2016_lamotrigine.R (multiplicative effect on parent apparent clearance: cl *= (1 + 1.17 * UGT2B7_372GG); homozygous variant carriers have +117% higher CL relative to the AA homozygous wild-type reference, Milosheska 2016 Table 4 row UGT2B7 372 A > G genotype GG vs AA).
• Notes: Companion canonical to UGT2B7_372AA and UGT2B7_372AG. The magnitude of the GG effect is by far the largest single-variant effect on lamotrigine apparent clearance identified in Milosheska 2016; the GG homozygote frequency in the paper’s Slovenian cohort was 2.0% (2 of 99 subjects), so per-model covariateData[[UGT2B7_372GG]]$notes should record the small-N caveat. Ratified canonically on 2026-06-20 alongside the Milosheska 2016 lamotrigine extraction.

Immunogenicity

ADA_POS (canonical for anti-drug antibody positive status indicator)

• Description: 1 = antidrug-antibody-positive, 0 = ADA-negative.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (ADA-negative).
• Source aliases:
  • ADA (semantically “ever positive”) – used in Zhu_2017_lebrikizumab.R. When translating from a paper that uses ADA as “ever positive,” verify the time-frame matches ADA_POS semantics before renaming.
  • ADA (time-varying positivity, primary covariate in Xu 2019) – used in Xu_2019_sarilumab.R.
  • NAB (neutralizing antibody positive – used in Petrov_2024_romiplostim.R). Strictly a subset of total ADA-positive (ADA antibodies that neutralize the drug’s biological effect). Document per-model when the source assay measured NAB only and the canonical column thus excludes binding-only ADA.
  • ATAPOSNEW (ADA-positive in the newer/updated-assay cohort) – used in Suri_2018_brentuximab.R as the modern-assay arm of a two-era ADA decomposition.
  • ADA_POSNEW (retired intermediate name; renamed to ADA_POS on 2026-04-29 for consistency across single- and multi-assay models).
• Example models: Clegg_2024_nirsevimab.R, Hu_2026_clesrovimab.R, Petrov_2024_romiplostim.R, Suri_2018_brentuximab.R (multi-assay; paired with ADA_POSOLD and ADA_MISSING; cl *= (1 + 0.125 * ADA_POS)), Xu_2019_sarilumab.R.
• Notes: In single-assay studies this is a straightforward binary. In studies pooling data across assay generations (different sensitivity / drug-tolerance characteristics), ADA_POS represents modern/current-assay positivity; companion indicators ADA_POSOLD and ADA_MISSING capture historical-assay-positive and missing-result sub-groups respectively. All three are mutually exclusive; reference is ADA-negative (all three = 0). Distinct from the continuous ADA quantities [[ADA_TITER]] (dimensionless dilution factor / assay titer) and [[CONC_ADA_NGML]] (calibrated mass concentration in ng/mL); a paper’s ADA_POS column is frequently derived from one of those by applying an assay cutoff, so when both are available prefer the continuous column if the final model estimated a continuous effect.

ADA_POSOLD (canonical for ADA-positive in older-assay study indicator)

• Description: 1 = subject is anti-drug-antibody-positive in a study that used the older lower-sensitivity, lower-drug-tolerance ADA assay; 0 = otherwise.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (ADA-negative or not in an older-assay study). Mutually exclusive with ADA_POS and ADA_MISSING.
• Source aliases:
  • ATAPOSOLD – used in Suri_2018_brentuximab.R.
• Example models: Suri_2018_brentuximab.R (multiplicative additive effect on ADC clearance: cl *= (1 + 0.177 * ADA_POSOLD)).
• Notes: Companion to ADA_POS (multi-assay form); see that entry’s Notes for the decomposition rationale. The “newer” vs “older” assay split is paper-specific (Suri 2018 newer assay: sensitivity 23.573 ng/mL, drug tolerance 25 ug/mL; older assay: sensitivity 4 ng/mL, drug tolerance 3,125 ng/mL). Time-varying once positive. Ratified canonically on 2026-04-28.

ADA_MISSING (canonical for ADA-result-missing indicator)

• Description: 1 = ADA value is missing (subject did not have a measured ADA result, distinct from a measured negative); 0 = ADA result is reported (positive or negative).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (ADA result reported). Mutually exclusive with ADA_POS and ADA_POSOLD.
• Source aliases:
  • ATAMISSING – used in Suri_2018_brentuximab.R.
• Example models: Suri_2018_brentuximab.R (multiplicative additive effect on ADC clearance: cl *= (1 + 0.192 * ADA_MISSING)).
• Notes: Used when a substantial fraction of the pooled cohort has no ADA measurement (Suri 2018: 205 of 380 patients) and the modeler retains ADA-missing as a separate level rather than collapsing missingness onto the ADA-negative reference. The non-zero positive estimate of e_adam_adc_cl indicates ADA-missing patients are not exchangeable with the ADA-negative reference – interpret with caution given the missingness mechanism is not random. Distinct from the continuous ADA quantities [[ADA_TITER]] and [[CONC_ADA_NGML]]; a missing-result indicator has no continuous analogue, so a model that carries a continuous ADA column and also has missing measurements needs an explicit per-model imputation rule rather than this flag. Ratified canonically on 2026-04-28.

ADA_TITER (canonical for continuous antidrug-antibody titer/titre)

• Description: Continuous antidrug-antibody titer/titre (time-varying; matched in time to the PK sample). Covers both the British-spelling reciprocal-dilution convention (ADA_TITRE, with ADA_TITRE = 1 for ADA-negative so log_e(1) = 0 cancels a log-linear effect) and the American-spelling linear-titer convention (ADA_TITER, with ADA_TITER = 0 for ADA-negative). The per-model covariateData[[ADA_TITER]]$description and notes must state which zero-encoding convention is in force so the covariate column cannot be misinterpreted.
• Units: Reciprocal dilution (e.g., 10, 20, 40, …, 2560) OR assay units (log2 or arbitrary) – document per-model in covariateData[[ADA_TITER]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – ADA-negative encoded per-model (see zero-encoding note).
• Source aliases:
  • ADA_TITRE – British spelling (reciprocal-dilution convention; 1 for negative).
  • ADA titre – British spelling long form.
  • ADAT – used in Moein_2022_etrolizumab.R (American linear-titer convention; 0 for negative).
• Example models: Jackson_2022_ixekizumab.R (reciprocal-dilution reference convention with ADA_TITER = 1 for negatives and (1 + coef * log_e(ADA_TITER)) on CL), Moein_2022_etrolizumab.R (linear-titer convention with ADA_TITER = 0 for negatives and exp(theta * ADA_TITER) on CL, per-unit-titer theta = 0.0365), Robbie_2012_palivizumab.R (reciprocal-dilution values 0/10/20/40/>=80 with category-specific multiplicative effects per titer bin; 0 = ADA negative reference).
• Notes: The prior separate ADA_TITRE (British, 1 = negative) and ADA_TITER (American, 0 = negative) canonicals were merged on 2026-04-20 into a single general-scope ADA_TITER. The zero-encoding convention is the load-bearing semantic and must be documented per-model. Distinct from ADA_POS (binary presence/absence); when the paper reports both, the final model usually keeps only one. Distinct from [[CONC_ADA_NGML]], a calibrated ADA MASS concentration in ng/mL from a drug-tolerant assay: that column has no zero-encoding at all (ADA-negatives carry real positive concentrations) and is dimensioned, whereas ADA_TITER is a dimensionless dilution factor or arbitrary assay unit. Do not use ADA_TITER for a ng/mL quantity – the zero-encoding semantic above would be false and the unit would be misstated. Imputation rules (LOCF / NOCB / baseline-as-negative) should be documented per-model.

Disease / treatment history

TRTPH_<phase> – canonical family for protocol-defined treatment-phase / treatment-block indicators. Mutually exclusive binary indicators identifying which named phase (block, course, element) of a multi-phase treatment protocol a record falls in. Time-varying per record. Intended for regimens whose protocol is divided into named blocks that differ materially in concomitant therapy, intensity, or physiological state – most commonly pediatric and adult oncology protocols (ALL, AML, lymphoma), but the concept generalises to any multi-block regimen. Source papers normally report this as a single multi-level categorical covariate (“treatment phase”, “protocol element”, “treatment block”) with one row per level in the parameter table; decompose it into one TRTPH_<phase> binary per non-reference level, following the register’s decomposed-binary convention for multi-level categoricals (ICU_ADM_*, RACE_*, TUMTP_*). The protocol’s induction (or first) phase is the reference category and is encoded as all indicators 0, so no TRTPH_ column is registered for it.

Each model MUST document the protocol name and the phase-to-column mapping in covariateData[[TRTPH_<phase>]]$notes, because the same phase word (“maintenance”, “intensification”) means different things across protocols. A future paper using a different protocol registers its own members under this prefix; if two protocols use the same phase word with materially different therapy, qualify the member with the protocol (TRTPH_ALL11_MAINT) rather than overloading the bare name. Distinct from TRT_PHASE, which is a binary double-blind active-treatment-on / off gate for placebo-controlled PD models, NOT a multi-level protocol-block categorical. Distinct from CYCLE (an integer dose-number / cycle counter within a single regimen) and from OCC (a sampling-occasion index for inter-occasion variability). Distinct from the CONMED_<INN> family: a TRTPH_<phase> column deliberately stands in for the entire concomitant-chemotherapy block of that phase, which is the right encoding when the individual agents are co-administered and cannot be separated – Kloos 2021 tested doxorubicin and methotrexate as individual covariates on top of the treatment phase and rejected both in backward elimination. Ratified canonically alongside the Kloos 2021 PEGasparaginase extraction (sidecar request-001 / response-001, question q3, option A).

TRTPH_1B (canonical for DCOG ALL-11 protocol 1B treatment-phase indicator)

• Description: 1 = the record falls within protocol 1B of the Dutch Childhood Oncology Group ALL-11 acute-lymphoblastic-leukemia protocol; 0 = any other phase. Protocol 1B follows induction protocol 1A and comprises cyclophosphamide, cytarabine and 6-mercaptopurine plus a 1,500 IU/m^2 PEGasparaginase dose at day 40. Member of the TRTPH_<phase> family described above.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0, with all other TRTPH_ indicators 0 = protocol 1A induction.
• Source aliases: 1B – Kloos 2021 Table 2 / Table 3 treatment-phase row label.
• Example models: Kloos_2021_pegasparaginase.R (clearance effect FIXED to 1, i.e. pooled with the 1A reference, because only two patients were treated as high risk and the effect was not estimable reliably; Kloos 2021 Table 2 prints “1 (fix)”).
• Notes: A fixed-to-reference member is still worth registering: dropping the column would silently erase the fact that the authors tested the phase and could not estimate it. See the family preamble above for the protocol-documentation requirement.

TRTPH_CONSOLIDATION (canonical for consolidation treatment-phase indicator)

• Description: 1 = the record falls within the consolidation phase of the treatment protocol; 0 = any other phase or population. For the founding model (QuANTUM-First, AC220-A-U302, newly diagnosed FLT3-ITD-positive AML) consolidation is standard high-dose cytarabine plus quizartinib or placebo, allogeneic hematopoietic cell transplantation, or both, in patients achieving remission. Member of the TRTPH_<phase> family described above.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0. Note that the all-TRTPH_-indicators-0 state is protocol-defined and is NOT necessarily the protocol’s own induction phase: for Vaddady 2024 the reference is relapsed/refractory AML patients on quizartinib monotherapy, “for whom no distinct treatment phases were reported”, so induction carries its own indicator rather than being folded into the reference. Any model using this column must state in covariateData notes what its all-zero state means.
• Source aliases: PHASE == 2 – Vaddady 2024 final NONMEM control stream (Supporting Information); Consolidation – Vaddady 2024 Table 3 treatment-phase row label.
• Example models: Vaddady_2024_quizartinib.R (fractional change -0.192 on quizartinib relative bioavailability, RSE 15.6 percent, Equation 9; and +0.272 on the parent-to-metabolite conversion fraction fMET, RSE 13.3 percent, Equation 16).
• Notes: Bare (unqualified) phase name, per the family preamble’s qualify-only-on-collision default; ratified by sidecar request-001 / response-001, question q2, option A. A later protocol whose consolidation phase differs materially in background therapy should qualify its own member (TRTPH_<PROTOCOL>_CONSOLIDATION) rather than overloading this entry.

TRTPH_CONTINUATION (canonical for continuation treatment-phase indicator)

• Description: 1 = the record falls within the continuation phase of the treatment protocol; 0 = any other phase or population. For the founding model (QuANTUM-First, AC220-A-U302) continuation is single-agent quizartinib or placebo for up to 3 years in patients with blood-count recovery, i.e. no background chemotherapy. Member of the TRTPH_<phase> family described above.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0; see the reference-category note on TRTPH_CONSOLIDATION – the all-zero state is protocol-defined and for Vaddady 2024 is relapsed/refractory AML on monotherapy, not induction.
• Source aliases: PHASE == 3 – Vaddady 2024 final NONMEM control stream (Supporting Information); Continuation – Vaddady 2024 Table 3 treatment-phase row label.
• Example models: Vaddady_2024_quizartinib.R (fractional change +0.418 on quizartinib relative bioavailability, RSE 12.5 percent, Equation 9; and -0.249 on fMET, RSE 8.47 percent, Equation 16).
• Notes: Bare (unqualified) phase name; ratified by sidecar request-001 / response-001, question q2, option A. This is the phase with the highest dose-normalised quizartinib exposure (about 1.4-fold the relapsed/refractory reference); Vaddady 2024 confirmed by a VPC restricted to patients who entered continuation that the effect is phase-related rather than driven by selection of patients who survived to that phase.

TRTPH_INDUCTION (canonical for induction treatment-phase indicator)

• Description: 1 = the record falls within the induction phase of the treatment protocol; 0 = any other phase or population. For the founding model (QuANTUM-First, AC220-A-U302) induction is quizartinib or placebo plus intravenous cytarabine and an anthracycline (daunorubicin or idarubicin). Member of the TRTPH_<phase> family described above.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0. This member exists precisely because the family preamble’s default – fold the protocol’s own induction phase into the all-zero reference – does NOT hold universally: Vaddady 2024 sets the reference to relapsed/refractory AML patients on quizartinib monotherapy, so induction is a non-reference level needing its own indicator. Register TRTPH_INDUCTION only when induction is genuinely non-reference in the source model; when induction IS the reference, encode it as all indicators 0 and register no column, per the preamble.
• Source aliases: PHASE == 1 – Vaddady 2024 final NONMEM control stream (Supporting Information); Induction – Vaddady 2024 Table 3 treatment-phase row label.
• Example models: Vaddady_2024_quizartinib.R (fractional change -0.419 on quizartinib relative bioavailability, RSE 4.92 percent, Equation 9; and +0.715 on fMET, RSE 6.38 percent, Equation 16).
• Notes: Bare (unqualified) phase name, per the family preamble’s qualify-only-on-collision default; ratified by sidecar request-001 / response-001, question q2, option A. In parent-plus-metabolite models the phase effects on parent bioavailability and on the conversion fraction act in opposite directions by construction: the parent bioavailability effect is carried over to the metabolite, and the conversion-fraction phase effect exists to counterbalance that spurious carry-over (Vaddady 2024 Discussion).

TRTPH_M (canonical for DCOG ALL-11 protocol M treatment-phase indicator)

• Description: 1 = the record falls within protocol M of the DCOG ALL-11 protocol (6-mercaptopurine plus high-dose methotrexate, 5,000 mg/m^2/dose at days 8, 22, 36 and 50); 0 = any other phase. Member of the TRTPH_<phase> family described above.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0, with all other TRTPH_ indicators 0 = protocol 1A induction.
• Source aliases: M – Kloos 2021 Table 2 / Table 3 treatment-phase row label.
• Example models: Kloos_2021_pegasparaginase.R (multiplicative on CL: 0.87, RSE 5.2 percent, bootstrap 95 percent CI 0.80-0.95).

TRTPH_MR_INTENS (canonical for DCOG ALL-11 medium-risk-group intensification treatment-phase indicator)

• Description: 1 = the record falls within the medium-risk-group intensification phase of the DCOG ALL-11 protocol (dexamethasone, vincristine, 6-mercaptopurine, plus doxorubicin in TEL/AML1-negative patients or methotrexate in TEL/AML1-positive patients); 0 = any other phase. Member of the TRTPH_<phase> family described above.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0, with all other TRTPH_ indicators 0 = protocol 1A induction.
• Source aliases: MRG intens. / MR intensification – Kloos 2021 Table 2 / Table 3 treatment-phase row labels.
• Example models: Kloos_2021_pegasparaginase.R (multiplicative on CL: 0.89, RSE 5.2 percent, bootstrap 95 percent CI 0.82-0.98).

TRTPH_MR_MAINT (canonical for DCOG ALL-11 medium-risk-group maintenance treatment-phase indicator)

• Description: 1 = the record falls within the medium-risk-group maintenance phase of the DCOG ALL-11 protocol (dexamethasone, vincristine, methotrexate and 6-mercaptopurine); 0 = any other phase. Member of the TRTPH_<phase> family described above.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0, with all other TRTPH_ indicators 0 = protocol 1A induction.
• Source aliases: MRG maint. / MR maintenance – Kloos 2021 Table 2 / Table 3 treatment-phase row labels.
• Example models: Kloos_2021_pegasparaginase.R (multiplicative on CL: 0.81, RSE 3.9 percent, bootstrap 95 percent CI 0.75-0.86).

TRTPH_SR_IV (canonical for DCOG ALL-11 standard-risk-group protocol IV treatment-phase indicator)

• Description: 1 = the record falls within protocol IV of the standard-risk arm of the DCOG ALL-11 protocol (dexamethasone and vincristine plus a single individualized PEGasparaginase dose at day 1); 0 = any other phase. Member of the TRTPH_<phase> family described above.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0, with all other TRTPH_ indicators 0 = protocol 1A induction.
• Source aliases: SRG protocol IV / SR protocol IV – Kloos 2021 Table 2 / Table 3 treatment-phase row labels.
• Example models: Kloos_2021_pegasparaginase.R (multiplicative on CL: 0.81, RSE 6.5 percent, bootstrap 95 percent CI 0.73-0.90).

HCT_COND_RIC (canonical for reduced-intensity conditioning regimen indicator)

• Description: 1 = subject received reduced-intensity conditioning (RIC) chemotherapy prior to allogeneic hematopoietic cell transplantation, 0 = subject received myeloablative conditioning (MAC). Conditioning intensity is fixed per subject for the analysis window (the conditioning regimen was completed before transplantation, before any of the post-transplant tacrolimus PK observations). RIC regimens use lower-dose chemotherapy / radiotherapy to preserve some host haematopoiesis and rely on graft-versus-tumour effect for cytoreduction; MAC regimens deliver high-dose chemotherapy and / or total-body irradiation that fully ablates host marrow.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (myeloablative conditioning, MAC).
• Source aliases:
  • RIC (Dunlap 2025 NM-TRAN convention; binary 0 / 1) – used directly in Dunlap_2025_tacrolimus.R.
• Example models: Dunlap_2025_tacrolimus.R (Dunlap 2025 Table 2 reduced-covariate-model column; exponential effect on apparent oral clearance: cl *= 0.63 ^ HCT_COND_RIC, so RIC recipients have ~37% lower apparent oral tacrolimus clearance than MAC recipients).
• Notes: Conditioning regimen intensity has been reported to associate with post-transplant tacrolimus apparent clearance, likely via gut / hepatic CYP3A activity, GVHD-related inflammatory response, and post-transplant haematopoietic state. The paper-specific definition of “RIC” follows the source publication’s own classification (e.g., Dunlap 2025 follows the institutional protocol at UNCMC, which pools non-myeloablative conditioning regimens into the RIC category when assigning the binary indicator); document the source paper’s RIC criteria in covariateData[[HCT_COND_RIC]]$notes. When a future paper distinguishes a third intensity tier (non-myeloablative, NMA) as a separate covariate level rather than pooling NMA into RIC, register a parallel canonical (e.g. HCT_COND_NMA) instead of overloading HCT_COND_RIC. Scope: specific because the column is meaningful only for allo-HCT recipients. Ratified canonically on 2026-05-09 alongside the Dunlap 2025 tacrolimus extraction.

AGVHD_LIVER (canonical for acute graft-versus-host disease – liver involvement indicator)

• Description: 1 = subject has documented evidence of acute graft-versus-host disease (aGvHD) involving the liver (any grade, I-IV) at the current model time; 0 = no documented liver aGvHD. Time-varying per subject in allo-HSCT cohorts: 0 before the first documented liver-aGvHD diagnosis and 1 from that time onward for the modeled observation window. Interpolation convention (Waterhouse 2024 Methods): next observation carried backward (NOCB), i.e., a subject with a positive liver-aGvHD diagnosis at any time during the study has AGVHD_LIVER = 1 from study start through the diagnosis time; use last-observation-carried-forward (LOCF) instead if the source paper documents that convention.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no documented liver aGvHD; the pooled reference includes both subjects who never developed liver aGvHD and time periods before diagnosis in subjects who eventually did).
• Source aliases:
  • LIVGVHD / GVHD_LIVER – Waterhouse 2024 NM-TRAN column for the time-varying liver-aGvHD indicator. Same orientation, no value transformation.
• Example models: Waterhouse_2024_vedolizumab.R (multiplicative power-form effect on CL: cl *= 1.05^AGVHD_LIVER, i.e., liver-aGvHD subjects show ~5% higher CL than the reference at the diagnosis time and thereafter; the paper’s Table 2 reports the estimate as 1.05 (0.834, 1.26) with 10.3% RSE, and the Results paragraph 3 concludes the 90% CI crosses 1 so the effect is not clinically meaningful).
• Notes: Sibling to AGVHD_SKIN and AGVHD_INTESTINE – a patient can have more than one organ involvement simultaneously (Table 1 footnote in Waterhouse 2024: “a patient could exhibit one or more types of GvHD”), so the three indicators are orthogonal binary covariates rather than mutually-exclusive levels of a categorical. Time-varying with the diagnosis-time boundary; supply per subject at every observation timestamp in the event dataset. Specific scope because the column is meaningful only for allo-HSCT cohorts. Distinct from TX_LIVER (liver-transplant recipient indicator, a solid-organ-transplant-cohort covariate). Future extractions distinguishing chronic-vs-acute GvHD should register sibling canonicals (e.g., CGVHD_LIVER) rather than overloading this name. Ratified canonically on 2026-07-25 alongside the Waterhouse 2024 vedolizumab extraction.

AGVHD_SKIN (canonical for acute graft-versus-host disease – skin involvement indicator)

• Description: 1 = subject has documented evidence of acute graft-versus-host disease (aGvHD) involving the skin (any grade, I-IV) at the current model time; 0 = no documented skin aGvHD. Time-varying per subject in allo-HSCT cohorts using the same NOCB interpolation convention as AGVHD_LIVER.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no documented skin aGvHD).
• Source aliases:
  • SKGVHD / GVHD_SKIN – Waterhouse 2024 NM-TRAN column for the time-varying skin-aGvHD indicator.
• Example models: Waterhouse_2024_vedolizumab.R (multiplicative power-form effect on CL: cl *= 1.03^AGVHD_SKIN; Waterhouse 2024 Table 2 reports 1.03 (0.940, 1.12) with 4.56% RSE; the 90% CI crosses 1 so the effect is not clinically meaningful).
• Notes: Sibling to AGVHD_LIVER and AGVHD_INTESTINE; see the AGVHD_LIVER entry for the shared multi-organ / orthogonal-indicator rationale. Ratified canonically on 2026-07-25 alongside the Waterhouse 2024 vedolizumab extraction.

AGVHD_INTESTINE (canonical for acute graft-versus-host disease – intestinal involvement indicator)

• Description: 1 = subject has documented evidence of acute graft-versus-host disease (aGvHD) involving the intestine / gastrointestinal tract (any grade, I-IV) at the current model time; 0 = no documented intestinal aGvHD. Time-varying per subject in allo-HSCT cohorts using the same NOCB interpolation convention as AGVHD_LIVER.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no documented intestinal aGvHD).
• Source aliases:
  • INTGVHD / GVHD_INTESTINE / GVHD_GI – Waterhouse 2024 NM-TRAN column for the time-varying intestinal-aGvHD indicator.
• Example models: Waterhouse_2024_vedolizumab.R (multiplicative power-form effect on CL: cl *= 1.07^AGVHD_INTESTINE; Waterhouse 2024 Table 2 reports 1.07 (0.870, 1.27) with 9.54% RSE; the 90% CI crosses 1 so the effect is not clinically meaningful).
• Notes: Sibling to AGVHD_LIVER and AGVHD_SKIN; see the AGVHD_LIVER entry for the shared multi-organ / orthogonal-indicator rationale. Vedolizumab’s mechanism (integrin alpha-4 beta-7 blockade) specifically targets gut-homing leukocyte trafficking, so AGVHD_INTESTINE is the mechanistically-motivated on-target-organ indicator in vedolizumab-for-GvHD-prophylaxis popPK studies; the paper notes the observed effect (~7% increase in CL at intestinal-aGvHD onset) is consistent with target-mediated drug disposition at the on-target site but the estimate is not statistically or clinically significant in this dataset. Ratified canonically on 2026-07-25 alongside the Waterhouse 2024 vedolizumab extraction.

DISEXT_EP (canonical for extensive colitis / pancolitis indicator)

• Description: 1 = extensive colitis or pancolitis disease extension, 0 = otherwise (any non-extensive disease extension, e.g. left-sided colitis or proctitis).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0. In papers that decompose the disease-extension categorical into both DISEXT_EP and DISEXT_OTHER, DISEXT_EP = 0 AND DISEXT_OTHER = 0 corresponds to the left-sided-colitis reference group; in papers that use a single binary indicator for extensive colitis, the reference is pooled non-extensive (left-sided + any other extension).
• Source aliases:
  • EXTCOL – used in Faelens_2021_infliximab.R (binary 0/1 for extensive colitis at baseline; no separate “other” category).
  • Derived from a multi-level DISEXT column in the source (levels: left-sided colitis, extensive/pancolitis, other): DISEXT_EP = as.integer(DISEXT == "extensive/pancolitis").
• Example models: Moein_2022_etrolizumab.R (paired with DISEXT_OTHER; multiplicative effect on CL, +8.2% vs. left-sided colitis), Faelens_2021_infliximab.R (single-binary encoding; multiplicative fold-change on V of 1.25 when DISEXT_EP = 1).
• Notes: Optionally paired with DISEXT_OTHER when the source paper decomposes a three-level disease-extension categorical (left-sided / extensive-pancolitis / other) into two indicators; the pairing is paper-specific and not required. Promoted from scope: specific to scope: general on 2026-04-27 because the binary “extensive colitis vs not” semantics generalize across UC popPK papers regardless of whether the original dataset additionally distinguished an “other” disease-extension category.

DISEXT_OTHER (canonical for ‘other disease extension’ indicator)

• Description: 1 = disease extension other than left-sided colitis or extensive/pancolitis, 0 = not.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (left-sided colitis, when paired with DISEXT_EP = 0).
• Source aliases: Derived from a multi-level DISEXT column: DISEXT_OTHER = as.integer(DISEXT == "other").
• Example models: Moein_2022_etrolizumab.R (multiplicative effect on CL, +18% vs. left-sided colitis; large uncertainty due to 2% prevalence).
• Notes: Paired with DISEXT_EP; together they encode the three-level disease-extension categorical.

PRIOR_TAXANE (canonical for binary prior-taxane chemotherapy indicator)

• Description: 1 = subject received any prior taxane regimen (docetaxel, paclitaxel, cabazitaxel, etc.) before study entry, 0 = taxane-naive. Time-invariant within a subject (records treatment history at baseline). Oncology-pretreatment indicator: relevant for cohorts in which prior taxane exposure plausibly alters disease biology (e.g., advanced / castration-resistant prostate cancer, where taxane pretreatment is associated with more advanced disease and selects for taxane-resistant clones).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (taxane-naive).
• Source aliases:
  • PTAX – used in vanHasselt_2015_eribulin.R (van Hasselt 2015 paper notation; binary 0 / 1 for prior docetaxel pretreatment).
• Example models: vanHasselt_2015_eribulin.R (multiplicative effect on baseline serum PSA: psa0 = exp(lpsa0 + etalpsa0) * e_prior_taxane_psa0^PRIOR_TAXANE with e_prior_taxane_psa0 = 3.23 – prior-taxane patients have ~3.2x higher baseline PSA than taxane-naive patients, consistent with more advanced disease at study entry).
• Notes: Pairs with PRIOR_TAXANE_DAYS when a continuous duration-of-pretreatment is also relevant (e.g., van Hasselt 2015 uses both: PTAX on PSA0 and NTRT on KD). Distinct from CONMED_* (which is concomitant medication during the study, not pretreatment history) and from generic PRIOR_* chemotherapy indicators (which would warrant a separate canonical when a paper differentiates by drug class rather than collapsing to taxanes). Scope: specific because the population semantics (CRPC) and the “any prior taxane” pooling are tied to van Hasselt 2015; future papers that distinguish per-drug pretreatment (docetaxel vs paclitaxel vs cabazitaxel separately) should register parallel canonicals rather than overloading PRIOR_TAXANE.

PRIOR_TAXANE_DAYS (canonical for cumulative days of prior taxane treatment)

• Description: Cumulative number of days of prior taxane chemotherapy at study entry. 0 for taxane-naive patients (i.e., for any subject with PRIOR_TAXANE = 0). Time-invariant within a subject (records the pretreatment history at baseline).
• Units: days
• Type: continuous
• Scope: specific
• Reference category: n/a – normalised by a population reference value (van Hasselt 2015 uses the population median of 720 days among prior-taxane-pretreated patients) and entered as a power covariate (1 + PRIOR_TAXANE_DAYS / 720) ^ e_prior_taxane_days_<param>. The +1 inside the bracket makes the covariate effect collapse to a multiplier of 1 for taxane-naive patients (PRIOR_TAXANE_DAYS = 0) regardless of the estimated exponent.
• Source aliases:
  • NTRT – used in vanHasselt_2015_eribulin.R (van Hasselt 2015 paper notation; cumulative number of days of prior taxane treatment).
• Example models: vanHasselt_2015_eribulin.R (van Hasselt 2015 Eq. 4 power covariate on drug PSA inhibition rate KD0: kd0 = exp(lkd0 + etalkd0) * (1 + PRIOR_TAXANE_DAYS / 720)^e_prior_taxane_days_kd0 with e_prior_taxane_days_kd0 = -4.00 – KD0 decreases with longer prior-taxane exposure, encoding cross-resistance between docetaxel and eribulin via the shared microtubule-inhibition mechanism).
• Notes: Pairs with PRIOR_TAXANE (binary). The paper also considered an alternative continuous parameterisation in cycles of prior taxane (NCYCL, median 30 cycles) which was deemed slightly less informative (dOFV = -8 for NCYCL vs -10 for NTRT) and was not retained in the final model. If a future model needs the cycle-count form, register a parallel canonical (e.g. PRIOR_TAXANE_CYCLES) rather than overloading this one. Scope: specific because the 720-day normalisation reference is tied to the van Hasselt 2015 study population (post-docetaxel mCRPC patients).

T_DIAG_DIAB (canonical for time since type 2 diabetes diagnosis)

• Description: Time elapsed since clinical diagnosis of type 2 diabetes mellitus (T2DM) at study entry, in years. Continuous time-fixed covariate; supply the per-subject duration in years. T2DM-specific (Type 1 cohorts use a separate T_DIAG_T1D canonical if registered in the future).
• Units: years
• Type: continuous
• Scope: general
• Reference category: n/a – enters as a power covariate (T_DIAG_DIAB / 2)^theta on the model-predicted baseline FPG, on Gmax (the maximal SGLT2-inhibition-driven FPG-reduction), and on kHbA1cout. Reference value 2 years per Baron 2016 Tables S3 and S4.
• Source aliases:
  • DUR – used in Baron_2016_empagliflozin.R (Baron 2016 Methods column “duration of diabetes”; PK/PD dataset categorisation reported as <1 year / 1-5 years / >5 years in Table S2 with 58.5% of patients in the >5 years group).
• Example models: Baron_2016_empagliflozin.R (power-form effects centred at 2 years on BFPG: (T_DIAG_DIAB / 2)^0.0512, on Gmax: (T_DIAG_DIAB / 2)^0.0117, on kHbA1cout: (T_DIAG_DIAB / 2)^-0.577).
• Notes: Disease-progression marker complementary to the binary DIS_DIAB indicator – DIS_DIAB records the comorbidity at baseline (1/0) while T_DIAG_DIAB quantifies how long that diabetes has been present. The covariate is a time-since-event under the canonical T_<event> family. Distinct from T_NUT_SUPP (time on nutritional supplementation) and T_POST_ECMO (time after ECMO decannulation) which are different T_<event> siblings. For a baseline-only continuous power-form effect, a 0-year subject (newly diagnosed at study entry) yields (0/2)^theta = 0 – supply at least a small floor value (e.g. 0.1 year) for newly-diagnosed subjects in simulation; the floor convention should be documented in the per-model covariateData[[T_DIAG_DIAB]]$notes. Ratified canonically on 2026-06-24 alongside the Baron 2016 empagliflozin extraction.

T_DIAG_CANCER (canonical for time since primary cancer diagnosis)

• Description: Time elapsed between the patient’s primary cancer diagnosis (histopathology / imaging confirmation date recorded in the clinical database) and study entry / re-baseline, in days. Continuous time-fixed covariate; supply the per-subject duration in days. Cancer-nonspecific (gastric, GEJC, NSCLC, breast, etc. all use this same canonical). Distinct from T_DIAG_DIAB (T2DM-specific; different disease and different reference form).
• Units: days
• Type: continuous
• Scope: general
• Reference category: n/a – entered as a power covariate (T_DIAG_CANCER / ref)^exponent on structural parameters. Reference values observed: 53 days (Terranova 2022 avelumab arm median at randomization for the JAVELIN Gastric 100 gastric-cancer cohort, per Supplementary Methods).
• Source aliases:
  • Tdiag – used in Terranova_2022_TGD_OS_gastric.R (Terranova 2022 Supplementary Methods equation form (Tdiag/53)^theta5 on the Gompertz tumor-growth rate constant Kg).
  • Log time since diagnosis – Terranova 2022 Table S2 OS TTE coefficient label; enters on log(median OS) via log(T_DIAG_CANCER) * beta.
• Example models: Terranova_2022_TGD_OS_gastric.R (power effect on Gompertzian Kg: Kg = tvKg * (T_DIAG_CANCER / 53)^-0.00291, reference 53 days; and additive linear-log effect on log-median OS: log_median_OS += 0.0436 * log(T_DIAG_CANCER)).
• Notes: Sibling of T_DIAG_DIAB under the canonical T_<event> family. Auto-approved without a naming sidecar per the T_ policy on 2026-07-24 (agcand_13066655 request-001 q1=A). For a 0-day subject (diagnosed at study entry) the power form (0/53)^theta evaluates to 0 when the exponent is positive, which is a boundary case; supply at least a small floor value (e.g. 1 day) for such subjects in simulation. Distinct from post-treatment intervals (T_POST_ECMO, etc.) which are time-since-intervention rather than time-since-diagnosis.

BL_PN_GR1 (canonical for active baseline grade 1 peripheral neuropathy indicator)

• Description: 1 = subject had active grade 1 peripheral neuropathy (CTCAE 4.0 grade 1: asymptomatic; clinical or diagnostic observations only; intervention not indicated) at study entry; 0 = no active PN at baseline. Time-fixed at study entry. The covariate captures pre-existing low-grade PN typically arising from prior antimicrotubule or platinum chemotherapy in heavily pretreated oncology cohorts and is used to assess whether baseline PN sensitizes patients to subsequent antimicrotubule-induced PN.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no active PN at baseline).
• Source aliases:
  • “active grade 1 PN at study entry (yes/no)” (Lu 2017 narrative; coded as a binary 0/1 in the source NONMEM dataset) – used in Lu_2017_polatuzumab_neuropathy.R.
• Example models: Lu_2017_polatuzumab_neuropathy.R (multiplicative effect on the grade >= 2 peripheral neuropathy hazard via exp(theta_baselinePN * BL_PN_GR1) with theta_baselinePN = -0.222, SE 0.324 – not detectable given the large SE; the paper reports a sensitivity analysis in which this indicator was replaced by a broader “history of prior PN” indicator with similarly inconclusive results).
• Notes: Specific to oncology cohorts where grade 1 PN is permitted at study entry (per the source paper’s eligibility criteria). Distinct from PREV_AE_SCORE (the time-varying ordinal Markov-state AE-score covariate from Girard 2012 pimasertib, which conditions a current-observation outcome on the previous observation’s grade); BL_PN_GR1 is a time-fixed baseline indicator that does not update during the analysis window. Distinct from DIS_DPN (painful diabetic peripheral polyneuropathy disease-state indicator from chronic-pain cohorts); DIS_DPN flags a diabetes-related primary diagnosis used as a stratification covariate, while BL_PN_GR1 flags a pre-existing low-grade adverse-event status from any prior cause in an oncology trial. When a future paper uses a different baseline-PN grade threshold (e.g., grade <= 2 permitted) or a different AE category, register a parallel canonical (e.g., BL_PN_GR2, BL_FATIGUE_GR1) rather than overloading this one. Ratified canonically on 2026-06-24 alongside the Lu 2017 polatuzumab vedotin TTE extraction.

Hypercholesterolemia biomarkers

PCSK9 (canonical for baseline unbound serum PCSK9 concentration)

• Description: Baseline unbound serum proprotein convertase subtilisin/kexin type 9 (PCSK9) concentration.
• Units: ng/mL (document per-model in covariateData[[PCSK9]]$units if a different unit – typically nM – is used in a given model; conversion uses a PCSK9 molecular weight of ~72 kDa, so 1 nM ~= 72 ng/mL).
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (PCSK9 / ref)^exponent. Reference values observed: 425 ng/mL (= 5.9 nM) in Kuchimanchi_2018_evolocumab.R (population median).
• Source aliases: none known.
• Example models: Kuchimanchi_2018_evolocumab.R (power exponent 0.194 on Vmax: Vmax * (PCSK9/425)^0.194).
• Notes: PCSK9 is the pharmacological target of anti-PCSK9 monoclonal antibodies (evolocumab, alirocumab, etc.); baseline PCSK9 drives the magnitude of target-mediated elimination and is a recurring covariate in anti-PCSK9 popPK models. Baseline (time-fixed) covariate; patients with missing baseline PCSK9 are typically excluded from analyses that include PCSK9 as a covariate.

Pharmacogenomic SNPs

Canonical pattern: SNP_<GENE>_<RSID>. Use one binary indicator per SNP genotype that the source paper tests as a model covariate. The SNP_ prefix makes the category unambiguous; the gene symbol disambiguates rsIDs grouped by gene; the rsID provides a globally unique identifier. Encoding follows the most common pharmacogenomic convention (also used by Papachristos 2020): 1 = mutant allele present (heterozygous or homozygous mutant); 0 = homozygous wild-type. When a paper uses a different encoding (e.g., per-allele dosage 0/1/2, dominant model with mutant homozygotes only, or recessive model), document the encoding explicitly in covariateData[[<COL>]]$notes and consider registering a separate canonical name. SNP indicators default to scope: specific because the parameter on which they act and the encoded reference category are tied to the source paper’s analysis plan; promote to general when a second paper ratifies identical semantics.

SNP_ABCG2_RS4148157 (canonical for ABCG2 rs4148157 variant indicator)

• Description: Binary genotype indicator for the ABCG2 (BCRP) rs4148157 single-nucleotide polymorphism (G > A; intron 11, population MAF ~0.10). 1 = at least one variant (A) allele present (heterozygous AG or homozygous AA carrier); 0 = homozygous wild-type (GG).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (homozygous wild-type GG).
• Source aliases:
  • GENECAT – Roberts 2016 (paper Results ‘Population Pharmacokinetic Analysis’ section: the individual-Ka equation Ka_i = Ka_pop * exp(theta_1 * GENECAT), with GENECAT = 1 for AG / AA and 0 for GG).
• Example models: Roberts_2016_topotecan.R (exponential effect on oral topotecan absorption rate constant Ka: Ka = Ka_pop * exp(1.06 * SNP_ABCG2_RS4148157); AG/AA carriers have Ka approximately 2.89x higher than GG homozygotes, corresponding to an observed Cmax approximately 1.7x higher).
• Notes: Time-fixed per subject (germline genotype). Variant carrier rate in the Roberts 2016 paediatric brain-tumour cohort: 19% (10 of 52 successfully genotyped patients; 9 AG heterozygotes plus 1 AA homozygote, with AA and AG pooled because only one AA homozygote was present). The rs4148157 variant is intronic and is in strong linkage disequilibrium with the better-characterised rs2231142 variant (Q141K, exon 6); Roberts 2016 Discussion proposes that rs4148157 may be acting as a surrogate marker for rs2231142 in this analysis. ABCG2 (also called BCRP, breast cancer resistance protein) is an intestinal and hepatic efflux transporter that influences oral bioavailability of topotecan and other substrates.

SNP_ABCG2_RS2231142_HOM (canonical for ABCG2 rs2231142 (Q141K) homozygous-variant indicator)

• Description: Binary genotype indicator for the ABCG2 (BCRP) rs2231142 single-nucleotide polymorphism (c.421C>A; exon 5; amino-acid Q141K / p.Gln141Lys; population MAF ~30-60% in East Asians, ~5-15% in Caucasians and African-Americans). 1 = homozygous variant (421A/A) carrier; 0 = otherwise (the union of heterozygous 421C/A and homozygous wild-type 421C/C). This is a recessive-model encoding: heterozygotes are pooled with wild-type homozygotes because Ueshima 2018 (the founding example) reported that only the 421A/A stratum had a distinct typical-value covariate effect on apixaban non-renal clearance. Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0. In a recessive-model (_HOM-only) encoding the 0-level is the union of homozygous wild-type 421C/C and heterozygous 421C/A. In a paired-indicator encoding (SNP_ABCG2_RS2231142_HET also present) the reference group is the 421C/C wild-type homozygote stratum, i.e. both indicators = 0.
• Source aliases:
  • ABCG2 421A/A – used in Ueshima_2018_apixaban.R (Ueshima 2018 Methods Eq. of the final-model CL/F: dichotomous parameter ABCG2 equals 1 for 421A/A and 0 for 421C/C or 421C/A).
  • rs2231142 TT – used in Jiang_2023_imatinib.R (Jiang 2023 reports the SNP on the opposite DNA strand as G>T, so the TT genotype is the 421A/A homozygous-variant stratum; Table 3 row rs2231142 TT).
• Example models: Ueshima_2018_apixaban.R (multiplicative factor on the non-renal arm of apparent oral clearance: cl_nonren = exp(lcl) * e_abcg2_homvar_cl_nonren^SNP_ABCG2_RS2231142_HOM with e_abcg2_homvar_cl_nonren = 0.341; 421A/A homozygotes have non-renal CL/F reduced by 65.9% relative to 421C/C or 421C/A; paper Table 4 theta6 = 0.341), Jiang_2023_imatinib.R (power-of-binary multiplicative factor on apparent oral clearance, paired with SNP_ABCG2_RS2231142_HET: e_abcg2_het_cl^SNP_ABCG2_RS2231142_HET * e_abcg2_hom_cl^SNP_ABCG2_RS2231142_HOM with e_abcg2_hom_cl = 0.976; TT homozygotes have CL/F 2.4% lower than GG wild-type homozygotes, imprecisely estimated on n = 6 TT subjects; paper Table 3).
• Notes: Distinct from SNP_ABCG2_RS4148157 in two ways: (a) a different SNP (rs2231142 is the well-characterised coding Q141K variant in exon 5, whereas rs4148157 is an intronic variant in intron 11 that is in strong linkage disequilibrium with rs2231142 and used by Roberts 2016 as a surrogate marker), (b) a different genetic model (recessive 421A/A-only in Ueshima 2018 vs dominant any-A-allele in Roberts 2016 rs4148157). Strand orientation: papers report this SNP either as c.421C>A (dbSNP reference orientation, variant allele A) or as G>T on the opposite strand (variant allele T); the two are the same variant, so 421C/C = GG, 421C/A = GT, and 421A/A = TT. Always map the paper’s genotype labels onto the reference-orientation strata before assigning indicator values. Genetic-model orientations: use SNP_ABCG2_RS2231142_HOM alone for a recessive encoding (Ueshima 2018); pair it with SNP_ABCG2_RS2231142_HET when the source paper assigns a distinct typical-value effect to each of the three genotype strata (Jiang 2023), following the SLCO1B1_HAP15_HET / SLCO1B1_HAP15_HOM and CYP3A5_STAR1_HET / CYP3A5_STAR1_HOM precedents. The per-subject column value is identical in both encodings (1 iff the subject is 421A/A); only the model’s reference-category interpretation changes, which is why the paired form does not need a separate canonical for the homozygote. When a future paper uses a dominant-model encoding (any-A-allele = 1, pooling heterozygotes with homozygous variants into a single carrier group), register a paired companion canonical SNP_ABCG2_RS2231142_CARRIER so the two genetic-model orientations remain separate canonicals. The Q141K variant impairs ABCG2 plasma-membrane localisation and function; it is the most commonly studied ABCG2 pharmacogenetic SNP in popPK literature (substrates include apixaban, rosuvastatin, sulfasalazine, topotecan, methotrexate). Ratified canonically on 2026-05-30 alongside the Ueshima 2018 apixaban extraction; scope promoted from specific to general on 2026-07-27 alongside the Jiang 2023 imatinib extraction (second independent paper using the same column definition across an unrelated drug class and a different genetic model, demonstrating the canonical generalizes beyond the Ueshima 2018 recessive encoding).

SNP_ABCG2_RS2231142_HET (canonical for ABCG2 rs2231142 (Q141K) heterozygote indicator)

• Description: Binary genotype indicator for the heterozygous stratum of the ABCG2 (BCRP) rs2231142 single-nucleotide polymorphism (c.421C>A; exon 5; amino-acid Q141K / p.Gln141Lys). 1 = subject carries exactly one variant allele (genotype 421C/A, equivalently GT on the opposite-strand reporting convention); 0 = otherwise (the union of 421C/C wild-type homozygotes and 421A/A homozygous-variant carriers; the paired indicator SNP_ABCG2_RS2231142_HOM flags the homozygous-variant group). Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (421C/C wild-type homozygote, when paired with SNP_ABCG2_RS2231142_HOM = 0). The reference group is the wild-type homozygote stratum; SNP_ABCG2_RS2231142_HOM flags the homozygous-variant stratum.
• Source aliases:
  • rs2231142 GT – used in Jiang_2023_imatinib.R (Jiang 2023 reports the SNP on the opposite DNA strand as G>T, so the GT genotype is the 421C/A heterozygous stratum; Table 3 row rs2231142 GT; the paper’s final-model equation writes the exponent as heterozygous).
• Example models: Jiang_2023_imatinib.R (power-of-binary multiplicative factor on apparent oral clearance: e_abcg2_het_cl^SNP_ABCG2_RS2231142_HET with e_abcg2_het_cl = 0.879; heterozygotes have CL/F 12.1% lower than GG wild-type homozygotes and therefore reach higher trough concentrations at the same dose; paper Table 3, RSE 5%, bootstrap 95% CI 0.785-0.968).
• Notes: Paired with SNP_ABCG2_RS2231142_HOM (see that entry’s Notes for the strand-orientation convention, the recessive-vs-paired genetic-model distinction, and the three-level decomposition rationale). Follows the SLCO1B1_HAP15_HET / SLCO1B1_HAP15_HOM, CYP3A5_STAR1_HET / CYP3A5_STAR1_HOM, and CYP2D6_STAR10_HET / CYP2D6_STAR10_HOM precedents: two binary indicators encode a three-level germline genotype with the wild-type homozygote as the implicit reference (both indicators = 0). Distinct from a hypothetical SNP_ABCG2_RS2231142_CARRIER (not yet registered), which would pool heterozygotes with homozygous variants under a dominant model; use the paired indicators when the source paper resolves a distinct typical-value effect for the heterozygous stratum specifically. Jiang 2023 is the founding example and motivated the three-level decomposition because the East Asian cohort had abundant heterozygotes (45/85, 53%) but few homozygous variants (6/85, 7%) – the heterozygote effect (0.879) is precisely estimated while the homozygote effect (0.976) is not. Genotype distribution in the Jiang 2023 cohort of 85 Chinese postoperative GIST adults (Table 2): GG 34 (40%), GT 45 (53%), TT 6 (7%); allele frequency G 0.66 / T 0.34; Hardy-Weinberg p = 0.22. Ratified canonically on 2026-07-27 alongside the Jiang 2023 imatinib extraction.

SNP_IL10_RS1800896_HET (canonical for IL-10 G-1082A (rs1800896) heterozygote indicator)

• Description: Binary genotype indicator for the heterozygous stratum of the IL10 rs1800896 promoter single-nucleotide polymorphism (-1082 G>A, reported as T>C on the opposite strand). 1 = subject carries exactly one variant allele (genotype C/T on the reporting strand used by the source); 0 = otherwise. Time-fixed per subject (germline genotype). IL-10 is a potent down-modulator of CYP3A enzyme activity, so carriers of the higher-IL-10-expression genotype show reduced CYP3A-mediated clearance.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (rs1800896 T/T homozygote on the source’s reporting strand).
• Source aliases:
  • rs1800896-TC – Pei 2023 Table S4 row label for the coefficient; the paper writes the locus as “IL-10 G-1082A (rs1800896)” in the text and tabulates the genotype counts under “IL-10 (rs1800896)” in Table 2.
• Example models: Pei_2023_tacrolimus.R (exponential effect on apparent oral clearance: exp(e_il10_het_cl * SNP_IL10_RS1800896_HET) with e_il10_het_cl = -0.35, i.e. heterozygotes have 0.70 times the tacrolimus CL/F of T/T homozygotes; Pei 2023 Table S4).
• Notes: Registered as a heterozygote indicator rather than a variant-allele count because the founding cohort observed only two of the three strata – Pei 2023 Table 2 reports TT 63 (73.3%) and CT 23 (26.7%) among 86 genotyped heart transplant recipients, with no CC subjects – so the single reported coefficient is a heterozygote-versus-TT contrast and a per-allele reading is not identifiable from the source. A future paper resolving all three strata should add the paired SNP_IL10_RS1800896_HOM following the SNP_ABCG2_RS2231142_HET / _HOM and CYP3A5_STAR1_HET / _HOM precedents, or register a _VAR_COUNT sibling if it fits a per-allele model. Auto-approved member of the SNP_<GENE>_RS<rsid> family. Distinct from the other two IL-10 loci genotyped in Pei 2023 (rs1800871 and rs1800872, Table S3), neither of which entered the final model. Ratified 2026-08-05 alongside the Pei 2023 tacrolimus extraction.

SNP_ICAM1_RS1799969 (canonical for ICAM-1 rs1799969 mutant indicator)

• Description: Binary genotype indicator for the ICAM1 rs1799969 single-nucleotide polymorphism (G > A; Gly241Arg / K469E in some references). 1 = at least one mutant (A) allele present (heterozygous or homozygous mutant); 0 = homozygous wild-type (GG).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (homozygous wild-type).
• Source aliases:
  • cat – Papachristos 2020 (the paper writes the indicator as cat in the CL covariate equation; no formal column name is given in the published narrative).
• Example models: Papachristos_2020_bevacizumab_pk.R, Papachristos_2020_bevacizumab_qss.R, Papachristos_2020_bevacizumab_pkpd.R (multiplicative effect on bevacizumab CL: CL * exp(-0.423 * SNP_ICAM1_RS1799969) in the PK and PK/PD models; CL * exp(-0.33 * SNP_ICAM1_RS1799969) in the binding QSS model – mutant carriers have lower CL and higher trough levels).
• Notes: Time-fixed per subject. Mutant carrier rate in the Papachristos 2020 mCRC cohort: 20% (Notes Table 1 of the paper). The biological mechanism by which the ICAM1 mutant slows bevacizumab clearance is unknown; the association is empirical and may be specific to mCRC.

SNP_VEGFA_RS1570360 (canonical for VEGF-A rs1570360 mutant indicator)

• Description: Binary genotype indicator for the VEGFA rs1570360 single-nucleotide polymorphism (-1154 G > A; promoter region). 1 = at least one mutant (A) allele present; 0 = homozygous wild-type (GG).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (homozygous wild-type).
• Source aliases:
  • cat1 – Papachristos 2020 (used as the first categorical indicator in the inter-compartmental clearance equation of the PK model; no formal column name in the narrative).
• Example models: Papachristos_2020_bevacizumab_pk.R (multiplicative effect on bevacizumab Q: Q * exp(0.378 * SNP_VEGFA_RS1570360) – mutant carriers have higher inter-compartmental clearance).
• Notes: Time-fixed per subject. Mutant carrier rate in the Papachristos 2020 mCRC cohort: 33%. The covariate is significant in the standalone PK model but does not appear in the binding QSS or PK/PD models because in those models the inter-compartmental clearance covariate effects are absorbed into the rs699947 effect on Q (PK/PD) or into the K_ss / BM0 effects (QSS).

SNP_VEGFA_RS699947 (canonical for VEGF-A rs699947 mutant indicator)

• Description: Binary genotype indicator for the VEGFA rs699947 single-nucleotide polymorphism (-2578 C > A; promoter region). 1 = at least one mutant (A) allele present; 0 = homozygous wild-type (CC).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (homozygous wild-type).
• Source aliases:
  • cat2 – Papachristos 2020 PK model (second categorical indicator on Q).
  • cat – Papachristos 2020 binding QSS model (effect on K_ss and BM0) and PK/PD model (effect on Q).
• Example models: Papachristos_2020_bevacizumab_pk.R (effect on Q: -0.429), Papachristos_2020_bevacizumab_qss.R (effect on K_ss: +1.22, on BM0: -0.851), Papachristos_2020_bevacizumab_pkpd.R (effect on Q: -0.414).
• Notes: Time-fixed per subject. Mutant carrier rate in the Papachristos 2020 mCRC cohort: 52%. The mutant allele is associated with lower baseline free VEGF-A levels and a higher in-vivo affinity (higher K_ss), consistent with reports that rs699947 mutants have prolonged overall survival on bevacizumab-based therapy.

SNP_SLCO1B1_RS11045819 (canonical for SLCO1B1 rs11045819 mutant indicator)

• Description: Binary genotype indicator for the SLCO1B1 rs11045819 single-nucleotide polymorphism (C > A; OATP1B1 transporter, exon 5, P155T). 1 = at least one mutant (A) allele present (heterozygous AC or homozygous AA); 0 = homozygous wild-type (CC). The Hennig 2015 cohort (n = 35 successfully genotyped of 44) reported 5 AC heterozygotes, 30 CC homozygotes, and 0 AA homozygotes, so the indicator is effectively heterozygous-vs-CC in that cohort.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (homozygous wild-type CC).
• Source aliases:
  • SLCO1B1 rs11045819 genotype – Hennig 2015 (paper text; the source NONMEM control stream is in the unrecovered AAC supplement, so the formal column name is not on disk).
• Example models: Hennig_2015_rifabutin.R (multiplicative effect on rifabutin bioavailability F: F * (1 + 0.304 * SNP_SLCO1B1_RS11045819) – AC carriers have ~30% higher rifabutin F than CC reference; dOFV = -6.5).
• Notes: Time-fixed per subject. Carrier rate in the Hennig 2015 South-African HIV/TB cohort: 14% (5 of 35 genotyped). SLCO1B1 encodes OATP1B1, a hepatic uptake transporter; rs11045819 has been associated with reduced rifampicin and lopinavir concentrations in prior studies but in Hennig 2015 was associated with INCREASED rifabutin bioavailability (note opposite direction of effect across rifamycins).

SNP_ABCB1_RS1045642 (canonical for ABCB1 rs1045642 (c.3435C>T) mutant allele carrier indicator)

• Description: Binary genotype indicator for the ABCB1 rs1045642 single-nucleotide polymorphism (c.3435C>T; exon 26; synonymous Ile1145Ile; encodes P-glycoprotein / MDR1 efflux transporter). 1 = subject carries at least one T (mutant) allele (heterozygous CT or homozygous TT; pooled because TT homozygote frequency was 1 of 262 in the Bisaso 2014 cohort); 0 = homozygous wild-type (CC). Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (homozygous wild-type CC).
• Source aliases:
  • ABCB13435 – used in Bisaso_2014_albumin.R (paper text “ABCB1c.3435C>T mutation” and Figure 3 stratification “ABCB13435==0 stands for ABCB1c.3435CC while ABCB13435==1 stands for ABCB1c.3435CT and ABCB1c.3435TT”; same orientation as the canonical).
• Example models: Bisaso_2014_albumin.R (multiplicative additive shift on baseline albumin secretion rate Q0: Q0 = exp(lq0) * (1 + e_snp_abcb1_rs1045642_q0 * SNP_ABCB1_RS1045642) with e_snp_abcb1_rs1045642_q0 = 0.167; T-carriers have 16.7% higher Q0 than CC wild-type, equivalent to the paper text’s “16% higher” framing).
• Notes: Distinct from ABCB1_HAP_TTT (which is the multi-SNP haplotype across rs1128503 / rs2032582 / rs1045642 jointly – a different concept even though rs1045642 is one of the three contributing SNPs); use ABCB1_HAP_TTT when the source paper reports a phased haplotype, and SNP_ABCB1_RS1045642 when the source paper reports the single c.3435C>T SNP alone. Heterozygote and homozygote T-carriers are pooled in Bisaso 2014 because the TT cohort was n = 1; extractions that instead assign the effect to the homozygous-variant stratum alone use the sibling SNP_ABCB1_RS1045642_HOM (registered alongside Gu_2025_rivaroxaban.R, which pools CC and CT into its reference group), and a source estimating a separate heterozygote effect would add the matching SNP_ABCB1_RS1045642_HET, following the SLCO1B1_HAP15_HET / SLCO1B1_HAP15_HOM precedent. ABCB1 c.3435C>T is associated with altered P-glycoprotein expression and has been linked in the literature to predisposition to ART and rifampicin-based anti-TB drug-induced liver injury (Yimer 2011, cited in Bisaso 2014 Discussion). Ratified canonically on 2026-05-20 alongside the Bisaso 2014 albumin extraction.

SNP_ABCB1_RS1045642_HOM (canonical for ABCB1 rs1045642 (c.3435C>T) homozygous-variant indicator)

• Description: Binary genotype indicator for the homozygous-variant stratum of the ABCB1 rs1045642 single-nucleotide polymorphism (c.3435C>T; exon 26; synonymous Ile1145Ile; encodes the P-glycoprotein / MDR1 efflux transporter). 1 = subject carries two variant alleles (genotype c.3435TT, written AA in the dbSNP A/G orientation); 0 = otherwise (the union of c.3435CC wild-type homozygotes and CT heterozygotes). Time-fixed per subject (germline genotype). Use this canonical – not the carrier-orientation SNP_ABCB1_RS1045642 – when the source paper assigns the covariate effect to the homozygous-variant group alone and pools heterozygotes with wild-type homozygotes.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (pooled c.3435CC wild-type homozygotes and CT heterozygotes).
• Source aliases:
  • AA – Gu 2025 Results section 3.2 final-model equation (AA = 1 for AA genotype of rs1045642, AA = 0 for other genotypes of rs1045642), and Table 3 row CL_A642. Gu 2025 genotypes rs1045642 on the A/G strand, so its AA stratum is the c.3435TT homozygous-variant group; same orientation as the canonical, no value transformation.
• Example models: Gu_2025_rivaroxaban.R (log-additive effect on apparent clearance: exp(-0.204 * SNP_ABCB1_RS1045642_HOM), equivalently the multiplicative factor 0.815 reported as CL_A642 in Gu 2025 Table 3, so AA subjects clear rivaroxaban about 18.5% more slowly than the pooled non-AA reference; Gu 2025 screened the SNP first as a four-category variable, found only the AA stratum separated, and reclassified it as this two-category covariate).
• Notes: The homozygous half of the paired-indicator decomposition anticipated in the SNP_ABCB1_RS1045642 notes, following the SLCO1B1_HAP15_HET / SLCO1B1_HAP15_HOM, CYP2D6_STAR10_HET / CYP2D6_STAR10_HOM and CYP3A5_STAR1_HET / CYP3A5_STAR1_HOM precedents. The companion SNP_ABCB1_RS1045642_HET indicator is deliberately NOT registered here because Gu 2025 pools heterozygotes into the reference group and estimates no separate heterozygote effect; register it when a source paper estimates one. Distinct from SNP_ABCB1_RS1045642 (1 = any T-allele carrier, het or hom) and from ABCB1_HAP_TTT (the phased three-SNP haplotype). Beware the nomenclature trap in the founding paper: the Gu 2025 Discussion calls the AA stratum the “wild genotype”, which contradicts both the standard dbSNP orientation (A = the c.3435T variant allele) and the paper’s own allele frequencies (G = 0.60, A = 0.40 in its NVAF cohort, matching the Han-Chinese c.3435C frequency); the canonical follows the genotype coding of the printed equation rather than the prose label.

SNP_ABCB1_RS4148738_HET (canonical for ABCB1 rs4148738 heterozygote (CT) indicator)

• Description: Binary genotype indicator for the heterozygous stratum of the ABCB1 rs4148738 single-nucleotide polymorphism (intronic; encodes P-glycoprotein / MDR1, the intestinal efflux transporter that limits dabigatran etexilate absorption). 1 = subject carries exactly one C and one T allele (genotype CT); 0 = otherwise (the union of CC and TT homozygotes). Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (non-CT, i.e. the pooled CC + TT homozygote group). Note this is a heterozygote-versus-both-homozygotes contrast, not the more usual heterozygote-versus-wild-type-homozygote contrast, because the founding paper carried separate CC / CT / TT dummy columns and only the CT dummy survived stepwise covariate modelling.
• Source aliases:
  • ABCB1CT – used in Yang_2024_dabigatran.R (Yang 2024 Supplementary Material $INPUT column list, which also carries the unused ABCB1C and ABCB1T dummies; the retained covariate block is IF(ABCB1CT.EQ.1) V2ABCB1CT = ( 1 + THETA(12))).
• Example models: Yang_2024_dabigatran.R (linear proportional deviation on the apparent central volume of distribution: vc = exp(lvc + etalvc) * (1 + e_abcb1_het_vc * SNP_ABCB1_RS4148738_HET) with e_abcb1_het_vc = 0.38; CT heterozygotes have 38% larger V2/F than the pooled CC + TT reference; Yang 2024 Table 4 row “rs4148738 on V2”, RSE 45.5%, bootstrap 95% CI 0.04-0.55).
• Notes: Distinct from SNP_ABCB1_RS1045642 (c.3435C>T in exon 26), SNP_ABCB1_RS3842 (c.4036A>G in the 3’ UTR), and ABCB1_HAP_TTT (the phased rs1128503 / rs2032582 / rs1045642 haplotype); use this entry only when the source paper genotypes rs4148738 specifically. Follows the SNP_ABCG2_RS2231142_HET and SNP_IL10_RS1800896_HET precedents for a heterozygote-stratum indicator, and is an auto-approved member of the SNP_<GENE>_RS<rsid> family. No paired SNP_ABCB1_RS4148738_HOM is registered yet: Yang 2024 tested a TT dummy in the same forward-selection step and it did not enter, so no homozygote effect size exists to encode. A future paper that resolves a distinct TT effect should add the _HOM sibling and, at that point, re-express the reference category as the CC wild-type homozygote alone. The founding cohort was 99 genotyped healthy Chinese adults (Yang 2024 Table 2): CC 20 (20%), CT 48 (48%), TT 31 (31%), minor (C) allele frequency 44.44%. The modelled CT-only effect is non-monotonic in allele dose, which the source paper does not explain; treat the estimate as an empirical stratum contrast in a heterozygote-rich cohort rather than a mechanistic gene-dose effect (Yang 2024 Results 3.4 similarly reports the CT stratum as the one with a distinctly low median trough concentration). rs4148738 has been associated with dabigatran peak concentrations in the RE-LY genome-wide analysis (Pare 2013, cited in Yang 2024 Introduction). Ratified canonically on 2026-08-11 alongside the Yang 2024 dabigatran extraction.

SNP_ABCB1_RS1045642_GA (canonical for ABCB1 rs1045642 heterozygous GA genotype indicator)

• Description: Binary genotype indicator for the ABCB1 rs1045642 single-nucleotide polymorphism (conventionally written c.3435C>T; exon 26; synonymous Ile1145Ile; encodes P-glycoprotein / MDR1). 1 = subject’s reported genotype is GA (heterozygous); 0 = otherwise. Paired with SNP_ABCB1_RS1045642_GG; both indicators are 0 for the AA reference group. Time-fixed per subject (germline genotype). Named by the reported genotype letters rather than by wild-type / variant status, because the founding source does not state which genotype is the wild type (see Notes).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 in combination with SNP_ABCB1_RS1045642_GG = 0, i.e. the AA genotype group (n = 12 of 67 in Yao 2025).
• Source aliases:
  • ABCB1 (rs1045642) GA – the Yao 2025 Table 2 row label for the CL coefficient of the GA group.
• Example models: Yao_2025_flurbiprofen_s.R (exponential effect on apparent plasma clearance of S(+)-flurbiprofen: cl = exp(lcl + etalcl) * exp(e_snp_abcb1_rs1045642_ga_cl * SNP_ABCB1_RS1045642_GA + e_snp_abcb1_rs1045642_gg_cl * SNP_ABCB1_RS1045642_GG) with e_snp_abcb1_rs1045642_ga_cl = -1.52, i.e. GA subjects have about 78% lower apparent CL than the AA reference group).
• Notes: Genotype-level (three-category) encoding, distinct from the pooled carrier indicator SNP_ABCB1_RS1045642 (1 = any T allele, reference = c.3435CC wild type) and from the phased haplotype ABCB1_HAP_TTT. The two encodings are NOT composable: SNP_ABCB1_RS1045642 takes the wild-type homozygote as its reference, whereas this pair takes the source’s AA group as reference, and the wild-type / variant orientation of the reported A / G alleles is unresolved. Yao 2025 reports genotypes as AA/GA/GG (12/40/15) while writing the SNP as “3435C > T” in its Methods, and never states which of AA / GG is the wild type. Two readings are defensible: (i) ABCB1 is transcribed from the minus strand, so cDNA C = genomic G and cDNA T = genomic A, making GG the c.3435CC wild type and AA the variant homozygote – the observed A-allele frequency of 0.478 is consistent with the reported 3435T frequency in Han Chinese cohorts; (ii) the source intended A as the wild type and simply ordered genotypes alphabetically. Reading (i) is the better supported for this locus, which would mean the source used the variant homozygote as its reference category. Because the model’s numerical predictions are identical under either reading, the founding extraction encodes the covariates by reported genotype letter so that no unverifiable wild-type claim is baked into the register; an extraction that needs wild-type-anchored semantics must resolve the orientation against the source’s genotyping assay before pooling with SNP_ABCB1_RS1045642. The _HET / _HOM suffix pair pre-authorised in the SNP_ABCB1_RS1045642 Notes was not used for exactly this reason: _HOM presupposes which homozygote is the variant, and here the reference category is itself a homozygote.

SNP_ABCB1_RS1045642_GG (canonical for ABCB1 rs1045642 homozygous GG genotype indicator)

• Description: Binary genotype indicator for the ABCB1 rs1045642 single-nucleotide polymorphism (conventionally written c.3435C>T). 1 = subject’s reported genotype is GG (homozygous for the G allele); 0 = otherwise. Paired with SNP_ABCB1_RS1045642_GA; both indicators are 0 for the AA reference group. Time-fixed per subject (germline genotype). Named by the reported genotype letters rather than by wild-type / variant status.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 in combination with SNP_ABCB1_RS1045642_GA = 0, i.e. the AA genotype group (n = 12 of 67 in Yao 2025).
• Source aliases:
  • ABCB1 (rs1045642) GG – the Yao 2025 Table 2 row label for the CL coefficient of the GG group.
• Example models: Yao_2025_flurbiprofen_s.R (exponential effect on apparent plasma clearance of S(+)-flurbiprofen with e_snp_abcb1_rs1045642_gg_cl = 0.19, i.e. GG subjects have about 21% higher apparent CL than the AA reference group).
• Notes: Always used together with SNP_ABCB1_RS1045642_GA; a model that references one without the other has mis-specified the reference category. See the SNP_ABCB1_RS1045642_GA Notes for the unresolved wild-type / variant orientation and for why this pair must not be pooled with the carrier indicator SNP_ABCB1_RS1045642. In Yao 2025 the genotype effects are non-monotonic (GA markedly reduces apparent CL, GG slightly increases it, relative to AA); the source reports this without mechanistic explanation beyond noting that P-glycoprotein polymorphisms modulate transport, and the AA reference group contained only 12 subjects.

SNP_POR_RS1057868_GA (**canonical for POR rs1057868 (POR*28) heterozygous GA genotype indicator**)

• Description: Binary genotype indicator for the POR rs1057868 single-nucleotide polymorphism (POR*28; conventionally written c.1508C>T, p.Ala503Val; encodes cytochrome P450 oxidoreductase, the flavoprotein that supplies electrons to microsomal CYP enzymes). 1 = subject’s reported genotype is GA (heterozygous); 0 = otherwise. Paired with SNP_POR_RS1057868_GG; both indicators are 0 for the AA reference group. Time-fixed per subject (germline genotype). Named by the reported genotype letters rather than by wild-type / variant status (see Notes).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 in combination with SNP_POR_RS1057868_GG = 0, i.e. the AA genotype group (n = 17 of 67 in Yao 2025).
• Source aliases:
  • POR (rs1057868) GA – the Yao 2025 Table 3 row label for the CL coefficient of the GA group.
• Example models: Yao_2025_flurbiprofen_r.R (exponential effect on apparent plasma clearance of R(-)-flurbiprofen: cl = exp(lcl + etalcl) * exp(e_snp_por_rs1057868_ga_cl * SNP_POR_RS1057868_GA + e_snp_por_rs1057868_gg_cl * SNP_POR_RS1057868_GG) with e_snp_por_rs1057868_ga_cl = -0.29, i.e. GA subjects have about 25% lower apparent CL than the AA reference group).
• Notes: First POR entry in the register. Genotype-level (three-category) encoding following the SNP_ABCB1_RS1045642_GA / _GG pattern. As with that pair, the wild-type / variant orientation of the reported A / G alleles is unresolved: Yao 2025 reports AA/GA/GG = 17/41/9 and writes the locus as “POR28 (rs1057868)” but never states which genotype is the wild type. Two readings conflict. (i) POR* is transcribed from the minus strand, so cDNA C = genomic G and cDNA T = genomic A, making GG the c.1508CC wild type and AA the 28/28 variant homozygote. (ii) The source’s own mechanism narrative points the other way – the Discussion attributes the reduced clearance of the GA and GG groups to”compromised electron transfer from POR to CYP450 enzymes”, i.e. it treats G as the loss-of-function (28) allele and therefore AA as the wild type. The allele-frequency check cannot break the tie because this locus deviates from Hardy-Weinberg equilibrium in the source cohort (the observed A-allele frequency of 0.560 far exceeds the roughly 0.36-0.44 POR28 frequency reported for East Asian cohorts, and the authors attribute the deviation to their small sample size). Because the model’s numerical predictions are identical under either reading, the founding extraction encodes the covariates by reported genotype letter so that no unverifiable wild-type claim is baked into the register. An extraction that needs wild-type-anchored POR semantics must resolve the orientation against the source’s genotyping assay first, and should register a separate wild-type-anchored canonical rather than redefining this one.

SNP_POR_RS1057868_GG (**canonical for POR rs1057868 (POR*28) homozygous GG genotype indicator**)

• Description: Binary genotype indicator for the POR rs1057868 single-nucleotide polymorphism (POR*28; conventionally written c.1508C>T, p.Ala503Val). 1 = subject’s reported genotype is GG (homozygous for the G allele); 0 = otherwise. Paired with SNP_POR_RS1057868_GA; both indicators are 0 for the AA reference group. Time-fixed per subject (germline genotype). Named by the reported genotype letters rather than by wild-type / variant status.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 in combination with SNP_POR_RS1057868_GA = 0, i.e. the AA genotype group (n = 17 of 67 in Yao 2025).
• Source aliases:
  • POR (rs1057868) GG – the Yao 2025 Table 3 row label for the CL coefficient of the GG group.
• Example models: Yao_2025_flurbiprofen_r.R (exponential effect on apparent plasma clearance of R(-)-flurbiprofen with e_snp_por_rs1057868_gg_cl = -2.01, i.e. GG subjects have about 87% lower apparent CL than the AA reference group).
• Notes: Always used together with SNP_POR_RS1057868_GA; a model that references one without the other has mis-specified the reference category. See the SNP_POR_RS1057868_GA Notes for the unresolved wild-type / variant orientation and the Hardy-Weinberg deviation at this locus. The Yao 2025 genotype effects are monotonic in G-allele count (GA about 25% lower, GG about 87% lower apparent CL than AA), which is the pattern expected if G is the reduced-function allele; only 9 of 67 subjects were GG, so the GG coefficient is imprecise (bootstrap 95% CI -5.2 to -1.2).

SLCO1B1_HAP15_HET (**canonical for SLCO1B1*15 haplotype heterozygote indicator**)

• Description: Binary haplotype indicator for the SLCO1B1 *15 reduced-function haplotype (the cis combination of the 388A>G / rs2306283 and 521T>C / rs4149056 variants; encodes the OATP1B1 N130D + V174A double mutant). 1 = subject carries exactly one 15 allele (heterozygous: *1a/*15 or *1b/*15), 0 = otherwise (the union of 15-noncarriers and *15-homozygotes; the paired indicator SLCO1B1_HAP15_HOM flags the homozygous group). Time-fixed per subject (germline haplotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (homozygous 1a/1a, 1a/1b, or 1b/1b – i.e., no 15 allele). The reference group is the union of the three non-15 diplotypes; SLCO1B1_HAP15_HOM flags the 15/15 group.
• Source aliases:
  • HT – Ide 2009 (paper text Eq. for Frel = 1 * theta1^HT * theta2^HM where HT = 1 for heterozygotes *1a/*15 and *1b/*15).
  • OATP1B1 phenotype – Jeong 2022 (paper Section 3.2 + Table 1 phenotype-to-haplotype mapping: ET = 1a/1a + 1a/1b + 1b/1b, IT = 1a/15 + 1b/15, PT = 15/15; IT subjects map to SLCO1B1_HAP15_HET = 1).
• Example models: Ide_2009_pravastatin.R (multiplicative effect on relative bioavailability Frel: Frel = 1.50^SLCO1B1_HAP15_HET * 1.95^SLCO1B1_HAP15_HOM – 15 heterozygotes have 50% higher Frel than 15-noncarriers; dOFV = 32.2 in backward elimination, p < 0.001), Jeong_2022_torsemide.R (linear-deviation effect on apparent central volume V/F: V/F = tvV/F * (1 + (-0.410) * SLCO1B1_HAP15_HET + (-0.646) * SLCO1B1_HAP15_HOM) – 15 heterozygotes have 41% lower V/F than 15-noncarriers; Jeong 2022 Table 4).
• Notes: Paired with SLCO1B1_HAP15_HOM to encode a three-level haplotype categorical (noncarrier / heterozygote / homozygote) with *15-noncarrier as the implicit reference (both indicators = 0). Distinct from the SNP-level canonical SNP_SLCO1B1_RS11045819 (which encodes only the C>A variant at a different position; rs11045819 = P155T) and from the 15 component SNPs rs2306283 (388A>G) and rs4149056 (521T>C) individually: future Ide-style extractions that pool 5 (521T>C only) with *15 should still record their values under this canonical and document the pooling rule in covariateData[[SLCO1B1_HAP15_HET]]$notes. Distribution in the Ide 2009 cohort of 57 healthy Japanese male volunteers (Table I): 28 noncarriers, 23 heterozygotes, 6 homozygotes; in the Jeong 2022 cohort of 112 healthy Korean male volunteers (Table 1): 86 ET noncarriers (76.8%), 23 IT heterozygotes (20.5%), 3 PT homozygotes (2.7%). Ratified canonically on 2026-05-12 alongside the Ide 2009 extraction; scope promoted from specific to general on 2026-05-17 alongside the Jeong 2022 torsemide extraction (second model using the same haplotype encoding across an unrelated drug class, demonstrating the canonical generalizes beyond statins).

SLCO1B1_HAP15_HOM (**canonical for SLCO1B1*15 haplotype homozygote indicator**)

• Description: Binary haplotype indicator for the SLCO1B1 *15 reduced-function haplotype. 1 = subject carries two 15 alleles (homozygous: *15/*15), 0 = otherwise (the union of 15-noncarriers and *15-heterozygotes; the paired indicator SLCO1B1_HAP15_HET flags the heterozygous group). Time-fixed per subject (germline haplotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (homozygous 1a/1a, 1a/1b, or 1b/1b – i.e., no 15 allele). The reference group is the union of the three non-15 diplotypes; SLCO1B1_HAP15_HET flags the *15-heterozygote group.
• Source aliases:
  • HM – Ide 2009 (paper text Eq. for Frel = 1 * theta1^HT * theta2^HM where HM = 1 for homozygotes *15/*15).
  • OATP1B1 phenotype – Jeong 2022 (paper Section 3.2 + Table 1 phenotype-to-haplotype mapping: PT = 15/15 corresponds to SLCO1B1_HAP15_HOM = 1).
• Example models: Ide_2009_pravastatin.R (multiplicative effect on relative bioavailability Frel: Frel = 1.50^SLCO1B1_HAP15_HET * 1.95^SLCO1B1_HAP15_HOM – 15 homozygotes have 95% higher Frel than 15-noncarriers; dOFV = 33.7 in backward elimination, p < 0.001), Jeong_2022_torsemide.R (linear-deviation effect on apparent central volume V/F: V/F = tvV/F * (1 + (-0.410) * SLCO1B1_HAP15_HET + (-0.646) * SLCO1B1_HAP15_HOM) – 15 homozygotes have 64.6% lower V/F than 15-noncarriers; Jeong 2022 Table 4).
• Notes: Paired with SLCO1B1_HAP15_HET to encode a three-level haplotype categorical (noncarrier / heterozygote / homozygote) with *15-noncarrier as the implicit reference (both indicators = 0). See SLCO1B1_HAP15_HET Notes for the broader context; population distribution in Ide 2009 was 6 of 57 (10.5%) homozygotes and in Jeong 2022 was 3 of 112 (2.7%) homozygotes. Ratified canonically on 2026-05-12 alongside the Ide 2009 extraction; scope promoted from specific to general on 2026-05-17 alongside the Jeong 2022 torsemide extraction (second model using the same haplotype encoding across an unrelated drug class).

CYP2C9_S1_COUNT (**canonical for CYP2C9*1 (wild-type) allele count**)

• Description: Continuous individual-level CYP2C91 allele count: 0 = no 1 allele, 1 = one 1 allele (heterozygous), 2 = two 1 alleles (homozygous wild-type). Time-invariant (germline genotype). Paired with CYP2C9_S2_COUNT and CYP2C9_S3_COUNT to encode the three loss-of-function-allele dosage form used by Hamberg-family warfarin models, where the subject’s CL is the sum of per-allele CL contributions across the two CYP2C9 alleles. The three count columns sum to 2 for each subject.
• Units: (count, 0/1/2 alleles per subject)
• Type: continuous
• Scope: general
• Reference category: n/a (continuous). Used directly as a multiplier on a fixed per-allele CL contribution (0.174 L/h per *1 allele in Hamberg / Xia 2024).
• Source aliases:
  • CYP2C9 (genotype string such as "*1/*1", "*1/*3"): derive CYP2C9_S1_COUNT = (length of *1 matches in the genotype string). The model Xia_2024_warfarin.R carries the source CYP2C9 genotype string mapped to the three count columns.
• Example models: Xia_2024_warfarin.R (per-allele CL contributions: cl = CYP2C9_S1_COUNT * 0.174 + CYP2C9_S2_COUNT * 0.0879 + CYP2C9_S3_COUNT * 0.0422, times an age effect).
• Notes: Hamberg’s warfarin K-PD model parameterises CL as a sum of two per-allele CL contributions (one per CYP2C9 allele on each chromosome), which is more flexible than a single genotype indicator because it naturally accommodates any combination of 1, 2, 3 alleles (six diplotypes: 1/1, 1/2, 1/3, 2/2, 2/3, 3/3). When a paper reports additional CYP2C9 alleles (e.g. 5, 6, 8, *11), register parallel canonicals (CYP2C9_S5_COUNT etc.) rather than overloading the existing three counts. Distinct from the categorical phenotype canonicals CYP3A5_EXPR (binary expresser) and from continuous-activity scores like CYP3A4 – the count form preserves loss-of-function-allele dosage exactly. Ratified canonically on 2026-05-16 alongside the Xia 2024 warfarin extraction.

CYP2C9_S2_COUNT (**canonical for CYP2C9*2 reduced-function allele count**)

• Description: Continuous individual-level CYP2C92 allele count: 0 = no 2 allele, 1 = one 2 allele (heterozygous), 2 = two 2 alleles (homozygous). Time-invariant (germline genotype). Paired with CYP2C9_S1_COUNT and CYP2C9_S3_COUNT; the three counts sum to 2 for each subject. The *2 allele (rs1799853, R144C) encodes a reduced-function CYP2C9 isoform.
• Units: (count, 0/1/2 alleles per subject)
• Type: continuous
• Scope: general
• Reference category: n/a (continuous). Used directly as a multiplier on a fixed per-allele CL contribution (0.0879 L/h per *2 allele in Hamberg / Xia 2024).
• Source aliases:
  • CYP2C9 (genotype string such as "*1/*2", "*2/*2", "*2/*3"): derive CYP2C9_S2_COUNT = (length of *2 matches in the genotype string).
• Example models: Xia_2024_warfarin.R (per-allele CL contributions; the Xia 2024 Han cohort had no *2 carriers, but the model retains the term for general use across CYP2C9 papers).
• Notes: See CYP2C9_S1_COUNT for the broader rationale. Ratified canonically on 2026-05-16 alongside the Xia 2024 warfarin extraction.

CYP2C9_S3_COUNT (**canonical for CYP2C9*3 reduced-function allele count**)

• Description: Continuous individual-level CYP2C93 allele count: 0 = no 3 allele, 1 = one 3 allele (heterozygous), 2 = two 3 alleles (homozygous). Time-invariant (germline genotype). Paired with CYP2C9_S1_COUNT and CYP2C9_S2_COUNT; the three counts sum to 2 for each subject. The *3 allele (rs1057910, I359L) encodes a strongly reduced-function CYP2C9 isoform and is the dominant CYP2C9 pharmacogenomic risk variant in East-Asian populations (warfarin / phenytoin sensitivity).
• Units: (count, 0/1/2 alleles per subject)
• Type: continuous
• Scope: general
• Reference category: n/a (continuous). Used directly as a multiplier on a fixed per-allele CL contribution (0.0422 L/h per *3 allele in Hamberg / Xia 2024).
• Source aliases:
  • CYP2C9 (genotype string such as "*1/*3", "*3/*3"): derive CYP2C9_S3_COUNT = (length of *3 matches in the genotype string).
• Example models: Xia_2024_warfarin.R (per-allele CL contributions; 5.7% of the Han cohort were 1/3 heterozygous per Xia 2024 Table 1), Ohara_2014_warfarin_s.R (dichotomised to a carrier flag via CYP2C9_S3_COUNT > 0; CL(S) in carriers is 0.543x the 1/1 reference).
• Notes: See CYP2C9_S1_COUNT for the broader rationale. Ratified canonically on 2026-05-16 alongside the Xia 2024 warfarin extraction.

CYP2C9_MISSING (canonical for CYP2C9 genotype-missing indicator)

• Description: Binary indicator for a subject whose CYP2C9 genotype was not measured / not available. 1 = CYP2C9 genotype missing; 0 = CYP2C9 genotype known (in which case CYP2C9_S1_COUNT + CYP2C9_S2_COUNT + CYP2C9_S3_COUNT = 2). Companion to the CYP2C9_S{1,2,3}_COUNT per-allele canonicals so popPK models that estimate a separate typical-value covariate effect for the missing-genotype subgroup (rather than imputing it as wild-type) can do so faithfully.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (CYP2C9 genotype known). When CYP2C9_MISSING == 1 the three CYP2C9_S{1,2,3}_COUNT columns must all be 0 so the diplotype-indicator products correctly evaluate to zero for the missing-genotype subject.
• Source aliases:
  • Missing (Lane 2011 Table 3 q_CYP2C9 Missing row – the paper treats unknown CYP2C9 genotype as a distinct categorical level with its own multiplicative CL effect, rather than imputing the missing subjects into the wild-type reference; 14 of 306 S-warfarin subjects were missing CYP2C9 genotype per Lane 2011 Table 1).
• Example models: Lane_2011_warfarin_s.R (multiplicative CL effect 0.782 for the missing-genotype subgroup vs the 1/1 wild-type reference; Lane 2011 Table 3).
• Notes: Follows the missing-data-indicator pattern established by ADA_MISSING (multiplicative CL effect for subjects with missing ADA results; Suri 2018 brentuximab) and HEPIMP_MOD_OR_MISSING (composite moderate-or-missing hepatic impairment indicator). Distinct from the strategy of imputing missing genotype as wild-type (which would set CYP2C9_S1_COUNT = 2 and not use a separate indicator); use CYP2C9_MISSING only when the source paper explicitly fits a separate coefficient for the missing-genotype subgroup. Ratified canonically on 2026-06-30 alongside the Lane 2011 R/S-warfarin extraction.

VKORC1_1639G_COUNT (canonical for VKORC1 -1639G allele count)

• Description: Continuous individual-level count of VKORC1 -1639G alleles (rs9923231, also reported as VKORC1 -1639 G > A or 1173 C > T depending on numbering convention). 0 = AA homozygous (warfarin-sensitive), 1 = GA heterozygous, 2 = GG homozygous (warfarin-resistant). Time-invariant (germline genotype). The complementary -1639A count is 2 - VKORC1_1639G_COUNT.
• Units: (count, 0/1/2 alleles per subject)
• Type: continuous
• Scope: general
• Reference category: n/a (continuous). In the Hamberg / Xia 2024 EC50 model the typical EC50 is a per-allele sum: ec50_typ = VKORC1_1639G_COUNT * ec50_per_G + (2 - VKORC1_1639G_COUNT) * ec50_per_A. Distribution in the Xia 2024 Han cohort (Table 1): AA 80.3%, GA 18.7%, GG 0.9% (G allele frequency ~10%, consistent with East-Asian populations).
• Source aliases:
  • VKORC1 (genotype string such as "AA", "GA", "GG" or "1639AA" etc.): derive VKORC1_1639G_COUNT = (count of G in the two-letter genotype).
• Example models: Xia_2024_warfarin.R (per-allele EC50 contributions, re-estimated for the Han Chinese cohort: 4.3 mg/L per G allele, 1.14 mg/L per A allele), Ohara_2014_warfarin_s.R (dichotomised to a G-carrier flag via VKORC1_1639G_COUNT > 0, with -1639A/A as the reference and a 2.07-fold IC50 increase in G carriers).
• Notes: VKORC1 -1639G > A is the strongest single-SNP determinant of warfarin sensitivity (the A allele reduces VKORC1 expression via a promoter-region effect, requiring less warfarin to achieve target anticoagulation). The per-allele count form is preferred over a binary carrier indicator because the heterozygous and homozygous mutant subjects respond detectably differently to warfarin. The count column also reconstructs a carrier-flag parameterisation deterministically (count > 0), which is what papers reporting a single “VKORC12” indicator require – note that such papers usually take the A/A genotype as the reference and report an IC50/EC50 increase* per G carriage, the mirror image of the Hamberg / Xia G/G-reference orientation. Ratified canonically on 2026-05-16 alongside the Xia 2024 warfarin extraction.

SNP_CYP4F2_RS2108622_T_COUNT (**canonical for CYP4F2 1297C>T (rs2108622, CYP4F2*3) T-allele count**)

• Description: Continuous individual-level count of CYP4F2 c.1297C>T (rs2108622, p.V433M) T alleles, i.e. the CYP4F2*3 allele: 0 = CC homozygous wild-type (1/1), 1 = CT heterozygous (1/3), 2 = TT homozygous variant (3/3). Time-invariant (germline genotype). CYP4F2 is the hepatic vitamin-K1 oxidase; the 433M variant protein has reduced activity, so *3 carriers retain more hepatic vitamin K and require a higher warfarin concentration for the same inhibition of vitamin-K-dependent clotting-factor synthesis. It is the third pharmacogene (after VKORC1_1639G_COUNT and the CYP2C9_S{1,2,3}_COUNT family) included in the CPIC and IWPC warfarin dosing guidance.
• Units: (count, 0/1/2 alleles per subject)
• Type: continuous
• Scope: general
• Reference category: n/a (continuous). In Ohara_2014_warfarin_s.R the count is dichotomised in model() to a carrier indicator (SNP_CYP4F2_RS2108622_T_COUNT > 0) because the source paper reports a single carriage effect rather than a per-allele dosage: IC50 is 1.30x higher in 1/3 and 3/3 subjects than in the 1/1 reference (Ohara 2014 Table 2).
• Source aliases:
  • CYP4F2*3 – Ohara 2014 / Shi 2024 (reported as a 0/1 carriage indicator; Shi 2024 Table 3 footnote c defines CYP4F2*3 = 0 for 1/1 and 1 otherwise, so the count column reconstructs it as SNP_CYP4F2_RS2108622_T_COUNT > 0).
  • CYP4F2 (genotype string such as "CC" / "CT" / "TT", or star-allele form "*1/*1" / "*1/*3" / "*3/*3"): derive the count as the number of T (equivalently *3) alleles in the diplotype.
  • V433M / rs2108622 / 1297C>T – alternative labels for the same variant.
• Example models: Ohara_2014_warfarin_s.R (multiplicative 1.30x effect on the S-warfarin IC50 for inhibition of normal-prothrombin synthesis, applied to carriers; Ohara 2014 Table 2, 95% CI 1.07-1.53).
• Notes: Distribution in the Ohara 2014 Taiwanese Chinese cohort (Table 1, n = 99): 50 CC, 43 CT, 6 TT (MAF 0.278), consistent with reported East-Asian frequencies. Shi 2024 assumed all simulated Chinese patients to be CYP4F21/1, so the column is 0 throughout their clinical-trial simulations. The count form (rather than a bare SNP_CYP4F2_RS2108622 indicator) follows the SNP_CYP2B6_RS3745274_T_COUNT and CYP2C9_S{1,2,3}_COUNT precedent: a single count column carries the full genotype without redundancy and lets model() derive either a per-allele effect (* count) or a carrier/HET/HOM decomposition depending on the source paper’s parameterisation. Ratified canonically on 2026-08-07 alongside the Ohara 2014 S-warfarin PK/PD extraction.

SNP_CYP1A2_RS762551_C_CARRIER (canonical for CYP1A2 rs762551 (C163A) C-allele carrier indicator)

• Description: Binary indicator for carriage of the CYP1A2 rs762551 promoter-region single-nucleotide polymorphism (also known as CYP1A2 c.-163C>A; the paper’s “C163A” shorthand). 1 = subject carries at least one C allele (heterozygous AC or homozygous CC); 0 = homozygous AA (the CYP1A2*1F reference / ultra-inducible form, associated with enhanced CYP1A2 substrate metabolism in smokers). Time-fixed per subject (germline genotype). AC heterozygous and CC homozygous carriers are pooled in Hopkins 2015 because the CC homozygous frequency (9/105 = 8.6%) was too low for a separate phenotype layer; future extractions distinguishing heterozygous vs homozygous C-allele effects should register paired SNP_CYP1A2_RS762551_C_HET / SNP_CYP1A2_RS762551_C_HOM indicators following the SLCO1B1_HAP15_HET / SLCO1B1_HAP15_HOM precedent.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (homozygous AA, i.e., CYP1A21F/1F).
• Source aliases:
  • CYP1A2 C163A – Hopkins 2015 (paper Table 3 caption defines the covariate: “carriers of the C allele of CYP1A2 rs762551 (i.e. CC or CA genotype)”; same orientation as the canonical).
• Example models: Hopkins_2015_leflunomide.R (multiplicative effect on the cessation-hazard covariate model in a leflunomide time-to-event analysis: hazard <- h0(t) * (1 + e_cyp1a2_haz * SNP_CYP1A2_RS762551_C_CARRIER) with e_cyp1a2_haz = 1.29; C-allele carriers have a 2.29-fold (95% CI 2.24, 2.34) increase in instantaneous cessation hazard compared with AA homozygotes; Hopkins 2015 Table 3 covariate-model theta10 and Results paragraph on CYP1A2).
• Notes: rs762551 lies in the CYP1A2 promoter region. The A allele defines the CYP1A2*1F ultra-inducible haplotype associated with enhanced CYP1A2 substrate metabolism in cigarette smokers (Sachse et al. 1999, cited in Hopkins 2015 Discussion). Downstream mechanistic hypotheses for the observed leflunomide-toxicity association include (a) altered relative contributions of CYP1A2 vs CYP2C19 / CYP3A4 in converting leflunomide to its active metabolite teriflunomide, potentially producing higher concentrations of alternate toxic metabolites in C-allele carriers, and (b) direct leflunomide toxicity that is unmasked when the CYP1A2 clearance arm is slower (Hopkins 2015 Discussion). No association between smoking status and leflunomide cessation was detected in the Hopkins 2015 cohort, but the smoking rate (37.1%) was modest. Distinct from S1A2 (a specific-scope categorical CYP1A2-modifying co-medication indicator carried per subject in the DDMORE lidocaine bundle; not the same concept). The naming pattern SNP_<GENE>_<RSID>_<ALLELE>_CARRIER follows the existing SNP_ABCB1_RS1045642 / SNP_ABCB1_RS3842 precedent while making the binarised allele explicit. Ratified canonically on 2026-06-21 alongside the Hopkins 2015 leflunomide extraction.

SNP_CYP2B6_RS3745274_T_COUNT (canonical for CYP2B6 516G>T (rs3745274) T-allele count)

• Description: Continuous individual-level CYP2B6 c.516G>T (rs3745274, p.Q172H) T-allele count: 0 = GG homozygous wild-type, 1 = GT heterozygous, 2 = TT homozygous variant. Time-invariant (germline genotype). The 516G>T variant reduces CYP2B6 enzyme activity in a gene-dose-dependent but non-additive manner; in nevirapine and efavirenz populations, the TT-vs-GG decrease in apparent oral clearance is approximately 2-3x larger than the GT-vs-GG decrease, motivating a heterozygous-vs-homozygous indicator parameterization rather than a linear per-allele model.
• Units: (count, 0/1/2 alleles per subject)
• Type: continuous
• Scope: general
• Reference category: n/a (continuous). In the Schipani 2011 nevirapine model the count is decomposed in model() into mutually-exclusive heterozygous (count == 1) and homozygous (count == 2) indicators, each multiplied by an independently estimated CL/F shift (-0.5 L/h for GT, -1.3 L/h for TT relative to the GG reference). In the Olagunju 2018 efavirenz model the count is summed together with SNP_CYP2B6_RS28399499_C_COUNT to derive a composite metaboliser status (n_variant == 0 fast, == 1 intermediate, >= 2 slow), and the per-group CL/F is encoded as log-ratio multiplicative shifts on the fast-metaboliser reference.
• Source aliases:
  • X_516GT / X_516TT – Schipani 2011 (paper Table 2 / final-model equation TVCL = theta0 + theta_BW * (BW - 72.5) + theta_516GT * X_516GT + theta_516TT * X_516TT + theta_983TC * X_983TC; the two indicators are mutually exclusive across the three 516 genotypes, so the canonical count column reconstructs them deterministically via X_516GT = as.integer(SNP_CYP2B6_RS3745274_T_COUNT == 1) and X_516TT = as.integer(SNP_CYP2B6_RS3745274_T_COUNT == 2)).
  • CYP2B6 516G>T (rs3745274) – Olagunju 2018 (paper Methods ‘Sample Collection …’ paragraph 1; genotype reported per-allele and used in combination with rs28399499 to define a composite CYP2B6 metaboliser status).
  • RSA – Vucicevic 2025 (paper Table 2 footnote: ‘binary parameter indicating whether the patient is a carrier of CYP2B6 516G>T (1) or not (0)’; a dominant-model carrier indicator that the canonical count column reconstructs via SNP_CYP2B6_RS3745274_T_COUNT >= 1. Results paragraph 2 reports the carriers as GT heterozygotes (n = 30) versus GG wild-type (n = 60) with no TT homozygotes observed, so within that cohort the carrier indicator and a heterozygote indicator coincide).
• Example models: Schipani_2011_nevirapine.R (additive linear shift on CL/F: cl = exp(lcl) + e_516gt_cl * (SNP_CYP2B6_RS3745274_T_COUNT == 1) + e_516tt_cl * (SNP_CYP2B6_RS3745274_T_COUNT == 2) + ...; 516TT homozygotes have approximately 37% lower CL/F than the GG reference; Schipani 2011 Table 2), Olagunju_2018_efavirenz.R (composite-metaboliser-status encoding: variant alleles from rs3745274 and rs28399499 are summed to classify subjects as fast / intermediate / slow, with per-group CL/F = 18.0 / 16.1 / 6.24 L/h reported in Olagunju 2018 Table 2), Vucicevic_2025_efavirenz.R (dominant-model carrier indicator raised as a power of a fractional-change factor on apparent oral clearance: (1 + e_cyp2b6_516t_cl)^(SNP_CYP2B6_RS3745274_T_COUNT >= 1) with e_cyp2b6_516t_cl = -0.364; carriers have CL/F 36.4% lower than GG wild-type; Vucicevic 2025 Table 2 theta_RSA).
• Notes: Distribution in the Schipani 2011 European HIV-positive cohort (Table 1, n = 275): 516GG 47%, 516GT 46%, 516TT 7%. In the Olagunju 2018 Nigerian HIV-positive pregnant-women cohort (Table 1, n = 77): 516GG 32%, 516GT 54%, 516TT 14%. The CYP2B6 516G>T variant is one of the two most-extensively-studied CYP2B6 pharmacogenomic polymorphisms (together with 983T>C / rs28399499, registered as SNP_CYP2B6_RS28399499_C_COUNT) and is consistently associated with reduced metabolism of nevirapine, efavirenz, bupropion, and methadone. The count-form encoding is preferred over paired binary HET / HOM indicators because (a) it follows the established _COUNT precedent used for CYP2C9_S{1,2,3}_COUNT and VKORC1_1639G_COUNT, (b) a single count column captures the underlying genotype without redundancy, and (c) the model code can deterministically derive either a linear per-allele effect (* count) or a non-additive HET / HOM decomposition (* (count == 1), * (count == 2)) depending on the source paper’s parameterization. Ratified canonically on 2026-05-21 alongside the Schipani 2011 nevirapine extraction; scope promoted to general on 2026-05-26 alongside the Olagunju 2018 efavirenz extraction, which uses the same count column under a composite-metaboliser-status encoding combining rs3745274 + rs28399499.

SNP_CYP2B6_RS28399499_C_COUNT (canonical for CYP2B6 983T>C (rs28399499) C-allele count)

• Description: Continuous individual-level CYP2B6 c.983T>C (rs28399499, p.I328T) C-allele count: 0 = TT homozygous wild-type, 1 = TC heterozygous, 2 = CC homozygous variant. Time-invariant (germline genotype). The 983T>C variant lies within the CYP2B6 J-helix and changes a highly conserved hydrophobic Ile to polar Thr; recombinant expression in COS-1 cells shows essentially zero enzyme activity (Klein et al. 2005), and clinical cohorts consistently show large reductions in apparent oral clearance of CYP2B6 substrates in heterozygous carriers.
• Units: (count, 0/1/2 alleles per subject)
• Type: continuous
• Scope: general
• Reference category: n/a (continuous). In the Schipani 2011 nevirapine model the count is decomposed in model() into a heterozygous indicator (count == 1) multiplied by an estimated CL/F shift (-1.4 L/h relative to the TT reference); the homozygous indicator (count == 2) is not estimated because no 983CC homozygotes have been reported in any published cohort. In the Olagunju 2018 efavirenz model the count is summed together with SNP_CYP2B6_RS3745274_T_COUNT to define a composite CYP2B6 metaboliser status (slow / intermediate / fast); a hypothetical 983CC subject is still classified as slow (each variant allele contributes to the composite count) although that substratum is not present in the fitted cohort.
• Source aliases:
  • X_983TC – Schipani 2011 (paper Table 2 / final-model equation; the heterozygous indicator is mutually exclusive with the TT-homozygous reference, so the canonical count column reconstructs it via X_983TC = as.integer(SNP_CYP2B6_RS28399499_C_COUNT == 1)).
  • CYP2B6 983T>C (rs28399499) – Olagunju 2018 (paper Methods ‘Sample Collection …’ paragraph 1; combined with rs3745274 to define a composite metaboliser status).
• Example models: Schipani_2011_nevirapine.R (additive linear shift on CL/F: cl = exp(lcl) + e_983tc_cl * (SNP_CYP2B6_RS28399499_C_COUNT == 1) + ...; 983TC heterozygotes have approximately 40% lower CL/F than the TT reference; Schipani 2011 Table 2), Olagunju_2018_efavirenz.R (composite-metaboliser-status encoding: variant alleles from rs3745274 and rs28399499 are summed to classify subjects as fast / intermediate / slow; Olagunju 2018 Table 2).
• Notes: Distribution in the Schipani 2011 European HIV-positive cohort (Table 1, n = 275): 983TT 97%, 983TC 3%, 983CC 0%. Distribution in the Olagunju 2018 Nigerian HIV-positive pregnant-women cohort (Table 1, n = 77): 983TT 75%, 983TC 25%, 983CC 0% – a much higher heterozygous-variant fraction than the Schipani 2011 cohort, consistent with the West-African ethnicity of the Olagunju 2018 study population. The 983T>C variant is also known as CYP2B6*18 and is found primarily in African and African-admixed populations (allele frequency ~6-8% in West Africans, ~0% in Europeans and East Asians); the Schipani 2011 cohort included 33% Black-ethnicity subjects, which explains the observed 3% heterozygote frequency, while the all-Nigerian Olagunju 2018 cohort shows the typical West-African 25% heterozygote frequency. No 983CC homozygotes have been described in the published literature as of the 2011 report (Schipani 2011 Results paragraph 4), so models that include the variant typically estimate only a heterozygous shift. Often co-tested with 516G>T (linkage-disequilibrium block: subjects homozygous for both 516TT and 983TC are CYP2B6 “slow metabolizers” of efavirenz / nevirapine). The count-form encoding follows the same rationale as SNP_CYP2B6_RS3745274_T_COUNT. Ratified canonically on 2026-05-21 alongside the Schipani 2011 nevirapine extraction; scope promoted to general on 2026-05-26 alongside the Olagunju 2018 efavirenz extraction, which uses the same count column under a composite-metaboliser-status encoding combining rs3745274 + rs28399499.

SNP_CYP2B6_RS4803419_T_COUNT (canonical for CYP2B6 c.485-18C>T (rs4803419) T-allele count)

• Description: Continuous individual-level CYP2B6 c.485-18C>T (rs4803419) T-allele count: 0 = CC homozygous wild-type, 1 = CT heterozygous, 2 = TT homozygous variant. Time-invariant (germline genotype). rs4803419 is an intron-3 splice-region variant (also reported as CYP2B6 15582C>T) that tags the CYP2B6*22-associated reduced-expression haplotype; it is in partial linkage disequilibrium with c.516G>T (rs3745274) but carries an independently detectable effect on efavirenz apparent oral clearance in some cohorts.
• Units: (count, 0/1/2 alleles per subject)
• Type: continuous
• Scope: specific
• Reference category: n/a (continuous). In the Vucicevic 2025 efavirenz model the count is decomposed in model() into a single recessive (TT-versus-rest) indicator, SNP_CYP2B6_RS4803419_T_COUNT == 2, multiplied into a fractional-change factor on CL/F; heterozygotes are pooled with CC wild-type in the reference group.
• Source aliases:
  • RSB – Vucicevic 2025 (paper Table 2 final-model equation CL/F = CLp * (1 + theta_RSA)^RSA * (1 + theta_RSB)^RSB; the Table 2 footnote describes RSB as a ‘carrier’ indicator, but Results paragraph 2 identifies the flagged stratum as the TT genotype (n = 12) and Table 3 contrasts the simulated strata as ‘CYP2B6 c.485-18TT’ versus ‘CYP2B6 c.485-18CC’, so the canonical count column reconstructs RSB via SNP_CYP2B6_RS4803419_T_COUNT == 2).
• Example models: Vucicevic_2025_efavirenz.R (recessive multiplicative factor on apparent oral clearance: (1 + e_cyp2b6_485tt_cl)^(SNP_CYP2B6_RS4803419_T_COUNT == 2) with e_cyp2b6_485tt_cl = -0.268; TT homozygotes have CL/F 26.8% lower than the pooled CC / CT reference; Vucicevic 2025 Table 2 theta_RSB, RSE 16%).
• Notes: Cohort distribution in the Vucicevic 2025 Serbian Caucasian HIV-1 cohort (Table 1, n = 89): 12 (13.48%) TT homozygotes; the CC / CT split within the remaining 77 subjects is not reported. The recessive (TT-only) encoding is the published parameterization, so a _HET sibling is not identifiable from this source; a future paper resolving all three strata should add SNP_CYP2B6_RS4803419_T_HET following the SNP_ABCG2_RS2231142_HET / _HOM precedent, or use the count directly for a per-allele model. The literature on this variant is split: Bertrand 2014 (Cambodian adults on efavirenz plus antituberculosis treatment) reported a 30% CL decrease, consistent with the Vucicevic 2025 estimate, whereas Bienczak 2016 (Ugandan and Zambian children) found no effect; the variant is not included in the CPIC CYP2B6-efavirenz dosing guideline, which covers only c.516G>T and c.983T>C. Follows the _COUNT encoding used by the sibling CYP2B6 canonicals SNP_CYP2B6_RS3745274_T_COUNT, SNP_CYP2B6_RS28399499_C_COUNT, and SNP_CYP2B6_RS35303484_G_COUNT: a single count column captures the germline genotype without redundancy and lets model() derive a per-allele effect (* count), a HET / HOM decomposition (* (count == 1), * (count == 2)), or a dominant / recessive indicator (* (count >= 1), * (count == 2)) as the source paper’s parameterization requires. Auto-approved member of the SNP_<GENE>_RS<rsid> family. Ratified 2026-08-17 alongside the Vucicevic 2025 efavirenz extraction.

SNP_CYP2C19_RS3814637_VAR_COUNT (canonical for CYP2C19 rs3814637 variant-allele count)

• Description: Continuous individual-level CYP2C19 rs3814637 variant-allele count: 0 = wild-type homozygote (the more common allele on both chromosomes), 1 = heterozygote, 2 = variant homozygote. Time-invariant (germline genotype). Companion missing-genotype indicator: SNP_CYP2C19_RS3814637_MISSING (when missing, all three genotype-indicator products evaluate to zero and the missing-multiplier is applied instead). The rs3814637 SNP is in the CYP2C19 gene region and is reported in Lane 2011 as significantly influencing R-warfarin clearance; the paper notes that this SNP is NOT in linkage disequilibrium with the canonical CYP2C19*2 loss-of-function allele (rs4244285) in the Lane 2011 patient population, so it is registered as a distinct canonical column and is not interchangeable with CYP2C19_S2_CARRIER.
• Units: (count, 0/1/2 alleles per subject)
• Type: continuous
• Scope: general
• Reference category: n/a (continuous). In the Lane 2011 R-warfarin model the count is decomposed in model() into mutually exclusive wild-homozygote (count == 2 - VAR_COUNT == 2), heterozygote (VAR_COUNT == 1), and variant-homozygote (VAR_COUNT == 2) indicators, each multiplied by a distinct typical-value CL multiplier.
• Source aliases:
  • CYP2C19 genotype / rs3814637 – Lane 2011 (paper Methods ‘Covariate selection and models’; coded as wild-type / heterozygote / mutant-homozygote with a separate Missing category). Heterozygote vs wild-homozygote CL multiplier 0.761, mutant-homozygote multiplier 0.494 (Lane 2011 Table 3).
• Example models: Lane_2011_warfarin_r.R (categorical CL multiplier 1.00 / 0.761 / 0.494 for wild / heterozygote / variant homozygote; Lane 2011 Table 3 final R-warfarin model).
• Notes: Follows the SNP_<GENE>_RS<rsid>_<allele>_COUNT pattern established by SNP_CYP2B6_RS3745274_T_COUNT and SNP_CYP2B6_RS28399499_C_COUNT. The _VAR token (rather than a specific allele letter) is used because Lane 2011 does not specify which allele is the wild-type vs variant in the paper text, only reporting the categorical wild-type / heterozygote / mutant-homozygote distinction. Future extractions that report rs3814637 with explicit allele letters (e.g. T/C) should add an aliased canonical such as SNP_CYP2C19_RS3814637_T_COUNT and cross-reference. Distinct from CYP2C19_S2_CARRIER (rs4244285): Lane 2011 explicitly notes that rs3814637 is not in linkage disequilibrium with CYP2C19*2 in their cohort. Ratified canonically on 2026-06-30 alongside the Lane 2011 R-warfarin extraction.

SNP_CYP2C19_RS3814637_MISSING (canonical for CYP2C19 rs3814637 genotype-missing indicator)

• Description: Binary indicator for a subject whose CYP2C19 rs3814637 genotype was not measured / not available. 1 = genotype missing; 0 = genotype known (in which case SNP_CYP2C19_RS3814637_VAR_COUNT carries the 0/1/2 variant-allele count). Companion to SNP_CYP2C19_RS3814637_VAR_COUNT so popPK models that estimate a separate typical-value covariate effect for the missing-genotype subgroup (rather than imputing it as wild-type) can do so faithfully.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (genotype known). When SNP_CYP2C19_RS3814637_MISSING == 1 the SNP_CYP2C19_RS3814637_VAR_COUNT column should be 0 so the genotype-indicator products correctly evaluate to zero for the missing-genotype subject.
• Source aliases:
  • Missing (Lane 2011 Table 3 q_CYP2C19 Missing row – the paper treats unknown CYP2C19 rs3814637 genotype as a distinct categorical level with its own multiplicative CL effect; 61 of 309 R-warfarin subjects were missing CYP2C19 genotype per Lane 2011 Table 1).
• Example models: Lane_2011_warfarin_r.R (multiplicative CL effect 0.804 for the missing-genotype subgroup vs the wild-homozygote reference; Lane 2011 Table 3 final R-warfarin model).
• Notes: Follows the same missing-data-indicator pattern as CYP2C9_MISSING, ADA_MISSING, and HEPIMP_MOD_OR_MISSING. Ratified canonically on 2026-06-30 alongside the Lane 2011 R-warfarin extraction.

SNP_CYP3A4_RS2242480_VAR_COUNT (canonical for CYP3A4 rs2242480 variant-allele count)

• Description: Continuous individual-level CYP3A4 rs2242480 variant-allele count: 0 = wild-type homozygote, 1 = heterozygote, 2 = variant homozygote. Time-invariant (germline genotype). Companion missing-genotype indicator: SNP_CYP3A4_RS2242480_MISSING. The rs2242480 SNP is one of the SNPs that defines the CYP3A4*1G haplotype (a 5’ UTR variant whose functional effect remains controversial – some studies report gain-of-function, others loss-of-function; Lane 2011 Discussion paragraph 5).
• Units: (count, 0/1/2 alleles per subject)
• Type: continuous
• Scope: general
• Reference category: n/a (continuous). In the Lane 2011 R-warfarin model the count is decomposed in model() into mutually exclusive wild-homozygote, heterozygote, and variant-homozygote indicators, each multiplied by a distinct typical-value CL multiplier.
• Source aliases:
  • CYP3A4 genotype / rs2242480 – Lane 2011 (paper Methods ‘Covariate selection and models’; coded as wild-type / heterozygote / mutant-homozygote with a separate Missing category). Heterozygote vs wild-homozygote CL multiplier 1.32, variant-homozygote multiplier 1.06 (Lane 2011 Table 3); Lane 2011 explicitly notes the heterozygote vs wild contrast is the significant effect (“Those patients with the heterozygote genotype for CYP3A4 had a 32% increased clearance for R-warfarin”, Lane 2011 Results paragraph ‘R-Warfarin models’).
• Example models: Lane_2011_warfarin_r.R (categorical CL multiplier 1.00 / 1.32 / 1.06 for wild / heterozygote / variant homozygote; Lane 2011 Table 3 final R-warfarin model).
• Notes: Follows the SNP_<GENE>_RS<rsid>_<allele>_COUNT pattern. The _VAR token (rather than a specific allele letter) is used because Lane 2011 does not specify allele letters in the genotype table. Lane 2011 Discussion paragraph 5 notes that the functional effect of rs2242480 / CYP3A4*1G has been subject to controversy in the literature (Gao 2008 and Du 2006 report gain-of-function; Wang 2011 and He 2011 report loss-of-function; a luciferase reporter study in Wang 2013 found higher transcriptional activity for the A allele than the G allele). The Lane 2011 finding of higher R-warfarin clearance in heterozygotes is consistent with a gain-of-function effect in heterozygotes, although the small variant-homozygote stratum (n=3) limits inference for that sub-group. Ratified canonically on 2026-06-30 alongside the Lane 2011 R-warfarin extraction.

SNP_CYP3A4_RS2242480_MISSING (canonical for CYP3A4 rs2242480 genotype-missing indicator)

• Description: Binary indicator for a subject whose CYP3A4 rs2242480 genotype was not measured / not available. 1 = genotype missing; 0 = genotype known. Companion to SNP_CYP3A4_RS2242480_VAR_COUNT so popPK models that estimate a separate typical-value covariate effect for the missing-genotype subgroup can do so faithfully.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (genotype known). When SNP_CYP3A4_RS2242480_MISSING == 1 the SNP_CYP3A4_RS2242480_VAR_COUNT column should be 0 so the genotype-indicator products correctly evaluate to zero.
• Source aliases:
  • Missing (Lane 2011 Table 3 q_CYP3A4 Missing row; 38 of 309 R-warfarin subjects were missing CYP3A4 genotype per Lane 2011 Table 1).
• Example models: Lane_2011_warfarin_r.R (multiplicative CL effect 0.937 for the missing-genotype subgroup vs the wild-homozygote reference; Lane 2011 Table 3 final R-warfarin model).
• Notes: Follows the same missing-data-indicator pattern as SNP_CYP2C19_RS3814637_MISSING, CYP2C9_MISSING, ADA_MISSING, and HEPIMP_MOD_OR_MISSING. Ratified canonically on 2026-06-30 alongside the Lane 2011 R-warfarin extraction.

SNP_CYP3A4_RS35599367 (**canonical for CYP3A4*22 (rs35599367) reduced-function allele carrier indicator**)

• Description: Binary genotype indicator for the CYP3A4*22 allele (rs35599367; c.522-191C>T; intron 6 T variant that lowers CYP3A4 mRNA splicing efficiency and hence hepatic CYP3A4 expression). 1 = subject carries at least one T (*22) allele (heterozygous *1/*22 or homozygous *22/*22); 0 = homozygous wild-type *1/*1. Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (CYP3A4 *1/*1 wild-type non-carrier).
• Source aliases:
  • CYP3A4 Genotype / CYP3A4*22 – used in MohammedAli_2023_tacrolimus.R (Mohammed Ali 2023 Methods section 2.3 CYP3A4*22 C > T (rs35599367) TaqMan genotyping; Table 1 reports *1/*1 86 (86.7%) vs *1/*22 12 (13.3%), so heterozygotes and the (unobserved) homozygotes are pooled into the carrier group exactly as the paper’s CYP3A4*22 carriers vs non-carriers contrast does).
• Example models: MohammedAli_2023_tacrolimus.R (one of the two binary inputs from which the three-level CYP3A4/CYP3A5 cluster metabolizer phenotype is reconstructed inside model(): is_hm <- (1 - SNP_CYP3A4_RS35599367) * CYP3A5_EXPR, is_pm <- SNP_CYP3A4_RS35599367 * (1 - CYP3A5_EXPR), is_im <- 1 - is_hm - is_pm; the three indicators then select one of three separately estimated typical CL/F values, 19.6 / 10.6 / 7.37 L/h).
• Notes: Distinct from the two already-registered CYP3A4 SNP canonicals: SNP_CYP3A4_RS2246709 (active-allele count) and SNP_CYP3A4_RS2242480_VAR_COUNT (variant-allele count for the *1G / c.1026+12G>A intronic variant) are different loci, and the continuous CYP3A4 canonical is a probe-substrate-derived activity score rather than a genotype. *22 is the principal reduced-function CYP3A4 allele in the tacrolimus / CYP3A pharmacogenetics literature and is nearly always analysed jointly with the CYP3A5 *3 expresser status (CYP3A5_EXPR), because the two genes together determine total CYP3A metabolic capacity. Follow the two-binary-input convention documented in the CYP3A5_EXPR_DONOR Notes: when a source paper fits a combined CYP3A4 + CYP3A5 “cluster” phenotype (high / intermediate / poor metabolizer), keep the underlying genotypes as separate canonical columns (CYP3A5_EXPR and SNP_CYP3A4_RS35599367) and reconstruct the cluster levels inside model(), rather than registering a single collapsed three-level cluster column – this keeps the underlying biology explicit in the dataset and lets a downstream user re-derive any other clustering rule. Ratified canonically on 2026-08-01 alongside the Mohammed Ali 2023 LCP-Tac tacrolimus extraction.

SNP_NR1I2_RS2461817_HOM (canonical for NR1I2 (PXR) rs2461817 homozygous-variant indicator)

• Description: Binary genotype indicator for the NR1I2 (pregnane X receptor, PXR) rs2461817 single-nucleotide polymorphism. 1 = homozygous variant; 0 = otherwise (the union of homozygous wild-type and heterozygous carriers). This is a recessive-model encoding: heterozygotes are pooled with wild-type homozygotes because the founding paper resolved a distinct typical-value effect only for the homozygous-mutant stratum. Time-fixed per subject (germline genotype). NR1I2 encodes PXR, the nuclear receptor that transcriptionally regulates CYP3A4, CYP3A5 and ABCB1, so NR1I2 variants act on drug disposition indirectly by modulating downstream enzyme and transporter expression rather than by altering an enzyme’s own catalytic activity.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (homozygous wild-type or heterozygous). In a recessive (_HOM-only) encoding the 0-level pools both non-homozygous-variant strata. If a future paper resolves a distinct heterozygote effect, register a paired SNP_NR1I2_RS2461817_HET companion following the SNP_ABCG2_RS2231142_HET / _HOM precedent; the per-subject value of the _HOM column is unchanged by that pairing.
• Source aliases:
  • GENE – used in Xie_2025_midazolam.R. Xie 2025 names the column GENE in its Figure 6 legend and defines the recessive pooling verbatim: “GENE = 1 indicates that the NR1I2 rs2461817 genotype reflects a homozygous mutation. GENE = 0 indicates that the NR1I2 rs2461817 genotype is wild-type homozygous or mutant heterozygous.” The bare name GENE is far too generic to be canonical, hence the explicit gene + rsID + genetic-model canonical.
• Example models: Xie_2025_midazolam.R (fractional-shift effect on midazolam clearance in mechanically ventilated Chinese ICU patients: nr1i2_factor <- 1 + e_nr1i2_hom_cl * SNP_NR1I2_RS2461817_HOM with e_nr1i2_hom_cl = -0.405; homozygous mutants have midazolam CL 40.5% lower, 13.4 vs 22.6 L/h; paper Table 2 “rs2461817 on CL MDZ”, RSE 29%, bootstrap 95% CI -0.591 to -0.0659).
• Notes: Functional form is fractional, not exponential. Xie 2025 does not print the covariate equation, but the fractional form (1 + theta * GENE) is the only one consistent with its own reported simulation clearances: 22.6 * (1 - 0.405) = 13.45 matches the reported 13.4 L/h, whereas 22.6 * exp(-0.405) = 15.07 does not. Check this before reusing the coefficient in another parameterisation. Distinct from the CYP3A4 / CYP3A5 genotype canonicals (SNP_CYP3A4_RS2246709, SNP_CYP3A4_RS2242480_VAR_COUNT, SNP_CYP3A4_RS35599367, CYP3A5_EXPR): those are the metabolising enzymes themselves, whereas NR1I2 is their upstream transcriptional regulator. Xie 2025 genotyped 23 loci across four genes (CYP3A4 rs2242480, rs2246709; CYP3A5 rs776746, rs15524; ABCB1 rs2032582, rs1128503, rs1045642; and 16 NR1I2 loci) and found only rs2461817 significant on midazolam clearance – the Discussion attributes the null CYP3A4 / CYP3A5 result to ICU drug-drug interactions and inflammatory cytokines masking the genetic signal. Other NR1I2 loci screened in that cohort and available for future canonicals: rs1464603, rs1464602, rs3732357, rs6785049, rs2276707, rs10934498, rs3814055, rs2472677, rs3732359, rs3814058, rs3732360, rs4688040, rs2276706, rs1523130, rs1523127. Ratified canonically on 2026-08-14 alongside the Xie 2025 midazolam extraction.

CYP2C19_S2_CARRIER (**canonical for CYP2C19*2 loss-of-function allele carrier indicator**)

• Description: Binary indicator for carriage of the CYP2C192 loss-of-function allele (rs4244285, c.681G>A; creates a cryptic splice site that abolishes CYP2C19 enzyme activity). 1 = subject carries at least one 2 allele (heterozygous 1/2 or homozygous 2/2); 0 = no 2 allele. Time-fixed per subject (germline genotype). Het and hom carriers are pooled in studies where the 2/2 (poor metabolizer) frequency is too low for a separate phenotype layer; future extractions that distinguish heterozygous-intermediate-metabolizer from homozygous-poor-metabolizer effects should register paired CYP2C19_S2_HET / CYP2C19_S2_HOM indicators (or, where the source uses the per-allele count form, a CYP2C19_S2_COUNT column following the CYP2C9_S2_COUNT precedent).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no 2 allele – 1/1, 1/17, or 17/*17).
• Source aliases:
  • CYP2C19*2 – Danielak 2017 (paper Methods ‘Determination of genetic polymorphisms’ and Table 2 final-model Effect of CYP2C19*2 on FM (COV) row; PCR-RFLP genotyping for rs4244285). The Danielak 2017 cohort had no 2/2 homozygous-poor-metabolizers, so heterozygous 1/2 carriers were pooled into the binary CYP2C19_S2_CARRIER = 1 group with no information loss.
• Example models: Danielak_2017_clopidogrel.R (linear-deviation effect on the fraction of clopidogrel metabolised to the active thiol H4: fm = TVFM * (1 + e_cyp2c19_s2_fm * CYP2C19_S2_CARRIER) with e_cyp2c19_s2_fm = -0.45; carriers convert 45% less of the absorbed clopidogrel to the active H4 metabolite, giving a 36.7% lower predicted AUC of H4 vs non-carriers; Danielak 2017 Table 2 final-model and Results page 1628).
• Notes: CYP2C192 is the dominant loss-of-function* CYP2C19 allele in clopidogrel pharmacogenetics; it reduces clopidogrel’s metabolic activation to the antiplatelet-active H4 thiol and is associated with elevated rates of stent thrombosis and major adverse cardiovascular events on clopidogrel therapy (FDA boxed warning, 2010). The paired *17 ultra-rapid-metabolizer allele (rs12248560) is typically registered separately when present (a CYP2C19_S17_CARRIER indicator following the same pattern). The continuous-individual-activity-score consolidation TODO logged on CYP2D6 line 3321 also applies prospectively to CYP2C19, but the binary carrier indicator remains the standard discrete encoding used by most published clopidogrel popPK / PD models. Ratified canonically on 2026-05-20 alongside the Danielak 2017 clopidogrel extraction.

CYP2C19_IM (canonical for CYP2C19 intermediate-metabolizer phenotype indicator)

• Description: 1 = subject is a CYP2C19 intermediate metabolizer (one functional and one loss-of-function allele; e.g., *1/*2, *2/*17); 0 = any other CYP2C19 phenotype (EM, UM, PM, or RM). Time-fixed per subject (germline genotype-derived phenotype). Paired with CYP2C19_PM to encode the three-level EM/UM (reference, both indicators 0) / IM (CYP2C19_IM = 1) / PM (CYP2C19_PM = 1) phenotype with two binary indicators.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (EM, UM, PM, or RM phenotype). Reference category when paired with CYP2C19_PM = 0 is the extensive / ultrarapid-metabolizer phenotype pool used in Zhao 2018.
• Source aliases:
  • CYP2C19 IM / IM – Zhao 2018 (paper Table 2 reports F_CYP2C19 IM = 0.449 relative to the EM/UM reference; genotypes pooled into IM: *1/*2, *2/*17).
• Example models: Zhao_2018_omeprazole.R (power-of-binary-indicator multiplicative factor on CLOMZ-M1 formation clearance: e_cyp2c19_im_kmet_5oh ^ CYP2C19_IM with e_cyp2c19_im_kmet_5oh = 0.449; IM subjects have ~55% lower formation clearance of 5-hydroxy-omeprazole than the EM/UM reference; Zhao 2018 Table 2), Jung_2024_clopidogrel.R (additive shift on the LOGIT scale of two nested metabolized fractions: e_cyp2c19_im_logitfm1 = -0.450 and e_cyp2c19_im_logitfm2 = -1.428, taking the active-metabolite fraction fmH4 from 0.120 in EM to 0.071 in IM; Jung 2024 Tables 2 and 3).
• Notes: Follows the CYP2B6_IM / CYP2B6_SM / CYP2B6_USM three-binary precedent for multi-level metabolizer phenotypes. The Zhao 2018 cohort pooled extensive (EM, *1/*1) and ultrarapid (UM, *1/*17, *17/*17) metabolizers into a single reference because the typical-value clearance of 5-hydroxy-omeprazole formation was indistinguishable between the two strata in n = 38 EM/UM subjects (Zhao 2018 Methods ‘Population pharmacokinetic-pharmacogenetic modelling’). Future extractions that fit a separate UM coefficient should register a paired CYP2C19_UM companion indicator following this pattern. Distinct from CYP2C19_S2_CARRIER (binary *2-allele carrier indicator used by Danielak 2017 clopidogrel) – CYP2C19_S2_CARRIER pools heterozygous and homozygous *2 carriers into a single 0/1 contrast, while CYP2C19_IM resolves the heterozygous *2 (IM) stratum separately from the homozygous *2/*2 (PM) stratum that CYP2C19_PM flags. Ratified canonically on 2026-05-25 alongside the Zhao 2018 omeprazole extraction.

CYP2C19_PM (canonical for CYP2C19 poor-metabolizer phenotype indicator)

• Description: 1 = subject is a CYP2C19 poor metabolizer (two loss-of-function alleles, e.g., *2/*2); 0 = any other CYP2C19 phenotype (EM, UM, IM, or RM). Time-fixed per subject (germline genotype-derived phenotype). Paired with CYP2C19_IM to encode the three-level EM/UM (reference) / IM / PM phenotype with two binary indicators.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (EM, UM, IM, or RM phenotype). Reference category when paired with CYP2C19_IM = 0 is the extensive / ultrarapid-metabolizer phenotype pool used in Zhao 2018.
• Source aliases:
  • CYP2C19 PM / PM – Zhao 2018 (paper Table 2 reports F_CYP2C19 PM = 0.125 relative to the EM/UM reference; genotype pooled into PM: *2/*2).
• Example models: Zhao_2018_omeprazole.R (power-of-binary-indicator multiplicative factor on CLOMZ-M1 formation clearance: e_cyp2c19_pm_kmet_5oh ^ CYP2C19_PM with e_cyp2c19_pm_kmet_5oh = 0.125; PM subjects have 87.5% lower formation clearance of 5-hydroxy-omeprazole than the EM/UM reference; Zhao 2018 Table 2), Marathe_2023_belzutifan.R (linear-deviation multiplicative factor on apparent clearance: cl_cyp2c19 = 1 + e_cyp2c19_pm_cl * CYP2C19_PM with e_cyp2c19_pm_cl = -0.36, i.e. -36.0% CL/F versus the non-poor-metabolizer reference (5.63 -> 3.60 L/h); Marathe 2023 Table 2 and Table 3. Here the reference pools ALL non-poor phenotypes (intermediate, extensive, rapid, and ultrarapid) because “only the PM category had a significantly different CL/F compared to all other categories”, so CYP2C19_IM is deliberately not used alongside it), Jung_2024_clopidogrel.R (additive shift on the LOGIT scale of two nested metabolized fractions: e_cyp2c19_pm_logitfm1 = -0.996 and e_cyp2c19_pm_logitfm2 = -2.432, taking the active-metabolite fraction fmH4 from 0.120 in EM to 0.034 in PM; Jung 2024 Tables 2 and 3).
• Notes: Companion to CYP2C19_IM. See CYP2C19_IM Notes for the three-level decomposition rationale. The PM phenotype indicator carries a separate typical-value coefficient because the Zhao 2018 cohort observed *2/*2 poor metabolizers (n = 2) had substantially lower 5-OH-omeprazole formation clearance than the heterozygous *1/*2 and *2/*17 intermediate metabolizers (12.5% vs 44.9% of EM/UM reference). Ratified canonically on 2026-05-25 alongside the Zhao 2018 omeprazole extraction.

UGT2B17_EM (canonical for UGT2B17 extensive-metabolizer phenotype indicator)

• Description: 1 = subject is a UGT2B17 extensive metabolizer (both alleles functional, corresponding to a wild-type diplotype at the highly polymorphic UGT2B17 copy-number / whole-gene-deletion locus); 0 = any other UGT2B17 phenotype (intermediate or poor). Time-fixed per subject (germline genotype-derived phenotype). Paired with UGT2B17_PM to encode the three-level EM (UGT2B17_EM = 1) / IM (both indicators 0, reference) / PM (UGT2B17_PM = 1) phenotype with two binary indicators.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (intermediate or poor metabolizer). When paired with UGT2B17_PM = 0 the reference collapses to the UGT2B17 intermediate-metabolizer stratum used in Marathe 2023.
• Source aliases:
  • UGT2B17P (three-level categorical, 1 = poor / 2 = intermediate / 3 = extensive, -99 = missing) – Marathe 2023 (supplement 2 NONMEM control stream branches IF(UGT2B17P.EQ.3) CLUGT2B17P = (1 + THETA(12)), with level 2 as “most common” reference and the -99 missing sentinel mapped onto the reference). Decomposed into the paired binary indicators UGT2B17_EM / UGT2B17_PM.
  • UGT2B17 EM / UGT2B17 (extensive) – Marathe 2023 Table 2 and Table 3 row labels.
• Example models: Marathe_2023_belzutifan.R (linear-deviation multiplicative factor on apparent clearance: cl_ugt2b17 = 1 + e_ugt2b17_em_cl * UGT2B17_EM + e_ugt2b17_pm_cl * UGT2B17_PM with e_ugt2b17_em_cl = 0.39, i.e. +39.1% CL/F for extensive metabolizers versus the intermediate-metabolizer reference (5.63 -> 7.83 L/h); Marathe 2023 Table 2 and Table 3).
• Notes: Follows the CYP2D6_PM / CYP2D6_EM and CYP2C19_IM / CYP2C19_PM paired-binary precedents for multi-level metabolizer phenotypes (two binary indicators encoding a three-level categorical with the intermediate stratum as the implicit reference). UGT2B17 is a phase-II glucuronidation enzyme whose poor-metabolizer phenotype arises from homozygous whole-gene deletion rather than from point mutations, which is why the phenotype – not a single SNP_UGT2B17_RS<rsid> genotype indicator – is the right canonical here. Note that Marathe 2023 retains all three UGT2B17 levels on CL/F but only two on bioavailability (EM and IM merged), so UGT2B17_EM has no bioavailability effect in that model while UGT2B17_PM does; a model needing the ultrarapid stratum should register a paired UGT2B17_UM on the same pattern. Distinct from the UGT2B7_* genotype canonicals above (a different UGT isoform, and genotype- rather than phenotype-based). Ratified canonically alongside the Marathe 2023 belzutifan extraction.

UGT2B17_PM (canonical for UGT2B17 poor-metabolizer phenotype indicator)

• Description: 1 = subject is a UGT2B17 poor metabolizer (homozygous UGT2B17 whole-gene deletion, i.e. del/del at the UGT2B17 copy-number locus, giving no enzyme activity); 0 = any other UGT2B17 phenotype (extensive or intermediate). Time-fixed per subject (germline genotype-derived phenotype). Paired with UGT2B17_EM to encode the three-level EM / IM (reference) / PM phenotype with two binary indicators.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (extensive or intermediate metabolizer). When paired with UGT2B17_EM = 0 the reference collapses to the UGT2B17 intermediate-metabolizer stratum. Note that a single model may use two different reference categories for two different effects of this indicator – in Marathe 2023 the CL/F effect is referenced to the intermediate stratum alone (all three levels retained) while the bioavailability effect is referenced to the pooled intermediate + extensive stratum (only two levels retainable on F).
• Source aliases:
  • UGT2B17P (three-level categorical, 1 = poor / 2 = intermediate / 3 = extensive, -99 = missing) – Marathe 2023 (supplement 2 NONMEM control stream branches IF(UGT2B17P.EQ.1) CLUGT2B17P = (1 + THETA(13)) for CL and IF(UGT2B17P.EQ.1) F1UGT2B17P = (1 + THETA(15)) for bioavailability). Decomposed into the paired binary indicators UGT2B17_EM / UGT2B17_PM.
  • UGT2B17 PM / UGT2B17 (poor) – Marathe 2023 Table 2 and Table 3 row labels.
• Example models: Marathe_2023_belzutifan.R (two distinct linear-deviation effects: on apparent clearance cl_ugt2b17 = 1 + e_ugt2b17_em_cl * UGT2B17_EM + e_ugt2b17_pm_cl * UGT2B17_PM with e_ugt2b17_pm_cl = -0.24, i.e. -24.2% CL/F versus the intermediate-metabolizer reference (5.63 -> 4.27 L/h); and on relative bioavailability f(depot) = exp(lfdepot) * (1 + e_ugt2b17_pm_fdepot * UGT2B17_PM) with e_ugt2b17_pm_fdepot = 0.11, i.e. +11.0% F versus the pooled intermediate + extensive reference; Marathe 2023 Table 2, Table 2 caption equation F = 1 * (1 + F-UGT2B17P), and Table 3).
• Notes: Companion to UGT2B17_EM; see that entry’s Notes for the three-level decomposition rationale and the phenotype-versus-genotype naming choice. Follows the CYP2C19_PM (Zhao 2018 omeprazole) and CYP2D6_PM (Knights 2015 aripiprazole) precedents. Marathe 2023 attributes the positive bioavailability effect to reduced first-pass glucuronidation in the gut when UGT2B17 activity is absent (Discussion), which is mechanistically consistent with the negative CL/F effect from the same deletion. The UGT2B17 deletion is strongly ancestry-stratified: Marathe 2023 reports the UGT2B17/CYP2C19 dual-PM phenotype frequency at ~0.5% in the overall US population but up to 15% in East Asians. Ratified canonically alongside the Marathe 2023 belzutifan extraction.

ABCB1_C1236T_HET (canonical for ABCB1 C1236T heterozygote indicator)

• Description: Binary genotype indicator for the ABCB1 (P-glycoprotein, MDR1) C1236T heterozygote group at rs1128503 (exon 12, synonymous Gly412Gly). 1 = subject carries exactly one variant allele (genotype T/C); 0 = otherwise (the union of C/C homozygous wild-type and T/T homozygous variant strata). Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: Model-dependent, because the two strata this indicator separates are “heterozygous” and “homozygous (either allele)”, not “variant” and “wild-type”. Document the reference explicitly per model. In Liu_2025_avatrombopag.R the reference is ABCB1_C1236T_HET = 1 (the T/C heterozygote stratum, for which the absorption fraction Fa = 1 under fasting); the pooled C/C + T/T homozygote stratum carries the estimated effect. When a source paper instead fits distinct heterozygote and homozygous-variant effects against a C/C wild-type reference, register a paired ABCB1_C1236T_MUT indicator on the ABCB1_C3435T_HET / ABCB1_C3435T_MUT pattern and use the two jointly.
• Source aliases:
  • ABCB1 – Liu 2025 (Table 3 footnote: “If the genotype is heterozygote, ABCB1 is 0; homozygote is 1”). The paper’s column is the exact complement of this canonical, so Liu_2025_avatrombopag.R writes (1 - ABCB1_C1236T_HET) wherever the paper writes ABCB1.
  • ABCB1 (C1236T) (CC/TC/TT) – Liu 2025 Table 1 demographic row; the three-level genotype from which the binary heterozygote indicator is derived.
• Example models: Liu_2025_avatrombopag.R (linear-deviation food-by-genotype co-effect on the absorption fraction: Fa = 1 + e_fed_highfat_fdepot * FED_HIGHFAT + e_abcb1hom_fdepot * (1 - ABCB1_C1236T_HET) + e_fed_abcb1hom_fdepot * FED_HIGHFAT * (1 - ABCB1_C1236T_HET) with e_abcb1hom_fdepot = -0.272 and e_fed_abcb1hom_fdepot = 0.177; fasting C/C-or-T/T homozygotes have 27.2% lower Fa than fasting T/C heterozygotes, and the deficit narrows to 16.0% when dosed after a high-fat meal).
• Notes: Sibling of ABCB1_C3435T_HET / ABCB1_C3435T_MUT (rs1045642, exon 26) and of SNP_ABCB1_RS1045642 / SNP_ABCB1_RS3842; distinct from ABCB1_HAP_TTT, which is the phased cis haplotype across rs1128503 / rs2032582 / rs1045642 jointly rather than the single rs1128503 SNP. Use ABCB1_C1236T_HET only when the source paper models rs1128503 alone. The heterozygote-versus-pooled-homozygote (rather than variant-versus-wild-type) contrast is unusual but is the source paper’s own empirical finding, not an extraction convenience: Liu 2025 Results 2.2.2 reports that the C/C and T/T strata had statistically indistinguishable avatrombopag exposure (Cmax ratio 102.4% fasting / 113.7% fed; AUC0-t ratio 108.7% fasting / 108.4% fed) while both differed materially from T/C, so the paper pooled them. Genotype frequencies in the 92-subject Chinese cohort were 13.0% C/C, 46.7% T/C, 40.2% T/T, consistent with other Asian populations (Liu 2025 Discussion paragraph 4). Mechanistically rs1128503 is a synonymous exon-12 variant whose functional consequences for P-gp expression and efflux are poorly characterised and inconsistent across substrates (Liu 2025 Discussion paragraph 5); the association should be treated as empirical. Ratified canonically alongside the Liu 2025 avatrombopag extraction.

ABCB1_C3435T_HET (canonical for ABCB1 C3435T heterozygote indicator)

• Description: Binary genotype indicator for the ABCB1 (P-glycoprotein, MDR1) C3435T heterozygote group at rs1045642 (exon 26, synonymous Ile1145Ile). 1 = subject carries exactly one variant allele (genotype C/T); 0 = otherwise (the union of C/C homozygous wild-type and T/T homozygous variant strata; the paired indicator ABCB1_C3435T_MUT flags the homozygous variant group). Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (C/C homozygous wild-type, when paired with ABCB1_C3435T_MUT = 0). The reference group is the homozygous wild-type C/C stratum; ABCB1_C3435T_MUT flags the homozygous-variant T/T stratum.
• Source aliases:
  • ABCB1 C3435T C/T / C/T – Zhao 2018 (paper Table 2 reports a multiplicative scaling factor of 1.86 on Ka for the heterozygote stratum relative to the C/C reference).
• Example models: Zhao_2018_omeprazole.R (power-of-binary-indicator multiplicative factor on absorption rate constant Ka: e_abcb1_c3435t_het_ka ^ ABCB1_C3435T_HET with e_abcb1_c3435t_het_ka = 1.86; C/T heterozygotes have an absorption rate constant approximately 86% higher than the C/C wild-type reference; paired with ABCB1_C3435T_MUT and used jointly).
• Notes: Follows the CYP3A5_STAR1_HET / CYP3A5_STAR1_HOM and SLCO1B1_HAP15_HET / SLCO1B1_HAP15_HOM paired-binary precedent for a three-level genotype where each stratum carries a distinct typical-value covariate effect. Distinct from the ABCB1_HAP_TTT haplotype canonical (which jointly tests the cis combination of rs1128503 / rs2032582 / rs1045642 SNPs as a single haplotype block; used in de Wit 2016 everolimus). Use ABCB1_C3435T_HET + ABCB1_C3435T_MUT when the source paper fits a distinct typical-value covariate effect to the single rs1045642 SNP without phasing it into a haplotype, and when both the heterozygous and homozygous-variant strata are large enough to identify independent effects (Zhao 2018 cohort: n = 22 heterozygotes, 43.1%, and n = 4 homozygous variant, 7.8%, with n = 25 wild-type, 49.0%, as the reference). Mechanistically the C3435T variant has been associated with altered P-gp expression and substrate efflux in some studies (often via linkage with functional variants in the same haplotype block), though directionality of the effect on substrate exposure varies across substrates and tissues. Ratified canonically on 2026-05-25 alongside the Zhao 2018 omeprazole extraction.

ABCB1_C3435T_MUT (canonical for ABCB1 C3435T homozygous-variant indicator)

• Description: Binary genotype indicator for the ABCB1 (P-glycoprotein, MDR1) C3435T homozygous-variant group at rs1045642 (exon 26, synonymous Ile1145Ile). 1 = subject carries two variant alleles (genotype T/T); 0 = otherwise (the union of C/C homozygous wild-type and C/T heterozygote strata; the paired indicator ABCB1_C3435T_HET flags the heterozygous group). Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (C/C homozygous wild-type, when paired with ABCB1_C3435T_HET = 0). The reference group is the homozygous wild-type C/C stratum; ABCB1_C3435T_HET flags the heterozygous C/T stratum.
• Source aliases:
  • ABCB1 C3435T T/T / T/T – Zhao 2018 (paper Table 2 reports a multiplicative scaling factor of 6.93 on Ka for the homozygous-variant stratum relative to the C/C reference).
• Example models: Zhao_2018_omeprazole.R (power-of-binary-indicator multiplicative factor on absorption rate constant Ka: e_abcb1_c3435t_mut_ka ^ ABCB1_C3435T_MUT with e_abcb1_c3435t_mut_ka = 6.93; T/T homozygotes have an absorption rate constant approximately 6.93-fold higher than the C/C wild-type reference; paired with ABCB1_C3435T_HET and used jointly).
• Notes: Companion to ABCB1_C3435T_HET. See ABCB1_C3435T_HET Notes for the three-level decomposition rationale, the distinction from the ABCB1_HAP_TTT haplotype canonical, and the Zhao 2018 cohort distribution. Ratified canonically on 2026-05-25 alongside the Zhao 2018 omeprazole extraction.

ABCB1_HAP_TTT (canonical for ABCB1 TTT haplotype carrier indicator)

• Description: Binary haplotype indicator for the ABCB1 TTT haplotype across the rs1128503 (1236C>T, exon 12, synonymous Gly412Gly) / rs2032582 (2677G>T/A, exon 21, Ala893Ser/Thr) / rs1045642 (3435C>T, exon 26, synonymous Ile1145Ile) SNP block. 1 = subject carries at least one TTT haplotype (heterozygous or homozygous; pooled because the homozygote frequency was < 0.1 in the de Wit 2016 cohort); 0 = no TTT haplotype. Time-fixed per subject (germline haplotype).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no TTT haplotype).
• Source aliases:
  • ABCB1 TTT haplotype – de Wit 2016 (paper text Methods ‘Pharmacogenetic analysis’ and Table 2 final-model theta TTT on F row; haplotypes phased in gPLINK with certainty > 0.97).
• Example models: deWit_2016_everolimus.R (multiplicative effect on apparent bioavailability F: F = 1 * 0.792^ABCB1_HAP_TTT – carriers have 20.8% lower F than non-carriers; dOFV = 9.6 in backward elimination, P < 0.01).
• Notes: The TTT haplotype of ABCB1 (P-glycoprotein, MDR1 efflux transporter) is associated with enhanced P-gp efflux activity and reduced everolimus bioavailability (de Wit 2016 Discussion paragraph 5); de Wit cites prior evidence that the same TTT haplotype also reduces exposure / efficacy of other P-gp substrates [refs 20-22 in the paper], although directionally inconsistent results have been reported [refs 7, 23, 24]. Het and hom carriers were pooled in de Wit 2016 because the homozygote frequency was < 0.1 (Methods ‘Pharmacogenetic analysis’); future extractions that estimate separate het / hom effects should register paired ABCB1_HAP_TTT_HET and ABCB1_HAP_TTT_HOM indicators following the SLCO1B1_HAP15_HET / SLCO1B1_HAP15_HOM precedent above. Distinct from any individual ABCB1 SNP indicator (rs1128503, rs2032582, rs1045642 alone) because the haplotype is the cis combination tested jointly. Ratified canonically on 2026-05-16 alongside the de Wit 2016 everolimus extraction.

ALDH2_S2_CARRIER (**canonical for ALDH2*2 inactive-variant carrier indicator**)

• Description: Binary genotype indicator for the ALDH2 *2 (inactive) variant allele (rs671 G>A; Glu487Lys, aldehyde dehydrogenase 2 mitochondrial isoform). 1 = subject carries at least one ALDH22 allele (heterozygous 1/2 or homozygous 2/2); 0 = ALDH21/*1 wild-type. Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (ALDH21/1 wild-type, fully active aldehyde dehydrogenase 2).
• Source aliases:
  • ALDH2 (the source NONMEM indicator used in Nemoto_2017_ethanol.R; 1 for 1/2 carriers, 0 for 1/1 wild-type; the Nemoto 2017 cohort had no 2/2 homozygotes so the indicator is effectively heterozygote-vs-wild-type in that cohort).
• Example models: Nemoto_2017_ethanol.R (additive shift on Vd/F: -20.4 L when ALDH2_S2_CARRIER = 1 vs ALDH21/1 reference; Nemoto 2017 Table II final model).
• Notes: ALDH22 is the canonical East-Asian aldehyde-dehydrogenase deficiency allele (rs671 GAA -> AAA, Glu487Lys); homozygous 2/2 subjects are essentially ALDH2-null and experience severe acetaldehyde-flush after even small ethanol doses, so are typically excluded from alcohol-PK studies. The Nemoto 2017 Japanese cohort contained 21/34 (62%) 1/1 and 13/34 (38%) 1/2 subjects; no 2/2 homozygotes were enrolled. The single binary carrier indicator pools heterozygous and homozygous variant carriers; future ethanol-PK extractions that distinguish 1/2 from 2/*2 effects should register paired ALDH2_S2_HET and ALDH2_S2_HOM indicators following the SLCO1B1_HAP15_HET / SLCO1B1_HAP15_HOM precedent. Ratified canonically on 2026-05-18 alongside the Nemoto 2017 ethanol extraction.

ADH1B_S2_HOM (**canonical for ADH1B*2 homozygote indicator**)

• Description: Binary genotype indicator for the ADH1B *2 (high-activity) variant allele (rs1229984 G>A; Arg47His, alcohol dehydrogenase 1B class I beta-subunit). 1 = subject is homozygous 2/2; 0 = otherwise (the default reference covers ADH1B2/1 heterozygotes; ADH1B1/1 wild-type subjects fall outside the published parameterization of Nemoto 2017 and are conventionally assigned the same reference value as heterozygotes). Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (ADH1B2/1 heterozygous; ADH1B1/1 wild-type by extension when the model is applied beyond the published parameter range).
• Source aliases:
  • ADH1B (the source NONMEM indicator used in Nemoto_2017_ethanol.R; 1 for 2/2 homozygotes, 0 for 2/1 heterozygotes; Nemoto 2017 Table II structural model encodes the Vmax conditional via two separate THETAs).
• Example models: Nemoto_2017_ethanol.R (additive shift on Vmax: +176 mg/h for 2/2 homozygotes relative to the 2/1 reference Vmax of 7790 mg/h; Nemoto 2017 Table II final model).
• Notes: ADH1B2 is the East-Asian high-activity alcohol-dehydrogenase variant (rs1229984 Arg47His); the 2 allele encodes a ~40x faster ethanol oxidation rate than the 1 wild-type. Allele frequency in Japanese populations is ~70%, so most subjects are 2/2 or 1/2 with a small 1/1 minority (~9% expected). Nemoto 2017 inherits the two-Vmax parameterization from Seng et al. 2014 (which estimated Vmax separately for 2/1 and 2/2 in a Chinese + Indian cohort) and does not report an ADH1B genotype distribution for its 34-subject Japanese cohort; the structural model is silent on ADH1B1/*1 subjects. Future ADH1B-aware ethanol-PK extractions that explicitly model all three genotypes should register ADH1B_S2_HET as a companion indicator following the SLCO1B1_HAP15_HET / SLCO1B1_HAP15_HOM precedent. Ratified canonically on 2026-05-18 alongside the Nemoto 2017 ethanol extraction.

PFMDR1_86Y (canonical for P. falciparum pfmdr1 codon-86 tyrosine mutant indicator)

• Description: Plasmodium falciparum pfmdr1 codon-86 tyrosine mutant indicator (1 = single-copy pfmdr1 with the 86Y mutation, Simpson 2013 Genotype 2; 0 = otherwise). Time-fixed per parasite isolate. This is a parasite-genome indicator (not host pharmacogenetics): the “subject” in the NLME framework is a clinical P. falciparum isolate, not a human patient.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = Simpson 2013 Genotype 1 single-copy wild-type 86N/1042N (when all four PFMDR1 indicators are 0). The four PFMDR1 indicators are mutually exclusive in the Simpson 2013 cohort (Thai pfmdr1 point mutations occur almost exclusively on single-copy parasites; amplifications occur exclusively on WT 86N/1042N parasites).
• Source aliases:
  • X1 – used in Simpson_2013_artesunate.R, Simpson_2013_chloroquine.R, Simpson_2013_lumefantrine.R, Simpson_2013_mefloquine.R (per-isolate Genotype-2 indicator).
• Example models: Simpson_2013_artesunate.R, Simpson_2013_chloroquine.R, Simpson_2013_lumefantrine.R, Simpson_2013_mefloquine.R (multiplicative proportional effect on the sigmoid-Emax EC50, 1 + e_pfmdr1_86y_ec50 * PFMDR1_86Y, drug-specific magnitude per Simpson 2013 Table 3 Genotype-2 percent-change column).
• Notes: Specific scope because the canonical name is a parasite-genome covariate tied to the Simpson 2013 in-vitro antimalarial study design. Explicitly DISTINCT from the human host-pharmacogenetics ABCB1_* canonicals (ABCB1 / MDR1 is the human P-glycoprotein efflux-transporter gene; pfmdr1 is the orthologous P. falciparum multidrug-resistance transporter). Member of the four-member mutually-exclusive PFMDR1_* Simpson 2013 genotype set (PFMDR1_86Y, PFMDR1_1042D, PFMDR1_CN2, PFMDR1_CN3PLUS), with single-copy WT 86N/1042N as the all-zero reference. Future pfmdr1-genotype antimalarial extractions should reuse this set or register sibling parasite-genome canonicals. Ratified canonically alongside the Simpson 2013 antimalarial in-vitro extractions.

PFMDR1_1042D (canonical for P. falciparum pfmdr1 codon-1042 aspartate mutant indicator)

• Description: Plasmodium falciparum pfmdr1 codon-1042 aspartate mutant indicator (1 = single-copy pfmdr1 with the 1042D mutation, Simpson 2013 Genotype 3; 0 = otherwise). Time-fixed per parasite isolate. Parasite-genome indicator (not host pharmacogenetics).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = Simpson 2013 Genotype 1 single-copy wild-type 86N/1042N (when all four PFMDR1 indicators are 0). Mutually exclusive with the other three PFMDR1 indicators.
• Source aliases:
  • X2 – used in Simpson_2013_artesunate.R, Simpson_2013_chloroquine.R, Simpson_2013_lumefantrine.R, Simpson_2013_mefloquine.R (per-isolate Genotype-3 indicator).
• Example models: Simpson_2013_artesunate.R, Simpson_2013_chloroquine.R, Simpson_2013_lumefantrine.R, Simpson_2013_mefloquine.R (multiplicative proportional effect on the sigmoid-Emax EC50, 1 + e_pfmdr1_1042d_ec50 * PFMDR1_1042D, drug-specific magnitude per Simpson 2013 Table 3 Genotype-3 percent-change column).
• Notes: Specific scope. Distinct from the human ABCB1_* canonicals (see PFMDR1_86Y notes). Member of the four-member mutually-exclusive PFMDR1_* Simpson 2013 genotype set with single-copy WT as the all-zero reference. Ratified canonically alongside the Simpson 2013 antimalarial in-vitro extractions.

PFMDR1_CN2 (canonical for P. falciparum pfmdr1 double-copy amplification indicator)

• Description: Plasmodium falciparum pfmdr1 double-copy amplification indicator (1 = two copies of pfmdr1, all wild-type 86N/1042N, Simpson 2013 Genotype 4; 0 = otherwise). Time-fixed per parasite isolate. Parasite-genome indicator (not host pharmacogenetics).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = Simpson 2013 Genotype 1 single-copy wild-type 86N/1042N (when all four PFMDR1 indicators are 0). Mutually exclusive with the other three PFMDR1 indicators.
• Source aliases:
  • X3 – used in Simpson_2013_artesunate.R, Simpson_2013_chloroquine.R, Simpson_2013_lumefantrine.R, Simpson_2013_mefloquine.R (per-isolate Genotype-4 indicator).
• Example models: Simpson_2013_artesunate.R, Simpson_2013_chloroquine.R, Simpson_2013_lumefantrine.R, Simpson_2013_mefloquine.R (multiplicative proportional effect on the sigmoid-Emax EC50, 1 + e_pfmdr1_cn2_ec50 * PFMDR1_CN2, drug-specific magnitude per Simpson 2013 Table 3 Genotype-4 percent-change column).
• Notes: Specific scope. Distinct from the human ABCB1_* canonicals (see PFMDR1_86Y notes). Member of the four-member mutually-exclusive PFMDR1_* Simpson 2013 genotype set with single-copy WT as the all-zero reference; PFMDR1_CN2 (two copies) and PFMDR1_CN3PLUS (three-or-more copies) jointly encode the copy-number amplification axis. Ratified canonically alongside the Simpson 2013 antimalarial in-vitro extractions.

PFMDR1_CN3PLUS (canonical for P. falciparum pfmdr1 triple-or-more-copy amplification indicator)

• Description: Plasmodium falciparum pfmdr1 triple-or-more-copy amplification indicator (1 = three or more copies of pfmdr1, all wild-type 86N/1042N, Simpson 2013 Genotype 5; 0 = otherwise). Time-fixed per parasite isolate. Parasite-genome indicator (not host pharmacogenetics).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = Simpson 2013 Genotype 1 single-copy wild-type 86N/1042N (when all four PFMDR1 indicators are 0). Mutually exclusive with the other three PFMDR1 indicators.
• Source aliases:
  • X4 – used in Simpson_2013_artesunate.R, Simpson_2013_chloroquine.R, Simpson_2013_lumefantrine.R, Simpson_2013_mefloquine.R (per-isolate Genotype-5 indicator).
• Example models: Simpson_2013_artesunate.R, Simpson_2013_chloroquine.R, Simpson_2013_lumefantrine.R, Simpson_2013_mefloquine.R (multiplicative proportional effect on the sigmoid-Emax EC50, 1 + e_pfmdr1_cn3plus_ec50 * PFMDR1_CN3PLUS, drug-specific magnitude per Simpson 2013 Table 3 Genotype-5 percent-change column; for artesunate and mefloquine the triple+ copy group carries the largest EC50 shift, +127% and +188% respectively).
• Notes: Specific scope. Distinct from the human ABCB1_* canonicals (see PFMDR1_86Y notes). Member of the four-member mutually-exclusive PFMDR1_* Simpson 2013 genotype set with single-copy WT as the all-zero reference; paired with PFMDR1_CN2 on the copy-number amplification axis. Ratified canonically alongside the Simpson 2013 antimalarial in-vitro extractions.

CYP2C19 (canonical for CYP2C19 individual metabolic-activity proxy)

• Description: Continuous individual-level CYP2C19 metabolic-activity proxy. The intent is a single canonical column for any CYP2C19 phenotype proxy that the source paper reports as a continuous number (probe-substrate model-based individual clearance, urinary or serum S/R-mephenytoin ratio, copy-number-corrected expression score, activity-score sum from *allele genotypes, etc.); the per-model covariateData[[CYP2C19]]$units, description, and notes document which proxy is in force, the orientation (higher value = higher or lower activity, which depends on the probe), and the population-mean reference value used inside the model. Time-invariant in all known examples (germline-genotype-determined enzyme expression measured once via probe-substrate assay).
• Units: Paper-specific – document per-model (e.g., unitless urinary S/R-mephenytoin ratio in Areberg 2006).
• Type: continuous
• Scope: general
• Reference category: n/a (continuous). Models center on a population mean (e.g., 0.769 in Areberg 2006); document the reference value per-model in covariateData[[CYP2C19]]$notes.
• Source aliases:
  • CYP2C19 – used directly in Areberg_2006_escitalopram.R (Areberg 2006 Equation 2 variable name; the value is the urinary S/R-mephenytoin ratio defined in the Materials and Methods ‘CYP2C19 Phenotyping’ section).
• Example models: Areberg_2006_escitalopram.R (linear-deviation effect on apparent oral clearance: CL/F = 19 - 17 * (CYP2C19 - 0.769); CYP2C19 = urinary S/R-mephenytoin ratio, population mean = 0.769; one unit of additional ratio = 17 L/h lower clearance, consistent with the biology that higher S/R ratio = lower CYP2C19 activity = lower escitalopram metabolic clearance; Areberg 2006 Equations 1-2 and Table 3).
• Notes: Companion to the continuous CYP2D6 (line 4055) and CYP3A4 (line 4089) canonicals, ratified here per the prospective registration TODO on the CYP3A4 line. Orientation depends on the probe. Two patterns coexist in the CYP2C19-aware popPK literature:
  • Probe-derived clearance form (analogous to the dextromethorphan-probe CYP2D6 / CYP3A4 columns): higher value = higher CYP2C19 activity. Use this orientation when the source reports a probe-substrate-derived individual clearance number.
  • Probe-derived metabolic ratio form (e.g., urinary or serum S/R-mephenytoin ratio): higher value = lower CYP2C19 activity, because high CYP2C19 activity selectively metabolizes S-mephenytoin and lowers the residual S enantiomer. Per-model covariateData[[CYP2C19]]$notes must document which orientation is in force alongside the population-mean reference value and the column’s units, mirroring the per-model documentation already used for the CYP2D6 and CYP3A4 continuous canonicals. Distinct from the binary genotype-derived canonicals CYP2C19_S2_CARRIER (loss-of-function *2 allele carrier), CYP2C19_IM (intermediate-metabolizer phenotype), and CYP2C19_PM (poor-metabolizer phenotype); use the binary indicators when the source paper reports only a discrete phenotype label, and use CYP2C19 (continuous) when the source reports a probe-derived continuous number. Ratified canonically on 2026-06-09 alongside the Areberg 2006 escitalopram extraction (founding example uses the urinary S/R-mephenytoin ratio form).

CYP2C19_NON_EM (canonical for composite CYP2C19 non-homozygous-extensive-metabolizer indicator)

• Description: 1 = subject is genotyped as a CYP2C19 poor metabolizer (homozygous loss-of-function, e.g. 2/2, 2/3, 3/3) OR a heterozygous-extensive metabolizer (one functional allele and one loss-of-function allele, e.g. 1/2, 1/3); 0 = subject is genotyped as a homozygous extensive metabolizer (both alleles functional, 1/1). Distinct from CYP2C19_PM (strict homozygous-PM only) and from CYP2C19_IM (intermediate-metabolizer-only); CYP2C19_NON_EM is a paper-defined composite of PM + IM that the source paper used as a single multiplicative covariate on clearance.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (homozygous extensive metabolizer; both *1 alleles).
• Source aliases:
  • PM (Wahlby 2004 / Walsh 2004 voriconazole source-column convention) – used in Wahlby_2004_voriconazole.R. Note that the column name PM in this paper denotes the composite non-EM group, not the strict CYP2C19_PM phenotype; users converting from the source paper’s data should not collapse PM to canonical CYP2C19_PM without re-checking the genotype-to-indicator mapping.
• Example models: Wahlby_2004_voriconazole.R (multiplicative effect on CL: (1 - 0.46 * CYP2C19_NON_EM), so a non-EM subject has 46 percent lower CL than a homozygous-EM subject).
• Notes: Specific scope because the PM+IM composite grouping is paper-defined; future papers that report PM and IM separately should use the strict CYP2C19_PM and CYP2C19_IM canonicals instead. The Wahlby 2004 / Walsh 2004 voriconazole encoding has been retained because the source paper does not provide separate per-genotype coefficient estimates. Ratified canonically alongside the Wahlby 2004 extraction.

SNP_ABCB1_RS3842 (canonical for ABCB1 rs3842 (c.4036A>G, 3’ UTR) mutant allele carrier indicator)

• Description: Binary genotype indicator for the ABCB1 rs3842 single-nucleotide polymorphism (c.4036A>G; in the 3’ untranslated region of the ABCB1 / MDR1 / P-glycoprotein gene). 1 = subject carries at least one G (mutant) allele (heterozygous AG or homozygous GG; pooled because the source paper reports the same effect for heterozygotes and homozygotes); 0 = homozygous wild-type (AA). Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (homozygous wild-type AA).
• Source aliases:
  • ABCB1 (rs3842) – Mukonzo 2009 (paper Table 1 ‘ABCB1’ section: position c.4036 A/G, rs number rs3842, allele New SNP, protein 3' UTR, observed SNP frequency 16.8%; Table 3 final-model ‘Effect of ABCB1 (rs 3842)’ row).
• Example models: Mukonzo_2009_efavirenz.R (linear-deviation effect on relative bioavailability Frel: f(depot) = (1 + e_rs3842_fdepot * SNP_ABCB1_RS3842) * exp(etalfdepot) with e_rs3842_fdepot = 0.257; mutant carriers (any G allele) have 25.7% higher Frel than AA wild-type carriers; Mukonzo 2009 Table 3 and Results paragraph ‘Pharmacokinetic modelling’).
• Notes: Distinct from SNP_ABCB1_RS1045642 (a different SNP at c.3435C>T in exon 26 that affects P-glycoprotein expression / function) and from ABCB1_HAP_TTT (the multi-SNP haplotype across rs1128503 / rs2032582 / rs1045642 jointly); use SNP_ABCB1_RS3842 only when the source paper genotypes the c.4036A>G variant in the 3’ UTR. Mukonzo 2009 selected rs3842 from a panel of 13 ABCB1 SNPs as a “previously uncharacterised polymorphism” predicted to have potential regulatory function by bioinformatics tools (Mukonzo 2009 Table 1 ‘Relevance’ = ‘Undetermined’ at extraction time); the modeled +25.7% effect on relative bioavailability was the strongest single-SNP signal among the 13 ABCB1 candidates in the Mukonzo 2009 backward-elimination step. Heterozygote and homozygote G-carriers are pooled in Mukonzo 2009 because the paper explicitly groups them (Results: “Mutant homozygote and heterozygote individuals for ABCB1 rs3842 exhibited 26% greater efavirenz bioavailability than wild-type carriers”); future extractions that estimate separate het / hom effects should register paired SNP_ABCB1_RS3842_HET and SNP_ABCB1_RS3842_HOM indicators following the SLCO1B1_HAP15_HET / SLCO1B1_HAP15_HOM precedent. The Mukonzo 2009 Discussion notes that the rs3842 minor-allele frequency is >20% in Asians, Europeans, and Sub-Saharan Africans according to dbSNP, so the canonical may have broad applicability once additional papers ratify the same encoding. Ratified canonically on 2026-06-13 alongside the Mukonzo 2009 efavirenz extraction.

SNP_SLC22A1_RS683369 (canonical for SLC22A1 (OCT1) rs683369 c.480C>G L160F variant carrier indicator)

• Description: Binary germline-genotype indicator for the SLC22A1 (OCT1, organic cation transporter 1) c.480C>G polymorphism, dbSNP rs683369, encoding the p.Leu160Phe substitution. 1 = carrier of at least one G (variant) allele, i.e. genotype CG or GG; 0 = CC wild-type homozygote. Time-fixed per subject. OCT1 is the principal hepatic uptake transporter for imatinib and several other cationic drugs, so a reduced-function variant lowers hepatocellular uptake and therefore apparent oral clearance.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (CC wild-type homozygote), whose typical-value multiplier is 1 by construction.
• Source aliases:
  • hOCT1 (Di Paolo 2014, which names the gene by its protein alias in the covariate column and titles the paper ‘The c.480C>G polymorphism of hOCT1 influences imatinib clearance’).
  • theta_SLC22A1 (Yang 2025 Table 1 symbol for the multiplicative coefficient, resolved by Table 1 footnote f).
  • rs683369 / c.480C>G / L160F – interchangeable identifiers for the same variant.
• Example models: DiPaolo_2014_imatinib.R (dominant genetic model: CL/F is multiplied by 0.882 for CG and GG carriers alike and by 1 for CC, per Yang 2025 Table 1 footnote f, ‘When SLC22A1 CG or GG theta_SLC22A1 = 0.882, else theta_SLC22A1 = 1 when SLC22A1 CC’).
• Notes: Encoded as a single CARRIER indicator rather than the paired _HET / _HOM indicators used by SNP_ABCG2_RS2231142_HET / _HOM in Jiang_2023_imatinib.R, because the founding model fits a DOMINANT genetic model: heterozygotes and variant homozygotes share one coefficient, so two indicators would be unidentifiable. A future paper that resolves CG and GG separately should add paired SNP_SLC22A1_RS683369_HET / _HOM entries rather than redefining this one. Shape follows the existing SNP_SLC22A2_808GT carrier-indicator precedent. Note that Jiang_2023_imatinib.R records SNP_SLC22A1_RS683369 under covariatesDataExcluded (screened, not retained) using the paper’s own three-level genotype description; that documentation-only entry predates this canonical and does not conflict with it. Ratified canonically on 2026-08-18 alongside the Yang 2025 imatinib external-evaluation extraction, as a well-formed member of the auto-approved SNP_<GENE>_RS<rsid> canonical family.

SNP_SLC22A2_808GT (canonical for SLC22A2 (OCT2) c.808G>T A270S variant carrier indicator)

• Description: Binary genotype indicator for the SLC22A2 (OCT2) c.808G>T single-nucleotide polymorphism (protein change A270S / p.Ala270Ser; widely reported in the pharmacogenomic literature as rs316019). 1 = at least one variant (T) allele present (heterozygous 808GT or homozygous 808TT carrier); 0 = homozygous wild-type 808GG. Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (homozygous wild-type 808GG).
• Source aliases:
  • OCT2-808 G>T – Yoon 2013 (paper Table I ‘OCT2 808 G>T’ row: 96 subjects genotyped as 81 GG + 15 GT + 0 TT, allele frequency 7.8%; Methods ‘Statistical Analysis for the Influence of Genetic Polymorphisms’ pooled heterozygotes and homozygous variants because the Korean cohort contained no homozygous variants).
• Example models: Yoon_2013_metformin.R (multiplicative fractional shift on CL/F: cl = exp(lcl + etalcl) * (1 + e_snp_oct2_cl * SNP_SLC22A2_808GT) * (1 + e_snp_octn1_cl * SNP_SLC22A4_917CT) with e_snp_oct2_cl = -0.248; 808G>T variant carriers have 24.8% lower apparent oral clearance than 808GG wild-type at the OCTN1 reference; Yoon 2013 Table III theta_OCT2 with RSE 15.8%).
• Notes: Position-based canonical name because the source paper reports the SNP by nucleotide position and protein change without an accompanying rsID; the SLC22A2 A270S variant is nevertheless documented across the pharmacogenomic literature as rs316019 and downstream extractions that report the rsID directly may register a paired canonical SNP_SLC22A2_RS316019 with an alias link back to this entry. OCT2 (organic cation transporter 2) is the basolateral renal-tubule uptake transporter for organic cations, including metformin; the A270S substitution reduces cellular metformin uptake in HEK293 in-vitro transport assays. In the Yoon 2013 Korean-male healthy-volunteer cohort the 808G>T variant allele frequency was 7.8% (Table I), comparable to previously reported frequencies of 16.8% in Japanese-Asian and 16% in European-American populations (Yoon 2013 Table I ‘Asian’ and ‘European’ columns). Ratified canonically on 2026-07-08 alongside the Yoon 2013 metformin extraction.

SNP_SLC22A4_917CT (canonical for SLC22A4 (OCTN1) c.917C>T T306I variant carrier indicator)

• Description: Binary genotype indicator for the SLC22A4 (OCTN1) c.917C>T single-nucleotide polymorphism (protein change T306I / p.Thr306Ile). 1 = at least one variant (T) allele present (heterozygous 917CT or homozygous 917TT carrier); 0 = homozygous wild-type 917CC. Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (homozygous wild-type 917CC).
• Source aliases:
  • OCTN1-917C>T – Yoon 2013 (paper Table I ‘OCTN1 917C>T’ row: 96 subjects genotyped as 10 CC + 52 CT + 34 TT, allele frequency 62.5%; Methods ‘Statistical Analysis for the Influence of Genetic Polymorphisms’ pooled heterozygotes and homozygous variants).
• Example models: Yoon_2013_metformin.R (multiplicative fractional shift on CL/F: cl = exp(lcl + etalcl) * (1 + e_snp_oct2_cl * SNP_SLC22A2_808GT) * (1 + e_snp_octn1_cl * SNP_SLC22A4_917CT) with e_snp_octn1_cl = -0.234; 917C>T variant carriers have 23.4% lower apparent oral clearance than 917CC wild-type at the OCT2 reference; Yoon 2013 Table III theta_OCTN1 with RSE 61.1%).
• Notes: Position-based canonical name because the source paper reports the SNP by nucleotide position and protein change without an accompanying rsID. Distinct from the well-characterised SLC22A4 c.1507C>T (L503F, rs1050152) OCTN1 variant which was also genotyped by Yoon 2013 but occurred with zero variant-allele frequency in the Korean cohort (Table I) and was not carried forward as a model covariate. OCTN1 (organic cation transporter, novel, type 1) is expressed at the apical membrane of renal proximal-tubule cells and contributes to the renal secretion of metformin; the T306I substitution is a candidate function-altering variant whose direct in-vitro effect on metformin transport has not been characterised (Yoon 2013 Discussion notes that in-vitro confirmation is still needed). Yoon 2013 Discussion further notes that the 917C>T variant-allele carrier rate is ~90% in Koreans, ~60% in other Asians, and ~33% in Caucasians, so the impact of OCTN1 917C>T on metformin PK is expected to be larger in Asian populations. Ratified canonically on 2026-07-08 alongside the Yoon 2013 metformin extraction.

SNP_CYP2B6_RS35303484_G_COUNT (canonical for CYP2B6 136A>G (rs35303484) G-allele count)

• Description: Continuous individual-level CYP2B6 c.136A>G (rs35303484, p.M46V) G-allele count: 0 = AA homozygous wild-type, 1 = AG heterozygous, 2 = GG homozygous variant. Time-invariant (germline genotype). The 136A>G variant defines the CYP2B6*11 haplotype (a phenotypic null allele in the CYP allele nomenclature) and is most commonly observed in Sub-Saharan African populations.
• Units: (count, 0/1/2 alleles per subject)
• Type: continuous
• Scope: specific
• Reference category: n/a (continuous). In the Mukonzo 2009 efavirenz model the count is decomposed in model() into a homozygous-mutant-only indicator (count == 2) multiplied by a -0.199 multiplicative shift on CL/F. Heterozygotes are pooled with wild-type and receive no shift, consistent with the source paper’s reported effect for “homozygous CYP2B6*11” only (Mukonzo 2009 Results paragraph ‘Pharmacokinetic modelling’).
• Source aliases:
  • CYP2B6*11 – Mukonzo 2009 (paper Table 1 SNP-table column ‘Allele’ and Table 3 ’Effect of CYP2B6*11’ row; rs35303484 is the defining SNP at c.136 A->G with p.M46V protein change).
• Example models: Mukonzo_2009_efavirenz.R (homozygous-mutant-only multiplicative shift on CL/F: cl = exp(lcl + etalcl) * (1 + e_2b6_6_cl * (SNP_CYP2B6_RS3745274_T_COUNT == 2)) * (1 + e_2b6_11_cl * (SNP_CYP2B6_RS35303484_G_COUNT == 2)) with e_2b6_11_cl = -0.199; homozygous *11 carriers have 19.9% lower CL/F than the wild-type / heterozygous reference; Mukonzo 2009 Table 3).
• Notes: Distribution in the Mukonzo 2009 Ugandan healthy-volunteer cohort (Table 1, n = 121): 136A>G allele frequency 13.6%, expected genotype frequencies under Hardy-Weinberg AA 74.6%, AG 23.5%, GG 1.9%. CYP2B611 is found primarily in African populations (allele frequency ~7-15% in Sub-Saharan Africans, ~0-1% in Europeans and East Asians); the M46V substitution lies in the substrate-binding pocket and abolishes detectable enzyme activity in recombinant expression studies. Often co-tested with the more common 516G>T (CYP2B66) variant; in the Mukonzo 2009 Ugandan cohort the two variants segregated independently. The count-form encoding follows the same rationale as SNP_CYP2B6_RS3745274_T_COUNT and SNP_CYP2B6_RS28399499_C_COUNT: a single count column captures the underlying genotype without redundancy and lets the model code derive either a linear per-allele effect (* count) or a non-additive HET / HOM decomposition (* (count == 1), * (count == 2)) depending on the source paper’s parameterization. Ratified canonically on 2026-06-13 alongside the Mukonzo 2009 efavirenz extraction.

Lifestyle / medical history

SMOKE (canonical for current-smoker binary indicator)

• Description: 1 = current smoker at baseline, 0 = non-smoker.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (non-smoker).
• Source aliases:
  • Smoking (case-insensitive) – used in Ma_2020_sarilumab_anc.R.
• Example models: Ma_2020_sarilumab_anc.R (power-form on baseline ANC: BASE * 1.15^SMOKE).
• Notes: Baseline-only indicator; does not track within-study smoking-cessation changes. Use this two-level (current vs non-smoker) encoding when the source paper does not split former and never smokers. When the source uses a 3-level smoking-status categorical (never / former / current), use the paired SMOKE_CURRENT + SMOKE_NEVER indicators below instead – the 3-level encoding cannot be reduced to a single SMOKE column without losing information.

SMOKE_CURRENT (canonical for current-smoker indicator (paired with SMOKE_NEVER))

• Description: 1 = current smoker at baseline, 0 otherwise (former or never smoker). Paired with SMOKE_NEVER to encode a 3-level smoking-status categorical with former smoker as the implicit reference (both indicators = 0).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (former smoker, when paired with SMOKE_NEVER = 0). The pairing follows the RACE_<GROUP> convention for paired indicators.
• Source aliases:
  • Smoking status = Current / SMOK = 2 (case-insensitive) – derived from a 3-level smoking-status column.
• Example models: Hwang_2023_monalizumab.R (proportional-shift effect on V1: (1 + 0.0484)^SMOKE_CURRENT; reference category former smoker).
• Notes: Baseline-only indicator. See also SMOKE_NEVER (paired indicator) and SMOKE (binary current-vs-non-smoker encoding when the source paper does not split former vs never).

SMOKE_NEVER (canonical for never-smoker indicator (paired with SMOKE_CURRENT))

• Description: 1 = never smoker at baseline, 0 otherwise (former or current smoker). Paired with SMOKE_CURRENT to encode a 3-level smoking-status categorical with former smoker as the implicit reference (both indicators = 0).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (former smoker, when paired with SMOKE_CURRENT = 0). The pairing follows the RACE_<GROUP> convention for paired indicators.
• Source aliases:
  • Smoking status = Never / SMOK = 0 (case-insensitive) – derived from a 3-level smoking-status column.
• Example models: Hwang_2023_monalizumab.R (proportional-shift effect on V1: (1 - 0.141)^SMOKE_NEVER; reference category former smoker).
• Notes: Baseline-only indicator. See also SMOKE_CURRENT (paired indicator) and SMOKE (binary current-vs-non-smoker encoding when the source paper does not split former vs never).

ALCOHOL_ABUSE (canonical for chronic alcohol abuse clinical indicator)

• Description: 1 = subject has a documented history of chronic alcohol abuse (defined per paper; commonly >= 6 standard drinks / units per day for a sustained period, per Swart 2004 which follows the Vrije Universiteit Medical Center clinical criteria); 0 = no such history. Subject-level baseline behavioural / clinical-history flag captured at study or ICU admission from patient / next-of-kin interview, not a laboratory measurement and NOT a pharmacogenomic marker. Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no chronic alcohol abuse).
• Source aliases:
  • alcohol abuse – printed prose label in Swart 2004 Results / Table 4 / Table 5 CL formulas (“CL no alcohol abuse” vs “CL alcohol abuse”); used in Swart_2004_lorazepam.R and Swart_2004_midazolam.R.
• Example models: Swart_2004_lorazepam.R (selector effect on CL: alcohol-abuse patients take a flat cl = 0.74 L/h; non-alcohol-abuse patients take the PEEP-adjusted cl = 4.13 - (PEEP - 5) * 0.417 L/h; contrast is a ~5.6-fold reduction in CL), Swart_2004_midazolam.R (selector effect on CL: alcohol-abuse patients take cl = 7.3 - (AGE - 57) * 0.145 L/h; non-alcohol-abuse patients take cl = 11.3 - (AGE - 57) * 0.145 L/h; same age slope in both strata, ~4 L/h lower baseline in the alcohol-abuse stratum at age 57).
• Notes: General scope because chronic alcohol abuse is a clinically-defined behavioural indicator applicable across drug classes and populations, and appears frequently as a hepatic-metabolism modifier (predominantly via CYP2E1 induction and cirrhosis-linked reductions in phase-I and phase-II capacity). Critically distinct from the pharmacogenomic ADH1B_S2_HOM / ADH1B_S2_CARRIER canonicals (Nemoto 2017 ethanol PK): those encode a germline high-activity aldehyde-dehydrogenase-1B allele (rs1229984) that changes intrinsic ethanol clearance; ALCOHOL_ABUSE encodes patient behaviour / history and would remain 1 regardless of ADH1B genotype. Per-model covariateData[[ALCOHOL_ABUSE]]$notes must document the paper’s exact definition (units/day, duration, ascertainment source); Swart 2004 uses “> 6 units per day” as the threshold. Future popPK papers using a different definition (e.g., DSM alcohol use disorder criteria, positive CAGE screen, ICD codes) can extend Example models provided they document the definition in per-model notes; if the paper uses a numerically-graded alcohol-use covariate rather than a binary flag, prefer a separate ordinal canonical rather than reusing this binary. Ratified canonically on 2026-07-26 alongside the Swart 2004 lorazepam / midazolam extraction.

CANNABIS_DAILY (canonical for daily-versus-occasional cannabis use indicator)

• Description: 1 = habitual daily cannabis user, 0 = occasional cannabis user. Subject-level baseline behavioural-history flag describing the participant’s habitual pattern of cannabis consumption before the study visit, ascertained by self-reported use-frequency interview rather than by a laboratory measurement. Time-fixed per subject and independent of the product consumed on the study day (flower versus concentrate).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (occasional user).
• Source aliases:
  • pattern of use (daily versus occasional) – prose label in Henthorn 2024 Methods (“a modeled analysis was conducted, in which the pattern of use was dichotomized (daily use versus occasional use) without regard to whether flower or concentrate was consumed in the dosing protocol”) and Table 3 footnote (“Covariate_daily user, value of the covariate for the estimated inhaled THC dose for both concentrate and flower users where the default is Covariate_occasional user = 0”); used in Henthorn_2024_tetrahydrocannabinol.R.
• Example models: Henthorn_2024_tetrahydrocannabinol.R (exponential effect on the estimated inhaled-dose fraction Fi: fdepot = exp(lfdepot + e_cannabis_daily_fdepot * CANNABIS_DAILY + etalfdepot) with e_cannabis_daily_fdepot = 1.79; daily users inhale exp(1.79) = 5.99-fold more bioavailable THC than the occasional-user reference, i.e. 10.78 mg versus 1.80 mg of a nominal 15 mg fully bioavailable dose).
• Notes: General scope because habitual cannabis use is a behavioural-history indicator applicable beyond a single drug: it plausibly modifies the disposition of co-administered drugs (CYP1A2 induction from combusted-material exposure, CYP2C9 / CYP3A interactions) and characterises co-use populations, matching how the sibling per-substance flags SMOKE (tobacco) and ALCOHOL_ABUSE (alcohol) are scoped. Distinct from SMOKE: a daily cannabis user may be a tobacco non-smoker and vice versa, and concentrate users (vape pens, dabs) do not combust plant material at all, so the two columns must be carried separately rather than collapsed. Distinct from the model-specific OPIOID_PATIENT_TYPE selector, which is scoped specific and names a parameter-set selector rather than a behavioural-history flag. Per-model covariateData[[CANNABIS_DAILY]]$notes must document the paper’s exact frequency definitions, because “occasional” is not standardised across the literature; Henthorn 2024 Methods defines occasional use as “an average of at least 2 days per month and no more than 3 days per week in the 90 days before enrollment” and daily use as daily consumption of flower or concentrate over the same window. A future paper that resolves usage pattern more finely (e.g. occasional / daily-flower / daily-concentrate, which Henthorn 2024 tested and rejected as non-significant, or a graded days-per-month count) should register a separate ordinal or paired-indicator canonical rather than overload this binary. Ratified canonically on 2026-08-05 alongside the Henthorn 2024 inhaled-THC extraction.

Formulation / assay / study

ROUTE_* family – section-header policy

All ROUTE_<TARGET> canonicals follow the same shape: a binary indicator where 1 = subject received the <TARGET> administration route and 0 = subject received the reference (non-target) route. The reference category is route-pair-specific and is documented per-canonical in the Reference category: field (e.g., ROUTE_IV references SC; ROUTE_IP references non-IP; ROUTE_NGT references oral). The 2026-06-19 canonical-register standardization audit reviewed renaming this family to a more explicit ROUTE_<TARGET>_VS_<REFERENCE> shape but the operator declined the rename and instead set the following documentation discipline:

Policy (2026-06-19 audit): every ROUTE_* canonical MUST document its reference category explicitly in the Reference category: field of its H3 block. The reference is what ROUTE_<TARGET> = 0 means in the source dataset and is paper-specific. New ROUTE_* canonicals registered after 2026-06-19 must follow this rule; existing entries already comply. The discipline eliminates the silent-mis-encoding risk (where a model file might assume the wrong reference category) by making the reference explicit in the register rather than relying on the column name alone.

ROUTE_IV (canonical for IV-vs-SC administration route indicator)

• Description: 1 = subject received intravenous (IV) administration, 0 = subcutaneous (SC) administration. Per-subject (study-fixed) covariate flagging the dosing route when a population analysis pools cohorts that differ by route, with covariate effects on PK parameters that capture route-specific disposition behaviour.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (SC).
• Source aliases:
  • “Admin route = IV” (categorical effect column in Yu 2022 covariate equations).
  • IV – used in Zierhut_2008_osteoprotegerin.R (DDMODEL00000233 dataObj column flagging IV vs SC cohort, switching the PK observation residual SD between CcpropSdIV and CcpropSdSC).
  • “IV” / “SC arm” – used in Wang_2021_pertuzumab.R (per-subject FeDeriCa arm indicator P+H IV vs PH FDC SC, switching the proportional residual SD between CcpropSdIv and CcpropSdSc).
• Example models:
  • Yu_2022_ofatumumab.R (exponential effect on R0, CL, Q, ksyninf).
  • Zierhut_2008_osteoprotegerin.R (per-subject indicator switching the PK observation residual SD between the IV cohort (CcpropSdIV) and the SC cohort (CcpropSdSC)).
  • Wang_2021_pertuzumab.R (per-subject indicator switching the proportional residual SD between the IV (CcpropSdIv = 0.175) and SC (CcpropSdSc = 0.155) cohorts of the FeDeriCa popPK).
  • Fiedler-Kelly_2019_fremanezumab.R (per-subject indicator switching both the central volume of distribution (Vc,IV = 2.98 L FIXED vs Vc,SC = 1.88 L) and the residual-error structure (IV: proportional-only with SD = sqrt(0.0467) = 0.21610; SC: combined additive sqrt(0.204) + proportional sqrt(0.0531)) in the pooled phase 1/2b/3 fremanezumab popPK).
  • Fanta_2007_ciclosporin.R (per-occasion indicator carrying a 23% lower typical CL on oral occasions than on IV occasions (exp(e_route_iv_cl * ROUTE_IV) with e_route_iv_cl = log(1/0.77) = 0.2614) and switching the residual-error structure between IV (propSdIv = 0.089 + addSdIv = 1.5 ug/L) and oral (propSdPo = 0.20) arms; the comparator non-IV route is oral microemulsion ciclosporin, not SC).
  • Koolen_2010_docetaxel.R (per-dose-record indicator switching the docetaxel central volume of distribution between the IV value (V_central_iv = 9.8 L, polysorbate-80-micelle-sequestered) and the oral value (V_central_po = 44.0 L) per Koolen 2010 Results ‘Volume of distribution’ section; the reference category is oral here, not SC, because the source paper pools IV and oral docetaxel cohorts).
  • Roepcke_2018_tak_079.R (per-subject / per-dose-record indicator switching the TAK-079 central volume of distribution between the IV reference (Vc = 0.141 L, monkey) and SC (Vc = 0.043 L, ca. 70% smaller) via vc = exp(lvc + etalvc) * (1 - e_route_iv_vc * (1 - ROUTE_IV)) with e_route_iv_vc = 0.697 per Roepcke 2018 Table 2 ‘ROUT on V_C’; SC data come from the four lower single-dose groups <= 1 mg/kg in studies 7 and 8).
  • Toutain_2025_doxycycline_pig.R (per-dose-record indicator separating the single-dose IV catheter arm of the VetCAST pig doxycycline meta-analysis from the four oral modalities; when ROUTE_IV = 1 both the absorption rate constant and the bioavailability collapse to 0 so the depot equation contributes nothing and the dose must be placed in the central compartment via cmt = 'central', and the IV residual-error pair (proportional 13.9%, additive 0.0128 ug/mL) is selected. Toutain 2025 Appendix S3 Phoenix script block A. The comparator non-IV route is oral, not SC).
  • vandenBerg_2021_uprifosbuvir_pbpk.R (per-dose-record indicator switching between IV and oral uprifosbuvir; when ROUTE_IV = 1 the oral-absorption fast/slow fractions F1/F2 are set to 0 so the depot compartments contribute no mass and the dose must be placed directly into the central compartment via cmt = 'central' on the event record; when ROUTE_IV = 0 the standard oral absorption applies per FORM_CAPSULE and CONMED_ITRACONAZOLE. Corresponds to NONMEM FORM = 10 for the IV cohort in van den Berg 2021 supplement Table 1).
  • Han_2025_midazolam_pbpk.R, Han_2025_fentanyl_pbpk.R, Han_2025_alfentanil_pbpk.R, Han_2025_sufentanil_pbpk.R (per-dose-record indicator gating the gut-wall unbound fraction of the whole-body age-dependent CYP3A4 PBPK. Han 2025 Supplementary Eq S5 states that fu,gut “was defaulted to 1 for oral administration and equaled to free fraction of drug (fu,b) in blood for intravenous administration”, so fu_gut = ROUTE_IV * fu_b + (1 - ROUTE_IV) * 1: an oral dose sees full first-pass extraction of the absorbed flux by duodenal, jejunal and ileal CYP3A4 while an intravenous dose sees only the protein-binding-limited rate. Same gating role as vandenBerg_2021_uprifosbuvir_pbpk.R. The reference category here is oral, not SC, because Han 2025 pool oral and intravenous midazolam; the three opioid siblings are intravenous throughout. Dose into stomach when ROUTE_IV = 0 and into venous when ROUTE_IV = 1).
• Notes: This is the per-subject covariate-equation indicator, distinct from the dosing-event cmt column that names the target compartment. When simulating, set ROUTE_IV = 1 for IV cohorts and dose into the central compartment; set ROUTE_IV = 0 for SC cohorts and dose into the depot. Scope: specific because the set of parameters that differ by route is paper-specific (Yu 2022 carries route-specific exponential effects on disposition parameters; Zierhut 2008, Wang 2021, and Fiedler-Kelly 2019 carry route-specific PK observation residual SDs; Fiedler-Kelly 2019 and Roepcke 2018 additionally carry a route-specific central volume of distribution; van den Berg 2021 gates the entire oral-absorption cascade including gut wall extraction, four-step transit chain, and pseudo-M4 gut pool so that IV doses bypass the first-pass metabolism to gut UMP-UTP-CTP-CMP cycle).

ROUTE_IP (canonical for intraperitoneal-vs-non-IP administration route indicator)

• Description: 1 = subject (or dose record) received intraperitoneal (IP) administration; 0 = subcutaneous (SC), intravenous (IV), or any other non-IP route. Per-dose-record covariate flagging the IP route when a preclinical popPK pools multiple routes with route-specific bioavailability and the IP arm is the only route that carries a non-unity F.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-IP, typically SC or IV with bioavailability fixed at 1).
• Source aliases:
  • “IP” (route label in Johnson 2011 Table I, per-study route designation across 12 rat studies – IP studies 1-6b, SC studies 7-11, IV study 12).
• Example models:
  • Johnson_2011_olanzapine_rat.R (per-dose-record indicator selecting the IP bioavailability FIP = 0.636 with 87% CV log-normal IIV; ROUTE_IP = 0 selects F = 1 for SC and IV. The encoding f(central) <- exp(ROUTE_IP * (lfip + etalfip)) collapses to 1 when ROUTE_IP = 0 because exp(0) = 1, so subjects dosed via SC or IV inherit complete bioavailability without IIV on F).
• Notes: This is the per-dose-record covariate-equation indicator, distinct from the dosing-event cmt column that names the target compartment (Johnson 2011 doses all routes directly into central because the absorption rate constant was not estimable from the available data). When simulating IP doses, set ROUTE_IP = 1 on the dose record(s); set ROUTE_IP = 0 for SC and IV dose records. Scope: specific because the IP-vs-other contrast and which parameter it modifies (here, bioavailability) is paper-specific; complementary to ROUTE_IV (IV-vs-SC indicator) – a future tri-route study could use both indicators jointly.

ROUTE_NGT (canonical for nasogastric-tube-vs-oral administration route indicator)

• Description: 1 = dose record administered by nasogastric tube (NGT), 0 = oral administration. Per-dose-record covariate flagging NGT delivery when an oral popPK pools whole-tablet / crushed-tablet oral dosing with crushed-tablet-via-NGT dosing, with the route effect captured on an absorption parameter.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (oral administration; the structural reference for the Denti 2018 typical absorption lag time T_lag of 0.242 h).
• Source aliases:
  • NGT – used in Denti_2018_levofloxacin.R (per-dose-record nasogastric-tube indicator).
  • SOL (ST) – used in Toutain_2025_doxycycline_pig.R (Toutain 2025 Table 3 route column: an oral doxycycline solution delivered by oro-gastric “stomach tubing” rather than taken spontaneously in drinking water).
• Example models: Denti_2018_levofloxacin.R (multiplicative effect on the absorption lag time: (1 + e_route_ngt_tlag * ROUTE_NGT) with e_route_ngt_tlag = -0.856, so NGT delivery shortens T_lag to ~14.4% of its oral value; in the cohort 90/109 (82.6%) children were dosed by crushed tablet via NGT, Denti 2018 Table 2).
  • Denti_2018_levofloxacin.R (multiplicative effect on the absorption lag time: (1 + e_route_ngt_tlag * ROUTE_NGT) with e_route_ngt_tlag = -0.856, so NGT delivery shortens T_lag to ~14.4% of its oral value; in the cohort 90/109 (82.6%) children were dosed by crushed tablet via NGT, Denti 2018 Table 2).
  • Toutain_2025_doxycycline_pig.R (selects the stomach-tube sub-model of the pig doxycycline meta-analysis – Ka = 0.725 1/h, F = 0.258, proportional residual 27.5% plus additive 0.0026 ug/mL – against the spontaneous drinking-water sub-model at ROUTE_NGT = 0 – Ka = 0.689 1/h, F = 0.307, proportional residual 29.3% plus additive 0.0057 ug/mL; Toutain 2025 Table 6. Only meaningful when ROUTE_IV = 0 and FORM_DOX_FEED = 0).
• Notes: This is the per-dose-record covariate-equation indicator, distinct from the dosing-event cmt column that names the target compartment. When simulating NGT doses, set ROUTE_NGT = 1 on the dose record(s); set ROUTE_NGT = 0 for oral dose records. Scope: specific because the NGT-vs-oral contrast and which absorption parameter it modifies are paper-specific. Distinct from the ROUTE_IV (IV-vs-SC) and ROUTE_IP (IP-vs-non-IP) parenteral-route indicators – ROUTE_NGT is an enteral-delivery-method indicator within oral administration. The canonical covers tube-delivered vs voluntarily-ingested enteral dosing generally, so a veterinary oro-gastric tube (Toutain 2025) uses the same column as a human nasogastric tube (Denti 2018); the reference category is spontaneous oral intake in both. Ratified canonically alongside the Denti 2018 levofloxacin extraction.

ROUTE_ORAL (canonical for oral-vs-non-oral administration route indicator)

• Description: 1 = dose record administered orally (capsule / tablet / oral solution), 0 = any non-oral route pooled in the same analysis (intranasal, intravenous, or other). Per-dose-record covariate flagging oral delivery when a population analysis pools an oral arm with one or more non-enteral arms and estimates a separate absorption parameter set – bioavailability, absorption rate constant, zero-order input duration, lag time – for each route, and/or restricts a systemic covariate effect to the oral route only.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-oral; the pooled reference is paper-specific and must be documented per model). In Comisar_2025_zavegepant.R the reference pools the intranasal nasal-spray arm and the intravenous-infusion arm: IV doses are placed directly into central by the dose record’s cmt and therefore never read the depot parameters at all, so ROUTE_ORAL = 0 selects the intranasal absorption set on the records that actually enter the depot.
• Source aliases:
  • “Administration route” – Comisar 2025 Table 1 per-study route column (Nasal spray / Oral / Intravenous infusion).
• Example models:
  • Comisar_2025_zavegepant.R (per-dose-record indicator selecting between the intranasal and oral absorption parameter sets of the three-compartment zavegepant popPK – F 5.1% vs 0.65%, ka 5.8 vs 0.81 1/h, D1 8.6 vs 57.2 min, and an oral-only 12.2 min absorption lag that collapses to zero via tlag = ROUTE_ORAL * exp(ltlag_oral); each switch is written as a log-scale convex combination exp((1 - ROUTE_ORAL) * l<param>_intranasal + ROUTE_ORAL * l<param>_oral + eta<param>) so the shared per-quantity eta of Comisar 2025 Table 3 rides on both routes. It additionally gates two effects that Comisar 2025 restricts to oral dosing: the itraconazole clearance effect, applied as (1 + e_conmed_itraconazole_cl * CONMED_ITRACONAZOLE * ROUTE_ORAL) = -27.9% on CL, and the fed-state bioavailability effect (1 + e_fed_fdepot_oral * FED * ROUTE_ORAL) = -50.9% on F.)
• Notes: This is the per-dose-record covariate-equation indicator, distinct from the dosing-event cmt column that names the target compartment. Complementary to ROUTE_IV (IV-vs-SC), ROUTE_IP (IP-vs-non-IP), ROUTE_SC (SC-vs-IM), ROUTE_NGT (NGT-vs-oral) and ROUTE_VAGINAL (vaginal-vs-buccal) – none of those carries an oral-vs-non-oral contrast, and ROUTE_NGT is its near-inverse (it distinguishes delivery methods within oral administration, with oral as its reference). In a three-or-more-route design a single ROUTE_ORAL column suffices whenever the remaining routes are separated by the target compartment rather than by a covariate, as in Comisar 2025; register an additional ROUTE_* indicator only when two non-oral routes must be told apart inside the same compartment’s parameter equations. Scope: specific because which parameters switch by route, and what the 0 level pools, are paper-specific. Registered under the ROUTE_* family section-header policy above, whose reference-category documentation discipline this entry follows.

ROUTE_VAGINAL (canonical for vaginal-vs-buccal administration route indicator)

• Description: 1 = subject received vaginal misoprostol administration, 0 = buccal administration. Per-subject (study-fixed) covariate flagging the dosing route when a population analysis pools vaginal and buccal cohorts of a mucosally absorbed drug, with the buccal cohort as the structural reference (F = 1) and vaginal-specific effects encoded as a relative bioavailability multiplier and separate absorption rate constants per route x dose combination.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (buccal administration; the structural reference in Vorontsova 2022, whose relative bioavailability parameter F_v/b = 2.3 is the ratio of vaginal to buccal bioavailability with the buccal value fixed at 1 per the paper’s model-construction statement ‘we included a relative bioavailability term (F_v/b) for the vaginal route relative to buccal (F_b, assumed to be one)’).
• Source aliases:
  • ROUTE – used in Vorontsova_2022_misoprostol.R (IMPROVE-trial per-subject randomization indicator flagging vaginal vs buccal misoprostol for full-term labor induction).
• Example models: Vorontsova_2022_misoprostol.R (per-subject indicator selecting between four absorption rate constants (ka_buccal_25, ka_buccal_50, ka_vaginal_25, ka_vaginal_50 indexed by ROUTE_VAGINAL x DOSE) and applying the relative bioavailability multiplier F_v/b = 2.3 on the depot dose for vaginal subjects only via fdepot = 1 - ROUTE_VAGINAL + ROUTE_VAGINAL * fvb; inter-occasion variability on F_v/b is estimated across the two sampled dose events).
• Notes: This is the per-subject covariate-equation indicator, distinct from the dosing-event cmt column that names the target compartment (Vorontsova 2022 doses both routes into the same depot absorption compartment; only the ka value and bioavailability switch by route). Complementary to ROUTE_IV (IV-vs-SC), ROUTE_IP (IP-vs-non-IP), ROUTE_NGT (NGT-vs-oral), and ROUTE_SC (SC-vs-IM) – ROUTE_VAGINAL fills the vaginal-vs-buccal contrast for mucosally absorbed drugs (misoprostol, obstetric prostaglandins, hormonal contraceptives) that neither of those cover. Scope: specific because the vaginal-vs-buccal contrast and which parameters switch by route are paper-specific (Vorontsova 2022 carries route-specific ka values crossed with dose level and a single relative bioavailability multiplier; a future paper could carry different route effects on CL, V, or Tlag). Ratified canonically alongside the Vorontsova 2022 misoprostol extraction.

ROUTE_SC (canonical for subcutaneous-vs-intramuscular administration route indicator)

• Description: 1 = dose record (or subject occasion) administered by subcutaneous (SC) injection, 0 = intramuscular (IM) injection. Per-dose-record / per-occasion covariate flagging SC delivery when a population analysis pools IM and SC injection cohorts of an injectable long-acting formulation, with the IM cohort as the structural reference and SC-specific effects encoded as multiplicative factors on absorption half-lives, dose-split ratios, and relative bioavailability.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (IM injection; the structural reference in Kado 2020, whose “Relative structural model parameters for SC administration” are all ratios of SC-value / IM-value with IM’s F fixed at 1).
• Source aliases:
  • SC / route = SC – Kado 2020 Table 1 SC-relative parameter block (SC vs IM crossover of benzathine benzylpenicillin G).
• Example models: Kado_2020_benzathine_benzylpenicillin_g.R (per-dose-record indicator switching the three absorption half-lives (t1/2,abs-1/-2/-3), the transit half-life (t1/2,tr), the two dose-split ratios (RAT-transit, RAT-slowfast), and the relative bioavailability (F_SC = 0.957) between the IM reference and the SC structural values; all IIVs on absorption parameters are also route-specific per Kado 2020 Methods ‘PK modelling’).
• Notes: This is the per-dose-record covariate-equation indicator, distinct from the dosing-event cmt column that names the target compartment (Kado 2020 doses all routes into the same triple-depot absorption chain; only the parameter values switch by route). Complementary to ROUTE_IV (IV-vs-SC, where SC is the reference) and ROUTE_IP (IP-vs-non-IP, where non-IP including SC is the reference) – ROUTE_SC fills the SC-vs-IM contrast that neither of those cover. Scope: specific because the IM-vs-SC contrast and the set of parameters that differ by route are paper-specific.

DEVICE_AI (canonical for autoinjector-vs-prefilled-syringe SC device indicator)

• Description: 1 = subject’s SC dose delivered via autoinjector (AI), 0 = prefilled syringe (PFS). Per-subject (study-fixed) covariate flagging the SC delivery device when a model carries device-specific PK effects.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (PFS).
• Source aliases: “Formulation = AI” (categorical effect column in Yu 2022 covariate equations).
• Example models: Yu_2022_ofatumumab.R (exponential effect on k_e(P) and R0), Diep_2026_donidalorsen.R (Phoenix linear-effect (1 + e_device_ai_ka * DEVICE_AI) on the typical SC absorption rate constant with theta = +0.262 -> multiplier 1.262 for autoinjector vs vial-and-syringe reference; characterized in the ISIS 721744-CS9 single-dose bioequivalence cohort).
• Notes: Set to 0 (PFS / vial reference) for IV subjects, since the device is undefined for IV; the IV-specific effects are captured by ROUTE_IV instead. Scope: specific because the AI / vial / PFS contrast and which parameters it affects depend on the study’s device-comparison design.

INJSITE_ARM (canonical for SC injection-site = arm indicator)

• Description: 1 = subject’s SC dose injected into the arm, 0 = abdomen (the universal SC reference site across the popPK literature). Per-dose-record covariate flagging the SC injection site when a population analysis estimates site-specific absorption parameters.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (abdomen).
• Source aliases: paper narrative “arm” / “abdomen” subgroup labels driving site-specific ka in Diep 2022.
• Example models: Diep_2022_eplontersen.R (additive log-shift e_injsite_arm_ka = log(ka_arm / ka_ab) on the typical absorption rate constant: ka_arm = 0.217 1/h vs ka_ab = 0.282 1/h, ~30% higher ka for abdomen; INJSITE_ARM = 1 selects the arm typical value), Diep_2026_donidalorsen.R (Phoenix linear-effect (1 + e_injsite_arm_ka * INJSITE_ARM) on the typical absorption rate constant with theta = -0.338 -> multiplier 0.662 for arm; the paper’s reference category is “abdomen or thigh” rather than “abdomen” alone, but is consistent with the canonical reference because abdomen is the universal SC reference site and the thigh effect is pooled into the reference category by the Diep 2026 model), CarlssonPetri_2018_semaglutide.R (multiplicative CL/F ratio 1.08 for upper-arm vs abdomen injection site, encoded as e_site_arm_cl^INJSITE_ARM per Table S3; per-subject dominant injection-site indicator since the paper assigns each subject their most frequently used site), Perlstein_2025_risperidone_tv46000.R (categorical multiplicative effect (1 + e_injsite_arm_ka1)^INJSITE_ARM on the fast direct-release rate constant of the TV-46000 subcutaneous risperidone depot, with theta = 0.331 -> 33% higher ka1 for the upper arm; per-dose-record, since the paper’s conclusion is that a patient may alternate sites between injections without an exposure consequence).
• Notes: Specific scope because the arm-vs-abdomen contrast is paper-specific. Sister canonical to INJSITE_THIGH (thigh-vs-abdomen indicator; founded 2026-07-09 alongside the CarlssonPetri_2018_semaglutide extraction). Per-administration rather than per-subject in the Diep 2022 / 2026 usage (a subject in a multi-dose simulation can switch SC injection sites between doses; supply the indicator on each dose record), but per-subject in the Carlsson Petri 2018 usage (each subject’s dominant / most frequently used injection site was used as the covariate value). Distinct from ROUTE_IV (IV vs SC route, not within-SC site) and from DEVICE_AI (autoinjector vs prefilled syringe, device rather than anatomical site).

INJSITE_THIGH (canonical for SC injection-site = thigh indicator)

• Description: 1 = subject’s SC dose injected into the thigh, 0 = abdomen (the universal SC reference site across the popPK literature) or a different non-thigh injection site. Per-dose-record OR per-subject covariate flagging the SC injection site when a population analysis estimates site-specific absorption or clearance effects.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (abdomen).
• Source aliases: paper narrative “thigh” / “abdomen” subgroup labels driving site-specific PK parameters.
• Example models: CarlssonPetri_2018_semaglutide.R (multiplicative CL/F ratio 1.04 for thigh vs abdomen injection site, encoded as e_site_thigh_cl^INJSITE_THIGH per Table S3; per-subject dominant injection-site indicator since the paper assigns each subject their most frequently used site), Bjornsson_2023_buprenorphine.R (multiplicative gate (1 - INJSITE_THIGH) on the CAM2038 Q1W fast-pathway dose fraction Fq1w1, i.e. a thigh injection routes the entire weekly buprenorphine depot dose through the slow absorption pathway; Bjornsson 2023 Results 3.1.1 estimated Fq1w1 for thigh injections “close to 0” and subsequently fixed it to 0, so the effect is encoded as a structural gate rather than an estimated coefficient; per-dose-record covariate, and the reference complement here is buttock / abdomen / upper arm, which Discussion 4.1 reports as indistinguishable from one another), Glatard_2025_octreotide.R (fractional change -0.351 on the fast-release mean absorption time MAT_fast of the CAM2029 octreotide depot relative to abdominal injection, per Table 3 / Eq 11; supplied per dose record alongside INJSITE_BUTTOCK, both 0 meaning abdomen).
• Notes: Specific scope because the thigh-vs-abdomen contrast is paper-specific. Sister canonical to INJSITE_ARM (arm-vs-abdomen). Per-administration or per-subject depending on the paper’s dosing granularity – in Carlsson Petri 2018 the covariate is per-subject because ‘the most frequently used injection site for an individual patient was used as the covariate value’ (Methods); Bjornsson 2023 supplies it per dose record because a single subject’s weekly depot injections can rotate between sites. The reference complement is paper-defined: strictly abdomen in Carlsson Petri 2018, and the pooled buttock / abdomen / upper arm group in Bjornsson 2023 (where those three sites showed no absorption differences). A thigh effect may be a structural gate rather than an estimated coefficient when the source paper fixes the thigh parameter at a boundary value. Distinct from ROUTE_IV (IV vs SC route, not within-SC site) and from DEVICE_AI (autoinjector vs prefilled syringe, device rather than anatomical site). Founded alongside the CarlssonPetri_2018_semaglutide extraction.

STUDY_APLIOS (canonical for APLIOS bioequivalence study indicator)

• Description: 1 = subject enrolled in the APLIOS bioequivalence study (NCT03560739; phase 2; ofatumumab AI vs PFS in RMS), 0 = other study in the Yu 2022 pooled analysis.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-APLIOS studies: OMS115102, MIRROR, ASCLEPIOS I, ASCLEPIOS II).
• Source aliases: “Study = APLIOS” (categorical effect column in Yu 2022 covariate equations).
• Example models: Yu_2022_ofatumumab.R (exponential effect on Emax of B cell lysis).
• Notes: Captures a between-study shift in the maximum B-cell lysis stimulatory effect not explained by the other covariates in the final model.

STUDY_MIRROR (canonical for MIRROR dose-finding study indicator)

• Description: 1 = subject enrolled in the MIRROR dose-finding study (NCT01457924; phase 2; SC ofatumumab dose-ranging in RRMS), 0 = other study in the Yu 2022 pooled analysis.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-MIRROR studies: OMS115102, APLIOS, ASCLEPIOS I, ASCLEPIOS II).
• Source aliases: “Study = MIRROR” (categorical effect column in Yu 2022 covariate equations).
• Example models: Yu_2022_ofatumumab.R (exponential effect on B cell elimination rate kout).
• Notes: Captures a between-study shift in the B cell elimination rate not explained by the other covariates in the final model.

STUDY_ARROW_PART2 (canonical for ARROW PK Substudy Part 2 study indicator)

• Description: 1 = record from the ARROW PK Substudy Part 2 (the antiretroviral-dosing substudy of the ARROW trial in African children living with HIV-1), 0 = record from any other study pooled into the analysis. Per-record (study-fixed) binary indicator.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (all other pooled studies; relative bioavailability fixed at 1).
• Source aliases:
  • F1 study term – used in Chandasana_2024b_abacavir.R; Chandasana 2024 Table 1 names the two estimates “F, tablet ARROW PK Substudy Part 2” and “F, solution ARROW PK Substudy Part 2”.
• Example models: Chandasana_2024b_abacavir.R (study-specific relative bioavailability on the abacavir depot, applied jointly with FORM_SOLUTION so that the ARROW Part 2 tablet gets 1.62 and the ARROW Part 2 oral solution gets 1.75, both relative to the all-other-studies reference of 1; encoded on the log scale as exp(e_arrow_tab_fdepot * STUDY_ARROW_PART2 * (1 - FORM_SOLUTION) + e_arrow_sol_fdepot * STUDY_ARROW_PART2 * FORM_SOLUTION)).
• Notes: The source model included this term because abacavir exposure observed in ARROW PK Substudy Part 2 was substantially higher than in the other pooled studies despite similar doses and formulations (Chandasana 2024 “ABC Pediatric PopPK Model”), i.e. it absorbs an unexplained between-study bioavailability shift rather than a mechanistic covariate effect. The effect is formulation-specific within the substudy, so it is always paired with a formulation indicator. Sibling of the other STUDY_<id> members; scoped specific because the reference category is the paper’s own pooled-study set. Ratified canonically alongside the Chandasana 2024 pediatric ABC/DTG/3TC extraction.

STUDY_ODYSSEY (canonical for ODYSSEY trial study indicator)

• Description: 1 = record from the ODYSSEY trial (a multicentre randomised trial of antiretroviral regimens in children and adolescents living with HIV-1), 0 = record from the comparator study pooled into the analysis. Per-record (study-fixed) binary indicator.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (the per-paper comparator pooled study; IMPAACT P1093 in Chandasana 2024).
• Source aliases:
  • study – used in Chandasana_2024b_dolutegravir.R; Chandasana 2024 Table 2 labels the residual-error rows “study P1093” and “ODYSSEY”.
• Example models: Chandasana_2024b_dolutegravir.R (selects between the two study-specific residual-error magnitudes of the pooled dolutegravir pediatric model: proportional 28.6% plus additive 0.00164 ug/mL for the IMPAACT P1093 reference, and proportional 11.1% plus additive 0.090 ug/mL for ODYSSEY; applied inside model() as propSd_i <- propSd * (1 - STUDY_ODYSSEY) + propSd_odyssey * STUDY_ODYSSEY and likewise for the additive term).
• Notes: Distinguishes the two pooled source studies of the pediatric dolutegravir popPK model. The between-study difference is confined to residual error (assay and sampling-design differences), not to any structural or covariate parameter. Records that belong to neither pooled study – for example the IMPAACT 2019 external-validation cohort – take 0 so the P1093 residual error applies, that being the pediatric dolutegravir single-entity study most comparable to IMPAACT 2019. Sibling of the other STUDY_<id> members. Ratified canonically alongside the Chandasana 2024 pediatric ABC/DTG/3TC extraction.

STUDY_TLS (canonical for the TLS on-farm field trial cohort indicator)

• Description: 1 = record from the TLS trial, the 215-pig on-farm field trial of doxycycline administered in medicated feed (del Castillo 2006); 0 = record from one of the laboratory-condition in-feed trials pooled into the same analysis (AFSSA, BIOEQ, PARADOX, Company 9203, Company 9204). Per-dose-record (trial-fixed) binary indicator separating field-condition from laboratory-condition in-feed medication.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (the laboratory-condition in-feed trials).
• Source aliases:
  • Trial ID = TLS – Toutain 2025 Table 1 trial identifier; Table 6 labels the two blocks “Trial TLS (feed, field conditions)” and “Trials AFSSA, BIOEQ, PARADOX, Company 9203 and Company 9204 (feed, laboratory conditions)”.
• Example models: Toutain_2025_doxycycline_pig.R (selects between the field-condition feed sub-model (Ka = 0.072 1/h, F = 0.501 with 84.8% between-subject variability, proportional residual 22.8% plus additive 0.113 ug/mL) and the laboratory-condition feed sub-model (Ka = 0.144 1/h, F = 0.340 with 36.4% between-subject variability, proportional residual 18.4% plus additive 0.019 ug/mL); Toutain 2025 Table 6).
• Notes: Well-formed member of the auto-approved STUDY_<id> canonical family; the mechanistic reading is field vs laboratory husbandry, but the paper’s own sub-model boundary is exactly the TLS trial, so the trial identifier is the honest column. Only meaningful when FORM_DOX_FEED = 1; set to 0 for every non-feed record. Sibling of STUDY_ODYSSEY (which likewise carries a purely between-study contrast) except that here the contrast spans absorption rate and bioavailability as well as residual error, because the field trial’s group-fed dosing process introduces competition between animals for access to the medicated meal. Ratified canonically alongside the Toutain 2025 pig doxycycline extraction.

STUDY_DORZA_EARLY (canonical for early-phase dorzagliatin study indicator (HMM0102 / HMM0103 / HMM0110))

• Description: 1 = subject enrolled in one of the three early-phase dorzagliatin studies pooled by Wang 2023 – HMM0102 (NCT02077452, multiple-ascending-dose), HMM0103 (NCT02386982, 28-day beta-cell-function study) or HMM0110 (NCT04324424, renal-impairment study with matched healthy volunteers); 0 = one of the three later-phase studies HMM0201 (NCT02561338, phase II dose-ranging), HMM0301 (NCT03173391, phase III monotherapy) or HMM0302 (NCT03141073, phase III metformin add-on). Used to switch apparent clearance between the two study groups.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (later-phase studies HMM0201 / HMM0301 / HMM0302, which used near-commercial preparations).
• Source aliases:
  • Study – used in Wang_2023_dorzagliatin.R (Wang 2023 Table 3 footnote: “Study: 1 for study 102/103/110, 0 for others”).
• Example models: Wang_2023_dorzagliatin.R (multiplicative exponential effect on CL/F: exp(0.203 * STUDY_DORZA_EARLY) = a 1.23-fold, i.e. 22.5% higher, apparent clearance in the early-phase studies; Wang 2023 Table 3 CL_STUDY = 1.23).
• Notes: Specific scope; tied to the six-trial Hua Medicine dorzagliatin development programme. Subject-level (time-fixed); set from the trial identifier. Wang 2023 Discussion paragraph 2 documents the mechanism and why the effect lands on CL/F rather than on F: the early studies showed systematically lower exposure, plausibly a formulation difference, but because the later studies were mainly sparsely sampled “the model was unable to correct for prediction bias by bioavailability”, so the between-trial difference was absorbed into apparent clearance. This is why the indicator is registered under the STUDY_<id> family rather than as a FORM_<drug>_<formulation> entry – the formulation attribution is the paper’s hypothesis, not its parameterisation. Directly analogous to STUDY_LBSL (a binary indicator over a group of early-phase studies switching CL and V magnitudes) and to STUDY_FARLETUZUMAB_PHASE2 / STUDY_POSA_PHASE3 / STUDY_NIPOCALIMAB_PHASE1. Follows the auto-approved STUDY_<id> canonical family.

REGI_BID (canonical for twice-daily dosing-regimen indicator)

• Description: 1 = subject’s dosing regimen is BID (twice daily), 0 = QD (once daily) or other non-BID regimen. Per-subject (regimen-fixed) categorical indicator for population analyses that pool QD and BID arms and test regimen as a covariate.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-BID regimen – typically QD).
• Source aliases:
  • BID – used in Girard_2012_pimasertib.R.
• Example models: Girard_2012_pimasertib.R (additive shift on the cumulative-logit AE-score model: theta_bid * REGI_BID; -0.399 logit units for BID vs QD).
• Notes: Specific scope because the QD-vs-BID contrast is study-specific; future regimen-comparison models that contrast different schedules should either extend this entry’s example list (when QD is the reference) or register a sibling indicator (REGI_TID, REGI_QW) following the same pattern. Distinct from DOSE (dose level in mg) and from total-daily-dose aggregates: a 60 mg/day cohort can include either a 60 mg QD subgroup or a 30 mg BID subgroup, and both share the same DOSE = 60 while differing in REGI_BID.

REGI_QM (canonical for once-monthly dosing-regimen indicator)

• Description: 1 = subject’s dosing regimen is once-monthly (QM, typically every 4 weeks for a long-half-life mAb), 0 = the per-paper comparator regimen (typically once-every-2-weeks, Q2W, for evolocumab-class mAbs). Per-subject (regimen-fixed) categorical indicator used by static Emax-on-AUC exposure-response models that pool QM and Q2W arms and need to distinguish them because the Emax-on-AUC formulation cannot represent the difference in target-saturation time courses between the two regimens. This is purely an exposure-response model device; dynamic ODE-based PK / PD layers do not need this indicator because they resolve the concentration time course explicitly.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (non-QM regimen – paper-defined; Q2W for Kuchimanchi_2018_evolocumab_ldlc.R, and sham / every-other-month for Crass_2025_pegcetacoplan_ga_doseresponse.R). Document the per-paper comparator in covariateData[[REGI_QM]]$notes.
• Source aliases:
  • REG – Kuchimanchi 2018 notation for the binary indicator i in the exposure-response formula Eff = Emax * AUC / (EC50 * REG^i + AUC). The linear-scale multiplier paired with this indicator (paper estimate 2.30) maps to the canonical parameter reg_qm.
• Example models: Kuchimanchi_2018_evolocumab_ldlc.R (founding example; exposure-response Emax on LDL-C distinguishes 420 mg SC QM from 140 mg SC Q2W via ec50_eff = ec50 * reg_qm^REGI_QM), Crass_2025_pegcetacoplan_ga_doseresponse.R (proportional step-function shift on the study-eye geographic-atrophy lesion progression rate, (1 + e_regi_qm_slope_study * REGI_QM + e_regi_q2m_slope_study * REGI_Q2M) with e_regi_qm_slope_study = -0.204, i.e. a 0.80-fold lower rate of lesion growth on intravitreal pegcetacoplan 15 mg monthly vs sham).
• Notes: Promoted from Scope: specific to Scope: general alongside the Crass 2025 pegcetacoplan geographic-atrophy extraction: “once-monthly dosing regimen” is a regimen-interval fact that is independent of the comparator, and a second unrelated analysis (a different drug, a different route, and a sham rather than Q2W comparator) now uses the same column. The comparator is paper-specific and is recorded per-model in covariateData[[REGI_QM]]$notes, not in the column name. Sibling of REGI_BID (within-QD-vs-BID schedules) and REGI_Q2M (every other month) under the REGI_* family pattern; future regimen-comparison models that contrast different intervals (e.g., Q4W vs Q8W) should register a sibling indicator (REGI_Q4W, REGI_Q8W) following the same pattern. Distinct from DOSE (dose level in mg) and from regimen-implied total-AUC aggregates: a steady-state exposure-matched 140 mg Q2W and 420 mg QM pair share the same AUC over 4 weeks but differ in REGI_QM. Pairs with the canonical parameter lreg_qm / reg_qm (the EC50 multiplier exponentiated by this indicator). Use only in static Emax-on-AUC exposure-response models; dynamic ODE-based PK / PD layers do not need this indicator.

REGI_Q2M (canonical for once-every-other-month dosing-regimen indicator)

• Description: 1 = subject’s dosing regimen is every other month (Q2M, i.e. once every 8 weeks or once every second monthly visit), 0 = the per-paper comparator regimen. Per-subject (regimen-fixed) categorical indicator used by exposure-response and dose-response models that pool arms differing only in dosing interval and need to distinguish them because the model’s drug-effect term is a per-regimen step function rather than a resolved concentration time course.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (not on the Q2M regimen – paper-defined). Document the per-paper comparator in covariateData[[REGI_Q2M]]$notes. Reference values observed: in Crass_2025_pegcetacoplan_ga_doseresponse.R the reference state is REGI_QM = REGI_Q2M = 0, i.e. sham treatment; 498 of 1501 patients were on the Q2M (every-other-month) pegcetacoplan arm and 505 on the monthly arm.
• Source aliases:
  • EOM – “every other month”, the Crass 2025 notation for the arm and for the corresponding drug-effect theta (“Every-other-month drug effect, proportion”).
• Example models: Crass_2025_pegcetacoplan_ga_doseresponse.R (proportional step-function shift on the study-eye GA lesion progression rate: (1 + e_regi_qm_slope_study * REGI_QM + e_regi_q2m_slope_study * REGI_Q2M) with e_regi_q2m_slope_study = -0.172, i.e. a 0.83-fold lower rate of lesion growth vs sham).
• Notes: Sibling of REGI_QM (once-monthly) and REGI_BID (twice-daily) under the REGI_* regimen-indicator family; the REGI_QM entry’s Notes explicitly anticipate registering siblings for additional dosing intervals. When a model pools three or more regimens, use one indicator per active arm and let all-zero select the reference arm – the indicators are then mutually exclusive and must be constructed so at most one is 1 per subject. Distinct from DOSE (dose level in mg): the Crass 2025 monthly and every-other-month arms received the identical 15 mg intravitreal dose and differ only in interval. Use only where the model cannot resolve the concentration time course; a dynamic ODE-based PK/PD layer expresses the same information through the dosing records. Ratified canonically alongside the Crass 2025 pegcetacoplan geographic-atrophy extraction.

MIL_REGIMEN (canonical for miltefosine monotherapy vs combination-with-LAmB regimen indicator)

• Description: Per-subject (time-fixed) binary indicator carrying the visceral-leishmaniasis miltefosine treatment-arm assignment in the Dorlo 2017 Eastern African LEAP-0208 study. 1 = monotherapy arm (28 days oral miltefosine 2.5 mg/kg/day, maximum 150 mg/day); 0 = combination arm (single IV liposomal amphotericin B 10 mg/kg on day 1 plus 10 days oral miltefosine 2.5 mg/kg/day, maximum 150 mg/day). Used to select the duration of the initial reduced-bioavailability window: 7 days for monotherapy, 1 day for the combination arm (Dorlo 2017 Table 2 footnote d).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (combination arm: single LAmB IV on day 1 + 10-day oral miltefosine).
• Source aliases: derived per subject from the trial-arm assignment in NCT01067443; the source paper does not name an NMTRAN column directly. Dorlo 2017 narrative labels the arms ‘MIL’ (monotherapy) and ‘combination therapy arm’.
• Example models: Dorlo_2017_miltefosine.R (sets tlowf <- 7 * MIL_REGIMEN + 1 * (1 - MIL_REGIMEN) inside model(); tlowf is then used in the (t > 0) * (t <= tlowf) predicate that gates the typical 74.3% reduction in relative bioavailability during the initial absorption window).
• Notes: Specific scope because the miltefosine-vs-miltefosine+LAmB head-to-head and the per-arm absorption-window durations are tied to the Dorlo 2017 design. The combination-arm reduction window is shorter than the monotherapy window because the LAmB infusion is hypothesised to accelerate the patients’ overall physiological recovery (Discussion paragraph ‘Pharmacokinetics’); future Eastern African VL combination-with-LAmB miltefosine popPK extractions that share the same regimen pair can extend this entry’s example list, while a contrast against a different miltefosine combination partner (e.g., paromomycin, fexinidazole) should register a sibling indicator. Distinct from REGI_BID (within-monotherapy QD vs BID schedule) and from the CONMED_* family (the LAmB co-administration in the combination arm is captured implicitly via the regimen indicator rather than via an explicit concomitant-medication binary, because the LAmB-driven effect modelled here is on the absorption-window duration rather than on miltefosine clearance or distribution).

MEAL_FLAG (canonical for intra-day meal-window indicator (time-varying))

• Description: 1 = the current observation time falls within a meal window (typically lunch or dinner, ~1 hour duration each); 0 = no meal-driven physiological perturbation active. Distinct from FED (per-dose-record fed-vs-fasted indicator): MEAL_FLAG is a time-varying intra-day flag that switches on at meal onset, stays on through the meal duration, and switches off afterward. Used by enterohepatic-recirculation / gallbladder-contraction models that need to scale post-prandial transport rate constants for the duration of a meal.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no meal effect active at this time).
• Source aliases: paper narrative “meal effect on K_BI” / “post-prandial gallbladder contraction” in Zuo 2016.
• Example models: Zuo_2016_UDCA.R (multiplies the biliary-to-intestine rate constants K_BI,0/1/2 by E_meal = 35.33 during meal windows to simulate gallbladder contraction; two meals modelled per day, lunch at +4 h and dinner at +10 h after the morning dose, each 1 hour long).
• Notes: Specific scope because the operational definition (meal duration, schedule, magnitude of the rate-constant scaling) is paper-defined. Time-varying: must be supplied at every observation row in the event dataset. Pair with SNACK_FLAG for studies that distinguish meal and snack effects. Distinct from FED and FED_HIGHFAT (per-dose-record meal-state indicators tied to a single dosing event); MEAL_FLAG is decoupled from any specific dose record and instead drives ongoing physiology over a multi-hour window.

SNACK_FLAG (canonical for intra-day snack-window indicator (time-varying))

• Description: 1 = the current observation time falls within a snack window (typically 0.5 hour duration, a smaller perturbation than a meal); 0 = no snack-driven physiological perturbation active. Used in tandem with MEAL_FLAG by enterohepatic-recirculation / gallbladder-contraction models that scale post-prandial transport rate constants with a smaller magnitude for snacks than for full meals.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no snack effect active at this time).
• Source aliases: paper narrative “snack effect on K_BI” in Zuo 2016.
• Example models: Zuo_2016_UDCA.R (multiplies the biliary-to-intestine rate constants K_BI,0/1/2 by E_snack = 9.53 during snack windows; one snack modelled per day at +7 h after the morning dose in the Xiang 2011 single-dose study, 0.5 hour long).
• Notes: Specific scope; same per-paper-defined operational definition as MEAL_FLAG. Time-varying.

FRACABS (canonical for dose-record fractional absorption supplied as a data column)

• Description: Per-dose-record fractional absorption (0-1) supplied as a covariate when the source paper reports F as a dose-dependent function rather than as a single estimable bioavailability parameter. The model wires the covariate into a bioavailability hook (f(<depot>) <- FRACABS); the user supplies the per-dose F value derived from the paper’s regression / lookup table.
• Units: fraction
• Type: continuous
• Scope: specific
• Reference category: n/a (continuous covariate).
• Source aliases: paper narrative “F = 0.66 at 150 mg” / “F = 0.31 at 1000 mg” / Walker 1992 / Crosignani 1991 / Dilger 2012 reported absorption values in Zuo 2016.
• Example models: Zuo_2016_UDCA.R (dose-dependent fractional absorption derived from a log-dose linear regression: F = 0.66 at 150 mg and F = 0.31 at 1000 mg, with R^2 = 0.99 over 200-2000 mg per the paper; combined with the UDCA molecular-weight conversion in the bioavailability hook f(stomach_udca) <- FRACABS / mw_udca).
• Notes: Specific scope because the operational definition (which doses get which F value; whether F is treated as a known input or as a parameter to be re-estimated) is paper-defined. Distinct from lfdepot / f(depot) in models where bioavailability is an estimable PK parameter rather than a supplied covariate. When the paper’s F regression is itself a function of DOSE, the user can derive FRACABS from the DOSE column upstream of rxSolve rather than carrying the regression inside model().

FASTED_STRICT (canonical for strict-fasting-vs-relaxed-fasting dose-record indicator)

• Description: 1 = the dose was taken under a strictly enforced fast, in which the protocol prohibits food for a stated interval both BEFORE and AFTER the dose; 0 = any less strict prandial state, i.e. a relaxed / partial fast in which food is permitted on one side of the dose, or a fed state. Encodes fast strictness, the axis that the binary FED indicator cannot express: FED = 0 pools every non-fed state into one category, so a study that contrasts two different non-fed protocols needs this second indicator to separate them. FASTED_STRICT composes with FED to span a three-level prandial factor: strict fast is FASTED_STRICT = 1, FED = 0; relaxed fast is FASTED_STRICT = 0, FED = 0; fed is FASTED_STRICT = 0, FED = 1.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (relaxed / partial fast, or fed). Document the two protocols verbatim in covariateData[[FASTED_STRICT]]$notes, since what counts as “strict” is protocol-defined and the pre-dose and post-dose intervals differ between papers.
• Source aliases:
  • prandial-state label (Table 2 row labels fast / mod fast / fed) – used in OlssonGisleskog_2025_ibrutinib.R, where the three-level prandial factor of Olsson Gisleskog 2025 Table 2 is decomposed into FASTED_STRICT (acting on F1) plus FED (acting on the zero-order input duration D1).
• Example models: OlssonGisleskog_2025_ibrutinib.R (multiplicative ratio effect on relative bioavailability: 0.666^FASTED_STRICT, i.e. 33.4% lower F1 under the strict fast than under the paper’s relaxed-fast or fed reference, per Olsson Gisleskog 2025 Table 2 rows F1 mod fast/fed = 1 FIX and F1 fast = 0.666 FIX. The D1 rows of the same table pool the two non-fed states at a single 2.45 h, so FED rather than FASTED_STRICT is the indicator that acts on the absorption duration – the two indicators land on different parameters).
• Notes: General scope: an enforced fasting window is a protocol-level design variable, and pre-dose fasting instructions of the form “nothing by mouth from midnight” appear in the approved labelling of more than one oral oncology product, so a study contrasting two fast strictnesses is expected to recur. Do NOT identify this indicator with the bare phrase “modified fasting.” That phrase is not portable between papers and must be read from each paper’s own definition, because two papers in the same extraction wave used it for opposite protocols: Olsson Gisleskog 2025 ibrutinib defines it as dose first, food later (Methods 2.1: ibrutinib “taken at least 30 min before or at least 2 h after a meal”), and that state is this entry’s reference category (FASTED_STRICT = 0), with the paper’s stricter PK-sampling-day instruction to “fast from midnight prior (or at a minimum, 2 h prior) to dosing and continue fasting until approximately 30 min after capsule intake” being FASTED_STRICT = 1. Mauro 2025 nilotinib uses the same phrase for food first, dose about 2 hours later, which is MEAL_PREDOSE_2H = 1 and belongs to a different family entirely. Always encode the protocol, never the phrase. Distinct from the rest of the meal family on the axis each one carries: FED / FED_HIGHFAT / FED_LOWFAT carry meal presence and composition; MEAL_DELAY_<n> carries a dose-then-meal interval and MEAL_PREDOSE_<n> a meal-then-dose interval, both of which describe when a meal happened relative to the dose; FASTED_STRICT carries how tightly food was excluded on BOTH sides of the dose, so both of its levels may be food-free. MEAL_FLAG / SNACK_FLAG are time-varying intra-day meal windows driving ongoing physiology and are a different construct again. Per dose record: in a study where routine dosing and PK-sampling-day dosing follow different fasting instructions (as in SHINE) the indicator varies within subject across days, which is also why the covariate is best set from the dosing-diary record rather than assigned once per subject. Register further strictness contrasts as siblings rather than redefining this entry’s levels.

FED (canonical for fed-vs-fasted dose-record indicator)

• Description: 1 = fed state at dosing, 0 = fasted.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (fasted).
• Example models: Kyhl_2016_nalmefene.R, Goel_2016_Sonidegib.R (the Goel 2016 healthy-fasted F effect is applied via e_healthy_fast_f ^ (DIS_HEALTHY * (1 - FED)); cancer-patient records have FED = 1, healthy-fasted-arm records have FED = 0, high-fat-meal arm records have FED = 1 and the additional FED_HIGHFAT = 1 indicator), Niebecker_2015_edoxaban.R (the Niebecker 2015 “study 6” indicator is encoded via FED – study 6 was the only Fed-state phase 1 study in the pooled analysis; all 12 other phase 1 studies and the Hokusai-VTE phase 3 study were overnight-fast. Two multiplicative effects: ka * (1 + (-0.690) * FED) for the -69% absorption slowdown and CLnr/F * (1 + 0.204 * FED) for the +20.4% non-renal CL with food), Chen_2023_nemonoxacin.R (three multiplicative power-form effects from Chen 2023 Eq. 8: ka * 0.44^FED, T_lag * 1.6^FED, and F1 * 0.88^FED; the paper’s operational definition is any food taken within 2 h before or 30 min after the dose, i.e. a general fed-vs-fasted flag rather than a high-fat-meal challenge, so FED applies rather than FED_HIGHFAT), Marathe_2023_belzutifan.R (linear-deviation effect on the absorption rate constant: ka * (1 + (-0.88) * FED), an 87.6% reduction in ka when dosed fed (2.40 -> 0.30 1/h) per Marathe 2023 Table 2 KA-FED and Table 3; assessed in the crossover food-effect Study 2, so a subject contributes both FED levels. Food had no effect on AUCss, and the food effect on absorption lag time reached SCM significance but could not be retained in the final model), Comisar_2025_rimegepant.R (two multiplicative fractional effects per Comisar 2025 Table 3: -0.315 on relative bioavailability F1 and -0.706 on the transit rate constant ktr. The only fed data come from the crossover food-effect study BHV3000-112, whose fed arm received a HIGH-FAT meal, so the effect size is calibrated on a high-fat challenge – but FED is used rather than FED_HIGHFAT because the paper labels the covariate generically as ‘Food effect (fasting/fed)’ in Table 2 and applies it as a general fed flag across all 11 pooled studies. Neither effect was judged clinically meaningful, partly because migraine attacks are often accompanied by nausea so a food-intake recommendation would not reflect clinical practice).

FED_HIGHFAT (canonical for high-fat-meal-at-dosing indicator)

• Description: 1 = oral dose administered after a high-fat meal, 0 = oral dose administered under any other meal condition (typically fasted or light meal). Refines the more general FED indicator for studies that specifically test the high-fat-meal food effect on bioavailability or absorption rate (a common solubility-limited absorption phenotype).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (non-high-fat meal condition; most often “fasted” or “2 h post light meal” as defined per study protocol). Document the operational reference per model.
• Source aliases:
  • Fatmeal – used in Goel_2016_Sonidegib.R (covariate on F).
  • FATM – used in Goel_2016_Sonidegib.R (covariate on Ka; same indicator as Fatmeal in that paper).
  • YS – used in Yang_2024_dabigatran.R (Yang 2024 Supplementary Material $INPUT column list and the KAYS / CLYS / ALAG1YS covariate blocks; 1 = dosed within 30 min after a standard high-fat meal, 0 = fasted, so the same orientation as the canonical).
• Example models: Goel_2016_Sonidegib.R (multiplicative effect on F: 5.74^FED_HIGHFAT – ~5.7-fold higher F under high-fat meal vs 2 h post-light-meal reference; multiplicative effect on Ka: 1.01^FED_HIGHFAT – no meaningful effect), Yang_2024_dabigatran.R (linear proportional deviations on three absorption / disposition parameters simultaneously: ka * (1 - 0.24 * FED_HIGHFAT), tlag * (1 + 2.65 * FED_HIGHFAT) and cl * (1 + 0.51 * FED_HIGHFAT) – the high-fat meal slows absorption, more than triples the absorption lag time, and raises apparent clearance, reproducing the observed 2.05 h Tmax delay and 23% AUC reduction; Yang 2024 Table 4).
• Notes: Distinct from FED (binary fed-vs-fasted): FED_HIGHFAT carries the specific “high-fat meal” semantic (typically >= 800 kcal, >= 50% calories from fat per FDA guidance). Document the per-protocol meal definition in covariateData[[FED_HIGHFAT]]$notes. When a paper reports a high-fat-meal arm and a separate fasted-healthy arm (e.g., Goel 2016), use FED_HIGHFAT for the high-fat semantic and the existing FED + DIS_HEALTHY indicators for the composite healthy-fasted effect (Goel 2016 applies the e_healthy_fast_f effect via (DIS_HEALTHY * (1 - FED))); the retired HV_FAST composite indicator was deleted on 2026-05-11. Sibling of FED_LOWFAT for the low-fat-meal end of the meal-type spectrum.

FED_LOWFAT (canonical for low-fat-meal-at-dosing indicator)

• Description: 1 = oral dose administered after a low-fat meal, 0 = oral dose administered under any other meal condition (typically fasted, moderate-fat, or high-fat). Refines the more general FED indicator for studies that specifically test a low-fat-meal food effect on bioavailability or absorption rate, most commonly for drugs whose apparent bioavailability decreases with a light / low-fat meal relative to fasted (e.g., HIV integrase inhibitors, some antiviral tablets whose dissolution is optimised under fasted or moderate-fat conditions).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (non-low-fat meal condition; most often “fasted” or “moderate-fat / high-fat meal” as defined per study protocol). Document the operational reference per model.
• Source aliases:
  • LOWFAT – used in Bukkems_2021_raltegravir.R (NONMEM covariate flag; source paper Bukkems 2021 Methods ‘Development population pharmacokinetic model’ subsection ‘A low-fat meal (389 kcal, 6.9% fat)’; same orientation as the canonical, 1 = low-fat meal).
• Example models: Bukkems_2021_raltegravir.R (linear additive effect on bioavailability: F *= (1 + e_lowfat_fdepot * FED_LOWFAT) with e_lowfat_fdepot = -0.459; low-fat-meal-associated raltegravir dose records have -46% relative bioavailability vs the fasted / moderate-fat reference, Bukkems 2021 Table 2 ‘Factor change in F low-fat meal’).
• Notes: Distinct from FED (binary fed-vs-fasted, generic) and from FED_HIGHFAT (high-fat-meal semantic). The low-fat semantic is typically defined per protocol as <= ~400 kcal with <= ~10% calories from fat (Bukkems 2021 references 389 kcal / 6.9% fat as the low-fat definition from the Rizk 2012 raltegravir food-effect study, and 650-844 kcal / 48% fat as the moderate-fat reference). Document the per-protocol meal definition in covariateData[[FED_LOWFAT]]$notes. When a paper reports discrete meal-type strata (fasted / low-fat / moderate-fat / high-fat), the recommended encoding is FED for any-food-vs-fasted plus one or both of FED_LOWFAT / FED_HIGHFAT for the specific low-/high-fat semantics; the moderate-fat stratum is the typical fed-reference when no FED_MODERATE sibling has been ratified. Sibling of FED_HIGHFAT for the high-fat-meal end of the meal-type spectrum. General scope because the low-fat semantic is well-defined across studies (rather than tied to a single paper’s protocol).

MEAL_DELAY_1H (canonical for delayed-meal indicator: food consumed >= 1 h after dosing rather than ~0.5 h after dosing)

• Description: 1 = the subject started eating at least 1 hour after taking the oral dose; 0 = the subject started eating approximately 0.5 hour after taking the dose. Encodes the dose-to-meal interval in studies where every subject is fed but the delay between dosing and the start of the meal is protocol-controlled or recorded. The concept it captures is how long the drug has to dissolve and empty from a relatively unfed stomach before the meal arrives, which shifts gastric-emptying-limited absorption timing (typically a zero-order absorption duration D1, a lag time, or ka) without necessarily changing overall bioavailability.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (food consumed ~0.5 h after dosing – the shorter dose-to-meal interval). Document the per-protocol interval definition in covariateData[[MEAL_DELAY_1H]]$notes, since the two levels a given paper contrasts may not be exactly 0.5 h vs 1 h.
• Source aliases:
  • FOOD – used in Wang_2023_dorzagliatin.R (Wang 2023 Table 3 footnote: “FOOD: 0 and 1 for the time of food consumption after 0.5 h and >= 1 h of drug administration, respectively”; same orientation as the canonical, 1 = the longer delay).
• Example models: Wang_2023_dorzagliatin.R (multiplicative exponential effect on the zero-order absorption duration D1: exp(0.816 * MEAL_DELAY_1H) = a 2.26-fold longer zero-order absorption window, 0.418 h -> 0.945 h, when the meal is delayed by at least 1 h; Wang 2023 Table 3 D1,FOOD = 2.26).
• Notes: Specific scope. The concept – the dose-to-meal interval – is genuinely new to the register, but the particular 1-hour threshold is Wang 2023’s own protocol design choice and has not yet been shown to be reusable, so the entry is scoped to that paper pending a second ratification. Promote to general when a second paper adopts the same 0.5 h vs >= 1 h dichotomy. Distinct from the whole FED family: FED is fed-vs-fasted (meal presence), FED_HIGHFAT / FED_LOWFAT / MEAL_A-MEAL_D are meal composition, and MEAL_FLAG / SNACK_FLAG are time-varying intra-day meal windows driving ongoing physiology. MEAL_DELAY_1H is orthogonal to all of these – both of its levels are fed, with identical meal composition, differing only in timing relative to the dose. A study that varies both composition and delay should carry FED_HIGHFAT (or FED_LOWFAT) and this indicator. Per-dose-record in principle; in a fixed-protocol study it is effectively subject-level. Because the effect typically lands on an absorption-timing parameter rather than on F or CL, the downstream exposure consequences are usually small: Wang 2023 Section 3.3 reports no change in steady-state AUCtau and only -1.26% / +2.71% shifts in Cmax,ss / Cmin,ss. Future models contrasting a different pair of intervals (e.g. immediately-with-food vs 2 h post-dose) should register a sibling indicator (MEAL_DELAY_2H) following the same pattern rather than redefining this entry’s levels. See MEAL_PREDOSE_2H for the mirror-image concept, in which the meal PRECEDES the dose.

MEAL_PREDOSE_2H (canonical for pre-dose-meal interval indicator: dose taken ~2 h after the start of the meal rather than ~0.5 h after it)

• Description: 1 = the oral dose was taken about 2 hours after the subject started eating; 0 = the oral dose was taken about 0.5 hour after the subject started eating, or the subject was fasted. Encodes the meal-to-dose interval for protocols in which the meal PRECEDES the dose, so the drug arrives in a stomach whose emptying and bile-salt state has already been perturbed for two hours rather than for thirty minutes. Composes with the meal-composition indicators (FED, FED_HIGHFAT, FED_LOWFAT), which continue to carry what was eaten; this entry carries only when it was eaten relative to the dose. The concept it captures is a bioavailability and absorption-timing difference between two fed protocols of identical meal composition.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (dose taken about 0.5 h after the start of the meal, or fasted). Document the per-protocol intervals in covariateData[[MEAL_PREDOSE_2H]]$notes, since the pair of intervals a given paper contrasts may not be exactly 0.5 h vs 2 h.
• Source aliases:
  • FOOD / prandial-state label – used in Mauro_2025_nilotinib.R and Mauro_2025_nilotinib_lowfat.R, where the four-level prandial-state factor of Mauro 2025 Methods, “Prandial state definitions”, is decomposed into FED + FED_HIGHFAT / FED_LOWFAT + this indicator. The paper’s level names are “fed” (MEAL_PREDOSE_2H = 0) and “modified fasting” (MEAL_PREDOSE_2H = 1).
• Example models: Mauro_2025_nilotinib.R (separates the paper’s “fed” state – high-fat meal started 0.5 h before the dose – from its “modified fasting with a high-fat meal” state – the same high-fat meal started 2 h before the dose. The two states carry different proportional effects on nilotinib tablet relative bioavailability: at 142 mg +48.4% at the 0.5 h interval vs +58.5% at the 2 h interval, and at 190 mg +61.7% vs +60.3%, per Mauro 2025 Supplemental Table 2. Applied as hf05h <- FED_HIGHFAT * (1 - MEAL_PREDOSE_2H) and hf2h <- FED_HIGHFAT * MEAL_PREDOSE_2H), Mauro_2025_nilotinib_lowfat.R (same decomposition with the low-fat 2 h state added as FED_LOWFAT * MEAL_PREDOSE_2H).
• Notes: General scope: the meal-to-dose interval is a protocol-level design variable, and a 2 h pre-dose interval is an approved-label dosing instruction for more than one marketed oral oncology product, so the concept is expected to recur. Direction matters and is the whole point of the name. MEAL_DELAY_<n> is the mirror concept – the meal comes AFTER the dose (MEAL_DELAY_1H = food consumed >= 1 h after dosing) – and MEAL_PREDOSE_<n> is the meal BEFORE the dose. Registering this as MEAL_DELAY_2H would have been the easy and wrong choice: it would have put a meal-precedes-dose concept into the meal-follows-dose family and inverted the physiology. Register further pre-dose intervals as siblings (MEAL_PREDOSE_4H, …) rather than redefining this entry’s levels. Do not identify this indicator with the bare phrase “modified fasting.” That phrase is not portable between papers and must be read from each paper’s own definition: Mauro 2025 nilotinib uses it for food first, dose about 2 hours later, whereas Olsson Gisleskog 2025 ibrutinib uses the same phrase for the opposite protocol – no food from about 30 min before to about 2 h after dosing, i.e. dose first, food later – which belongs to FASTED_STRICT, not to this entry. Always encode the protocol, never the phrase. Distinct from FED / FED_HIGHFAT / FED_LOWFAT (meal composition and presence) and from MEAL_FLAG / SNACK_FLAG (time-varying intra-day meal windows driving ongoing physiology). Per dose record in principle; in a fixed-protocol crossover it varies within subject across periods and is naturally paired with OCC.

MULTI_DOSE_PT (canonical for multiple-dose-phase-in-patients indicator)

• Description: 1 = dose record from the multiple-dose phase of a clinical-pharmacology study in patients, 0 = otherwise (single-dose run-in records, healthy-volunteer records, or first-dose records in patient studies). Captures any systematic shift in apparent bioavailability between the controlled run-in and the longer multiple-dose phase, typically driven by variable food-restriction compliance over many dosing days.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (single-dose / run-in / healthy-volunteer dose records).
• Source aliases:
  • FMDD – used in Goel_2016_Sonidegib.R (Goel 2016 covariate on F).
• Example models: Goel_2016_Sonidegib.R (multiplicative effect on F: 1.16^MULTI_DOSE_PT – ~16% higher apparent F during the multiple-dose phase relative to first dose, attributed in the paper to occasional non-fasting compliance), Fang_2010_etanercept.R (multiplicative effect on F: 0.674^MULTI_DOSE_PT – ~33% lower apparent F during the multiple-dose phase relative to the single-dose reference; Fang 2010 attributes the reduction to partitioning of the rhTNFR-Fc fusion protein into local subcutaneous adipose tissue with repeated injection. In Fang 2010 the multi-dose cohort is the AS-patient arm and the single-dose cohort is the healthy-volunteer arm, so MULTI_DOSE_PT is effectively subject-level: source column M), vanIersel_2018_posaconazole.R (multiplicative effect on apparent clearance: cl *= (1 + 0.750 * MULTI_DOSE_PT) – 75% higher CL in multiple-dose records relative to the single-dose reference; van Iersel 2018 Table 2 final-model ‘Dosing regimen on CL’ = 0.750).
• Notes: Specific scope because the indicator’s exact definition (dose-record level vs subject level, run-in inclusion, occasion boundary) is paper-specific. In Goel 2016, the dataset distinguishes the run-in single dose from the daily multiple-dose phase; the indicator switches at the start of the multiple-dose phase for cancer patients. In Fang 2010, the indicator is subject-level (all dose records of a multi-dose AS subject carry MULTI_DOSE_PT = 1; all dose records of a healthy-volunteer subject carry MULTI_DOSE_PT = 0). Distinct from FED and FED_HIGHFAT (which are per-record meal-state indicators) and from REGI_BID (regimen indicator). Future models that need a generic “occasion boundary” effect should consider the existing ooc<n> IOV pattern instead.

FORM_TABLET (canonical for tablet vs non-tablet oral liquid formulation indicator)

• Description: 1 = tablet formulation, 0 = the per-paper non-tablet oral liquid comparator (solution or suspension). Document the reference oral liquid form per-model in covariateData[[FORM_TABLET]]$notes.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (per-paper non-tablet oral liquid comparator; per-model documentation identifies whether the reference is a solution or a suspension).
• Source aliases:
  • TABLET – earlier name used by Kyhl_2016_nalmefene.R and Tikiso_2021_abacavir.R; renamed to FORM_TABLET for consistency with the FORM_* family (FORM_CAPSULE, future FORM_SUSPENSION, etc.).
• Example models: Kyhl_2016_nalmefene.R (additive shift on residual error: tablet vs solution), Tikiso_2021_abacavir.R (multiplicative effect on the absorption mean transit time MTT: tablet (abacavir + lamivudine fixed-dose-combination tablet) vs liquid solution; mtt *= (1 + 0.249 * FORM_TABLET), i.e. 24.9% slower absorption for the FDC tablet relative to the abacavir liquid reference), Kleideiter_2017_cebranopadol.R (tablet is the typical-value reference, FORM_TABLET = 1 leaves the formulation effects on ka, klag, and bioavailability at zero; paired with FORM_CAPSULE and the derived is_solution = (1 - FORM_TABLET) * (1 - FORM_CAPSULE) to encode a three-level formulation stratification), Fisher_2008_fosamprenavir.R (relative bioavailability of tablet vs fed-suspension reference: F_tab = 1.09; encoded as log(F) <- log(F_tab) * FORM_TABLET + log(F_food,sus) * (1 - FORM_TABLET) * (1 - FED) so tablet bioavailability does not additionally depend on food, while fasted suspension carries the F_food,sus reduction), Aruldhas_2021_R_methadone.R, Aruldhas_2021_S_methadone.R (per-oral-dose covariate that selects between two independently-estimated first-order absorption rate constants: suspension is the canonical reference Ka and tablet is a log-additive multiplicative shift; ka <- exp(lka + e_form_tablet_ka * FORM_TABLET)), Wada_2023_sparsentan.R (three-level sparsentan formulation stratification {100 mg capsule reference, whole tablet, crushed tablet} encoded into FORM_TABLET plus the sibling new canonical FORM_CRUSHED_TABLET, with both = 0 selecting the capsule; the whole tablet is absorbed more slowly than the capsule via ka * exp(-0.306) and has a shorter lag via tlag * exp(-0.269). NOTE: here the comparator is a CAPSULE, not the non-tablet oral liquid named in this entry’s default reference category – document the comparator per-model).
• Notes: Scope promoted from specific to general on 2026-07-24 alongside the Aruldhas 2021 methadone extraction (the sixth example model), where the non-tablet comparator is an oral suspension rather than an oral solution. Per-model documentation should identify which non-tablet oral liquid formulation is the reference (solution or suspension) in covariateData[[FORM_TABLET]]$notes. Distinct from the FORM_FDC canonical (Wilkins 2008 antitubercular fixed-dose-combination of multiple drugs, contrasted against single-drug tablets) because here the comparator is a non-tablet oral liquid rather than a separate tablet product. Future formulation-comparison models should extend this entry’s example list when the comparator is any non-tablet oral liquid (solution or suspension); register a sibling canonical when contrasting two tablet products.

FORM_CLO_GENERIC (canonical for generic-vs-Plavix clopidogrel product indicator)

• Description: 1 = the generic 75 mg film-coated clopidogrel test tablet evaluated in a bioequivalence study, 0 = the Plavix 75 mg film-coated reference tablet (Sanofi Winthrop Industrie, Ambares, France). Both products are film-coated tablets of the same strength, so this indicator distinguishes two products rather than two dosage forms; the contrast it carries is relative bioavailability, not a change in absorption mechanism.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (Plavix film-coated tablet – the reference medicine, whose bioavailability F is the fixed 100% anchor that makes the generic’s relative bioavailability identifiable).
• Source aliases: formulation, treatment (the crossover treatment-arm column of a 2-treatment bioequivalence dataset; FORM_CLO_GENERIC = as.integer(treatment == "test")).
• Example models: Pejcic_2024_clopidogrel.R (selects between the fixed reference F = 1 and the estimated study-specific generic relative bioavailability Fgen_st1 = 1.08 / Fgen_st2 = 0.960; frel <- exp(lfdepot) * (1 - FORM_CLO_GENERIC) + exp(lfgenTv) * FORM_CLO_GENERIC).
• Notes: Follows the auto-approved FORM_<drug>_<formulation> canonical family. Time-varying within subject in a crossover design – each subject receives both products, one per period – so it must be set per dose record and is naturally paired with OCC. Distinct from FORM_TABLET / FORM_CAPSULE, which contrast dosage forms; here both arms are the same dosage form and the covariate identifies the manufacturer’s product. Register a sibling FORM_<drug>_GENERIC canonical for other generic-vs-reference bioequivalence extractions rather than reusing this drug-specific entry.

FORM_CAPSULE (canonical for capsule formulation indicator)

• Description: 1 = capsule formulation, 0 = the per-paper comparator non-capsule formulation. The complement formulation is paper-defined: solution for Hennig 2006 / 2007 itraconazole, tablet for Gupta 2016 lenvatinib, Doryx delayed-release tablet (with FORM_DOX_DORYX_MPC = 0) for Hopkins 2017 doxycycline. Document the comparator and the reference-category bioavailability per-model in covariateData[[FORM_CAPSULE]]$notes.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: Either FORM_CAPSULE = 0 or FORM_CAPSULE = 1, paper-defined. Hennig 2006 and Gupta 2016 anchor F = 1 on the non-capsule comparator (FORM_CAPSULE = 0): solution in Hennig 2006 with fdepot fixed to 1 for the solution arm; tablet in Gupta 2016 with F fixed to 1 for the tablet arm. Salem 2014 anchors F = 1 on the capsule arm (FORM_CAPSULE = 1): the capsule is the structural F = 1 reference and the liquid (suspension or solution) arm carries the estimated age-dependent relative bioavailability. The IIV (when present) is gated to the arm that carries the estimated F; document the orientation in covariateData[[FORM_CAPSULE]]$notes.
• Source aliases:
  • PREP – used in Hennig_2006_itraconazole.R (Clin Pharmacokinet 2006;45(11):1099-1114; PREP = 1 = capsule, PREP = 0 = oral solution) and in Hennig_2007_itraconazole.R (Br J Clin Pharmacol 2007;63(4):438-450; DOI 10.1111/j.1365-2125.2006.02778.x; same orientation, capsule typical absorption parameters as the published reference).
  • CAPSULE – earlier name used in the Hennig_2006_itraconazole.R and Hennig_2007_itraconazole.R model files before the FORM_* rename.
• Example models: Hennig_2006_itraconazole.R (Hennig 2006 Table II final estimates: capsule ka 0.09 h^-1 vs solution ka 0.96 h^-1 and capsule relative bioavailability 0.55 vs solution 1; the etalfdepot IIV applies only to the capsule arm), Hennig_2007_itraconazole.R (selects between lka_cap and lka_sol typical-value absorption rate constants and applies f(depot) <- (1 - FORM_CAPSULE) + FORM_CAPSULE * fdepot so the relative bioavailability F_rel = 0.817 is applied only to the capsule arm), Gupta_2016_lenvatinib.R (relative bioavailability of capsule vs tablet is 0.896; F1 fixed to 1 for the tablet reference; the etalfcap IIV (30.2% CV) applies only to the capsule arm), Lacy_2018_cabozantinib.R (multiplicative fractional effect of capsule (vs tablet reference) on Ka = -0.579 (57.9% slower absorption for capsule) and on overall bioavailability F = -0.144 (14.4% lower exposure for capsule); tablet F fixed at 1 as the reference; comparator capsule = Cometriq 140 mg approved for MTC), Kleideiter_2017_cebranopadol.R (paired with FORM_TABLET to encode the three-level cebranopadol formulation stratification: tablet reference, oral solution, liquid-filled capsule; multiplicative effects on ka (log shift log(2.09 / 0.864) = 0.883), klag (log shift log(0.077 / 0.087) = -0.122), and bioavailability (factor 1.174) for capsules relative to the tablet reference), Kleideiter_2018_cebranopadol.R (three-level formulation factor {tablet, oral solution, liquid-filled capsule} encoded into two binary indicators FORM_CAPSULE and the sibling new canonical FORM_SOLUTION; tablet is the reference when both indicators are 0; capsule multiplicative effects per Kleideiter 2018 Table 13 are 2.09 / 0.864 = 2.419 on Ka, 0.077 / 0.087 = 0.885 on klag, and 1.174 on bioavailability F), Hopkins_2017_doxycycline.R (three-level Doryx formulation factor {Doryx tablet (delayed-release), Doryx MPC (modified-acid-resistance delayed-release), Doryx capsule (conventional-release)} encoded into two binary indicators FORM_CAPSULE and the sibling canonical FORM_DOX_DORYX_MPC; Doryx tablet is the reference when both indicators are 0; Doryx-capsule relative bioavailability vs the Doryx-tablet reference is 0.978 per Hopkins 2017 Table 3 F1CAP, shared 0.115 h absorption lag with FORM_DOX_DORYX_MPC per Table 3 ALAG1, and the Doryx-capsule food effect on KTR matches the Doryx-tablet -20.9% reduction per Table 3 COVFED rather than the Doryx-MPC -54.9% reduction), Salem_2014_efavirenz.R (capsule is the structural F = 1 reference; the oral suspension and oral solution arms are pooled as ‘liquid’ since Salem 2014 found no difference in F between them, and the liquid arm carries an Emax age-dependent relative bioavailability with mature asymptote 0.79 (Salem 2014 Table 2 TVF, RSE 12.5%) and TM50,F = 10.6 months (Salem 2014 Table 2 TM50,F, RSE 38.7%); IIV on the mature liquid F is 39.9% CV (etaltvf_liq) and is gated to the liquid arm via fdepot <- FORM_CAPSULE * 1 + (1 - FORM_CAPSULE) * f_liquid), Sano_2023_fesoterodine.R (fesoterodine beads-in-capsule (BIC) vs tablet in pediatric patients with neurogenic detrusor overactivity; same orientation as Gupta 2016 lenvatinib, with the tablet arm fixed at F = 1 and the BIC arm carrying the estimated relative bioavailability 0.648 per Sano 2023 Table 2, applied as f(depot) <- (1 - FORM_CAPSULE) + FORM_CAPSULE * exp(lfdepot); note that in the source studies BIC was given only to the 25-kg-or-less cohort, so formulation is confounded with body weight and dose), vandenBerg_2021_uprifosbuvir_pbpk.R (uprifosbuvir capsule vs tablet formulation stratification per van den Berg 2021 Table 3: capsule multiplicative factors are 2.02 on KA1, 3.23 on the pre-logit for the fast-absorption fraction FDOS1 (corresponding to F1 = 0.91 for capsules vs 0.67 for tablets), 1.17 on bioavailability, and 1.65 on the fast-absorption lag time ALAG1; the capsule dose-slope on KA1 is -0.00189 (linear on log-KA1 per mg above the 150 mg reference); the gut-M6 concentration-dependent elimination rate uses a separate capsule-specific baseline KelM6g = 5.76 with a capsule dose slope of -0.00420), Majid_2024_lenvatinib.R (relative bioavailability of the lenvatinib capsule versus the tablet reference: f(depot) <- 0.882^FORM_CAPSULE, Majid 2024 Table 1 footnote F1 = 1 * 0.882^FORM; unlike the Gupta 2016 predecessor model no inter-individual variability is estimated on F1), Koh_2025_aspirin.R (enteric-coated aspirin capsule (Astrix, Boryungbio) vs enteric-coated tablet (Aspirin Protect, Bayer); the ONLY entry in this list where the indicator does not touch bioavailability at all – every Koh 2025 parameter is apparent (/F) and F is common to both arms, so the formulation acts solely on the first-order absorption rate constant, ka = 0.22 1/h for the capsule (estimated, RSE 21.8%) vs 0.053 1/h for the tablet (fixed during covariate analysis); the tablet is the reference category because it carries the fixed value, and Koh 2025 tested and rejected the same formulation split on Tk0, Lag0 and fr), Xu_2025_aficamten.R (formulation affects only the absorption lag time: two separately estimated lag times, 0.229 h for the pooled phase 2 / phase 3 / commercial tablet reference and 0.248 h for the phase 1 capsule, selected as tlag <- exp(ltlag_tab) * (1 - FORM_CAPSULE) + exp(ltlag_cap) * FORM_CAPSULE per Xu 2025 Table 1 and the Data S1 control stream line TVALAG1 = (THETA(6)*(1-CAPSULE) + CAPSULE*THETA(7)). Unlike the other members of this family no relative-bioavailability and no Ka effect of formulation was retained in the final model, so F1 is anchored at 1 on both arms and the two arms differ by 8% in lag time alone; source column FORM, where FORM <= 1 is capsule and FORM 2 / 3 / 4 are the phase 2, phase 3, and commercial tablets), Comisar_2025_rimegepant.R (three-level formulation factor {immediate-release tablet, capsule, orally disintegrating tablet} encoded into two binary indicators FORM_CAPSULE and the sibling new canonical FORM_ODT, with the tablet as the reference when both indicators are 0; the capsule effect is on the transit rate constant only, ktr * (1 + 2.03) per Comisar 2025 Table 3 ‘Capsule formulation on ktr’, i.e. 3.03-fold faster transit, with formulation effects on relative bioavailability and on CL/F both screened and then fixed to 0 in the final model – so unlike the other capsule-vs-tablet examples here this one is bioequivalent to its tablet reference in extent of absorption and differs only in rate. Only 17 subjects (4.9%) received the capsule, consistent with the wide interval on the effect).
• Notes: Scoped specific because the complement reference category is paper-defined (solution for Hennig itraconazole, tablet for Gupta lenvatinib). Sibling to FORM_TABLET (Kyhl 2016 / Tikiso 2021 tablet vs solution) under the FORM_* family. Future formulation-comparison models that need a capsule indicator should reuse this canonical, extending the example list and documenting the comparator in per-model notes.

FORM_ODT (canonical for orally disintegrating tablet formulation indicator)

• Description: 1 = the dose was administered as an orally disintegrating tablet (ODT), a fast-dispersing solid oral dosage form placed sublingually or on the tongue that disintegrates in saliva without water; 0 = the per-paper comparator formulation (usually a conventional swallowed immediate-release tablet). Per-dose-record indicator. Use this canonical when a source paper carries an ODT arm as a distinct level of a formulation factor and estimates separate absorption parameters (ka, transit rate constant, lag time, or relative bioavailability) for it. An ODT is pharmaceutically distinct from a conventional tablet because disintegration is not gastric-transit-limited, which typically manifests as a faster absorption rate rather than a change in extent of absorption.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (the per-paper comparator formulation; conventional immediate-release tablet in Comisar_2025_rimegepant.R). Document the comparator per-model in covariateData[[FORM_ODT]]$notes.
• Source aliases:
  • FORM (value 3) – used in Comisar_2025_rimegepant.R (Comisar 2025 Supplementary Listing 1 three-level formulation factor IF(FORM.EQ.1) ... ; Most common = tablet reference, FORM.EQ.2 = capsule, FORM.EQ.3 = ODT).
• Example models: Comisar_2025_rimegepant.R (three-level formulation factor {immediate-release tablet, capsule, orally disintegrating tablet} encoded into two binary indicators FORM_ODT and the sibling canonical FORM_CAPSULE, with the tablet as the reference when both indicators are 0; multiplicative fractional effect on the transit rate constant only, ktr * (1 + 0.355) per Comisar 2025 Table 3 ‘Oral disintegrating tablet on ktr’, a 35.5% faster transit versus the tablet. Formulation was also screened on relative bioavailability and on CL/F but both were fixed to 0 in the final model, so the ODT is bioequivalent to the tablet in extent of absorption and differs only in rate – the expected ODT signature. The rimegepant ODT is the marketed Nurtec / Vydura presentation).
• Notes: Auto-approved member of the FORM_* family, registered as a general dosage-form type alongside FORM_TABLET, FORM_CAPSULE, FORM_SOLUTION, FORM_SUSPENSION, FORM_SYRUP, FORM_GRANULE, and FORM_POWDER rather than as a drug-specific FORM_<drug>_<formulation> entry, because orally-disintegrating tablets are a generic presentation used across many drugs (rimegepant, ondansetron, olanzapine, rizatriptan, donepezil, …). Distinct from FORM_TABLET (which contrasts a conventional tablet against a non-tablet oral liquid) – an ODT arm compared against a conventional tablet needs its own indicator, since both arms are tablets. Distinct from FORM_SUBLINGUAL_FILM or similar buccal / transmucosal-absorption forms should one be needed later: an ODT disintegrates in saliva but is then swallowed and absorbed enterally, so it does not bypass first-pass metabolism, whereas a genuine sublingual / buccal product does. Where a paper administers the ODT both sublingually and on top of the tongue and finds no difference (as Comisar 2025 study BHV3000-110 part 2 did), a single FORM_ODT indicator covers both placements.

FORM_CRUSHED_TABLET (canonical for crushed-tablet (dry, not resuspended) formulation indicator)

• Description: 1 = the dose was administered as a tablet crushed immediately before dosing and swallowed dry (or sprinkled on food) without being suspended in a liquid vehicle; 0 = the per-paper comparator intact formulation. Per-dose-record indicator. Use this canonical when a source paper carries a crushed-tablet arm as a distinct level of a formulation factor and estimates separate absorption parameters (Ka, lag time, bioavailability, or transit rate) for it.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (per-paper intact comparator; document which formulation is the reference per-model). For Wada 2023 sparsentan the reference is the 100 mg capsule, selected when FORM_CRUSHED_TABLET and the sibling FORM_TABLET are both 0.
• Source aliases:
  • Formulation three-level categorical column (capsule / tablet / crushed tablet) – the crushed-tablet level maps to FORM_CRUSHED_TABLET = 1. Used in Wada_2023_sparsentan.R.
• Example models: Wada_2023_sparsentan.R (third member of the three-level sparsentan formulation stratification {100 mg capsule reference, whole tablet, crushed tablet}, encoded into the two binary indicators FORM_TABLET and FORM_CRUSHED_TABLET; the crushed tablet is absorbed faster than either intact form via ka * exp(0.080) (Wada 2023 Table 2, RSE 159.1%) and has a markedly shorter absorption lag via tlag * exp(-1.175) (0.099 h vs the 0.32 h capsule reference); formulation had no effect on relative bioavailability, so AUC is unchanged and Cmax differs by <10%).
• Notes: Distinct from FORM_SUSPENSION (Svensson 2018: tablets crushed AND suspended in a liquid vehicle, i.e. an extemporaneously-prepared liquid dosage form) – the defining feature here is that no liquid vehicle is involved, so the dissolution and gastric-emptying behaviour differ. Also distinct from ROUTE_NGT (Denti 2018: crushed tablet delivered through a nasogastric tube), which is a route-of-administration indicator rather than a formulation state; a model pooling dry-crushed oral dosing with crushed-via-NGT dosing needs both columns. Sibling to FORM_TABLET / FORM_CAPSULE / FORM_SOLUTION / FORM_POWDER / FORM_GRANULE under the generic FORM_* family. Promote to general scope when a second paper ratifies the same encoding. Ratified canonically alongside the Wada 2023 sparsentan extraction.

FORM_SOLUTION (canonical for oral-solution formulation indicator)

• Description: 1 = subject received the modelled drug as an oral solution, 0 = the per-paper comparator non-solution formulation (tablet or capsule). The complement reference is paper-defined; document the comparator and the reference-category absorption / bioavailability per-model in covariateData[[FORM_SOLUTION]]$notes.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (per-paper non-solution comparator; for Kleideiter 2018 cebranopadol the film-coated tablet is the typical-value reference, with the sibling FORM_CAPSULE also at 0 in the reference state).
• Source aliases:
  • FORM (three-level formulation column {tablet, oral solution, liquid-filled capsule}) – used in Kleideiter_2018_cebranopadol.R; the oral-solution level maps to FORM_SOLUTION = 1 and is paired with FORM_CAPSULE so that both = 0 selects the tablet reference.
• Example models: Kleideiter_2018_cebranopadol.R (third member of the three-level cebranopadol formulation stratification {tablet reference, oral solution, liquid-filled capsule}, encoded into the two binary indicators FORM_SOLUTION and FORM_CAPSULE; oral-solution multiplicative effects relative to the tablet reference per Kleideiter 2018 Table 13 are 2.43 / 0.864 = 2.813 on Ka, 0.077 / 0.087 = 0.885 on klag, and 1.045 on bioavailability F), Othman_2013_ABT_102.R (two-level oral-solution vs solid-dispersion comparator for the TRPV1 antagonist ABT-102; solution-vs-solid-dispersion-reference effects per Othman 2013 PK/PD-model Results paragraph 1 are 0.40 multiplicative on bioavailability F and 0.5 multiplicative on absorption lag time), PerezRuixo_2006_tipifarnib.R (oral-solution-vs-solid (capsule or tablet) absorption-effect indicator; ratio-form multiplicative effects on D1 (0.348), Ka (2.07), and tlag (0.183) applied as ratio^FORM_SOLUTION; reference category is the solid oral form – Perez-Ruixo 2006 found the absorption profile statistically indistinguishable between capsule and tablet, so the two solid forms are pooled as the reference, and the solution shows ~3-fold faster zero-order release, 2-fold higher Ka, and a 5.5-fold shorter lag time relative to the solid reference), Petric_2023_vinpocetine.R (third member of the three-level Petric 2023 vinpocetine formulation stratification {Ultra Vinca sustained-release beta-cyclodextrin tablet reference, Cavinton immediate-release tablet, 10 mg / 5 mL oral solution}, encoded into the two binary indicators FORM_SOLUTION and the sibling drug-specific FORM_VINP_IR; oral-solution log-additive effects relative to the SR-tablet reference per Petric 2023 Table 1 are exp(beta = -0.68) on Tk0 (49% shorter zero-order absorption duration) and exp(beta = -1.24) on V1/F (71% lower apparent central volume, i.e. higher metabolite exposure); metabolite AVA is the observed compound, so V/F folds in both the vinpocetine oral bioavailability and the vinpocetine-to-AVA conversion fraction), Chandasana_2024b_lamivudine.R (two-level oral-solution vs solid-oral-dosage-form contrast for absolute bioavailability F1; f(depot) <- exp(lfdepot * (1 - FORM_SOLUTION) + lfdepot_sol * FORM_SOLUTION) with F1 = 0.609 for the solid reference (tablet, capsule, and the ABC/DTG/3TC dispersible tablet) and F1 = 0.496 for the oral solution per Chandasana 2024 Table 3; the dispersible tablet is assigned to the solid reference because that assignment reproduces Chandasana 2024 Table 4 to within 2.4% in every weight band while the solution value under-predicts by ~26%), Chandasana_2024b_abacavir.R (used only as the formulation half of the ARROW PK Substudy Part 2 relative-bioavailability term, paired with STUDY_ARROW_PART2: the substudy tablet gets 1.62 and the substudy oral solution 1.75; outside that substudy the abacavir model applies no formulation effect on bioavailability, so this indicator is inert when STUDY_ARROW_PART2 = 0), Nguyen_2025_valbenazine.R (two-level oral-solution vs capsule contrast on the transit absorption rate constant KTR; per Nguyen 2025 Supplemental Table S2 equation (3) the coefficient is a PROPORTIONAL shift, KTR * (1 + 1.51 * FORM_SOLUTION), i.e. a 2.51-fold higher absorption rate constant for the oral solution than for the capsule reference. The oral solution appeared only in the six phase 1 studies; both phase 3 studies – KINECT-HD and KINECT 3 – used capsules, so the indicator is 0 throughout the phase 3 simulations).
• Notes: Scoped specific because the complement reference category is paper-defined. Third member of the oral solid-dosage FORM_* family alongside FORM_TABLET (tablet vs solution) and FORM_CAPSULE (capsule vs per-paper comparator); register prose for those entries already anticipates this name. Where a model carries all three formulations, FORM_CAPSULE + FORM_SOLUTION form the two-indicator encoding with tablet as the all-zero reference. Distinct from FORM_ASV_LIQUID (a drug-specific suspension/solution-vs-capsule/tablet indicator for asunaprevir). Ratified canonically alongside the Kleideiter 2018 cebranopadol extraction.

FORM_ASV_LIQUID (canonical for asunaprevir liquid (suspension/solution) formulation indicator)

• Description: Asunaprevir (ASV) formulation indicator. 1 = ASV given as a suspension or oral solution (the higher zero-order absorption-fraction route); 0 = ASV given as a capsule or tablet (the reference formulation). The covariate has no effect when no ASV dose is administered.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (capsule or tablet; the reference formulation in the Wang 2018 ASV PK fit).
• Source aliases: none – the source Formulation column (“Suspension” / “Solution” -> 1; “Capsule” / “Tablet” -> 0, Wang 2018 Table 1) maps directly onto the canonical orientation; the model column is the canonical FORM_ASV_LIQUID.
• Example models: Wang_2018_daclatasvir_asunaprevir.R (switches the structural zero-order absorption fraction FK between fk_cap_asv = 0.184 (capsule/tablet) and fk_sol_asv = 0.334 (suspension/solution); both estimated with a shared 65.0% inter-arm-variability CV encoded as a single eta on the logit of FK, Wang 2018 Table 3).
• Notes: Specific scope because the liquid-vs-solid ASV-formulation contrast and the FK absorption-fraction switch are paper-specific to the Wang 2018 daclatasvir + asunaprevir analysis. Drug-specific member of the formulation indicator family; distinct from the generic oral solid-dosage FORM_TABLET / FORM_CAPSULE / FORM_SOLUTION indicators because the contrast here lumps both liquid forms (suspension and solution) against both solid forms (capsule and tablet) for a single named drug (ASV). Per-dose-occasion in principle (a participant could receive both formulations across occasions), but in the Wang 2018 trials each subject received a single ASV formulation. Ratified canonically alongside the Wang 2018 daclatasvir/asunaprevir extraction.

FORM_VOSO_SOLN02 (canonical for the 0.2 mg/mL vosoritide dosing-solution indicator)

• Description: Vosoritide dosing-solution strength indicator. 1 = the subcutaneous dose was prepared from the 0.2 mg/mL vosoritide solution; 0 = prepared from the 0.8 mg/mL or 2 mg/mL solution. Per-dose-record indicator. The contrast is between reconstituted-solution concentrations of the same lyophilised drug product, not between dosage forms, so the covariate belongs to the FORM_<drug>_<formulation> drug-specific branch of the FORM_* family rather than to the oral solid-dosage members.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (the 0.8 mg/mL and 2 mg/mL solutions – the strengths used in the phase III studies and in the commercial stock keeping units; Qi 2024 Discussion states these “did not have any effect on F”, so no adjustment is needed between commercial products).
• Source aliases:
  • SOLNC (“solution concentration”, a three-level column {0.2, 0.8, 2 mg/mL}) – used in Qi_2024_vosoritide.R; only the 0.2 mg/mL level carries an effect, so the three-level column collapses to this single binary indicator.
• Example models: Qi_2024_vosoritide.R (multiplies relative bioavailability by 1.56, Qi 2024 Table 5 “Effect of SOLNC (0.2 mg/mL)”; encoded on the log scale as e_form_voso_soln02_fdepot = log(1.56) and confirmed by Table 3, where the 0.2 mg/mL column is a constant 56% above the time-only reference at every tabulated time).
• Notes: Specific scope because the effect is tied to vosoritide’s reconstituted-solution strengths. The 0.2 mg/mL solution was used only in study 111-202 of the pooled five-trial analysis, so the indicator is 0 for every phase III and commercial record. Qi 2024 draws the analogy to insulin, where dilution of the dosing solution is also known to change subcutaneous bioavailability. A future paper reporting a dosing-solution-strength effect for a different drug should register a parallel FORM_<drug>_SOLN<strength> canonical rather than overloading this name. Ratified canonically alongside the Qi 2024 vosoritide extraction.

FORM_UNDIL_SUSP (canonical for undiluted oral suspension formulation indicator)

• Description: 1 = subject received the modelled drug as an undiluted oral suspension, 0 = the per-paper comparator non-undiluted-suspension formulation (tablet or diluted oral suspension in Willmann 2018; documents the comparator per-model in covariateData[[FORM_UNDIL_SUSP]]$notes). The undiluted oral suspension was distinguished from the diluted oral suspension because pre-clinical in-vitro dissolution work showed the rivaroxaban suspension excipients limit dissolution of drug particles at low pH unless diluted before administration (Willmann 2018 Discussion paragraph 5).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (per-paper comparator non-undiluted-suspension; for Willmann 2018 this lumps the tablet and the diluted oral suspension as a single reference because the paper found their ka statistically indistinguishable).
• Source aliases: derived per dose record from the EINSTEIN-Jr phase I formulation column (Tablet / Undiluted suspension / Diluted suspension); FORM_UNDIL_SUSP = 1 iff the formulation column is “Undiluted suspension”.
• Example models: Willmann_2018_rivaroxaban.R (multiplicative log-shift on the absorption rate constant: ka <- exp(lka + e_undilsusp_ka * FORM_UNDIL_SUSP) with lka = log(0.717) h^-1 and e_undilsusp_ka = log(0.208 / 0.717) = -1.238 per Willmann 2018 Table 1, RSE 21.3% and 15.4% respectively; the undiluted-suspension ka is ~3.4-fold slower than the tablet / diluted-suspension reference, consistent with the in-vitro dissolution result reported in the Discussion).
• Notes: Specific scope because the “undiluted suspension vs tablet-or-diluted-suspension” lumping is tied to the Willmann 2018 EINSTEIN-Jr phase I formulation design (which deliberately tested both undiluted and diluted variants of the same oral suspension after observing delayed absorption with the undiluted formulation). Sibling to FORM_TABLET / FORM_CAPSULE / FORM_SOLUTION under the FORM_* family. Future paediatric or low-pH-sensitive-drug models that distinguish an undiluted suspension from a diluted one with the same composition may extend this entry’s example list; models that contrast a suspension against a tablet without the diluted-vs-undiluted distinction should reuse FORM_TABLET (= 0 for the suspension comparator) instead.

FORM_ABA_PHASE2 (canonical for the abatacept SC phase-2 formulation indicator)

• Description: 1 = subject received the abatacept Phase-2 SC formulation (lower-pH excipient blend, ~56% absolute bioavailability), 0 = subject received the Phase-3 / commercial SC formulation (the ~81% bioavailability reference). Per-dose-occasion indicator: a single subject can carry both indicator values across phase-2-to-phase-3 transition arms.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (Phase-3 / commercial 125 mg SC formulation; the typical-value bioavailability reference).
• Source aliases: none – the canonical name matches the source-paper column.
• Example models: Li_2019_abatacept.R (additive shift on the logit-scale bioavailability: logit_F = logit_F_TV + FORM_ABA_PHASE2 * (-1.16); Li 2019 Table 2 reports the Phase-2 formulation effect as -1.16 logit-scale units, mapping to an absolute F of ~0.56 vs the Phase-3 reference ~0.81).
• Notes: Scoped specific because the Phase-2 vs Phase-3 formulation contrast is tied to the abatacept clinical-development timeline and does not generalise to other drugs. Future Phase-2-vs-commercial bioavailability comparisons for unrelated drug programmes should register their own FORM_<drug>_PHASE2 canonical rather than reuse this entry. Distinct from the generic FORM_TABLET / FORM_CAPSULE / FORM_SUSPENSION family (oral solid-dosage manipulations) because the contrast here is two SC injectable formulations with different excipient pH. Ratified canonically on 2026-05-28 per the naming-audit D20 review.

FORM_PEX_PHASE1 (canonical for the pexidartinib Phase 1 clinical formulation indicator)

• Description: 1 = subject received the pexidartinib Phase 1 clinical formulation (used in Phase 1 dose-ranging studies PLX108-01 and the relative-bioavailability study U114; relative bioavailability 0.855 vs the Phase 3 / commercial formulation reference); 0 = subject received the Phase 3 / commercial formulation (used in healthy-volunteer studies U116-U121 and in the Phase 3 ENLIVEN trial; the typical-value bioavailability reference at F1 = 1.0). Per-dose-occasion indicator: within a single study the formulation is fixed, but a subject can in principle carry both values across studies.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (Phase 3 / commercial formulation; the typical-value bioavailability reference).
• Source aliases: none – Yin 2020 codes the formulation as a categorical “Phase 1” vs “Phase 3” column; the canonical FORM_PEX_PHASE1 name matches the indicator direction (1 = Phase 1).
• Example models: Yin_2020_pexidartinib.R (multiplicative effect on the depot bioavailability: F1 = 1.0 when FORM_PEX_PHASE1 = 0 (Phase 3 reference) and F1 = 0.855 * exp(etalfdepot) when FORM_PEX_PHASE1 = 1, where the 0.855 typical-value anchor is held fixed in the source model and the IIV etalfdepot ~ N(0, 0.101) is gated by FORM_PEX_PHASE1 so it has no effect on Phase 3 subjects).
• Notes: Scoped specific because the Phase-1-vs-Phase-3 formulation contrast is tied to the pexidartinib clinical-development timeline and does not generalise to other drugs. Future drug-specific Phase-1-vs-commercial bioavailability comparisons should register their own FORM_<drug>_PHASE1 canonical rather than reuse this entry (sibling to FORM_ABA_PHASE2 for abatacept). Distinct from the generic FORM_TABLET / FORM_CAPSULE / FORM_SUSPENSION family (oral solid-dosage manipulations) because the contrast here is two oral formulations differentiated by clinical-development phase rather than by dosage form. For typical simulations of the approved 800 mg/day TGCT regimen used in ENLIVEN, set FORM_PEX_PHASE1 = 0 for every subject.

FORM_GEF_F02 (canonical for the gefapixant F02 wet-granulation citric-acid tablet formulation indicator)

• Description: 1 = dose record administered as gefapixant F02, the earlier wet-granulation film-coated immediate-release tablet (7.5, 20, and 50 mg strengths) that contains citric acid as an acidulant; 0 = dose record administered as F04 (gefapixant citrate active ingredient with a 20A film coating, used in two phase I studies) or F04A (gefapixant citrate with a 03K film coating, used in the phase III COUGH-1 and COUGH-2 trials). Per-dose-occasion indicator: within a single study the formulation is fixed, but a subject in a relative-bioavailability crossover study can carry both values across periods.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (F04 / F04A gefapixant-citrate formulations).
• Source aliases:
  • FORM – Chawla 2023 codes the formulation as a categorical F02 / F04 / F04A column (Table S2); the canonical name matches the indicator direction (1 = F02).
• Example models: Chawla_2023_gefapixant.R (carries NO main effect on any PK parameter – no F02-vs-F04 relative-bioavailability difference was retained in the final model – and exists solely to gate the FED effect on the absorption rate constant to F02 records: ka = exp(lka + etalka) * (1 + e_fed_ka * FED * FORM_GEF_F02) with e_fed_ka = -0.594, a 59.4% reduction in Ka when an F02 dose is taken fed. Dedicated phase I relative-bioavailability studies had already established that fed status and concomitant proton-pump-inhibitor use affect gefapixant exposure for F02 but not for F04, so food and PPI effects were tested for F02 only.).
• Notes: Scoped specific because the F02-vs-F04 contrast is tied to the gefapixant clinical-development formulation timeline and does not generalise to other drugs. This is a gating indicator rather than an effect-carrying covariate – the distinguishing pattern in the FORM_* family, where siblings such as FORM_PEX_PHASE1 and FORM_SUSPENSION carry their own multiplicative effect on bioavailability or absorption. When a formulation indicator exists only to restrict another covariate’s effect to a formulation subset, encode it in covariateData (it IS referenced in model()) and state the gating role explicitly in covariateData[[FORM_GEF_F02]]$notes. The marketed F04B formulation is compositionally identical to F04A except that it omits citric acid, and was shown bioequivalent to F04A, so F04B records take FORM_GEF_F02 = 0. The excluded F01 formulation predates F02 and does not appear in the analysis data set; a future extraction that needs it should register a separate FORM_GEF_F01. For typical simulations of the approved 45 mg b.i.d. chronic-cough regimen, set FORM_GEF_F02 = 0 for every subject (COUGH-1 and COUGH-2 used F04A), which makes the food effect inoperative. Ratified canonically alongside the Chawla 2023 gefapixant extraction.

FORM_SUSPENSION (canonical for extemporaneously-prepared liquid suspension formulation indicator)

• Description: 1 = subject received the modelled drug as an extemporaneously-prepared liquid suspension (the solid drug substance – typically a tablet, but also possibly powder or another solid form – compounded into a liquid suspension at bedside or in the clinical-pharmacy immediately before administration, rather than dispensed as a formally manufactured suspension product); 0 = subject received the comparator solid oral formulation (typically the same tablet swallowed whole or a sugar-coated tablet). Per-dose-occasion (not per-subject) indicator because a single participant can receive both formulations across study occasions in a crossover bioequivalence design.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (the per-paper solid oral comparator: tablets swallowed whole in Svensson 2018; sugar-coated tablet swallowed whole in Valle 2005). Document the per-model comparator in covariateData[[FORM_SUSPENSION]]$notes.
• Source aliases:
  • FORM – used in Svensson_2018_bedaquiline.R (paper’s narrative “whole vs suspended” with the suspended formulation as the 1 level in the analytical control stream).
  • Treatment-arm column “Treatment 2 (suspension after fasting)” vs “Treatment 1 (SCT after fasting)” – used in Valle_2005_exemestane.R (3x3 Latin-square crossover with the extemporaneous suspension as the 1 level).
• Example models: Svensson_2018_bedaquiline.R (multiplicative effect on the typical mean absorption time MAT: mat_typ = exp(lmat) * (1 + e_susp_mat * FORM_SUSPENSION) with e_susp_mat = +0.23 (suspended-tablet MAT is 23% longer than whole-tablet MAT; Svensson 2018 Table 2, 95% CI 2.1-48%, P = 0.03); relative bioavailability F is held identical between formulations because the paper found no statistically significant difference (95% nonparametric CI 94-108% within the 80-125% bioequivalence criteria)), Valle_2005_exemestane.R (multiplicative effects on absorption rate ka (suspension/SCT-fasting ratio 7.6/2.35 = 3.234x; suspension absorbs ~3.2x faster than the SCT swallowed whole) and on apparent bioavailability F (suspension/SCT-fasting ratio = 1.2x); intrinsic V/F is shared across formulations; the paper’s per-treatment V/F values 1360 vs 1120 L collapse to a constant intrinsic V at V/F_SCT_fasting / F_suspension = 1133 ~= 1120 L).
• Notes: Specific scope because the per-paper comparator solid-oral form is paper-defined (tablets swallowed whole in Svensson 2018 paediatric-tuberculosis; sugar-coated tablet swallowed whole in Valle 2005 healthy-postmenopausal-women bioequivalence). Both papers contrast the same drug substance prepared two different ways (extemporaneous liquid suspension vs swallowed-whole solid) rather than two different manufactured drug products – distinct from FORM_TABLET (Kyhl 2016 / Tikiso 2021 tablet vs liquid solution), FORM_CAPSULE, and FORM_POWDER. Future extemporaneous-suspension bioequivalence extractions should reuse this canonical and extend the example list, documenting the per-paper comparator solid-oral form. Ratified canonically on 2026-05-16 alongside the Svensson 2018 bedaquiline extraction.

FORM_UNDILUTED_SUSP (canonical for undiluted ready-to-use oral suspension preparation-state indicator)

• Description: 1 = subject received a manufactured ready-to-use oral suspension administered undiluted (squirted directly into the mouth without pre-administration mixing with another liquid); 0 = subject received any of the rapidly-absorbed comparator formulations (e.g. tablet, granules for oral suspension after reconstitution, or the same ready-to-use suspension after dilution with a defined volume of non-sparkling liquid). Per-dose-occasion indicator because a single pediatric participant can switch preparation state across study phases. Distinct from FORM_SUSPENSION (Svensson 2018 / Valle 2005 extemporaneously-compounded suspension contrasted with a whole-swallowed solid) and from FORM_GRANULE (Ayyoub 2016 reconstitution-from-granules contrasted with a tablet) because here all subjects receive the same manufactured suspension product and the contrast is whether the suspension was diluted or not before swallowing.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (rapidly-absorbed comparator pool: tablet, granules for oral suspension after reconstitution, or diluted ready-to-use suspension; these share the same ka in Willmann 2021).
• Source aliases:
  • FORM,DILU (composite: FORM = 2 for ready-to-use suspension AND DILU = 1 for undiluted) – used in Willmann_2021_rivaroxaban.R (supplement S2 NONMEM $PK block IF(FORM.EQ.2.AND.DILU.EQ.1) FD = 2 ; IF(FD.EQ.2) KA = THETA(6)).
• Example models: Willmann_2021_rivaroxaban.R (piecewise selection of the typical first-order absorption rate constant Ka: ka = exp(lka) * (1 - FORM_UNDILUTED_SUSP) + exp(lka_susp_undiluted) * FORM_UNDILUTED_SUSP; the undiluted suspension Ka = 0.226 1/h (95% CI 0.154-0.297) is ~3.5x slower than the comparator pool Ka = 0.799 1/h (95% CI 0.655-0.944); the relative bioavailability F is unaffected by preparation state because absorption rate does not enter the area under the curve at the dose levels studied per Willmann 2021 Discussion p. 1201 ‘A lower rate of absorption of the undiluted ready-to-use suspension was found in this model … but the extent of absorption is not affected by ka and, thus, AUC(0-24h),ss is independent of the formulation’).
• Notes: Specific scope because the contrast pairs a specific preparation step (mix the suspension with another liquid before swallowing vs swallow undiluted) against itself for one specific drug product. The dilution step was introduced in EINSTEIN-Jr for the ready-to-use suspension after the delayed oral absorption became obvious when administered undiluted (Willmann 2021 Discussion p. 1201). Sits in the FORM_* family alongside FORM_SUSPENSION (extemporaneous suspension), FORM_GRANULE (granules for reconstitution), and the tablet/capsule/solution siblings, capturing a third type of liquid-administration contrast (preparation state of the same manufactured product). Future ready-to-use-suspension dilution-preparation extractions should reuse this canonical with the per-paper comparator pool documented in covariateData[[FORM_UNDILUTED_SUSP]]$notes. Promote to general scope when a second paper ratifies the same encoding. Ratified canonically alongside the Willmann 2021 rivaroxaban pediatric popPK extraction.

FORM_GRANULE (canonical for granule-for-oral-suspension pediatric formulation indicator)

• Description: 1 = subject received the modelled drug as a pediatric granule (sachet) formulation intended for reconstitution with water or food immediately before administration to children (i.e. a regulated “granules for oral suspension” dosage form); 0 = subject received the comparator solid oral formulation (typically a tablet, the adult / older-child reference; document the per-paper comparator in covariateData[[FORM_GRANULE]]$notes). Per-subject (regimen-fixed) categorical indicator typical of pediatric-development popPK pooled analyses.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (tablet; the typical-value Ka reference in Ayyoub 2016 Table 2).
• Source aliases:
  • FORM / FORMULATION – used in Ayyoub_2016_pyronaridine.R (paper’s formulation indicator with the granule formulation as the 1 level; matches the canonical encoding directly without value transformation).
• Example models: Ayyoub_2016_pyronaridine.R (additive-multiplier effect on the typical absorption rate constant Ka: ka_typ = exp(lka) * (1 + e_form_granule_ka * FORM_GRANULE) with e_form_granule_ka = +1.63 (granule Ka is 2.63x the tablet Ka, i.e. 47.1 1/day vs 17.9 1/day; Ayyoub 2016 Table 2, %RSE 37.8); CL/F, V2/F, V3/F, and Q/F are held identical between formulations because backward elimination (P < 0.001) retained formulation only on Ka).
• Notes: Specific scope because the “pediatric granule vs tablet” contrast is tied to pediatric formulation-development popPK pooled analyses where the granule is a regulated dosage form (typically granules / sachets for oral suspension after reconstitution with water or food), distinct from the bedside-improvised FORM_SUSPENSION (Svensson 2018: tablets crushed-and-suspended) and from FORM_TABLET / FORM_SOLUTION / FORM_POWDER (different sibling formulation contrasts). Future pediatric-granule popPK extractions (a common pattern in antimalarial, antiretroviral, and antitubercular pediatric trials – e.g. Pyramax granules, Coartem dispersible, lopinavir-ritonavir pellets) should reuse this canonical and extend the example list. Promote to general scope when a second paper ratifies the same encoding. Ratified canonically alongside the Ayyoub 2016 pyronaridine extraction.

FORM_DTG_DT (canonical for the dolutegravir dispersible-tablet / granule formulation indicator)

• Description: 1 = dolutegravir given as the pediatric dispersible tablet or as granules for oral suspension; 0 = dolutegravir given as the film-coated tablet (FCT). The dispersible tablet and the granules share a single set of absorption and bioavailability estimates in the source model, so one indicator covers both.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (film-coated tablet; the typical-value Ka and relative-bioavailability reference in Chandasana 2024 Table 2).
• Source aliases:
  • formulation – used in Chandasana_2024b_dolutegravir.R; Chandasana 2024 Table 2 names the levels “FCT” and “DT and granules”.
• Example models: Chandasana_2024b_dolutegravir.R (two simultaneous effects: a blend selecting the typical absorption rate constant, lka <- lka_fct * (1 - FORM_DTG_DT) + lka_dt * FORM_DTG_DT with Ka = 0.854 1/h for the FCT and 2.04 1/h for the DT / granules, and a multiplicative relative-bioavailability effect exp(e_form_dt_fdepot * FORM_DTG_DT) with exp(e_form_dt_fdepot) = 1.53 for the fasted DT / granules relative to the fasted FCT; the sibling FED food effect is gated on (1 - FORM_DTG_DT) because Chandasana 2024 Table 2 reports a fed estimate for the FCT only).
• Notes: A dispersible tablet is a solid dosage form dispersed in water immediately before administration, so it is pharmaceutically distinct from the bedside-improvised FORM_SUSPENSION (crushed tablets), from FORM_SOLUTION (a manufactured oral liquid), and from FORM_GRANULE (sachet granules alone) – but the source model pools the dispersible tablet with the granules against the film-coated tablet, which is why this is a single drug-scoped indicator rather than a combination of the general siblings. Dispersible tablets are ubiquitous in pediatric antiretroviral, antimalarial and antitubercular development, so a general FORM_DISPERSIBLE_TABLET canonical is the natural promotion target: promote when a second paper ratifies a dispersible-tablet-vs-solid contrast that is not pooled with granules. Ratified canonically alongside the Chandasana 2024 pediatric ABC/DTG/3TC extraction.

FORM_POWDER (canonical for oral powder formulation indicator)

• Description: 1 = subject received the oral powder formulation of the modelled drug; 0 = subject received the comparator solid oral formulation (typically a tablet, but document the per-paper comparator in covariateData[[FORM_POWDER]]$notes).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (tablet; F = 1 fixed in Yukawa 1990 Model 2).
• Source aliases:
  • FORM_POWDER – used in Yukawa_1990_phenytoin.R (paper’s BA indicator inverted: source BA = 1 if tablet, 0 if powder; canonical FORM_POWDER = 1 - BA_indicator so 0 is the tablet reference).
• Example models: Yukawa_1990_phenytoin.R (Yukawa 1990 Model 2 dose-dependent powder bioavailability F_powder = 1 - exp(-9.92 / DOSE_PHT_MGKGD); tablet F fixed at 1), Retlich_2015_linagliptin.R (multiplicative shift on the linagliptin first-order absorption rate constant Ka: powder-in-bottle Ka = 0.933 1/h vs tablet formulation 2 reference Ka = 0.441 1/h; the tablet formulation 1 comparator is captured by the sibling canonical FORM_LINAG_TAB1), Hong_2011_atazanavir.R (multiplicative linear-deviation effect on relative bioavailability: fdepot *= (1 + e_form_powder_frel * FORM_POWDER) with e_form_powder_frel = -0.355, i.e. the pediatric atazanavir oral powder has 35.5% lower bioavailability than the capsule reference; Hong 2011 Table 4).
• Notes: Specific scope because the “powder vs tablet” contrast is tied to a particular drug-product manufacturing comparison (Yukawa 1990 contrasts Aleviatin brand phenytoin powder with Aleviatin tablets, both from Dainippon Pharmaceutical Co.; Retlich 2015 contrasts an early-phase linagliptin powder-in-bottle formulation against the marketed linagliptin tablet). Mirrors the sibling FORM_TABLET (Kyhl 2016 / Tikiso 2021 tablet vs solution) and FORM_CAPSULE (Hennig 2006 / Hennig 2007 capsule vs solution) under the FORM_* family. Future powder-formulation models should reuse this canonical, extending the example list and documenting the per-paper comparator. Ratified canonically on 2026-05-10 alongside the Yukawa 1990 phenytoin extraction.

FORM_PEGCET_LYOPHILIZED (canonical for the pegcetacoplan lyophilized-powder formulation indicator)

• Description: 1 = the subcutaneous pegcetacoplan dose was given as the lyophilized powder reconstituted before administration; 0 = the dose was given as one of the ready-to-use solution formulations. Per-regimen categorical indicator (a subject may change formulation between study periods, so set it per dose record rather than per subject when the source data do).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (ready-to-use solution formulations; the source FORM column levels 1 = sorbitol vehicle, 2 = dextrose vehicle, 3 = mannitol vehicle, which the Crass 2024 control stream pools as the reference because only the powder level carries an effect).
• Source aliases:
  • FORM (level 4 = POWDER) – Crass 2024 ESM Table 1 PK control stream, which expands the four-level FORM column into indicators FORM1..FORM4 and retains only FORM4 in the final model.
  • LYO, FORM_LYO – plausible alternative NONMEM $INPUT forms.
• Example models: Crass_2024_pegcetacoplan.R (fractional linear effect on subcutaneous bioavailability, F1 = THETA(4)*(1+FORM4*THETA(8)) with THETA(8) = 0.220, i.e. the lyophilized powder gives about 22% higher relative bioavailability than the solution reference, raising F from 0.758 to 0.925; Crass 2024 ESM Table 3 theta 8, 95% CI 0.145-0.296), Crass_2024_pegcetacoplan_hemoglobin.R, Crass_2024_pegcetacoplan_ldh.R.
• Notes: Well-formed member of the auto-approved FORM_<drug>_<formulation> family, alongside FORM_ASV_LIQUID, FORM_VOSO_SOLN02, FORM_ABA_PHASE2, FORM_PEX_PHASE1, FORM_GEF_F02, and FORM_DTG_DT. Deliberately NOT folded into the generic FORM_POWDER canonical: that entry is scoped to oral powder-versus-tablet product comparisons (Yukawa 1990 phenytoin, Retlich 2015 linagliptin, Hong 2011 atazanavir), whereas this contrast is a reconstituted lyophilisate versus a ready-to-use solution for subcutaneous injection, where the mechanism at stake is injection-site depot behaviour rather than oral dissolution. A future generic FORM_LYOPHILIZED could be promoted if a second parenteral lyophilisate-versus-solution model appears; until then the drug-specific form keeps the paper-defined solution reference unambiguous. Scope: specific because the reference category pools three named vehicle formulations defined by the pegcetacoplan development programme. Ratified canonically alongside the Crass 2024 pegcetacoplan extraction.

FORM_THEO_APNECUT (canonical for Apnecut vs theophylline-alcohol in-house preparation oral-theophylline product indicator)

• Description: 1 = subject received the commercial Apnecut (APC) oral theophylline product (4 mg/mL aqueous internal-use solution; Kowa Co., Ltd., Japan; launched August 2006), 0 = subject received the theophylline-alcohol (TA) in-house oral preparation (5 mg/mL theophylline dissolved in ethanol then diluted with sterile purified water to give 10 percent final ethanol concentration; compounded at the National Center for Child Health and Development pharmacy). Per-subject (regimen-fixed) categorical indicator in the Suda 2008 retrospective neonatal-apnea cohort.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (TA in-house preparation; the typical-value CL/F reference in Suda 2008 final model).
• Source aliases:
  • FORM_APNECUT – prior canonical name (pre-2026-06-19 standardization audit).
  • AP – used in Suda_2008_theophylline.R (Suda 2008 NONMEM AP indicator; same orientation, no transformation; AP = 1 if Apnecut, 0 if theophylline alcohol).
• Example models: Suda_2008_theophylline.R (multiplicative effect on CL/F per Suda 2008 final model page 638: cl <- exp(lcl + etalcl) * (WT / 1)^1.08 * (1 + e_form_apnecut_cl * FORM_THEO_APNECUT) with e_form_apnecut_cl = -0.282; the APC formulation has approximately 0.71x the CL/F of the TA reference, equivalent to approximately 1.41x higher dose-normalised exposure consistent with the higher trough concentrations the authors observed clinically that motivated the analysis).
• Notes: Specific scope because the Apnecut-vs-TA contrast is a Japan-specific paediatric-theophylline drug-product comparison local to the Suda 2008 cohort: the TA in-house preparation is unique to the National Center for Child Health and Development pharmacy (compounded ad hoc from bulk theophylline + ethanol + sterile water), and Apnecut is a Kowa Co. commercial Japanese product not marketed elsewhere. Both are oral liquid formulations - distinct from FORM_POWDER/FORM_SYRUP/FORM_TABLET/FORM_CAPSULE because the contrast here is between two specific liquid drug products rather than between dosage-form categories. Suda 2008 attributes the CL/F difference primarily to absorption (per Discussion, page 641: HPLC content analysis confirmed both products were within label, so the formulation effect on apparent oral clearance is interpreted as a bioavailability difference rather than an actual clearance difference) but the parameter is encoded as an effect on CL/F to match the published equation. Mirrors the sibling drug-product-version FORM_* entries (FORM_SAR_DP2 sarilumab, FORM_ISA_P2F2 isatuximab, FORM_LINAG_TAB1 linagliptin, FORM_VISMO_PHASEI vismodegib, FORM_TAC_IR tacrolimus) under the FORM_* family. Set to 0 to simulate the TA reference; in the absence of TA-specific dosing in a downstream cohort, set FORM_THEO_APNECUT = 1 to represent Apnecut. Ratified canonically on 2026-05-24 alongside the Suda 2008 theophylline extraction.

FORM_SYRUP (canonical for oral syrup / liquid-suspension formulation indicator)

• Description: 1 = subject received the modelled drug as an oral syrup or liquid-suspension formulation (paediatric or extemporaneously-compounded oral suspension); 0 = subject received the comparator solid oral formulation (typically a capsule, but document the per-paper comparator in covariateData[[FORM_SYRUP]]$notes). Per-dose-occasion indicator in principle; in paediatric cohorts often time-fixed per subject by clinical convention.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (per-paper non-syrup comparator: capsule in Nanga 2019 tacrolimus; tablet in Sarashina 2005 epinastine).
• Source aliases:
  • syrup formulation – used in Nanga_2019_tacrolimus_metaanalysis.R (Table 3 covariate-effect label; relative bioavailability of syrup vs capsule = 0.53).
  • FORM (dry syrup vs tablet) – used in Sarashina_2005_epinastine.R (Table 4 theta_9 multiplier on CL/F: dry-syrup / tablet ratio 1.06; paediatric arm received dry syrup only, adult arm received tablet or dry syrup).
• Example models: Nanga_2019_tacrolimus_metaanalysis.R (multiplicative effect on bioavailability per Eq. 4: f(depot) <- 0.53^FORM_SYRUP, so capsule users have F = 1 and syrup users have F = 0.53; Nanga 2019 Table 3 ‘Bioavailability for syrup formulation’ = 0.53 with 95% bootstrap CI 0.31 - 0.75), Sarashina_2005_epinastine.R (multiplicative ratio 1.06 on CL/F for dry syrup vs tablet; paediatric atopic-dermatitis patients all received dry syrup, healthy adults received either tablet or dry syrup; tablet is the reference (FORM_SYRUP = 0) and dry syrup is FORM_SYRUP = 1).
• Notes: Specific scope because the comparator solid-oral reference is paper-defined (capsule in Nanga 2019). Distinct from FORM_SUSPENSION (Svensson 2018: tablets extemporaneously suspended in water at bedside immediately before swallowing – same tablet swallowed two different ways, where the manipulation affects MAT rather than F) because here the contrast is between two distinct drug products (commercial capsules vs paediatric syrup / suspension). Distinct from FORM_CAPSULE, FORM_TABLET, and FORM_POWDER, which compare those solid-oral forms against a liquid solution or against each other. Future paediatric-syrup / oral-suspension formulation comparisons should reuse this canonical; if a future model contrasts syrup against tablet (rather than capsule), extend the per-model notes rather than registering a sibling canonical. Ratified canonically on 2026-05-18 alongside the Nanga 2019 tacrolimus meta-analysis extraction.

FORM_CACO3 (canonical for calcium-carbonate-tablet vs calcium-containing mineral-water formulation indicator)

• Description: 1 = subject received calcium as a calcium carbonate (CaCO3) tablet; 0 = subject received calcium as calcium-containing thermal mineral water (Geumjin thermal spring water, the Ahn 2014 reference comparator). Per-subject (treatment-arm) categorical indicator in the Ahn 2014 parallel-arm calcium-absorption design.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (Geumjin thermal spring water; the typical-value bioavailability reference in Ahn 2014, F = 1 fixed for the reference arm).
• Source aliases: none known; Ahn 2014 narratives the treatment factor as “thermal spring water versus calcium carbonate tablet” without a single NMTRAN column name.
• Example models: Ahn_2014_parathyroidHormone.R (multiplicative effect on the depot bioavailability per Ahn 2014 Table 2 ‘Relative F1’ = 1.98: fdepot = exp(lfdepot + e_form_caco3_fdepot * FORM_CACO3) with lfdepot fixed at log(1) for the thermal-water reference and e_form_caco3_fdepot = log(1.98) for the CaCO3 arm; 95% CI on relative F1 1.06-2.90).
• Notes: Specific scope because the contrast pairs a specific calcium product (calcium carbonate tablet) against a specific natural mineral-water source (Geumjin thermal spring water) rather than a generic tablet-vs-solution contrast (FORM_TABLET) – the 0-level here is calcium-containing spring water naturally rich in dissolved calcium, not a manufactured aqueous solution of the modelled drug. Future calcium-bioavailability studies that compare CaCO3 against a different calcium source (citrate, gluconate, dietary-calcium baseline) should reuse this canonical with the reference clearly documented in per-model notes, or register a sibling canonical if the comparator is meaningfully different. Sits in the FORM_* family alongside FORM_FDC (Wilkins 2008 rifampicin co-formulation), FORM_SYRUP (Nanga 2019 tacrolimus paediatric syrup), and FORM_TABLET (Kyhl 2016 nalmefene / Tikiso 2021 abacavir tablet vs solution). Ratified canonically on 2026-05-21 alongside the Ahn 2014 parathyroid-hormone calcium-absorption extraction.

FORM_FDC (canonical for fixed-dose-combination tablet formulation indicator)

• Description: 1 = subject received a fixed-dose-combination (FDC) tablet co-formulating the modelled drug with one or more other drugs in a single tablet; 0 = subject received the modelled drug as a single-drug tablet (or, in the antitubercular context, as a separate-drug-combination “SDC” of single-drug tablets). Per-subject (regimen-fixed) categorical covariate flagging the formulation when a population analysis pools FDC and single-drug-tablet arms and tests formulation as a covariate on absorption / disposition parameters.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: Paper-defined (the typical-value reference is not fixed across the register because different popPK papers anchor their typical-value parameters to different formulation arms). Wilkins 2008 set FDC = 1 as the typical-value reference (SDC = 0 enters as a deviation on MTT and CL); Choi 2018 set the single-agent tablet (FORM_FDC = 0) as the typical-value reference (FDC = 1 enters as a deviation on Ka and F). The canonical column value semantics (1 = FDC, 0 = single-drug tablet) are preserved across both papers; the choice of which arm is the structural reference is documented per-model in covariateData[[FORM_FDC]]$notes. Reference values observed: 1 = FDC (Wilkins 2008 rifampicin + isoniazid + pyrazinamide +/- ethambutol antitubercular FDC); 0 = single-agent metformin tablet (Choi 2018 metformin vs metformin-containing antidiabetic FDC).
• Source aliases:
  • FDC – used in Wilkins_2008_rifampicin.R (DDMODEL00000280 NMTRAN $INPUT column; values 0 / 1 with the same orientation as the canonical, 1 = FDC).
  • formulation (lower-case as printed in the paper) – used in Choi_2018_metformin.R (Choi 2018 Methods ‘Covariate analysis’ equation: formulation = 0 single-agent reference, formulation = 1 FDC test).
• Example models:
  • Wilkins_2008_rifampicin.R (multiplicative (1 + e_fdc0_mtt * (1 - FORM_FDC)) shift on MTT and (1 + e_fdc0_cl * (1 - FORM_FDC)) shift on CL; SDC subjects (FDC = 0) had 104% longer MTT and 23.6% higher CL than the FDC = 1 reference per Wilkins 2008 final estimates. Antitubercular co-formulation: rifampicin + isoniazid + pyrazinamide +/- ethambutol).
  • Choi_2018_metformin.R (multiplicative effects on first-order absorption rate Ka and on relative bioavailability F: ka *= 0.83^FORM_FDC (Ka shrinks to 83.0% of the single-agent value for FDC) and f_rel *= 0.94^FORM_FDC (relative bioavailability shrinks to 94.0% of the single-agent value for FDC) per Choi 2018 Table 3 final estimates. Antidiabetic co-formulation: the FDC drug-product is metformin co-formulated with an unspecified second antidiabetic agent – typical Korean metformin FDCs co-formulate with sitagliptin / glimepiride / vildagliptin / dapagliflozin; the paper does not name the specific co-formulant. Reference category 0 = single-agent metformin tablet).
• Notes: General scope: the FDC vs single-drug-tablet contrast applies to any drug class where a popPK study compares the modelled drug as a single-drug tablet against an FDC tablet co-formulating it with one or more other drugs. Promoted from specific to general scope on 2026-05-30 when Choi 2018 metformin (antidiabetic FDC) ratified the canonical alongside the pre-existing Wilkins 2008 rifampicin (antitubercular FDC) example. The drug-class context (antitubercular / antidiabetic / antihypertensive / antiretroviral / etc.) and the specific co-formulant identity (isoniazid + pyrazinamide / sitagliptin / etc.) are documented per-model in covariateData[[FORM_FDC]]$notes. The mechanism is co-formulation-driven perturbation of absorption (excipient interactions, dissolution rate, gastric residence) rather than drug-product manufacturing of the single drug – distinct from FORM_TABLET (Kyhl 2016 nalmefene tablet vs solution) and the rest of the FORM_* (drug-product-version) family because the FDC-vs-single-drug-tablet contrast compares two tablet products that both contain the modelled drug, not a tablet vs a non-tablet. When the source paper’s typical-value parameters are anchored to the FDC arm, set the per-paper note accordingly; when anchored to the single-drug-tablet arm, set the per-paper note accordingly. Both orientations are valid and the canonical column semantics (1 = FDC, 0 = single-drug tablet) preserve a consistent input column across simulations.

RIA_ASSAY (canonical for radioimmunoassay vs LC-MS/MS bioanalytical method indicator)

• Description: 1 = radioimmunoassay; 0 = LC-MS/MS.
• Units: (binary)
• Type: binary
• Scope: specific
• Example models: Kyhl_2016_nalmefene.R.
• Notes: Switches the additive residual-error magnitude. Use this canonical only when the source paper specifically identifies the immunoassay as radioimmunoassay. For non-radioactive immunoassay methods (microparticle enzyme immunoassay MEIA, chemiluminescence microparticle immunoassay CMIA, EMIT, or generic “immunoassay”), use the sibling canonical IMMUNOASSAY below.

IMMUNOASSAY (canonical for non-radioactive immunoassay vs LC-MS/MS bioanalytical method indicator)

• Description: Binary indicator selecting between a non-radioactive immunoassay bioanalytical method (microparticle enzyme immunoassay MEIA, chemiluminescence microparticle immunoassay CMIA, EMIT, ELISA-based, or generic “immunoassay”) and an LC-MS/MS reference method. 1 = immunoassay; 0 = LC-MS/MS. Time-varying per sample (per-row) in pooled datasets that span the historical introduction of LC-MS/MS at the analytical lab. Used to switch the additive and/or proportional residual-error magnitudes between the two analytical methods, which typically have different precision, accuracy, and cross-reactivity profiles.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (LC-MS/MS – the modern reference bioanalytical method with lower LLOQ, higher specificity, and less cross-reactivity with parent drug metabolites; selected as the reference because most current popPK datasets are LC-MS/MS based and the indicator captures the residual-error shift for the legacy-immunoassay subset).
• Source aliases:
  • IMMUNOASSAY / IA / ASSAY – common NONMEM $INPUT forms.
• Example models: Andrews_2017_tacrolimus.R (per-sample binary indicator switching the additive + proportional residual error between immunoassay (9% of samples; pre-2013 microparticle / chemiluminescence immunoassay) and LC-MS/MS (91% of samples; lower LLOQ 1.0 ng/mL vs 1.5 ng/mL) – both magnitudes estimated jointly in the final model per Andrews 2017 Section 2.3 and Table 2).
• Notes: Distinct from the radioimmunoassay-specific RIA_ASSAY canonical (use that one when the paper identifies the immunoassay as radioimmunoassay; use this IMMUNOASSAY for MEIA / CMIA / EMIT / ELISA / generic immunoassay). The two siblings could in principle be unified into a single IMMUNOASSAY canonical with per-model notes documenting the immunoassay subtype, but the existing RIA_ASSAY registration is preserved for backwards compatibility with Kyhl_2016_nalmefene.R. Per-row time-varying when the dataset spans the historical introduction of LC-MS/MS at the analytical lab (e.g., Andrews 2017’s 2009-2016 sampling window crosses the lab’s switch from immunoassay to LC-MS/MS; the per-sample assay method is recorded). When the dataset is fully LC-MS/MS, set IMMUNOASSAY = 0 for every row and the immunoassay residual-error parameters become non-identifiable – consider dropping them from the model. Ratified canonically on 2026-05-25 alongside the Andrews 2017 tacrolimus extraction.

ASSAY_OSA (canonical for one-stage clotting (OSA) vs chromogenic substrate assay (CSA) bioanalytical method indicator for factor VIII activity)

• Description: 1 = factor VIII activity measured by the one-stage activated partial thromboplastin time (APTT) clotting assay (OSA); 0 = measured by the chromogenic substrate assay (CSA). Per-observation (per-row) binary indicator. Used to switch (a) a multiplier capturing the systematic activity bias between the two assay platforms – applied either to bioavailability (Abrantes 2017) or directly to the predicted FVIII activity (Valke 2024) – and (b) the residual-error magnitudes, which differ between the two platforms.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (CSA). The column’s coding is fixed (1 = OSA, 0 = CSA), but which level a given model treats as its structural reference scale is a per-model property and must be recorded in that model’s covariateData notes. Abrantes_2017_moroctocog.R estimates its typical parameters on the CSA scale (F = 1 anchor at CSA, the assay used to calibrate the European-Union potency labels for Refacto AF) and applies its effects to the OSA rows; Valke_2024_factorviii.R and its PD siblings estimate CL / V1 on the OSA scale and therefore apply their effects to the CSA (ASSAY_OSA = 0) rows.
• Source aliases:
  • METH1 – used in Abrantes_2017_moroctocog.R (Abrantes 2017 Table 2 footnote g: OSA-central indicator on the bioavailability multiplier and on the proportional residual-error magnitude).
  • Assay method – used in Valke_2024_factorviii.R, Valke_2024_factorviii_thrombinPeak.R, Valke_2024_factorviii_thrombinPotential.R, Valke_2024_factorviii_plasminPeak.R (Valke 2024 Results 3.4: every sample was assayed by both methods and both were fitted simultaneously).
• Example models: Abrantes_2017_moroctocog.R (multiplicative effects: F multiplier (1 - 0.390 * ASSAY_OSA) so OSA-assayed observations have F reduced by 39.0% relative to the CSA reference; proportional residual SD multiplier (1 + 0.403 * ASSAY_OSA) so OSA-assayed observations have 40.3% larger residual error magnitude), Valke_2024_factorviii.R, Valke_2024_factorviii_thrombinPeak.R, Valke_2024_factorviii_thrombinPotential.R, Valke_2024_factorviii_plasminPeak.R (OSA-referenced: Cc <- fviii * 0.939^(1 - ASSAY_OSA) so CSA-assayed observations read 0.939x the one-stage value, with the proportional and additive residual SDs likewise switched by 0.840^(1 - ASSAY_OSA) and 5.012^(1 - ASSAY_OSA); the theta^flag form follows Valke 2024 Supplementary Methods Eq. 6).
• Notes: Specific scope because OSA-vs-CSA is a FVIII-domain bioanalytical contrast (the OSA is a functional clotting assay that uses patient APTT-based clot formation; the CSA is an amidolytic chromogenic substrate assay). The direction and magnitude of the OSA-CSA discrepancy are product-dependent: B-domain-deleted recombinant FVIII products read lower by OSA than CSA (Abrantes 2017 Discussion; Lippi 2008; Hubbard 2013), whereas for the plasma-derived VWF/FVIII concentrate of Valke 2024 the CSA read 6.1% lower than the OSA (correction factor 0.939). Do not carry a magnitude across models; source it per product. Per-observation (per-row) when a dataset mixes assays – Valke 2024 assayed every sample by both methods, so the same nominal sampling time appears twice with different ASSAY_OSA values. Paired with ASSAY_OSA_LOCAL to capture an additional inter-laboratory shift for OSA performed at non-central laboratories. Ratified canonically on 2026-06-21 alongside the Abrantes 2017 moroctocog extraction.

ASSAY_OSA_LOCAL (canonical for OSA performed at a local (vs central) laboratory indicator for factor VIII activity)

• Description: 1 = factor VIII activity measured by the OSA at a local (study-site or regional) laboratory rather than the centralised analytical laboratory; 0 = central-laboratory OSA, or any non-OSA-local observation. Per-observation (per-row) binary indicator. Sub-variant of ASSAY_OSA: when ASSAY_OSA_LOCAL = 1, ASSAY_OSA is also 1 (the local-lab samples are a subset of OSA-assayed samples). Used to capture an additional inter-laboratory variability term on the bioavailability multiplier.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (central laboratory OSA, or any non-OSA-local observation – the dominant subset in pooled FVIII PK datasets).
• Source aliases:
  • METH2 – used in Abrantes_2017_moroctocog.R (Abrantes 2017 Table 2 footnote g: OSA-local indicator on the bioavailability multiplier; only one study in the Abrantes 2017 13-study pool, B1831003, used a local laboratory).
• Example models: Abrantes_2017_moroctocog.R (multiplicative effect on F: (1 - 0.146 * ASSAY_OSA_LOCAL) so OSA-local-laboratory observations have an additional 14.6% F reduction beyond the central-laboratory OSA effect captured by ASSAY_OSA).
• Notes: Specific scope because OSA inter-laboratory variability is a FVIII-domain phenomenon. Paired with ASSAY_OSA. When the pooled dataset contains no local-laboratory observations, set ASSAY_OSA_LOCAL = 0 for every row and the corresponding F multiplier collapses to 1. Ratified canonically on 2026-06-21 alongside the Abrantes 2017 moroctocog extraction.

TRACER_TC99M_DTPA (canonical for the 99mTc-DTPA-vs-51Cr-EDTA exogenous GFR-tracer identity indicator)

• Description: 1 = the record’s administered exogenous glomerular-filtration tracer is technetium-99m diethylenetriaminepentaacetate (99mTc-DTPA); 0 = chromium-51 ethylenediaminetetraacetate (51Cr-EDTA). Per-record (per-occasion) binary covariate identifying which tracer compound a plasma-concentration record and its PK parameters belong to, when a population analysis fits two exogenous GFR tracers simultaneously in order to estimate the systematic bias between their measured clearances.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (51Cr-EDTA, the historical GFR reference tracer).
• Source aliases:
  • MES (“GFR measurement”) – used in Gracia_2025_cr51edta_tc99mdtpa.R (Gracia 2025 Methods “Pharmacokinetic analysis” and Table 2 footnote: “MES = 0 or = 1, respectively, for 51Cr-EDTA or 99mTc-DTPA data”).
• Example models: Gracia_2025_cr51edta_tc99mdtpa.R (multiplicative (1 - theta * TRACER_TC99M_DTPA) on both the typical CL and the typical V, with theta6 = -0.009 on CL and theta8 = +0.017 on V, and switching the proportional residual SD between 4.99% CV for 51Cr-EDTA and 7.18% CV for 99mTc-DTPA).
• Notes: Distinct from CRCL, which carries the measured GFR value regardless of which tracer produced it (the CRCL entry already lists iohexol, inulin, 99mTc-DTPA and 51Cr-EDTA as interchangeable measurement methods for that value); TRACER_TC99M_DTPA carries the tracer identity for a record in a joint two-tracer fit. Distinct from the ASSAY_* / RIA_ASSAY / IMMUNOASSAY bioanalytical-method family, which distinguishes how a single fixed analyte was measured – here the two tracers are different administered molecules with their own disposition, and the covariate names which compound the record belongs to. Also distinct from a STUDY_* cohort indicator: in Gracia 2025 tracer and source study happen to be perfectly confounded (Cysped NCT02822404 supplied all 51Cr-EDTA data, CysPedVal RnIPH 2020-101 all 99mTc-DTPA data, and “No patient successively received both radioisotopic tracers”), but the estimand is the tracer bias, and a future study administering both tracers to the same patients would break the confounding with no way to recover the distinction from a study label. Founds a TRACER_<agent> family; a future iohexol-vs-inulin or 99mTc-DTPA-vs-iohexol comparison should register a sibling (e.g. TRACER_IOHEXOL) with the same shape. Ratified canonically on 2026-08-20 alongside the Gracia 2025 extraction.

FORM_LEB_NS0 (canonical for lebrikizumab NS0 cell-line formulation indicator)

• Description: 1 = NS0 cell-line formulation (lebrikizumab), 0 = other.
• Units: (binary)
• Type: binary
• Scope: specific
• Source aliases:
  • FORM_NS0 – prior canonical name (pre-2026-06-19 FORM__ standardization).
• Example models: Zhu_2017_lebrikizumab.R.
• Notes: Renamed from FORM_NS0 to FORM_LEB_NS0 on 2026-06-19 per the canonical-register standardization audit (operator decision: insert the drug stem so the formulation indicator is namespaced to the specific drug, matching the FORM_<DRUG>_<FEATURE> pattern used by FORM_DOX_DORYX_MPC, FORM_ITR_SUBA, etc.).

FORM_LEB_CHO_PHASE2 (canonical for lebrikizumab CHO Phase 2 formulation indicator)

• Description: 1 = CHO Phase 2 formulation (lebrikizumab), 0 = other.
• Units: (binary)
• Type: binary
• Scope: specific
• Source aliases:
  • FORM_CHO_PHASE2 – prior canonical name (pre-2026-06-19 FORM__ standardization).
• Example models: Zhu_2017_lebrikizumab.R.
• Notes: Renamed from FORM_CHO_PHASE2 to FORM_LEB_CHO_PHASE2 on 2026-06-19 per the canonical-register standardization audit (operator decision: insert the drug stem so the formulation indicator is namespaced to lebrikizumab specifically, matching the FORM_<DRUG>_<FEATURE> pattern).

FORM_GCSF_PEG (canonical for pegylated vs non-pegylated recombinant human G-CSF formulation indicator)

• Description: 1 = pegfilgrastim (recombinant human G-CSF conjugated to a 20 kDa monomethoxy-polyethylene-glycol moiety, apparent MW approximately 39 kDa), 0 = filgrastim (non-pegylated recombinant human G-CSF, MW 18.8 kDa). Categorical drug-type covariate on the SC absorption / distribution / clearance / binding-affinity parameters (FSC, KSC, VD, CLD, KD). Subject-level (each subject received one drug per study cohort in the pooled analyses).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (filgrastim; the structurally-simpler non-pegylated parent molecule and the SC bioavailability anchor FSC = 1).
• Source aliases:
  • DRUG – generic paper-text label for the two-level filgrastim / pegfilgrastim covariate in Melhem_2018_g_csf.R (Melhem 2018 Methods ‘Covariate analysis’; the paper reports separate FIL and PEG parameter estimates in Table 2 rather than encoding a base + shift form directly).
• Example models: Melhem_2018_g_csf.R.
• Notes: Follows the FORM_<DRUG>_<FEATURE> naming pattern used by FORM_LEB_NS0, FORM_ITR_SUBA, FORM_DOX_DORYX_MPC, FORM_TAC_IR, FORM_LINAG_TAB1, FORM_SAR_DP2, FORM_ISA_P2F2, FORM_VISMO_PHASEI, FORM_XYNTHA, and FORM_AXI_XLI for drug-specific product / molecular-form indicators. Distinct from CONMED_IFNALPHA (pegylated interferon alpha coadministration flag) because FORM_GCSF_PEG is the primary G-CSF drug being modelled, not a coadministered agent. Distinct from CONMED_PLDH (PEGylated liposomal doxorubicin coadministration flag) for the same reason. Ratified canonically alongside the Melhem 2018 g_csf extraction. Auto-approved sibling of the FORM_<DRUG>_<FEATURE> family.

CONMED_EOX (canonical for concomitant EOX (epirubicin + oxaliplatin + capecitabine) chemotherapy backbone indicator)

• Description: 1 = concomitant epirubicin + oxaliplatin + capecitabine (EOX) chemotherapy backbone, 0 = other backbone (e.g., mFOLFOX6, CAPOX, or single-agent).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-EOX backbone).
• Source aliases: COMB (used by Yamada 2025 Table 1 with the EOX level coded as the non-reference category; renamed to CONMED_EOX to preserve the semantic meaning of the 1-level).
• Example models: Yamada_2025_zolbetuximab.R (fractional effect on V1).
• Notes: Disease-backbone indicator. If a future model needs more backbone categories, encode each as its own indicator (COMB_CAPOX, COMB_FOLFOX, …) with a single reference group.

CONMED_CCB (canonical for concomitant calcium-channel blocker coadministration indicator)

• Description: 1 = subject was receiving a calcium-channel blocker (CCB) as a co-medication during the study, 0 = no CCB. Captures the documented CYP3A4-inhibition CCB-tacrolimus interaction (CCBs reduce tacrolimus apparent oral clearance because amlodipine, diltiazem, and verapamil inhibit CYP3A4 in the gut wall and liver where tacrolimus is metabolised).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no CCB).
• Source aliases: none.
• Example models: Passey_2011_tacrolimus.R (multiplicative effect on apparent oral CL: cl_typ * e_ccb_cl^CONMED_CCB with e_ccb_cl = 0.812, i.e. CCB coadministration reduces tacrolimus CL/F by ~19% in adult kidney transplant recipients; Passey 2011 final model Table 4 row “CCB”).
• Notes: Specific scope because the CCB-tacrolimus interaction is documented for CYP3A4 substrates and the magnitude is drug-pair specific. Future popPK models for CYP3A4 substrates that need a CCB conmed indicator should reuse this canonical. Ratified 2026-05-28 per the naming audit.

CONMED_ATV_DOSE, CONMED_FLV_DOSE, CONMED_LOV_DOSE, CONMED_PRV_DOSE, CONMED_RSV_DOSE, CONMED_SMV_DOSE, CONMED_EZT_DOSE, CONMED_INH_DOSE (canonical for daily dose of a co-administered named drug)

• Description: Daily dose of the named drug (suffix = INN lowercase abbreviation: atv atorvastatin, flv fluvastatin, lov lovastatin, prv pravastatin, rsv rosuvastatin, smv simvastatin, ezt ezetimibe, inh isoniazid). 0 = the named drug is not part of the regimen for this study arm / subject; positive value = total daily dose. Captures the dose-response amplitude in MBMA or co-administered-drug PK/PD models where each drug arm contributes its own dose-effect curve.
• Units: mg/day for the statin / ezetimibe series; mg/kg for CONMED_INH_DOSE (paper-specific unit, documented in covariateData[[CONMED_INH_DOSE]]$units).
• Type: continuous
• Scope: specific
• Reference category: 0 (the drug is not given in this arm; equivalent to “this drug-arm contributes 0 to its dose-effect term”).
• Source aliases: DOSE_<drug> legacy form (used in Vargo 2014 statins / ezetimibe MBMA pre-rename, Chen 2017 TB mouse pre-rename); aliases of the canonical CONMED_<drug>_DOSE form per the 2026-05-28 naming audit rename.
• Example models: Vargo_2014_statins_ezetimibe_mbma.R (each CONMED_<statin>_DOSE drives the corresponding statin’s Hill / Emax dose-response curve; per-arm dose level in mg/day, default 0 outside the named statin arm; ezetimibe and statin arms combine via the sub-additive gamma_int term), Chen_2017_TB_MTP_GPDI_mouse.R (CONMED_INH_DOSE drives the isoniazid CL adjustment: cl_inh = cl_inh_lowdose * (1 - slope_inh * (CONMED_INH_DOSE - 12.5))).
• Notes: The CONMED_<drug>_DOSE shape replaces the earlier DOSE_<drug> / DOSE_<drug>_<unit> names (which conflated a covariate with a dose-amount column). New co-medication dose-effect MBMA models should reuse this family and add the appropriate <drug> INN abbreviation. The units field is per-paper. Ratified 2026-05-28 per the naming audit.

FORM_FLV_BID_XR (canonical for fluvastatin twice-daily / extended-release formulation indicator)

• Description: 1 = fluvastatin arm used twice-daily (BID) dosing or an extended-release (XR) formulation; 0 = once-daily immediate-release fluvastatin (the model reference).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (q.d. IR fluvastatin).
• Source aliases: BID_XR_FLV legacy form (used in Vargo 2014 statins / ezetimibe MBMA pre-rename); alias of the canonical FORM_FLV_BID_XR form per the 2026-05-28 naming audit rename.
• Example models: Vargo_2014_statins_ezetimibe_mbma.R (multiplies the fluvastatin ED50 by 0.645 when set to 1; Vargo 2014 Table 3 row “ED50,fluvastatin (b.i.d.|XR) / ED50,fluvastatin”; same ratio used for either regimen because the paper found b.i.d. and XR ED50 estimates were similar).
• Notes: Specific scope because the b.i.d.-vs-q.d. / IR-vs-XR formulation comparison is paper-specific to fluvastatin meta-analyses. Sibling of FORM_LOV_BID_XR (lovastatin) under the FORM_<drug>_BID_XR family pattern. Ratified 2026-05-28 per the naming audit.

FORM_LOV_BID_XR (canonical for lovastatin twice-daily / extended-release formulation indicator)

• Description: 1 = lovastatin arm used twice-daily (BID) dosing or an extended-release (XR) formulation; 0 = once-daily immediate-release lovastatin (the model reference).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (q.d. IR lovastatin).
• Source aliases: BID_XR_LOV legacy form (used in Vargo 2014 statins / ezetimibe MBMA pre-rename); alias of the canonical FORM_LOV_BID_XR form per the 2026-05-28 naming audit rename.
• Example models: Vargo_2014_statins_ezetimibe_mbma.R (multiplies the lovastatin ED50 by 0.59 when set to 1; Vargo 2014 Table 3 row “ED50,lovastatin (b.i.d.|XR) / ED50,lovastatin”).
• Notes: Specific scope because the b.i.d.-vs-q.d. / IR-vs-XR formulation comparison is paper-specific to lovastatin meta-analyses. Sibling of FORM_FLV_BID_XR (fluvastatin) under the FORM_<drug>_BID_XR family pattern. Ratified 2026-05-28 per the naming audit.

DIS_ACS (canonical for acute coronary syndrome cohort indicator)

• Description: 1 = study arm enrolled patients with acute coronary syndrome (ACS), 0 = non-ACS cohort (the model reference).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-ACS arm).
• Source aliases: ACS legacy form (used in Vargo 2014 statins / ezetimibe MBMA pre-rename); alias of the canonical DIS_ACS form per the 2026-05-28 naming audit rename.
• Example models: Vargo_2014_statins_ezetimibe_mbma.R (additive shift of -0.117 on the statin Emax in ACS arms; greater statin LDL-C lowering in ACS patients than in the non-ACS reference).
• Notes: Specific scope because the ACS-cohort effect on statin response is paper-specific to the Vargo 2014 MBMA. Sibling of DIS_HEFH (HeFH cohort indicator) and the broader DIS_<indication> family. Ratified 2026-05-28 per the naming audit.

DIS_HEFH (canonical for heterozygous familial hypercholesterolemia cohort indicator)

• Description: 1 = study arm enrolled patients with heterozygous familial hypercholesterolemia (HeFH), 0 = non-HeFH cohort (the model reference).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-HeFH arm).
• Source aliases: HEFH legacy form (used in Vargo 2014 statins / ezetimibe MBMA pre-rename); alias of the canonical DIS_HEFH form per the 2026-05-28 naming audit rename.
• Example models: Vargo_2014_statins_ezetimibe_mbma.R (additive shift of +0.127 on the statin Emax in HeFH arms; smaller statin LDL-C lowering in HeFH patients than in the non-HeFH reference; biologically consistent with the LDLR-pathway disruption in HeFH), Kuchimanchi_2018_evolocumab_ldlc.R (multiplicative exponent 1.28 on baseline LDL-C in the evolocumab Emax-on-AUC exposure-response layer, Table 4; HeFH patients have higher baseline LDL-C than the non-HeFH reference), Jadhav_2023_bempedoicAcid_ldlc.R (proportional shift of +0.0671 on the indirect-response baseline LDL-C, Jadhav 2023 Table 3; two of the four pivotal phase 3 studies enrolled patients with prior ASCVD and/or HeFH on maximally tolerated statin therapy).
• Notes: Specific scope because the HeFH-cohort effect on statin response is paper-specific. Sibling of DIS_HOFH (homozygous form, Pu_2021_evinacumab) and the broader DIS_<indication> family. Ratified 2026-05-28 per the naming audit.

CONMED_RIF_CC, CONMED_INH_CC, CONMED_EMB_CC, CONMED_STR_CC, CONMED_CAB_CC, CONMED_COL_CC, CONMED_MER_CC, CONMED_GEN_CC, CONMED_CIP_CC (canonical for time-varying plasma / in-vitro concentration of a co-administered named drug)

• Description: Time-varying plasma (or in-vitro experiment) concentration of the named drug (suffix = INN lowercase abbreviation: rif rifampicin, inh isoniazid, emb ethambutol, str streptomycin, cab capreomycin / a second antibiotic, col colistin, mer meropenem, gen gentamicin, cip ciprofloxacin). Used as the driving exposure for the corresponding drug-effect term in combination PK/PD or in vitro time-kill models. 0 = the named drug is not part of the regimen for this subject / arm.
• Units: mg/L (or ug/mL, equivalent) for in vitro time-kill experiments; ug/mL for clinical plasma-PK-driven PD subsystems. Per-paper unit is documented in covariateData[[CONMED_<drug>_CC]]$units.
• Type: continuous
• Scope: specific
• Reference category: 0 (drug not in regimen).
• Source aliases: Ccol / Cmer / Cgen / Ccip (Mohamed 2016, Sadouki 2025) and bare EMB / INH / RIF / STR / CAB (Clewe 2018, Khan 2015) – legacy forms used before the 2026-05-28 naming-audit rename. All map to the canonical CONMED_<drug>_CC form.
• Example models: Clewe_2018_rifampicin.R (in vitro time-kill of rifampicin + isoniazid + ethambutol against M. tuberculosis; each drug exposure is a fixed concentration driving the Hill-Emax effect on the F / S / N bacterial states), Khan_2015_ciprofloxacin.R (in vitro streptomycin + capreomycin time-kill), Mohamed_2016_colistin_meropenem.R (in vitro colistin + meropenem time-kill against WT and meropenem-resistant P. aeruginosa), Sadouki_2025_meropenem_gentamicin_ciprofloxacin.R (combination PD with meropenem / gentamicin / ciprofloxacin time-varying exposures).
• Notes: Companion to the dose covariate CONMED_<drug>_DOSE (registered above). The _CC suffix distinguishes the dynamic concentration covariate from the dose-level covariate; both share the <drug> INN abbreviation. Ratified 2026-05-28 per the naming audit.

CONMED_MER, CONMED_GEN, CONMED_CIP (canonical for concomitant coadministration indicators of meropenem / gentamicin / ciprofloxacin)

• Description: Binary indicator: 1 = the named antibiotic is coadministered with the index drug for this subject / record / study arm, 0 = the antibiotic is not coadministered. The indicator covers two related use cases that share the same data semantic: (1) in combination-antibiotic PD models, the indicator gates the corresponding drug-effect term (and any pairwise-interaction term that requires both partners present); (2) in popPK models of the index drug, the indicator enters as a covariate on a PK parameter (typically clearance) to capture a multiplicative shift attributable to (or correlated with) the coadministered antibiotic.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (drug not coadministered; the corresponding CONMED_<drug>_CC concentration covariate is then unused / 0).
• Source aliases: MER_PRESENT, GEN_PRESENT, CIP_PRESENT (Sadouki 2025) – the legacy _PRESENT suffix is dropped because binary semantics are conveyed by the type field; renamed 2026-05-28 per the naming audit. GENT (Cohen-Wolkowiez 2014) maps to CONMED_GEN (1 if gentamicin was given concurrently with piperacillin-tazobactam, 0 otherwise).
• Example models: Sadouki_2025_meropenem_gentamicin_ciprofloxacin.R (used together to compute a combination-of-three indicator that triggers a categorical -1 shift on the BETA parameter when all three antibiotics are co-administered), CohenWolkowiez_2014_tazobactam.R (CONMED_GEN enters the tazobactam clearance covariate equation as a multiplicative power-form factor 1.52^CONMED_GEN; in the source paper the gentamicin coadministration covariate was found to be confounded by postmenstrual / postnatal age differences between infants who received gentamicin and those who did not, so the effect should be interpreted as a marker of an older / sicker subset rather than a mechanistic drug-drug interaction).
• Notes: Companion to the concentration covariate CONMED_<drug>_CC (above). For a given drug, CONMED_<drug> is the binary “coadministered?” indicator and CONMED_<drug>_CC is the time-varying plasma concentration. Scope promoted to general on 2026-06-01 alongside the Cohen-Wolkowiez 2014 piperacillin-tazobactam extraction; the underlying semantic (binary coadministration of a named antibiotic) is the same whether the indicator gates a PD interaction term or scales a PK covariate equation, so a single canonical name is preferred over splitting into PD-only and PK-only variants. Ratified 2026-05-28 per the naming audit; scope updated 2026-06-01.

BACT (canonical for bacterial-strain indicator in in vitro time-kill models)

• Description: Integer-coded indicator of the bacterial strain used in an in vitro time-kill experiment. Each level represents a structurally distinct strain (e.g., wild-type ATCC reference vs a clinical isolate with a specific resistance pattern); models switch between strain-specific structural parameters at the indicator value. Paper-specific level coding is documented per-file in covariateData[[BACT]]$notes.
• Units: (categorical)
• Type: categorical
• Scope: specific
• Reference category: paper-specific (typically the wild-type ATCC reference strain; e.g., BACT = 2 in Mohamed 2016).
• Source aliases: none.
• Example models: Mohamed_2016_colistin_meropenem.R (1 = ARU552 meropenem-resistant clinical isolate, 2 = ATCC 27853 wild-type; hard switch on 25+ strain-specific structural parameters), Zhao_2024_ciprofloxacin_colistin_invitro.R, Zhao_2024_ciprofloxacin_colistin_plasma.R, Zhao_2024_ciprofloxacin_colistin_kidney.R (47 = C47 clinical urinary isolate, 347 = MG1655 wild type LM347, 378 = LM378 gyrA1 S83L, 421 = LM421 gyrA1 S83L plus marR knockout; levels are the source FSTRAIN codes and switch the colistin Emax, EC50, Hill and interaction parameters plus the pre-existing-resting inoculum fraction), HernandezLozano_2025_apramycin_invitro.R, HernandezLozano_2025_apramycin_mouse.R, HernandezLozano_2025_apramycin_human.R (591 = Escherichia coli EN591, an MDR rmtB clinical isolate; 700336 = Escherichia coli ATCC 700336 / EN1085, a trimethoprim/sulfamethoxazole-resistant urinary isolate. In the in vitro file the level selects the growth rate constant, and jointly with PH_MEDIUM the three drug-effect parameters SlopeS / SlopeR / kada and the MIC; in the two in vivo files it selects the per-organ growth and death rate constants and the strain’s MICs at pH 7.4 and pH 6, while the drug-effect parameters are shared across strains).
• Notes: Specific scope because the per-level strain identity is defined by the source experiment. New in vitro time-kill extractions should reuse this canonical and document the per-level strain mapping in covariate notes. Levels are integer codes with no shared meaning across papers, and there need be no reference level: Zhao 2024 gives every level its own parameter set rather than contrasting against a baseline, whereas Mohamed 2016 treats the wild-type ATCC strain as the reference. A level codes the strain and nothing else: when the source parameter table is indexed by strain and a separately-varied experimental condition, register (or reuse) a canonical for that condition and switch on the pair, rather than multiplying the two factors into composite BACT levels. HernandezLozano_2025_apramycin_invitro.R is the founding example – Table 1 of that paper indexes the in vitro drug-effect parameters by strain and by growth-medium pH, so the file carries BACT (591 / 700336) alongside PH_MEDIUM (6 / 7.4); composite levels would have made a single canonical carry two independent factors, so a model varying strain at fixed pH could not be expressed. Ratified 2026-05-28 per the naming audit.

PH_MEDIUM (canonical for the pH of the growth medium or site of action in an antibacterial PD model)

• Description: pH of the growth medium in which an antibacterial pharmacodynamic experiment was conducted (in vitro), or of the tissue / fluid at the site of action to which a site-of-action MIC is referenced (in vivo). Carried as a covariate whenever pH is a factor the experiment deliberately varied, because the MIC of a pH-sensitive antibacterial – and, for some strains, its drug-effect parameters – changes with it. Enters the model as a selector on the MIC and on any pH-specific drug-effect parameter set, not as a continuous slope, so a model that has only been characterised at the pH levels the source studied should switch on those levels rather than interpolating between them.
• Units: pH units
• Type: continuous
• Scope: general
• Reference category: n/a – not a contrast against a baseline. pH 7.4 is the conventional neutral condition used in standard broth time-kill work and is the natural default for a model that has to pick one; the founding example’s other level is pH 6, chosen because urine is acidic.
• Source aliases: none.
• Example models: HernandezLozano_2025_apramycin_invitro.R (levels 6 and 7.4; apramycin’s MIC is 4-fold higher at pH 6 for both strains – EN591 32 vs 8 mg/L, ATCC 700336 16 vs 4 mg/L – and for EN591 the pH levels additionally take separate SlopeS / SlopeR / kada estimates, whereas MIC-normalization alone lets ATCC 700336 share one drug-effect set across both).
• Notes: General scope because medium / site pH is an experimental condition with the same meaning in any antibacterial PD experiment, unlike the paper-specific integer coding of BACT. Recorded on the natural pH scale (a numeric such as 6 or 7.4), not as an integer level code, so the value is self-describing across papers. Pairs with BACT, which codes the strain and only the strain: the two factors are switched on jointly when a source parameter table is indexed by both. Do not use this column for a model in which pH is fixed rather than varied – the in vivo siblings HernandezLozano_2025_apramycin_mouse.R and HernandezLozano_2025_apramycin_human.R deliberately omit it, because there pH is a property of the organ (kidney assumed 7.4, bladder assumed 6) that is baked into which per-site MIC applies, and the MICs are fixed() ini() parameters a user overrides for a different isolate. The motivating methodological point of the founding paper is that time-kill studies are conventionally run at neutral pH while the target site in a urinary tract infection is acidic, so this covariate is expected to recur in future urinary and other site-specific antibacterial PD extractions. Ratified 2026-08-21 alongside the Hernandez-Lozano 2025 apramycin extraction.

LOWINOC (canonical for low-inoculum experiment indicator)

• Description: Binary indicator: 1 = study arm / replicate used the low-inoculum experimental design (typically 10^5 - 10^6 CFU/mL starting bacterial count), 0 = standard or high inoculum (typically 10^7 - 10^9 CFU/mL). Captures the inoculum-effect deviation in bacterial-kill kinetics across initial-density groups.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (standard / high inoculum).
• Source aliases: none.
• Example models: Sadouki_2025_meropenem_gentamicin_ciprofloxacin.R (gates a low-inoculum-specific shift on the bacterial-kill amplitude; the paper found the low-inoculum arm had a measurably different kill rate from the standard-inoculum arm).
• Notes: Specific scope because the per-paper definition of “low” varies by experiment design. Ratified 2026-05-28 per the naming audit.

FLARE (canonical for flare-design study-arm indicator)

• Description: Binary study-arm indicator: 1 = the trial used a flare design (subjects washed out of pain medication and required a predefined pain flare-up before randomisation), 0 = non-flare design. A property of the trial design in a model-based meta-analysis (MBMA), not of an individual patient.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-flare design).
• Source aliases:
  • If – used in Boucher_2018_naproxen_mbma.R (Boucher 2018 Eqs 2-3).
• Example models: Boucher_2018_naproxen_mbma.R (shifts both baseline WOMAC pain e0 and emax; 12 of 18 trials were flare designs).
• Notes: MBMA study-arm-level covariate (a property of the trial design). Specific scope because “flare design” is defined per the osteoarthritis-pain trial-design literature. Registered 2026-05-30 (promoted from the in-file MBMA-covariate documentation convention so checkModelConventions() recognises it).

NAPROXEN (canonical for naproxen treatment-arm indicator)

• Description: Binary study-arm treatment indicator: 1 = the arm received naproxen, 0 = the arm received placebo. A property of the trial arm in a model-based meta-analysis (MBMA), not of an individual patient.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (placebo arm).
• Source aliases:
  • In – used in Boucher_2018_naproxen_mbma.R (Boucher 2018 Eqs 3-4).
• Example models: Boucher_2018_naproxen_mbma.R (shifts emax and shortens et50; all 18 included trials had both a naproxen and a placebo arm).
• Notes: MBMA study-arm-level treatment indicator. Specific scope (a per-drug arm indicator). Registered 2026-05-30 (promoted from the in-file MBMA-covariate documentation convention so checkModelConventions() recognises it).

TRAMADOL (canonical for tramadol treatment-arm indicator)

• Description: Binary study-arm treatment indicator: 1 = the arm received tramadol, 0 = the arm did not receive tramadol (placebo, tapentadol, or another active-control arm). A property of the trial arm in a model-based meta-analysis (MBMA), not of an individual patient.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (arm did not receive tramadol).
• Source aliases: none (the source paper uses the drug name directly to select the extent-of-reduction term).
• Example models: Mercier_2014_tramadol_tapentadol_mbma.R (selects the tramadol Emax-in-dose term e_tramadol_emax * CONMED_TRAMADOL_DOSE / (CONMED_TRAMADOL_DOSE + ED50) * TRAMADOL in the extent-of-reduction R; 43 of 81 arms in the pooled 45-trial database were tramadol arms).
• Notes: MBMA study-arm-level treatment indicator, sibling of NAPROXEN in the per-drug MBMA arm-indicator family. Specific scope (a per-drug arm indicator). Pairs with TAPENTADOL and CONMED_TRAMADOL_DOSE in the Mercier 2014 chronic-non-malignant-pain extraction; the two treatment indicators are mutually exclusive (a single arm can be tramadol OR tapentadol, never both). Registered 2026-07-26 alongside the Mercier 2014 tramadol + tapentadol MBMA extraction.

TAPENTADOL (canonical for tapentadol treatment-arm indicator)

• Description: Binary study-arm treatment indicator: 1 = the arm received tapentadol, 0 = the arm did not receive tapentadol (placebo, tramadol, or another active-control arm). A property of the trial arm in a model-based meta-analysis (MBMA), not of an individual patient.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (arm did not receive tapentadol).
• Source aliases: none (the source paper uses the drug name directly).
• Example models: Mercier_2014_tramadol_tapentadol_mbma.R (enters the extent-of-reduction R additively via e_tapentadol_emax * TAPENTADOL; no dose-response is fit because tapentadol was studied only over the narrow 100-250 mg bid range in the Mercier 2014 database; 8 of 81 arms were tapentadol arms).
• Notes: MBMA study-arm-level treatment indicator, sibling of NAPROXEN and TRAMADOL in the per-drug MBMA arm-indicator family. Specific scope. Pairs with TRAMADOL in the Mercier 2014 extraction; the two are mutually exclusive per arm. Registered 2026-07-26 alongside the Mercier 2014 tramadol + tapentadol MBMA extraction.

QBL, QEFF (canonical for baseline / effective renal Q in HD/CRRT renal-replacement models)

• Description: Renal-replacement-therapy clearance terms used in hemodialysis (HD) / continuous renal replacement therapy (CRRT) popPK models for renally cleared drugs.
  • QBL = baseline (off-HD / off-CRRT) renal Q (L/h); the typical patient’s residual renal clearance.
  • QEFF = effective on-HD/CRRT renal Q (L/h); the apparent dialysis-augmented clearance during a session (including the effective renal Q plus the dialyser/filter clearance, depending on the paper’s parameterisation).
• Units: L/h
• Type: continuous
• Scope: specific
• Reference category: none (continuous).
• Source aliases: none.
• Example models: Leuppi-Taegtmeyer_2019_colistin.R (colistin / colistimethate sodium popPK in CRRT recipients; QBL drives the off-CRRT typical-value CL, QEFF drives the on-CRRT augmented CL during the session window), Zhang_2025_fluconazole.R (QEFF is the per-record fluconazole CRRT clearance CLcrrt, supplied as a data column rather than estimated, and removes drug from the paper-specific crrt compartment at QEFF * RRT_CRRT_ACTIVE / vcrrt; computed from CRRT mode and circuit flows as (Q_uf + Q_d) * S, equivalently dose [mL/kg/h] * WT [kg] * 0.67 / 1000 at the cohort sieving/saturation coefficient – Zhang 2025 Supplementary Material Section 1. This model uses QEFF without a companion QBL: residual body clearance is a separately estimated structural parameter stratified by DIS_ARF, not a covariate).
• Notes: Specific scope because the on-/off- HD/CRRT splitting is paper-specific. Future renal-replacement popPK extractions should reuse the QBL/QEFF pair. Note that the pair need not be used together: QEFF alone is correct when the paper supplies the extracorporeal clearance per record and estimates residual body clearance as a structural parameter (Zhang 2025), whereas the QBL/QEFF pair is used when both the off- and on-therapy renal Q are covariate-supplied (Leuppi-Taegtmeyer 2019). Ratified 2026-05-28 per the naming audit.

DIS_CHD_PERCENT (canonical for coronary-heart-disease cohort prevalence percentage)

• Description: Study-arm-level percentage (0-100) of the enrolled cohort who carry a coronary heart disease (CHD) diagnosis. Continuous covariate scaled in percent (not fraction).
• Units: %
• Type: continuous
• Scope: specific
• Reference category: 0% (healthy non-CHD cohort).
• Source aliases: CHD_PCT legacy form (used in Vargo 2014 statins / ezetimibe MBMA pre-rename); alias of the canonical DIS_CHD_PERCENT form per the 2026-05-28 naming audit rename.
• Example models: Vargo_2014_statins_ezetimibe_mbma.R (linear coefficient e_chd_emax_statin = -0.000649 per percentage point on Emax_statin, i.e. a 24% CHD arm reduces Emax_statin by 0.000649 * 24 = 0.016; the paper’s typical-patient definition uses 24% CHD).
• Notes: MBMA study-arm-level covariate; the canonical register’s individual-level pop-PK covariates do not directly fit aggregate-percentage columns, so this canonical is specific-scope and explicitly carries a study-arm aggregate meaning. Future MBMA models should reuse for the CHD-cohort prevalence column. Ratified 2026-05-28 per the naming audit.

TUMTP_SQUAM_PCT (canonical for squamous-tumor-histology cohort prevalence percentage)

• Description: Study-arm-level percentage (0-100) of the enrolled cohort whose tumor histology is squamous (versus non-squamous). Continuous covariate scaled in percent (not fraction). In non-small cell lung cancer (NSCLC) MBMA papers the arm’s squamous fraction interacts with the chemotherapy treatment class (chemotherapy tends to be more effective on squamous NSCLC than on non-squamous NSCLC).
• Units: %
• Type: continuous
• Scope: specific
• Reference category: 0% (all-non-squamous arm).
• Source aliases: %squamous histology (Franzese 2026 Table 1 / Table S1 covariate label).
• Example models: Franzese_2026_pdl1_nsclc_mbma.R (linear coefficients on ORR chemotherapy intercept (+0.282), OS chemotherapy hazard (+0.213), and PFS chemotherapy hazard (+0.186) in a mNSCLC MBMA of PD-(L)1 immunotherapy).
• Notes: MBMA study-arm-level covariate. Family precedent: DIS_CHD_PERCENT (Vargo 2014). Ratified 2026-07-24 alongside the Franzese 2026 mNSCLC MBMA. Distinct from a per-subject binary squamous-histology indicator; here the arm’s squamous fraction is a continuous proportion of participants.

PS_ECOG_0_PCT (canonical for ECOG-performance-status-0 cohort prevalence percentage)

• Description: Study-arm-level percentage (0-100) of the enrolled cohort with an Eastern Cooperative Oncology Group (ECOG) Performance Status score of 0 at baseline (fully active / asymptomatic). Continuous covariate scaled in percent (not fraction). Higher arm-level ECOG-0 fraction typically indicates a healthier / more-active enrolled cohort and is associated with better survival outcomes.
• Units: %
• Type: continuous
• Scope: specific
• Reference category: 0% (no ECOG-0 patients; all ECOG >= 1).
• Source aliases: %ECOG PS score of 0 / ps.0 (Franzese 2026 Table 1 / Table S1 covariate label).
• Example models: Franzese_2026_pdl1_nsclc_mbma.R (linear coefficient on OS non-chemotherapy hazard (-0.400) and on PFS global hazard (-0.293) in a mNSCLC MBMA of PD-(L)1 immunotherapy).
• Notes: MBMA study-arm-level covariate. Distinct from the per-subject ECOG_GE1 and ECOG_GE2 binaries (individual-level indicators). Family precedent: DIS_CHD_PERCENT (Vargo 2014). Ratified 2026-07-24 alongside the Franzese 2026 mNSCLC MBMA.

RACE_ASIAN_PCT (canonical for Asian-race cohort prevalence percentage)

• Description: Study-arm-level percentage (0-100) of the enrolled cohort who are Asian (any Asian subgroup, matching the individual-level RACE_ASIAN canonical). Continuous covariate scaled in percent (not fraction).
• Units: %
• Type: continuous
• Scope: specific
• Reference category: 0% (all-non-Asian arm).
• Source aliases: %Asian race / Race.Asian (Franzese 2026 Table 1 / Table S1 covariate label).
• Example models: Franzese_2026_pdl1_nsclc_mbma.R (enters the OS ORR-slope as an interaction term (eta_orr_os - 0.595) * (ORR/100) * (RACE_ASIAN_PCT/100); the paper’s Discussion attributes the effect to regional trial-conduct differences rather than an inherent race effect).
• Notes: MBMA study-arm-level covariate. Distinct from the per-subject binary RACE_ASIAN canonical (individual-level 0 or 1); here the arm’s Asian fraction is a continuous proportion of participants. Also distinct from Yang 2010’s use of RACE_ASIAN as a binary at the arm level (whole-arm-Asian vs whole-arm-Western), which is coarser than the continuous-fraction form. Family precedent: DIS_CHD_PERCENT (Vargo 2014). Ratified 2026-07-24 alongside the Franzese 2026 mNSCLC MBMA.

FORM_ISA_P2F2 (canonical for isatuximab P2F2 drug-material indicator)

• Description: 1 = isatuximab P2F2 drug material (intended commercial / phase III material, used in the EFC14335 / ICARIA-MM study), 0 = P1F1 drug material (early-phase material used in TED10893 / TED14154 / TCD14079).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (P1F1).
• Source aliases:
  • FORM_P2F2 – prior canonical name (pre-2026-06-19 standardization audit).
  • Drug_mat – used in Fau_2020_isatuximab.R. Values 0 / 1 with the same orientation as the canonical (1 = P2F2 / commercial-bound material).
• Example models: Fau_2020_isatuximab.R (exponential effect on Vc with coefficient -0.137; P2F2 patients had ~13% lower Vc than P1F1).
• Notes: Phase III / commercial-bound formulation indicator for isatuximab; the FORM_* family stays scope-specific per nlmixr2lib policy that drug-product-version indicators are kept model-specific unless they generalize across multiple drugs. Set to 1 to simulate the marketed material.

FORM_LINAG_TAB1 (canonical for linagliptin tablet formulation 1 indicator)

• Description: 1 = subject received the linagliptin “tablet formulation 1” (used in Retlich 2015 Study 2), 0 = subject received tablet formulation 2 (the marketed linagliptin tablet, used in Studies 3 and 4) OR the powder-in-bottle formulation (used in Study 1). The powder-vs-tablet contrast is captured by the sibling canonical FORM_POWDER; this indicator switches between the two tablet formulations.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (tablet formulation 2 = marketed linagliptin tablet, the typical-value Ka reference; or FORM_POWDER = 1 for the powder).
• Source aliases: none known.
• Example models: Retlich_2015_linagliptin.R (multiplicative shift on the linagliptin first-order absorption rate constant Ka; typical Ka = 0.441 1/h for tablet formulation 2 (reference), 0.795 1/h for tablet formulation 1, 0.933 1/h for the powder formulation).
• Notes: Specific scope because the linagliptin “tablet 1 vs tablet 2” distinction is a drug-product-version comparison local to the Retlich 2015 popPK dataset; tablet formulation 1 was a development formulation that is not marketed. Mirrors the FORM_SAR_DP2 (sarilumab) and FORM_ISA_P2F2 (isatuximab) entries under the FORM_* family. Set to 0 for routine marketed-formulation simulation. Ratified canonically alongside the Retlich 2015 linagliptin extraction.

FORM_SAR_DP2 (canonical for sarilumab drug-product version 2 indicator)

• Description: 1 = sarilumab drug product 2 formulation (used in some phase I studies and the dose-ranging phase II study), 0 = other drug product (DP1 or DP3; DP3 is the commercial formulation).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (DP1 or DP3).
• Source aliases:
  • FORM_DP2 – prior canonical name (pre-2026-06-19 standardization audit).
  • DP2 – used in Xu_2019_sarilumab.R.
• Example models: Xu_2019_sarilumab.R.
• Notes: Affects both CLO/F (1.30x multiplier) and Ka (0.663x multiplier) in Xu 2019. Set to 0 for routine commercial-formulation simulation.

FORM_POSA_AB (canonical for posaconazole prototype tablet (A or B) vs later (C or D) tablet formulation indicator)

• Description: 1 = subject received the posaconazole delayed-release solid tablet formulation A or B (the two early prototype formulations used in some phase 1 studies of the van Iersel 2018 pooled analysis); 0 = subject received tablet formulation C or D (the later formulations, with D being the marketed / commercial formulation and the predominant formulation across the pooled data set). Per-dose-record categorical indicator switching relative bioavailability F1 between the prototype and later tablet formulations.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (tablet C or D; the predominant formulation across the pooled data set and the typical-value F1 = 1 reference in van Iersel 2018 final model per Results ‘Posaconazole exposure … and impact of covariates’: ‘tablet formulation D is the marketed image and the predominant formulation used across the data set and was taken as the reference formulation for the relative bioavailability estimate for tablet formulations A and B’).
• Source aliases: none – the paper narrates the contrast as “tablet formulation A/B versus tablet formulation C/D” without a single NMTRAN column name.
• Example models: vanIersel_2018_posaconazole.R (multiplicative effect on relative bioavailability F1: fdepot *= (1 + e_formposaab_fdepot * FORM_POSA_AB) with e_formposaab_fdepot = +0.247, i.e. the prototype A/B formulations have 24.7% higher F1 than the later C/D reference; van Iersel 2018 Table 2 final-model ‘Tablet formulation A/B on F1’ = 0.247, RSE 21.5%).
• Notes: Specific scope because the prototype-A-or-B vs later-C-or-D contrast is paper-specific to the van Iersel 2018 posaconazole solid-tablet clinical-development pooled analysis. Drug-specific member of the FORM_<drug>_<contrast> family alongside FORM_ASV_LIQUID (asunaprevir), FORM_SAR_DP2 (sarilumab), FORM_VISMO_PHASEI (vismodegib), FORM_LINAG_TAB1 (linagliptin), FORM_ABA_PHASE2 (abatacept). Set to 0 for routine commercial-formulation simulation (tablet D). Ratified canonically alongside the van Iersel 2018 posaconazole extraction.

FORM_VISMO_PHASEI (canonical for vismodegib Phase I (dry-blend capsule) formulation indicator)

• Description: 1 = subject received the Phase I clinical-development vismodegib formulation (dry-blend capsules used in early-phase studies SHH3925g and the Phase I cohort of SHH4610g); 0 = subject received the Phase II / commercial-bound formulation (wet-granulation capsules used in SHH4476g, SHH4433g, and most of SHH4683g / SHH4871g). Per-subject (regimen-fixed) categorical indicator.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (Phase II / commercial wet-granulation capsule; F = 1 fixed as the reference in Lu 2015 Eq. 4 and the typical-value ka reference in Eq. 4).
• Source aliases: none known; Lu 2015 reports formulation as a paper-defined “Phase I vs Phase II formulation” categorical without committing to a single column name.
• Example models: Lu_2015_vismodegib.R (multiplicative shifts on Ka and relative bioavailability F: typical Ka = 9.025 1/day for the Phase II reference in patients, with exp(-0.602) = 0.55x for the Phase I formulation and exp(0.671) = 1.96x for healthy volunteers; F = 1 for the Phase II reference, F = 0.346 for the Phase I formulation in patients and 0.836 in HV).
• Notes: Specific scope because the “Phase I (dry blend) vs Phase II (wet granulation) capsule” contrast is tied to the vismodegib drug-product-version comparison in Lu 2015. Mirrors the FORM_SAR_DP2 (sarilumab), FORM_ISA_P2F2 (isatuximab), and FORM_LINAG_TAB1 (linagliptin) entries under the FORM_* family of drug-product-version indicators. Set to 0 for routine commercial-formulation simulation. Ratified canonically alongside the Lu 2015 vismodegib extraction.

FORM_DOX_DORYX_MPC (canonical for Doryx MPC delayed-release tablet doxycycline formulation indicator)

• Description: 1 = subject received the Doryx MPC delayed-release tablet (Mayne Pharma International; modified-acid-resistance delayed-release formulation, 120 mg doxycycline hyclate per tablet, formulation code MP336); 0 = subject received any other Doryx doxycycline product (the Doryx delayed-release tablet reference at 75 / 100 / 150 / 200 mg, or the conventional-release Doryx capsule at 100 mg, flagged separately by FORM_CAPSULE). Per-subject (regimen-fixed) categorical indicator used in popPK analyses that pool the three Doryx formulations and test formulation as a covariate on relative bioavailability, absorption lag, and the transit absorption rate’s food effect.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (Doryx delayed-release tablet; the typical-value reference for relative bioavailability F = 1 and the typical-value reference for the food effect on KTR in Hopkins 2017 Table 3).
• Source aliases:
  • FORM_DORYX_MPC – prior canonical name (pre-2026-06-19 standardization audit).
  • FMPC – used in Hopkins_2017_doxycycline.R (Hopkins 2017 Methods ‘Base model’ paragraph: “Structural covariates … a formulation effect (for Doryx MPC, FMPC; for Doryx capsule, FCAP) on relative bioavailability (RELF)”).
• Example models: Hopkins_2017_doxycycline.R (multiplicative effects per Hopkins 2017 Table 3 final model: relative bioavailability F1MPC = 0.863 vs the Doryx-tablet reference (Theta 8); shared absorption lag ALAG1 = 0.115 h with the Doryx capsule (Theta 12); strengthened food effect on the transit rate constant KTR: fed state reduces KTR by 54.9 % for Doryx MPC (COVFED2 = -0.549, Theta 11) vs 20.9 % for Doryx tablet / capsule (COVFED = -0.209, Theta 10)).
• Notes: Specific scope because the Doryx MPC vs Doryx tablet vs Doryx capsule contrast is tied to the doxycycline drug-product-version comparison in Hopkins 2017 (Mayne Pharma International’s modified-acid-resistance MP336 formulation vs the marketed Doryx delayed-release tablet vs the conventional-release Doryx capsule). Paired with FORM_CAPSULE (the Doryx capsule indicator) so that both indicators = 0 selects the Doryx-tablet reference; the two indicators are mutually exclusive at any given dose record. Mirrors the existing FORM_SAR_DP2 (sarilumab), FORM_ISA_P2F2 (isatuximab), FORM_LINAG_TAB1 (linagliptin), FORM_VISMO_PHASEI (vismodegib), FORM_TAC_IR (tacrolimus IR vs PR), and FORM_THEO_APNECUT (theophylline) entries under the FORM_* family of drug-product-version indicators. Set to 0 for routine Doryx-tablet simulation, 1 for Doryx MPC simulation. Ratified canonically on 2026-06-02 alongside the Hopkins 2017 doxycycline extraction.

FORM_DOX_FEED (canonical for doxycycline-in-medicated-feed vs doxycycline-in-aqueous-solution indicator)

• Description: 1 = the oral doxycycline dose was administered mixed into medicated feed (meal or pelleted ration); 0 = the oral dose was administered as an aqueous solution, either taken spontaneously in drinking water or delivered by gastric tube. Per-dose-record indicator used in veterinary oral-doxycycline population analyses that pool in-feed and in-water medication, which differ substantially in both absorption rate and absolute bioavailability.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (doxycycline given as an aqueous solution; in Toutain 2025 this reference splits further into drinking water vs stomach tube via ROUTE_NGT).
• Source aliases:
  • “Route of administration = oral feed” – Toutain 2025 Table 1 route column, distinguishing the six in-feed trials (AFSSA, BIOEQ, PARADOX, TLS, Company 9203, Company 9204) from the five aqueous-solution trials (104NL, 3205NL, GHENT, KING_NL, Bea).
• Example models: Toutain_2025_doxycycline_pig.R (selects between the two in-feed absorption sub-models and the two aqueous-solution sub-models of the pig meta-analysis; combined with STUDY_TLS it picks Ka = 0.072 / F = 0.501 for feed under field conditions or Ka = 0.144 / F = 0.340 for feed under laboratory conditions, and combined with ROUTE_NGT it picks Ka = 0.689 / F = 0.307 for drinking water or Ka = 0.725 / F = 0.258 for stomach tube, each stratum also carrying its own additive-plus-proportional residual error; Toutain 2025 Table 6).
• Notes: Well-formed member of the auto-approved FORM_<drug>_<formulation> family, using the same DOX drug token as the existing FORM_DOX_DORYX_MPC entry. Distinct from a food-effect covariate: this is not “drug taken with a meal” but “drug incorporated into the ration”, which in production animals also changes the dosing process (group access to a medicated meal, competition between animals) and is the reason Toutain 2025 reports a between-subject variability on bioavailability of 84.8% in feed versus 34.3% in drinking water. Ignored when ROUTE_IV = 1. Ratified canonically alongside the Toutain 2025 pig doxycycline extraction.

FORM_RAL_600 (canonical for raltegravir 600 mg vs 400 mg tablet formulation indicator)

• Description: 1 = subject received the raltegravir 600 mg film-coated tablet formulation (Isentress HD; the 1200 mg once-daily reformulation approved 2017); 0 = subject received the raltegravir 400 mg film-coated tablet formulation (Isentress; the original 400 mg twice-daily formulation approved 2007). Per-dose-record indicator used in raltegravir popPK analyses that pool the two tablet strengths to test formulation-specific effects on bioavailability, inter-occasion variability magnitude, or food-effect susceptibility. The 600 mg tablet is a “reformulated” polymer-based product with faster disintegration / dissolution than the original 400 mg tablet at physiological gastric pH.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (400 mg tablet reference; typical-value F, IOV F, and food-effect coefficient reported for the original 400 mg formulation in Bukkems 2021 Table 2).
• Source aliases:
  • NEW – used in Bukkems_2021_raltegravir.R (NONMEM covariate flag; source paper Bukkems 2021 Supporting Information S4 $PK section: IF (NEW.EQ.1) selects the 600 mg formulation multipliers on F and IOV F).
• Example models: Bukkems_2021_raltegravir.R (two multiplicative effects per Bukkems 2021 Table 2: linear additive effect on typical bioavailability F *= (1 + e_ral_600_fdepot * FORM_RAL_600) with e_ral_600_fdepot = 0.209 – 600 mg tablet has +21% bioavailability vs 400 mg reference; and multiplicative effect on the IOV F magnitude SD_iov_f *= (1 + e_ral_600_iov_fdepot * FORM_RAL_600) with e_ral_600_iov_fdepot = -0.718 – 600 mg tablet has 72% smaller inter-occasion variability magnitude on F than the 400 mg reference).
• Notes: Specific scope because the raltegravir 600 mg vs 400 mg contrast is tied to the two-tablet-strength Merck reformulation program (Isentress HD / SWITCHmrk regulatory package). Mirrors the existing FORM_DOX_DORYX_MPC (doxycycline Doryx MPC vs Doryx tablet), FORM_ITR_SUBA (itraconazole SUBA vs Sporanox), FORM_TAC_IR (tacrolimus Prograf IR vs Advagraf PR), FORM_SAR_DP2 (sarilumab DP2), FORM_ISA_P2F2 (isatuximab P2F2), FORM_LINAG_TAB1 (linagliptin), and FORM_VISMO_PHASEI (vismodegib Phase I) entries under the FORM_<drug>_<formulation> family of drug-product-version indicators. Set to 0 for routine 400 mg BID simulation, 1 for 600 mg (typically dosed as two 600 mg tablets = 1200 mg QD) simulation. Ratified canonically alongside the Bukkems 2021 raltegravir extraction.

FORM_NMV_TAB150 (canonical for nirmatrelvir 150-mg tablet formulation indicator)

• Description: 1 = the nirmatrelvir dose was given as the 150-mg film-coated tablet (the commercial PAXLOVID tablet strength used for the bulk of the phase II/III EPIC-HR population); 0 = the nirmatrelvir dose was given as the oral suspension or as the 100-mg tablet. Per-dose-record indicator used in nirmatrelvir popPK analyses that pool the three clinical formulations and estimate a relative-bioavailability offset for the 150-mg tablet only.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (oral suspension or 100-mg tablet; both share the F1 = 1 structural anchor because the source estimates an effect only for the 150-mg level of the three-level formulation column).
• Source aliases:
  • FFORM (three-level formulation column; 0 = suspension, 1 = 100-mg tablet, 2 = 150-mg tablet) – used in Chan_2023_nirmatrelvir.R; the 150-mg-tablet level maps to FORM_NMV_TAB150 = 1 (Chan 2023 Data S1 $PK: F1FFORM = 1; IF(FFORM.EQ.2) F1FFORM = 1+THETA(15)).
• Example models: Chan_2023_nirmatrelvir.R (linear fractional effect on relative bioavailability F1 *= (1 + e_form_nmv_tab150_fdepot * FORM_NMV_TAB150) with e_form_nmv_tab150_fdepot = -0.379, i.e. the 150-mg tablet carries 37.9% lower relative bioavailability than the suspension / 100-mg-tablet reference, applied on top of the dose power function (DOSE/300)^-0.409).
• Notes: Specific scope because the reference category pools two distinct products (suspension and 100-mg tablet) rather than contrasting a single pair, which is a consequence of Chan 2023 estimating an effect only for the FFORM = 2 level. Sibling of FORM_RAL_600 (raltegravir 600 mg vs 400 mg tablet), FORM_LINAG_TAB1, FORM_TAC_IR, FORM_ITR_SUBA and the rest of the FORM_<drug>_<formulation> family of drug-product-version indicators. Chan 2023 Discussion notes that the 150-mg tablet was evaluated only among healthy participants in a single single-dose study, so the estimated formulation effect on F1 is likely partially confounded with the COVID-19 effect on CL (DIS_COVID19); set both to their reference values when simulating the phase I suspension / 100-mg-tablet condition. A separate phase I study (NCT05263895) evaluated relative bioequivalence of the two tablet strengths but did not contribute to this analysis. Ratified canonically alongside the Chan 2023 nirmatrelvir extraction.

FORM_ITR_SUBA (canonical for SUBA-itraconazole vs Sporanox capsule formulation indicator)

• Description: 1 = subject received the SUBA-itraconazole formulation (a solid dispersion of itraconazole in a pH-dependent polymeric matrix that enhances dissolution and targets drug release in the proximal small intestine; marketed as Lozanoc in Australia, Itragerm in Spain); 0 = subject received the Sporanox capsule (Janssen Pharmaceuticals innovator product). Per-dose-record categorical indicator used in itraconazole bioequivalence-comparison popPK analyses.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (Sporanox capsule; relative bioavailability F = 1 and FVAR-variability scaling ETASCALE = 1 as the structural references in Abuhelwa 2015 Table 3).
• Source aliases:
  • FORM_SUBA – prior canonical name (pre-2026-06-19 standardization audit).
  • DRUG – used in Abuhelwa_2015_itraconazole.R (Appendix S1 NONMEM control stream: IF (DRUG.EQ.0) THEN DRUGF = 1 (Sporanox) ELSE DRUGF = (1 + THETA(8)) (SUBA-itraconazole)).
• Example models: Abuhelwa_2015_itraconazole.R (multiplicative effects per Abuhelwa 2015 Table 3: SUBA-itraconazole has +73% relative bioavailability vs Sporanox (F = 1 + 0.729 x FORM_ITR_SUBA) and 21.3% less F variability (ETASCALE = 1 + (-0.213) x FORM_ITR_SUBA = 0.787 scales the shared FVAR random effect)).
• Notes: Specific scope because the SUBA-itraconazole vs Sporanox capsule contrast is tied to the Mayne Pharma bioequivalence-development programme. Mirrors the FORM_SAR_DP2 (sarilumab), FORM_ISA_P2F2 (isatuximab), FORM_LINAG_TAB1 (linagliptin), and FORM_VISMO_PHASEI (vismodegib) entries under the FORM_* family of drug-product-version / formulation-comparison indicators. Distinct from FORM_CAPSULE (Hennig 2006 / 2007 capsule-vs-solution contrast for the same itraconazole molecule) because here both arms are capsule formulations and the contrast is between two capsule drug products. Ratified canonically alongside the Abuhelwa 2015 itraconazole extraction.

FORM_TAC_IR (canonical for tacrolimus immediate-release vs prolonged-release formulation indicator)

• Description: 1 = subject received the twice-daily immediate-release tacrolimus formulation (Prograf, Astellas) administered every 12 hours; 0 = subject received the once-daily prolonged-release tacrolimus formulation (Advagraf in Europe, Astagraf XL in the US; both Astellas) administered every 24 hours. Per-subject (regimen-fixed; per-occasion in cross-over conversion studies) categorical indicator used in popPK analyses that pool the two oral tacrolimus formulations and test formulation as a covariate on absorption and disposition parameters.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (Advagraf / Astagraf XL prolonged-release; the typical-value reference for Ktr and Vc/F in Woillard 2011 Table 4).
• Source aliases:
  • study – used in Woillard_2011_tacrolimus.R (Woillard 2011 Methods: “study factor (assumed to be similar to drug formulation) … study = 1 for the Prograf cohort, study = 0 for the Advagraf cohort”). The source paper’s study and formulation factors are aliased; the canonical preserves the source paper’s orientation (Prograf = 1 = IR, Advagraf = 0 = PR), which also matches the standard convention of treating the older immediate-release formulation as the non-reference level.
  • FORMULATION (with 1 = PR-T, opposite polarity) – used in Lu_2019_tacrolimus_industry_meta.R. Lu 2019 writes the Ka covariate equation as Ka = 0.375 * (1 - (1 - theta_form) * FORMULATION) with FORMULATION = 1 for prolonged-release; the model derives form_pr = 1 - FORM_TAC_IR inside model() to match the paper’s published equation while keeping the canonical column oriented Prograf = 1.
• Example models: Woillard_2011_tacrolimus.R (multiplicative effects per Woillard 2011 Table 4: Ktr = theta1 * theta2^FORM_TAC_IR with theta1 = 3.34/h and theta2 = 1.53 (Prograf ~53 % faster absorption than Advagraf); Vc/F = theta6 * theta7^FORM_TAC_IR with theta6 = 486 L and theta7 = 0.29 (Prograf Vc/F is 29 % of the Advagraf reference)), Lu_2019_tacrolimus_industry_meta.R (linear effect on Ka per Lu 2019 Table 3 ‘Prolonged-release tacrolimus on Ka’ = 0.499, encoded as Ka(PR-T) = Ka(IR-T) * 0.499 via form_pr = 1 - FORM_TAC_IR; 50 % slower absorption for PR-T vs IR-T).
• Notes: Scope promoted to general after Lu 2019 corroborated the Prograf-vs-Advagraf contrast in a second, larger-cohort population (Woillard 2011 n = 173 + 174, Lu 2019 n = 408 across 8 Astellas Phase II studies). Mirrors the existing FORM_SAR_DP2 (sarilumab), FORM_ISA_P2F2 (isatuximab), FORM_LINAG_TAB1 (linagliptin), and FORM_VISMO_PHASEI (vismodegib) entries under the FORM_* family of drug-product-version indicators. The Woillard 2011 paper notes the formulation effect partially confounds with time-post-transplant (Prograf cohort sampled within the first 6 months post-transplant, Advagraf cohort > 12 months post-transplant); Lu 2019 has no such confounding because most studies are within-subject IR-T-to-PR-T conversions. Future tacrolimus models that include Envarsus XR (modified-release once-daily granules) or LCP-Tacro (life-cycle-pharma melt-extrusion tablets) should register a sibling canonical (e.g., FORM_TAC_ENVARSUS) rather than overloading FORM_TAC_IR. Ratified canonically alongside the Woillard 2011 tacrolimus extraction.

FORM_TAC_ENVARSUS (canonical for the LCP-Tac / Envarsus MeltDose extended-release tacrolimus formulation indicator)

• Description: 1 = subject (or occasion) received once-daily LCP-Tac, the life-cycle-pharma MeltDose melt-extrusion extended-release tacrolimus tablet marketed as Envarsus / Envarsus XR (Chiesi Farmaceutici in Europe, Veloxis in the US); 0 = subject received a non-LCP-Tac oral tacrolimus product, in practice the twice-daily immediate-release formulation IR-Tac (Prograf, Astellas). Per-subject in parallel-group designs and per-occasion in the within-patient conversion designs that dominate this literature. MeltDose technology raises oral bioavailability and spreads release along the whole gastrointestinal tract to the colon, which is why LCP-Tac needs a lower daily dose than IR-Tac for equal exposure.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (IR-Tac immediate-release Prograf; the F = 1 reference arm is selected jointly with CYP3A5_EXPR in Mohammed Ali 2025, where the LCP-Tac nonexpresser group is the anchor).
• Source aliases:
  • formulation – used in MohammedAli_2025_tacrolimus.R (Mohammed Ali 2025 Methods section 2.2; patients were converted from Prograf to Envarsus, giving one IR-Tac and one LCP-Tac 24 h profile each).
• Example models: MohammedAli_2025_tacrolimus.R (switches three quantities at once: the first-order absorption rate constant (ka 0.111/h on LCP-Tac vs a 2.04/h mesor on IR-Tac), the absorption lag time (1.4 h vs 0.465 h), and the relative bioavailability F crossed with CYP3A5_EXPR (Mohammed Ali 2025 Table 3: F = 1 FIX for LCP-Tac nonexpressers, 0.745 for IR-Tac nonexpressers, 0.693 for LCP-Tac expressers, 0.427 for IR-Tac expressers). The circadian rhythm on the absorption rate constant is IR-Tac-specific and is gated by 1 - FORM_TAC_ENVARSUS).
• Notes: Registered as the sibling canonical that the FORM_TAC_IR Notes field explicitly directs Envarsus / LCP-Tacro extractions to create rather than overloading FORM_TAC_IR. The distinction is load-bearing and not cosmetic: FORM_TAC_IR = 0 means the Advagraf / Astagraf XL prolonged-release capsule, which is a different once-daily product from LCP-Tac with different absorption characteristics and a different conversion ratio from IR-Tac (1:1 for Advagraf versus 1:0.7 for Envarsus in the European guidelines), so a dataset that pooled Advagraf and Envarsus into one “prolonged-release” level would confound two distinct products. A study covering all three oral products encodes them with the two columns jointly: FORM_TAC_IR = 1 selects Prograf, FORM_TAC_ENVARSUS = 1 selects Envarsus, and both = 0 selects Advagraf / Astagraf XL. Mirrors the FORM_* family of drug-product-version indicators (FORM_TAC_IR, FORM_VINP_IR, FORM_SAR_DP2, FORM_ISA_P2F2, FORM_LINAG_TAB1, FORM_VISMO_PHASEI). Scoped general because the IR-Tac-to-LCP-Tac conversion is a routine clinical manoeuvre in solid-organ transplantation and the contrast recurs across the tacrolimus popPK literature (ASERTAA, and this group’s own earlier LCP-Tac analyses). Frequently interacts with CYP3A5_EXPR: Mohammed Ali 2025 found the bioavailability gain on conversion to be genotype-dependent, which is the basis for its genotype-specific conversion ratios (1:0.6 in CYP3A5*1 expressers, 1:0.7 in nonexpressers). Ratified canonically alongside the Mohammed Ali 2025 tacrolimus extraction.

FORM_VINP_IR (canonical for vinpocetine immediate-release Cavinton tablet formulation indicator)

• Description: 1 = subject received the Cavinton immediate-release 5 mg vinpocetine tablet (Organon / Gedeon Richter Ltd., Budapest, Hungary; the reference immediate-release solid dosage form of vinpocetine); 0 = subject received the Ultra Vinca sustained-release beta-cyclodextrin-complex 10 mg vinpocetine tablet (Tecnimede, Portugal; the Petric 2023 reference formulation). Per-dose-occasion indicator (used in the Petric 2023 crossover design where each subject received all three formulations across separate occasions with a 7-day washout).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (Ultra Vinca sustained-release beta-cyclodextrin complex tablet; the typical-value Tk0 and V1/F reference in Petric 2023 Table 1).
• Source aliases:
  • Formulation#2 – the second level of the three-level Formulation categorical covariate in Petric 2023 Table 1; maps directly to FORM_VINP_IR = 1 with Formulation#1 (Ultra Vinca SR) as the reference.
• Example models: Petric_2023_vinpocetine.R (log-additive effect on Tk0 (exp(beta = -0.4) = 33% shorter zero-order absorption duration relative to the Ultra Vinca SR reference) and on V1/F (exp(beta = -1.26) = 72% lower apparent central volume relative to the SR reference, consistent with a higher vinpocetine oral bioavailability / metabolite yield for the Cavinton IR arm; Petric 2023 Table 1 beta_Tk0_Formulation#2 = -0.4 (RSE 29.0%) and beta_V1/F_Formulation#2 = -1.26 (RSE 5.44%))…….
• Notes: Scoped specific because the Cavinton-IR-vs-Ultra-Vinca-SR contrast is tied to the Petric 2023 relative-bioavailability crossover design (a three-level formulation stratification of vinpocetine as its main active metabolite apovincaminic acid). Paired with the general FORM_SOLUTION canonical (both = 0 selects the Ultra Vinca SR reference; FORM_SOLUTION = 1 selects the 10 mg / 5 mL oral solution) to encode the full three-level factor. Sibling to the other drug-specific FORM_*_IR / FORM_*_<formulation> entries (FORM_TAC_IR, FORM_LINAG_TAB1, FORM_SAR_DP2, FORM_ISA_P2F2, FORM_VISMO_PHASEI) under the FORM_* family of drug-product-version indicators. Future vinpocetine-formulation extractions comparing other vinpocetine products against Ultra Vinca SR (or Cavinton IR) should reuse this canonical and extend the example list; future SR-vs-IR contrasts for unrelated drugs should register their own drug-specific sibling canonical rather than overloading this entry. Ratified canonically alongside the Petric 2023 vinpocetine extraction.

FORM_XYNTHA (canonical for Xyntha vs Refacto / Refacto AF moroctocog-alfa product indicator)

• Description: 1 = Xyntha (Wyeth Pharmaceuticals / Pfizer; OSA-calibrated potency label, marketed in the United States, Canada, and other regions); 0 = Refacto or Refacto AF (CSA-calibrated potency label, marketed in the European Union and other regions). Per-observation (per-row) binary indicator. Moroctocog alfa is the same B-domain-deleted recombinant factor VIII active moiety in all three products; Xyntha and Refacto AF are the same drug product with different potency-assay calibrations (OSA vs CSA) for label dosing. Used as a multiplicative effect on bioavailability F to harmonise the model-predicted FVIII activity across products.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (Refacto or Refacto AF; CSA-calibrated potency label).
• Source aliases:
  • PROD – used in Abrantes_2017_moroctocog.R (Abrantes 2017 Table 2 footnote g and Methods: F multiplier 1.38^PROD so Xyntha effectively delivers 1.38x the labelled dose in CSA-reference units).
• Example models: Abrantes_2017_moroctocog.R (multiplicative effect on F: (1 + 0.38 * FORM_XYNTHA) – equivalent to 1.38^FORM_XYNTHA when binary; the model anchors F = 1 for the CSA-reference Refacto/Refacto AF products and applies the +38.0% multiplier when the product administered is Xyntha).
• Notes: Specific scope because the contrast is tied to the Pfizer / Wyeth moroctocog-alfa product line (Refacto, Refacto AF, and Xyntha). Mirrors the FORM_<drug>_<variant> family pattern (FORM_LEB_NS0 lebrikizumab cell-line, FORM_SAR_DP2 sarilumab drug-product version, FORM_DOX_DORYX_MPC doxycycline Doryx-MPC). Per-observation (per-row) when a subject could switch between products across trials (e.g., Abrantes 2017 study B1831066 enrolled both Refacto and Refacto AF in the same protocol); subject-level otherwise. The 1.38 OSA-vs-CSA potency ratio for B-domain-deleted FVIII is documented in Mikaelsson 1998 and Hubbard 2013 (cited in Abrantes 2017 reference 13). Ratified canonically on 2026-06-21 alongside the Abrantes 2017 moroctocog extraction.

FORM_FVIII_BDD (canonical for B-domain-deleted (vs full-length) recombinant factor VIII product indicator)

• Description: 1 = the administered factor VIII (FVIII) concentrate is a B-domain-deleted recombinant product (e.g., Refacto, Refacto AF, Xyntha – moroctocog alfa); 0 = the administered product is a full-length recombinant FVIII (e.g., Kogenate FS, Helixate FS, Advate, Recombinate) or a plasma-derived FVIII (e.g., Aafact, Hemofil M). Per-observation (per-row) binary indicator. B-domain-deleted recombinant FVIII proteins retain the FVIII coagulation activity but omit the heavily glycosylated central B-domain; the one-stage clotting assay (OSA) commonly used clinically under-detects B-domain-deleted FVIII activity in plasma by a well-documented ~30-50 % relative to full-length FVIII (mechanism: sub-optimal thrombin-mediated activation kinetics in the OSA reagent milieu), so PK models that pool observations across BDD and full-length products commonly include this indicator as a multiplicative correction on the predicted plasma concentration to reconcile the assay artefact. Note: Xyntha vs Refacto/Refacto AF is a separate potency-calibration contrast within the BDD product family (see FORM_XYNTHA); the two canonicals are independent and can coexist in a single dataset.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (full-length recombinant or plasma-derived FVIII product).
• Source aliases:
  • bdp (paper-prose 0/1 indicator, 1 = B-domain-deleted product) – used in Hazendonk_2016_factor_viii.R (Hazendonk 2016 Methods: C_pred,bdp = C_pred * (1 - theta_bdp) with theta_bdp = 0.34 = 34 % assay-driven under-prediction of the B-domain-deleted product Refacto AF).
• Example models: Hazendonk_2016_factor_viii.R (multiplicative effect on the predicted FVIII plasma concentration: Cc = (central / vc) * (1 - theta_bdp * FORM_FVIII_BDD); reference category 0 = full-length recombinant or plasma-derived FVIII).
• Notes: General scope because the OSA vs CSA assay artefact for B-domain-deleted FVIII is a class-level phenomenon that recurs across BDD-product FVIII popPK models. Distinct from FORM_XYNTHA (which contrasts Xyntha vs Refacto / Refacto AF within the BDD moroctocog product family, capturing the +38 % OSA-vs-CSA potency calibration difference; a subject may have FORM_FVIII_BDD = 1 and FORM_XYNTHA = 0 or 1 depending on which Pfizer moroctocog product they received). Distinct also from ASSAY_OSA (which is the bioanalytical-method indicator: OSA vs CSA); when both FORM_FVIII_BDD and ASSAY_OSA are available, the correct interaction structure is FORM_FVIII_BDD * ASSAY_OSA (only OSA-assayed BDD-product samples show the under-detection), but many perioperative datasets are OSA-only and collapse the correction onto FORM_FVIII_BDD alone (Hazendonk 2016). Per-observation (per-row) when subjects can switch between products across surgical procedures; subject-level otherwise. Mirrors the FORM_<drug>_<variant> family pattern. Ratified canonically on 2026-07-11 alongside the Hazendonk 2016 perioperative FVIII extraction.

dilution (canonical for diluted-drug-product indicator)

• Description: 1 = drug diluted (Soehoel 2022 study D2213C00001), 0 = not diluted.
• Units: (binary)
• Type: binary
• Scope: specific
• Example models: Soehoel_2022_tralokinumab.R.
• Notes: Lower-case preserved from source; future models should rename to DILUTION. Kept as alias here to match existing file.

nonECZTRA (canonical for non-ECZTRA-trial indicator)

• Description: 1 = not the ECZTRA trial; 0 = ECZTRA.
• Units: (binary)
• Type: binary
• Scope: specific
• Example models: Soehoel_2022_tralokinumab.R.
• Notes: Mixed case preserved from source; future models should rename to NON_ECZTRA or STUDY_NON_ECZTRA.

SEASON2 (canonical for second RSV season at dosing indicator)

• Description: 1 = second RSV season at dosing, 0 = first RSV season.
• Units: (binary)
• Type: binary
• Scope: specific
• Example models: Clegg_2024_nirsevimab.R.
• Notes: Study-specific but semantically general (second-exposure indicator). Promote to general if a second RSV-season model adopts the same semantics.

COHDOSE (canonical for randomized dose cohort (mg/kg))

• Description: Randomized dose cohort expressed in mg/kg. Subject-level (time-fixed) covariate carrying the per-subject cohort dose in a study where each subject remained on a single escalating-cohort dose for the full dosing period.
• Units: mg/kg
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (COHDOSE / ref)^exponent. Reference values observed: 1 mg/kg in Narwal 2013 (human IgG1), 3 mg/kg in Niloy 2026 (preclinical mouse small molecule).
• Source aliases:
  • DOSE – used in Narwal_2013_sifalimumab.R (the paper’s Eq. 3 variable name; renamed to COHDOSE here to avoid colliding with the rxode2/nlmixr2 event-column convention where DOSE or AMT carries the administered dose).
  • Dose_i – used in Niloy_2026_MTMSATrp_mouse.R (Niloy 2026 Methods Eq. 2 notation; the per-mouse mg/kg cohort dose).
• Example models: Narwal_2013_sifalimumab.R (reference 1 mg/kg, exponent 0.0542 on CL), Niloy_2026_MTMSATrp_mouse.R (preclinical mouse; reference 3 mg/kg, exponent -0.30 on CL describing empirical dose-dependent CL over 0.3-10 mg/kg).
• Notes: Scope promoted from specific to general on 2026-06-22 with the Niloy 2026 MTMSA-Trp extraction (second model ratifying the canonical, this time as a preclinical mouse small-molecule dose-cohort covariate with a negative exponent on CL describing decreasing apparent clearance with increasing dose – complementing Narwal 2013’s positive exponent in a Phase Ib human mAb context). The shared functional form (COHDOSE / ref)^exponent on apparent clearance and the shared escalating-cohort design (each subject on one dose for the full study) carry across drug class, species, and study size. For fixed-dose simulations in a weight-based-dosing paper, set COHDOSE = nominal_dose_mg / WT per subject. When the subject receives a per-kg dose label directly (preclinical mg/kg dosing or human mg/kg cohort labels like the MI-CP152 0.3 / 1 / 3 / 10 mg/kg arms), COHDOSE is the mg/kg label.

DOSE (canonical for current administered dose level supplied as a data column)

• Description: Continuous covariate carrying the administered dose level (in mg) as a per-record data column. Two complementary use cases share this canonical:
  1. Per-subject assigned dose – each subject’s fixed assigned dose level (in mg) across the study, used as a power-style or stratified covariate when the population PK model detects a dose-dependent shift in a PK parameter (central volume, clearance, etc.).
  2. Time-varying current administered dose – the current daily dose at the time of the record, used in PD-only models that derive a per-cycle exposure metric (e.g., AUC = DOSE / CLI) from a posthoc-CL covariate without instantiating a PK ODE. The column is set to 0 during off-treatment periods (drug holidays, placebo arm) so the derived exposure becomes 0.
• Units: mg (document per-model in covariateData[[DOSE]]$units if a different dose unit is used).
• Type: continuous
• Reference category: n/a – used with power scaling (DOSE / ref)^exponent for use case (a), or directly inside derived-exposure expressions for use case (b). Reference values observed: 600 mg in Zheng_2016_sifalimumab.R (middle of the 200/600/1200 mg phase IIb dose range).
• Source aliases:
  • Dose – used in Zheng_2016_sifalimumab.R and Castro-Surez_2020_nimotuzumab.R.
  • DOS – used in the Hansson 2013 sunitinib biomarker / TGI / fatigue PD-model family (DDMODEL00000197 and siblings) as a per-record sunitinib dose column.
• Example models: Zheng_2016_sifalimumab.R (power effect on V1 with exponent 0.06), Castro-Surez_2020_nimotuzumab.R (binary-indicator usage (DOSE == 50) applying a 53 % decrease in V1 for the 50 mg cohort), Hansson_2013a_sunitinib.R (DDMODEL00000197; time-varying record-level dose feeding AUC = DOSE / CLI), Hansson_2013b_sunitinib.R (DDMODEL00000198; same time-varying AUC = DOSE / CLI form for the tumor growth inhibition model), Schindler_2016_sunitinib.R (DDMODEL00000221; same AUC = DOSE / CLI form, with the daily-dose column toggling between 50 mg/day on-cycle and 0 on dose-holiday records), Schindler_2017_imatinib.R (time-varying daily imatinib dose in mg/day feeding the size and density drug-effect terms as Kdrug,S * (DOSE / 400) * exp(-k * t) and Kdrug,D * (DOSE / 400), with 400 mg/day as the reference normalisation), Girard_2012_pimasertib.R (linear coefficient on the dropout-hazard log-rate: exp(beta * DOSE) Weibull multiplier; per-subject daily dose, observed range 1-255 mg), Wada_2023_sparsentan.R (use case (a): per-record administered dose drives a dose-dependent relative bioavailability Frel = (max(DOSE, 200) / 400)^-0.495 applied as f(depot), giving Frel = 1.41 / 1.00 / 0.71 at 200 / 400 / 800 mg – less-than-dose-proportional exposure, with the 200 mg clamp reproducing the paper’s published lower branch), Comisar_2025_rimegepant.R (use case (a) with BOTH a continuous and a categorical use of the same column in one model: a power term on relative bioavailability f(depot) <- (DOSE/10)^0.191 * ... reproducing greater-than-dose-proportional exposure over 10-150 mg attributed to dose-dependent autoinhibition of CYP3A first-pass metabolism, plus a categorical low-dose indicator on the transit rate constant, ktr * (1 + 0.536 * (DOSE < 50)), which reproduces the control stream’s IF(DOSE.EQ.10) / IF(DOSE.EQ.25) KTRDOSE = (1 + THETA(29)) exactly over the four dose levels studied (10, 25, 75, 150 mg) with 75 mg as the reference).
• Notes: Distinct from DOSE_70MG (binary indicator for a specific dose group in a trinary-dose design) and from the rxode2/nlmixr2 event column amt (which carries the administered dose at dose events). For use case (a), the values are typically time-fixed per subject; for use case (b), the values are time-varying with on/off cycling – for sunitinib 4-weeks-on / 2-weeks-off cycling, set DOSE = nominal_daily_mg (e.g., 50) during on-cycles and 0 during off-cycles or for the placebo arm. Per-model covariateData[[DOSE]]$notes should state which use case applies.

CD (canonical for cumulative cladribine dose (time-varying))

• Description: Time-varying cumulative cladribine dose (mg total dose, not body-weight-normalized) administered to each subject up to the current observation time. Stays at zero during the placebo arm and during the pre-dose baseline phase, rises stepwise across the dosing schedule (cladribine is given as short oral pulses), and remains constant between dose events.
• Units: mg
• Type: continuous
• Scope: specific
• Reference category: n/a – used inside EXPS = CD * 104.5 / CRL as an exposure surrogate driving an Emax-style symptomatic effect on disease progression, not as a power-form covariate.
• Source aliases: CD – column name used in the DDMODEL00000223 input dataset (Simulated_Novakovic_2016_multiplesclerosis_cladribine_irt.csv).
• Example models: Novakovic_2017_cladribine.R.
• Notes: Distinct from DOSE (per-subject assigned dose level, time-fixed) and COHDOSE (mg/kg cohort label, time-fixed). CD is the cumulative dose accrued at each timepoint, supplied as a time-varying covariate column rather than via dosing events because the Novakovic 2017 model does not carry an explicit cladribine-PK compartment. Scope: specific because the constant 104.5 inside the exposure-surrogate equation is hard-coded for cladribine in the source.

TRT (canonical for treatment-cohort indicator)

• Description: Per-subject treatment-cohort / treatment-arm integer indicator. Carries the categorical assignment that selects per-arm structural parameters (e.g., per-arm typical tumor-growth rate, per-arm dose-response slope, on-treatment vs placebo gating). Value coding is per-model: each consuming model defines its own integer levels via covariateData[[TRT]]$notes, because the same TRT column header carries different cohort semantics in different studies.
• Units: (categorical / integer-coded)
• Type: categorical
• Scope: general
• Reference category: per-model (each consuming model declares its own reference level in covariateData[[TRT]]$notes).
• Source aliases: TRT, ARM, TRTARM, STUDY_ARM – common column-name variants for the per-subject treatment-arm integer indicator; all map directly to canonical TRT with no value transformation. Per-model integer coding is documented inline.
• Example models: Novakovic_2017_cladribine.R.
  • Novakovic_2017_cladribine.R (DDMODEL00000223): 0 = placebo, 1 = cladribine 3.5 mg/kg cumulative-dose cohort, 2 = cladribine 5.25 mg/kg cumulative-dose cohort. Reference = 0 (placebo). Gates the symptomatic and protective drug-effect terms via TRT >= 1 && t > 0; the categorical level (1 vs 2) is informational because the dose-response is driven by the time-varying CD covariate and the per-cohort dosing schedule, not by TRT itself.
  • Struemper_2025_tumorsize_OS_nsclc.R: 1 = PEMBRO (pembrolizumab, INTR@PID LUNG 037, n = 152), 2 = FELAD (feladilimab, INDUCE-1, n = 52), 3 = CHEMO (docetaxel, Entree Lung Part 2, n = 34), 4 = FELAD+CHEMO (pooled INDUCE-1 + Entree Lung Part 2, n = 78), 5 = IO-COMBO (feladilimab + IO; pooled INDUCE-1 + INDUCE-2, n = 23), 6 = DOSTAR (dostarlimab, GARNET, n = 67), 7 = DOSTAR+CHEMO (PERLA, n = 121), 8 = PEMBRO+CHEMO (PERLA, n = 122), 9 = COBO100MG+DOSTAR cohort B (AMBER, n = 14), 10 = COBO300MG+DOSTAR cohort B (AMBER, n = 41), 11 = COBO900MG+DOSTAR cohort B (AMBER, n = 29), 12 = COBO300MG+DOSTAR cohort D (AMBER, n = 53). Reference = 1 (PEMBRO; the largest single-agent PD-1 cohort and the model’s canonical default). Selects per-arm TVKG and TVKS via a 12-branch if-else cascade and gates the PDL1_TUM effect on ks via a derived has_pd1 indicator equal to 1 when TRT is 1 or in {6, 7, 8, 9, 10, 11, 12}.
• Notes: Two models now use the TRT canonical with different integer codings: Novakovic 2017 (3 levels, placebo reference) and Struemper 2025 (12 levels, pembrolizumab reference). The column header is shared because both source NONMEM control streams use TRT; the integer-to-cohort mapping is intentionally per-model. Scope upgraded from specific to general on 2026-06-22 when Struemper 2025 added a second consuming model. Future models adding new TRT codings extend the Example-models list rather than registering a new canonical, so long as the column header is TRT; a model that uses a fundamentally different semantics (e.g., a generic on-treatment 0/1 flag distinct from per-arm cohort indexing) should register a new canonical name (e.g., ON_TREATMENT) rather than reusing TRT.

DRUG_ORMU (canonical for Ormutivimab vs HRIG drug-product comparator indicator)

• Description: 1 = subject received Ormutivimab (a recombinant human anti-rabies IgG1 monoclonal antibody, also referred to in the source paper as rHRIG); 0 = subject received plasma-derived human rabies immunoglobulin (HRIG) comparator (or placebo + vaccine without passive antibody). Per-subject (time-fixed) categorical indicator carrying the head-to-head drug-arm assignment in a randomized rabies-vaccine pharmacodynamics study where rabies virus neutralizing antibody (RVNA) activity is modelled with a time-dependent Emax response to the vaccine and the passive antibody product modifies the typical-value Emax / ET50.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (HRIG / no passive antibody).
• Source aliases:
  • antibody type – Zhang 2022 Results section 3.4 covariate-effect prose (“antibody type was determined as the covariate significantly affecting the model”); the source NMTRAN column name is not separately reported.
• Example models: Zhang_2022_ormutivimab.R (additive typical-value shifts on the linear-scale Emax and ET50 of the vaccine-induced RVNA Emax model: Emax_tv = exp(lEmax) + e_drug_ormu_Emax * DRUG_ORMU with e_drug_ormu_Emax = +0.143 IU/mL and ET50_tv = exp(lET50) + e_drug_ormu_ET50 * DRUG_ORMU with e_drug_ormu_ET50 = -3.8 day, yielding a higher and faster vaccine-induced antibody peak in the Ormutivimab arms relative to the HRIG comparator).
• Notes: Distinct from the FORM_* family (within-product formulation-version contrasts of a single drug) because the contrast here is between two biologically distinct products: HRIG is a polyclonal plasma-derived immunoglobulin, while Ormutivimab is a recombinant monoclonal antibody (CHO-cell-produced; the first rhRIG approved in China). Specific scope because the head-to-head HRIG-vs-rHRIG comparator design is tied to the Zhang 2022 phase II rabies-vaccine study. Future head-to-head rhRIG-vs-HRIG popPD models (e.g., SII Rabishield or Twinrab against HRIG) should register a sibling canonical (DRUG_SIIRMAB, DRUG_TWINRAB) rather than overloading DRUG_ORMU; cross-product comparisons that need both indicators in the same dataset can carry them as independent binaries with HRIG as the shared reference. Ratified canonically alongside the Zhang 2022 ormutivimab extraction.

DRUG_PRED (canonical for prednisone vs fosdagrocorat drug-arm comparator indicator)

• Description: 1 = subject is in the prednisone (oral glucocorticoid comparator) arm; 0 = subject is in the fosdagrocorat (PF-04171327, dissociated agonist of the glucocorticoid receptor) arm or the placebo arm. Per-subject (time-fixed) binary indicator carrying the head-to-head drug-arm assignment in a phase II randomized trial in adults with rheumatoid arthritis where serum bone-formation biomarkers (P1NP, osteocalcin) are modelled with a K-PD framework and the two drugs share the rebound and response-side parameters but carry separate KDE / Imax / EDK50 values.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (fosdagrocorat or placebo).
• Source aliases: derived per subject from the trial-arm assignment; the source paper does not name an NMTRAN column.
• Example models: Shoji_2017_fosdagrocorat_p1np.R, Shoji_2017_fosdagrocorat_oc.R (binary multiplier switching the active KDE and the active sigmoid-Emax inhibition parameters between fosdagrocorat and prednisone: lkde_active = lkde_fos * (1 - DRUG_PRED) + lkde_pred * DRUG_PRED, with analogous switching of logitimax_active / imax_active and ledk50_active; the rebound parameters RBmax, T50 and the response-side parameters Kd, BL, SLP are shared between the drugs and do not multiply by DRUG_PRED).
• Notes: Sibling of DRUG_ORMU (rhRIG vs HRIG) and follows the same “head-to-head drug-arm” pattern: the contrast is between two structurally distinct active comparators in a randomized trial. Specific scope because the fosdagrocorat-vs-prednisone head-to-head is tied to the Shoji 2017 P1NP / OC analyses; future DAGR-vs-prednisone (or DAGR-vs-other glucocorticoid) popPK/PD models can extend this entry’s example_models list, while a contrast between a different test drug and prednisone (e.g., methylprednisolone vs prednisone) should register a sibling canonical rather than reusing DRUG_PRED. Ratified canonically alongside the Shoji 2017 P1NP / OC extractions.

DOSE_BALCINRENONE_MG (canonical for administered balcinrenone per-administration dose amount)

• Description: Continuous per-dose-record covariate carrying the balcinrenone (AZD9977) dose amount, in mg, associated with the current record. Used when a balcinrenone popPK model makes a PK parameter an explicit function of the administered dose level rather than treating dose only as an event amount – in the founding model, a dose-dependent power function on the first-order absorption rate constant capturing solubility-limited absorption.
• Units: mg
• Type: continuous
• Scope: specific
• Reference category: n/a – used with power scaling (DOSE_BALCINRENONE_MG / ref)^exponent. Reference values observed: 150 mg (Parkinson 2025, the single dose used in the renal-impairment study NCT04469907 and the normalisation anchor at which the reported KA = 0.381 1/h applies).
• Source aliases:
  • DOSE – NONMEM $PK symbol used in Parkinson_2025_balcinrenone.R (Parkinson 2025 Supplementary Materials ‘Final NONMEM Model’: KADOSE = (DOSE/150)**THETA(11); Table 1 footnote Dose KA = (dose/150)**KA~Dose).
• Example models: Parkinson_2025_balcinrenone.R (power effect on the absorption rate constant, ka *= (DOSE_BALCINRENONE_MG / 150)^-0.262, so a 50 mg dose absorbs about 34% faster than the 150 mg reference and a 300 mg dose about 17% more slowly; the paper reports a correspondingly 33% higher dose-normalised Cmax at 50 mg than at 300 mg).
• Notes: A dedicated mg column is required for two independent reasons. First, the general rxode2 constraint documented under DOSE_NMV_MG: a data column literally named DOSE is consumed by rxode2’s event translation, so a model referencing DOSE in model() fails to solve. Second, and specific to this model, Parkinson_2025_balcinrenone.R supplies its amt in nmol (the amount unit the model’s nmol/L concentration states demand) while the published absorption relationship is calibrated in mg; carrying both in one column would silently mix units. Convert with the balcinrenone molar mass 399.4 g/mol (C20H18FN3O5; PubChem CID 118599727, not reported in Parkinson 2025): amt [nmol] = DOSE_BALCINRENONE_MG * 1e6 / 399.4. Same shape as DOSE_MTX_MGM2, which carries the identical molar-amt / mg-covariate split. Sibling of DOSE_NMV_MG, DOSE_EMPA_MGD, DOSE_SEMAGLUTIDE_MG and the rest of the DOSE_<drug>_<units> family; distinct from the DOSE_<N>MG binary dose-level indicators. Ratified canonically alongside the Parkinson 2025 balcinrenone extraction.

DOSE_NMV_MG (canonical for administered nirmatrelvir per-administration dose amount)

• Description: Continuous per-record covariate carrying the nirmatrelvir dose amount, in mg, associated with the current record. Used when a nirmatrelvir popPK model makes a PK parameter an explicit function of the administered dose level rather than treating dose only as an event amount – e.g. a dose-dependent relative-bioavailability power function capturing saturable absorption.
• Units: mg
• Type: continuous
• Scope: specific
• Reference category: n/a – used with power scaling (DOSE_NMV_MG / ref)^exponent. Reference values observed: 300 mg (Chan 2023, the authorized nirmatrelvir dose and the normalisation anchor at which F1 is fixed to 1).
• Source aliases:
  • DOSE – NONMEM $INPUT column name used in Chan_2023_nirmatrelvir.R (Chan 2023 Data S1 header comment: ;AMT: DOSE IN MG; $PK: F1 = TVF1*(DOSE/300)**THETA(11)).
• Example models: Chan_2023_nirmatrelvir.R (power effect on relative bioavailability, F1 *= (DOSE_NMV_MG / 300)^-0.409, so a 150 mg dose carries about 33% higher relative bioavailability than the 300 mg reference; applied on top of the fractional 150-mg-tablet formulation effect FORM_NMV_TAB150).
• Notes: The drug-qualified name is mandatory here rather than the bare DOSE canonical: rxode2’s event translation consumes a data column literally named DOSE, so a model referencing DOSE in model() fails to solve with The following parameter(s) are required for solving: DOSE even when the column is present in the event table. Any new model that needs the administered dose as a model covariate (as opposed to the amt event column) should register a DOSE_<drug>_<units> sibling for the same reason; existing models using the bare DOSE canonical are PD-only or posthoc-CL models that never route the column through an rxode2 event translation. Sibling of DOSE_EMPA_MGD, DOSE_SEMAGLUTIDE_MG, DOSE_CIPARGAMIN_MG, DOSE_TBPPI_MG and the rest of the DOSE_<drug>_<units> family. Distinct from the DOSE_<N>MG binary dose-level indicators. Ratified canonically alongside the Chan 2023 nirmatrelvir extraction.

DOSE_1P8MG (canonical for 1.8 mg dose-level indicator)

• Description: 1 = subject or dose record is in the 1.8 mg dose-level cohort, 0 = any other dose level. Route-neutral (SC / IV / oral); typically per-subject in fixed-arm randomizations (e.g., Overgaard 2016’s SCALE Diabetes trial arm assignment 1.8 mg vs 3.0 mg liraglutide). “P” is the decimal-point token used across the DOSE_<N>MG family for fractional doses (analogous to 1P2, 2P4 if those siblings are added in future).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (any non-1.8 mg dose level in the source study; for Overgaard 2016 the reference is the 3.0 mg liraglutide arm).
• Source aliases:
  • Dose 1.8 mg – derived per-subject from the trial-assigned dose level in Overgaard_2016_liraglutide.R (Overgaard 2016 Fig. 2 / Table S1 “Cov. 1.8 mg”; flags subjects in the SCALE Diabetes 1.8 mg arm alongside the 3.0 mg reference).
• Example models: Overgaard_2016_liraglutide.R (multiplicative log-scale effect on CL/F, coefficient +0.02 per Table S1; small and not pharmacokinetically relevant, consistent with dose proportionality in the 1.8-3.0 mg range).
• Notes: Sibling of DOSE_50MG, DOSE_70MG, DOSE_130MG, DOSE_260MG, DOSE_400MG; member of the DOSE_<N>MG family of dose-level indicators. The 1P8 token spells the decimal 1.8 with P = period; if a future paper adds a DOSE_2P4MG or DOSE_0P6MG sibling, follow the same convention. In Overgaard 2016 the indicator marks the SCALE Diabetes 1.8 mg-arm subjects vs the 3.0 mg reference so the paper can quantify (and formally rule out) a dose-linearity break within the SCALE-Diabetes cohort; the estimated log-scale coefficient of +0.02 (95% CI -0.03 to 0.08) confirms dose proportionality. Ratified canonically on 2026-07-10 alongside the Overgaard 2016 extraction.

DOSE_70MG (canonical for 70 mg dose regimen indicator)

• Description: 1 = subject is on the 70 mg SC Q4W dose regimen, 0 = subject is on the 210 or 490 mg SC Q4W regimen.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (210 mg or 490 mg Q4W regimen).
• Source aliases: derived per subject from the trial-assigned dose level.
• Example models: Kotani_2022_astegolimab.R.
• Notes: Zenyatta-study categorical covariate flagging the 70 mg group (lowest dose), modeled as a -15.3% relative change on relative bioavailability. Modeled by Kotani 2022 as 70 mg vs {210 mg, 490 mg} combined reference.

DOSE_50MG (canonical for 50 mg dose-level indicator)

• Description: 1 = subject or dose record is in the 50 mg dose-level cohort, 0 = any other dose level. Route-neutral (covers SC, IV, oral); per-record or per-subject depending on the source design (record-level when a single subject crossed dose levels, subject-level when subjects were randomized to a fixed dose arm).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (any non-50 mg dose level in the source study, e.g. 100-300 mg SC for Othman 2014, 200 mg or 400 mg t.i.d. oral for Jorga 2000).
• Source aliases:
  • derived per dose record from the administered amount (AMT) – used in Othman_2014_daclizumab.R and the Diao 2016 family.
  • I_Dose50mg – subject-level indicator derived from study-arm randomization, used in Jorga_2000_tolcapone_fluctuators.R for the 50 mg t.i.d. tolcapone fluctuator arm.
• Example models: Othman_2014_daclizumab.R (record-level SC), Diao_2016_daclizumab_cd25.R, Diao_2016_daclizumab_cd56bright.R, Diao_2016_daclizumab_treg.R, Jorga_2000_tolcapone_fluctuators.R (subject-level oral t.i.d.).
• Notes: Othman 2014 estimated two separate absolute bioavailabilities because of non-linear dose-normalized exposure at the 50 mg SC dose – F = 0.84 for the therapeutic 100-300 mg SC range and F = 0.57 for the 50 mg SC cohort. Encoded as a record-level indicator so e_dose_50mg_f = 0.57/0.84 - 1 = -0.321 scales bioavailability only on 50 mg SC dose records. For clinical-range simulation (150 mg SC Q4W Phase III regimen) leave DOSE_50MG = 0. The Diao 2016 PK/PD models inherit the Othman 2014 PK backbone verbatim. Jorga 2000 uses the indicator subject-level on the central and peripheral volumes of distribution: (1 + e_dose_50mg_vc_vp * DOSE_50MG) with e_dose_50mg_vc_vp = -0.45 (V is 55% of the 200 mg reference at the 50 mg t.i.d. arm); paired with DOSE_400MG in the same fluctuator model so the 200 mg arm is the joint reference (both indicators = 0).

DOSE_10MG (canonical for 10 mg dose-level indicator)

• Description: 1 = subject or dose record is in the 10 mg dose-level cohort, 0 = any other dose level. Route-neutral; per-record or per-subject depending on the source design (subject-level for fixed-arm randomizations such as Zhang 2015’s dolutegravir dose-ranging cohorts).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (any non-10 mg dose level in the source study; for Zhang 2015 dolutegravir the reference is the pooled 25 mg / 50 mg dose levels).
• Source aliases:
  • DOSE (= 1 for the 10 mg dose, 0 for 25 / 50 mg) – used in Zhang_2015_dolutegravir.R (per Zhang 2015 Table 3 footnote, F = 1.21^GEND * 1.24^DOSE).
• Example models: Zhang_2015_dolutegravir.R (exponential effect on bioavailability: f(depot) <- exp(lfdepot + e_dose_10mg_fdepot * DOSE_10MG) with e_dose_10mg_fdepot = log(1.24) = 0.215, so F is 24% higher at the 10 mg dose vs the pooled 25 / 50 mg reference), Gu_2025_rivaroxaban.R (exponential effect on relative bioavailability with e_dose_10mg_fdepot = fixed(log(1.363)), so relative F at the 10 mg rivaroxaban dose is 1.363 times the 15 mg reference; the value was estimated at the base-model stage and then held fixed because only 2 of 105 patients received 10 mg; paired with DOSE_20MG).
• Notes: Sibling of DOSE_50MG, DOSE_70MG, DOSE_130MG, DOSE_260MG, and DOSE_400MG; member of the DOSE_<N>MG family of dose-level indicators. Zhang 2015 reports that continuous dose was tested as a covariate on F but was not significant, and no F difference was found between the 25 and 50 mg dose levels; the higher relative bioavailability at the 10 mg dose is attributed to better dispersion of the lower-strength tablet (Zhang 2015 Discussion). Ratified canonically alongside the Zhang 2015 dolutegravir extraction.

DOSE_20MG (canonical for 20 mg dose-level indicator)

• Description: 1 = subject or dose record is in the 20 mg dose-level cohort, 0 = any other dose level. Route-neutral; per-record or per-subject depending on the source design (per-record for Gu 2025, whose pooled analysis mixes a 20 mg single-dose healthy-volunteer study with a 10 / 15 / 20 mg once-daily patient cohort).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (any non-20 mg dose level in the source study; for Gu 2025 paired with DOSE_10MG = 0 to select the 15 mg reference dose level).
• Source aliases:
  • F 20mg – Gu 2025 Table 3 row for the relative bioavailability of the 20 mg dose group.
• Example models: Gu_2025_rivaroxaban.R (exponential effect on relative bioavailability: f(depot) <- exp(lfdepot + e_dose_10mg_fdepot * DOSE_10MG + e_dose_20mg_fdepot * DOSE_20MG) with e_dose_20mg_fdepot = log(0.537), so relative F at 20 mg is 0.537 of the 15 mg reference; paired with DOSE_10MG whose fixed effect is log(1.363)).
• Notes: Sibling of DOSE_10MG, DOSE_50MG, DOSE_70MG, DOSE_130MG, DOSE_260MG and DOSE_400MG; member of the DOSE_<N>MG family of dose-level indicators. Gu 2025 attributes the falling relative bioavailability with increasing dose to the limited aqueous solubility of rivaroxaban, a BCS class II compound, and reports 15 mg relative F as 1.86-fold that of 20 mg – consistent with the 1.43-fold value reported for the same contrast by Zhao 2022 in Chinese NVAF patients (Gu 2025 Discussion). Distinct from the continuous per-administration rivaroxaban dose canonicals DOSE_RIV_MGKG (mg/kg, used by the Willmann 2021 pediatric model’s exponential-decay bioavailability function): use the dose-level indicators when the source estimates one relative-F value per fixed dose level, and the continuous column when the source fits a smooth function of dose.

DOSE_400MG (canonical for 400 mg dose-level indicator)

• Description: 1 = subject or dose record is in the 400 mg dose-level cohort, 0 = any other dose level. Route-neutral; per-record or per-subject depending on the source design (subject-level for fixed-arm randomizations such as Jorga 2000’s t.i.d. tolcapone study arms).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (any non-400 mg dose level in the source study; for Jorga 2000 paired with DOSE_50MG = 0 to select the 200 mg t.i.d. reference).
• Source aliases:
  • I_Dose400mg – subject-level indicator derived from study-arm randomization, used in Jorga_2000_tolcapone_fluctuators.R for the 400 mg t.i.d. tolcapone fluctuator arm.
• Example models: Jorga_2000_tolcapone_fluctuators.R (subject-level oral t.i.d.).
• Notes: Sibling of DOSE_50MG and DOSE_70MG; member of the DOSE_<N>MG family of dose-level indicators. Jorga 2000 uses the indicator subject-level on the central and peripheral volumes of distribution: (1 + e_dose_400mg_vc_vp * DOSE_400MG) with e_dose_400mg_vc_vp = +0.40 (V is 140% of the 200 mg reference at the 400 mg t.i.d. arm). The dose-dependent V was an empirical finding in the fluctuator cohort that the authors could not confirm in the nonfluctuator cohort (only 200 and 400 mg arms were enrolled there); the effect plausibly reflects a few high-V outliers in the small-volume cohort rather than a true mechanistic dose-V relationship (Jorga 2000 Discussion). Ratified canonically alongside the Jorga 2000 tolcapone extraction.

DOSE_130MG (canonical for 130 mg dose-level indicator)

• Description: 1 = subject or dose record is in the 130 mg dose-level cohort, 0 = any other dose level. Route-neutral; per-record or per-subject depending on the source design (per-dose-record for Hajjar 2018’s adult Pompe-disease ATB200+AT2221 study where the AT2221 dose changes across successive dosing occasions within a subject).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (any non-130 mg dose level in the source study; for Hajjar 2018 paired with DOSE_260MG = 0 to select the no-AT2221 reference for ATB200 linear CL).
• Source aliases:
  • derived per-dose-record from the co-administered AT2221 dose amount – used in Hajjar_2018_cipaglucosidase.R as a categorical multiplier on ATB200 linear CL (e_dose130mg_cl ^ DOSE_130MG).
• Example models: Hajjar_2018_cipaglucosidase.R (per-dose-record oral co-medication indicator).
• Notes: Sibling of DOSE_50MG, DOSE_70MG, DOSE_260MG, and DOSE_400MG; member of the DOSE_<N>MG family of dose-level indicators. In Hajjar 2018 the indicator marks the AT2221 (miglustat) co-administration dose paired with the same-occasion 20 mg/kg ATB200 IV infusion; mutual exclusivity with DOSE_260MG is enforced by the study design (a single dose occasion uses at most one AT2221 dose level). Ratified canonically alongside the Hajjar 2018 ATB200 / AT2221 extraction.

DOSE_260MG (canonical for 260 mg dose-level indicator)

• Description: 1 = subject or dose record is in the 260 mg dose-level cohort, 0 = any other dose level. Route-neutral; per-record or per-subject depending on the source design (per-dose-record for Hajjar 2018’s adult Pompe-disease ATB200+AT2221 study where the AT2221 dose changes across successive dosing occasions within a subject).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (any non-260 mg dose level in the source study; for Hajjar 2018 paired with DOSE_130MG = 0 to select the no-AT2221 reference for ATB200 linear CL).
• Source aliases:
  • derived per-dose-record from the co-administered AT2221 dose amount – used in Hajjar_2018_cipaglucosidase.R as a categorical multiplier on ATB200 linear CL (e_dose260mg_cl ^ DOSE_260MG).
• Example models: Hajjar_2018_cipaglucosidase.R (per-dose-record oral co-medication indicator).
• Notes: Sibling of DOSE_50MG, DOSE_70MG, DOSE_130MG, and DOSE_400MG; member of the DOSE_<N>MG family of dose-level indicators. In Hajjar 2018 the indicator marks the AT2221 (miglustat) co-administration dose paired with the same-occasion 20 mg/kg ATB200 IV infusion; mutual exclusivity with DOSE_130MG is enforced by the study design. Ratified canonically alongside the Hajjar 2018 ATB200 / AT2221 extraction.

DOSE_18G (canonical for 18 g dose-level indicator)

• Description: 1 = subject or dose record is in the 18 g dose-level cohort, 0 = any other dose level. Gram-scale member of the DOSE_<N><UNIT> dose-level-indicator family, for drugs dosed in grams rather than milligrams.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (any non-18 g dose level; for Darwish 2025 the reference comprises the 6-12 g therapeutic weight-banded doses).
• Source aliases:
  • DoseGrp1 – used in Darwish_2025_trofinetide.R (Darwish 2025 Results, definition of the DoseGrp1_I indicator variable in the typical-value equation for F1).
• Example models: Darwish_2025_trofinetide.R (proportional shift on oral bioavailability from Darwish 2025 Table 2: fdepot * (1 + e_dose18g_f * DOSE_18G) with e_dose18g_f = -0.132, a 13.2% reduction in F1 at the 18 g supratherapeutic dose. The dose level was studied in the thorough-QTc study ACP-2566-008; the effect captures the less-than-proportional rise in trofinetide exposure above the 6-12 g therapeutic range. Paired with DOSE_24G, which carries the larger 28.4% reduction – founding example).
• Notes: Sibling of the milligram-scale DOSE_1P8MG / DOSE_10MG / DOSE_50MG / DOSE_70MG / DOSE_130MG / DOSE_260MG / DOSE_400MG entries; the unit token is G rather than MG because trofinetide is dosed in grams throughout its literature and spelling this as DOSE_18000MG would be gratuitously unreadable. Follow the same P-for-decimal-point convention as DOSE_1P8MG if a fractional-gram sibling is ever needed. The covariate-effect parameter form is e_dose18g_<param>: unlike the DIS_/FORM_/AE_ families, the DOSE token is retained because it carries the semantic content (the effect is about the dose level) rather than being a bare family prefix. Mutually exclusive with DOSE_24G; both = 0 selects the therapeutic-dose reference. Distinct from the abstract DOSE_HIGH, which names the “in the highest cohort” role without fixing a numerical value – use the numerically-explicit entries when a source estimates a separate effect for each of two or more named supratherapeutic levels, as Darwish 2025 does. Ratified canonically alongside the Darwish 2025 trofinetide extraction.

DOSE_24G (canonical for 24 g dose-level indicator)

• Description: 1 = subject or dose record is in the 24 g dose-level cohort, 0 = any other dose level. Gram-scale member of the DOSE_<N><UNIT> dose-level-indicator family.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (any non-24 g dose level; for Darwish 2025 the reference comprises the 6-12 g therapeutic weight-banded doses).
• Source aliases:
  • DoseGrp2 – used in Darwish_2025_trofinetide.R (Darwish 2025 Results, definition of the DoseGrp2_I indicator variable in the typical-value equation for F1).
• Example models: Darwish_2025_trofinetide.R (proportional shift on oral bioavailability from Darwish 2025 Table 2: fdepot * (1 + e_dose24g_f * DOSE_24G) with e_dose24g_f = -0.284, a 28.4% reduction in F1 at the 24 g supratherapeutic dose – roughly twice the 18 g reduction, consistent with a saturating-absorption interpretation – founding example).
• Notes: Paired sibling of DOSE_18G; see that entry for the family, unit-token, and covariate-effect-naming rationale. Mutually exclusive with DOSE_18G. Ratified canonically alongside the Darwish 2025 trofinetide extraction.

DOSE_HIGH (canonical for highest-dose-cohort binary indicator)

• Description: 1 = subject is in the source paper’s highest-dose cohort (numerical threshold documented per model in covariateData[[DOSE_HIGH]]$notes); 0 = subject is in any lower-dose cohort. Time-fixed per subject in escalating-cohort or parallel-dose designs where each animal / subject remained on a single dose level for the full study and the paper detected a step-function shift in a PK parameter (typically apparent oral clearance or bioavailability) that was significant enough to warrant a separate typical-value estimate for the highest cohort alone.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (any dose cohort below the paper-defined highest-dose threshold).
• Source aliases: derived per subject from the trial- or cohort-assigned dose level.
• Example models: Viberg_2012_AZD6088_rat.R (Viberg 2012 Results, PK model paragraph: ‘The highest dose group deviated in the plots. This might be due to higher bioavailability or saturation of elimination at the highest dose and after estimating different clearance values for this group OFV decreased 32 units’. The 40 umol/kg = ~16.26 mg/kg oral dose group of AZD6088 in male Sprague-Dawley rats is flagged as DOSE_HIGH = 1 and gates a separate typical-value apparent oral clearance lcl_highdose = log(3.92 L/h/kg) in place of the standard-dose lcl = log(10.87 L/h/kg); the initial-efficacy-study cohorts at 1, 2.5, 5, 10, 20 umol/kg are DOSE_HIGH = 0), Maleki_2024_brepocitinib.R (first human ratification: DOSE_HIGH = 1 for an administered daily dose above 100 mg – the 175 and 200 mg phase I arms, 5% of the analysis population – and gates a 30% uplift in relative bioavailability, Frel = 1 * (1 + 0.3 * DOSE_HIGH), per Maleki 2024 Table 3 footnote c. The 100 mg cohort itself sits in the reference group. Same “step-function switch at the top of the dose range” semantics as the Viberg 2012 founding example, but expressed on bioavailability rather than clearance; the paper reached it from single-dose NCA showing dose-dependent PK above 100 mg q.d. plus a correlation between administered dose and the CL/F and Vc/F individual estimates).
• Notes: Abstract-form sibling of the numerically-explicit DOSE_<N>MG family (DOSE_50MG, DOSE_70MG, DOSE_130MG, DOSE_260MG, DOSE_400MG) and of the drug-suffixed DOSE_HIGH_EFL (Jansson 2008 eflornithine >= 3000 mg/kg indicator): DOSE_HIGH names the abstract “in the highest cohort” role without fixing the mg-threshold in the canonical name, and each model’s covariateData[[DOSE_HIGH]]$notes documents the per-paper dose value that maps to DOSE_HIGH = 1. Distinct from the continuous COHDOSE (mg/kg per-subject dose cohort) and DOSE (per-record administered dose level, mg) canonicals, which are numeric rather than binary; use DOSE_HIGH when the source paper reports a step-function switch in a PK parameter at the top of the dose range (Viberg 2012 pattern: single distinct THETA for the highest cohort, all other cohorts share the standard THETA), use COHDOSE when the source reports a power-form or continuous covariate effect across all cohorts, and use DOSE_HIGH_EFL (or a similar drug-suffixed sibling) when the numerical threshold is enshrined in the canonical name. Scope: specific because the numerical highest-dose value differs per paper; promote to general once a second model registers under the same abstract semantics. Ratified canonically alongside the Viberg 2012 AZD6088 rat extraction (nlmixr2lib task frompeople-633).

DOSE_LOW (canonical for lowest-dose-cohort binary indicator)

• Description: 1 = the record’s administered dose is in the source paper’s low-dose group (the specific dose levels documented per model in covariateData[[DOSE_LOW]]$notes); 0 = any higher dose level. Used where a paper detects a step-function shift in a PK parameter at the bottom of the studied dose range and estimates a separate multiplicative effect for the low-dose levels alone, rather than a continuous dose-response across all levels.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (any dose level above the paper-defined low-dose group).
• Source aliases:
  • 10/25 mg dose – used in Comisar_2025_rimegepant.R (Comisar 2025 Table 2 row label ‘10/25mg dose effect on k tr’).
• Example models: Comisar_2025_rimegepant.R (founding example; multiplicative fractional effect on the transit absorption rate constant, ktr * (1 + 0.596 * DOSE_LOW) per Comisar 2025 Table 2. DOSE_LOW = 1 for the 10 mg and 25 mg rimegepant dose levels and 0 for 75 mg and 150 mg. The positive sign is an increase in ktr, i.e. faster transit at low dose, per Table 2 footnote a ‘Increase in the transit rate constant for 10-25 mg doses’. The 6 pediatric participants weighing 15 to 30 kg who received 25 mg ODT carry DOSE_LOW = 1).
• Notes: Mirror-image sibling of [[DOSE_HIGH]], and registered on the same rationale: it names the abstract “in the lowest cohort” role without fixing the mg-threshold in the canonical name, with each model’s covariateData[[DOSE_LOW]]$notes documenting the per-paper dose levels that map to 1. Drug-suffixed sibling DOSE_LOW_AMG221 enshrines its threshold in the name instead. Encode as an explicit indicator column rather than deriving it from an inequality on a continuous dose covariate such as [[DOSE_RIMEGEPANT_MG]] – papers define these groups by the studied dose levels, not by a threshold, so an inequality would silently misclassify an intermediate dose that was never studied. Scope: specific because the numerical low-dose values differ per paper; promote to general once a second model registers under the same abstract semantics.

STUDY1 (canonical for Study-1 cohort indicator)

• Description: 1 = subject enrolled in Study 1 of the Cirincione 2017 pooled analysis, 0 = other. Used to switch the residual-error magnitude per study.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (all other studies; combined with STUDY5 = 0 selects the pooled “other” residual error).
• Source aliases:
  • DVID = "study1" (character-valued study identifier; STUDY1 = as.integer(DVID == "study1")) – legacy form previously used in Cirincione_2017_exenatide.R.
• Example models: Cirincione_2017_exenatide.R.
• Notes: Paired with STUDY5. When both are 0, the subject is in the pooled “other studies” residual-error group.

STUDY5 (canonical for Study-5 cohort indicator)

• Description: 1 = subject enrolled in Study 5 of the Cirincione 2017 pooled analysis, 0 = other. Used to switch the residual-error magnitude per study.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (all other studies).
• Source aliases:
  • DVID = "study5" (character-valued study identifier; STUDY5 = as.integer(DVID == "study5")) – legacy form previously used in Cirincione_2017_exenatide.R.
• Example models: Cirincione_2017_exenatide.R.
• Notes: Paired with STUDY1. When both are 0, the subject is in the pooled “other studies” residual-error group.

STUDY_MD (canonical for Cirincione 2017 AAPS J ER exenatide multi-dose study cohort indicator)

• Description: 1 = subject enrolled in the phase II multi-dose study (weekly SC ER exenatide for 15 weeks) of the Cirincione 2017 AAPS J combined single- and multiple-dose population analysis; 0 = phase II single-dose study (one SC ER exenatide dose). Used to switch the study-specific relative bioavailability (f_rel) value and the log-scale residual-error magnitude per study.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (single-dose phase II study).
• Source aliases: derived per subject from the dose-record structure (a subject with > 1 dose record in the multi-dose phase II study -> 1; subjects with a single dose record in the single-dose phase II study -> 0).
• Example models: Cirincione_2017_exenatide_er.R.
• Notes: Cirincione 2017 AAPS J Table II reports f_rel(single-dose study) = 8.86% and f_rel(MD study) = 15.5%, and log-scale residual SDs 0.684 (single-dose) vs 0.376 (multi-dose). The STUDY_MD indicator selects between them. For the phase III external validation cohort (multi-dose weekly 2 mg ER for 24 weeks), use STUDY_MD = 1 so the multi-dose f_rel and residual magnitudes apply. Specific scope because the indicator is tied to the AAPS J 2017 ER exenatide combined analysis (single-dose phase II + multi-dose phase II) – a future pooled analysis with additional study cohorts would justify its own canonical or a promotion to general.

STUDY_PKU015 (canonical for sapropterin PKU-015 pediatric study cohort indicator)

• Description: 1 = subject enrolled in study PKU-015 (pediatric population pharmacokinetic study of sapropterin in infants and young children, 0-6 years old, of the Qi 2014 pooled analysis); 0 = study PKU-004 (adolescent / adult open-label extension study, >= 9 years old). Used to switch the residual-error magnitude per study under the log-transform-both-sides (LTBS) constant-CV residual model.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (PKU-004 adolescent / adult cohort).
• Source aliases: derived per subject from the trial identifier (PKU-015 -> 1, PKU-004 -> 0).
• Example models: Qi_2014_sapropterin.R.
• Notes: Qi 2014 Table 3 reports separate residual-error estimates for the two studies – PKU-004 = 21.1% CV, PKU-015 = 30.2% CV under the LTBS approach. The STUDY_PKU015 indicator selects between them. Specific scope because the indicator is tied to the BioMarin sapropterin clinical-development program (PKU-004 = phase 3b extension, PKU-015 = phase 3b pediatric).

STUDY_FARLETUZUMAB_PHASE2 (canonical for Phase II study cohort indicator in the Farrell 2012 farletuzumab pooled analysis)

• Description: 1 = subject enrolled in the Phase II study (MORAb-003-002) of the Farrell 2012 pooled farletuzumab analysis; 0 = Phase I study (MORAb-003-001). Used to switch the residual-error magnitude per study.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (Phase I).
• Source aliases:
  • PHASE2 – prior canonical name (pre-2026-06-19 paper-specific-disambiguation standardization). Generic single-token name collided with the parallel PHASE1 canonical from Valenzuela 2025 that picks the opposite reference category.
  • Derived per subject from the trial identifier (MORAb-003-001 -> 0, MORAb-003-002 -> 1).
• Example models: Farrell_2012_farletuzumab.R.
• Notes: Farrell 2012 Table 3 reports separate residual-error estimates for the two studies – Phase I uses a proportional-only model (sigma = 20.5%); Phase II uses a combined additive + proportional model (sigma_prop = 34.9%, sigma_add = 7.94 ug/mL). The STUDY_FARLETUZUMAB_PHASE2 indicator selects between them. Renamed from generic PHASE2 to paper-specific STUDY_FARLETUZUMAB_PHASE2 on 2026-06-19 per the canonical-register standardization audit (operator decision: study-phase indicators must be paper-specific because two papers can pick opposite reference categories - Farrell 2012 picks Phase II as 1-level while Valenzuela 2025 picks Phase I as 1-level - and a generic “PHASE2” canonical conflates the two encodings).

STUDY_BALCINRENONE_PHASE1 (canonical for single-dose phase 1 study cohort indicator in the Parkinson 2025 balcinrenone pooled analysis)

• Description: 1 = the participant is from one of the four single-dose phase 1 studies of the Parkinson 2025 pooled balcinrenone analysis (NCT03843060 drug-drug-interaction, NCT03804645 and NCT04798222 bioavailability, NCT04469907 renal impairment – healthy participants and participants with renal impairment but without heart failure); 0 = the participant is from one of the two multiple-dose phase 1b/2b studies in patients with heart failure and chronic kidney disease (NCT03682497, NCT04595370). Used as a multiplicative covariate on apparent clearance.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (phase 1b/2b patient with heart failure and CKD). This matches the paper’s own reference participant, defined in Methods 2.6 as ‘a patient with HF and CKD … with an eGFR of 60 mL/min/1.73 m2 who received balcinrenone in a fasted state’.
• Source aliases:
  • PTSFLAG – NONMEM $PK column name used in Parkinson_2025_balcinrenone.R, coded with the opposite polarity (PTSFLAG = 1 for the patient studies, which are the most common group at 135 of 189 participants). The canonical value is STUDY_BALCINRENONE_PHASE1 = 1 - PTSFLAG. The reference category is unchanged by the recoding: the control stream sets CLPTSFLAG = 1 on the PTSFLAG.EQ.1 branch and (1 + THETA(12)) on the PTSFLAG.EQ.0 branch, so the patient level carries the unit multiplier under either encoding.
• Example models: Parkinson_2025_balcinrenone.R (multiplicative fractional effect cl *= (1 + 1.06 * STUDY_BALCINRENONE_PHASE1), i.e. 2.06-fold higher CL/F in the phase 1 participants, corresponding to 0.48-fold AUCss; Parkinson 2025 Table 1 CL/F~Study = 1.06, 95% CI 0.685-1.44, and Results 3.3).
• Notes: A study-design cohort indicator rather than a mechanistic subject covariate, and the paper says so: the effect survives adjustment for both baseline eGFR and body weight, and the Discussion offers three competing explanations (single-dose phase 1 versus repeated-dose patient designs, i.e. possible time-dependent PK; less accurate dosing- and sampling-time records in the patient studies; and unrecorded food state in the patient studies, where the fed state was assumed). It is partially collinear with DIS_HEALTHY in this dataset but not identical – the phase 1 stratum also contains the renal-impairment cohort NCT04469907, whose participants are not healthy – so DIS_HEALTHY would misdescribe the level. Follows the paper-specific STUDY_<drug>_<phase> convention established by STUDY_NIPOCALIMAB_PHASE1, STUDY_FARLETUZUMAB_PHASE2, STUDY_NMV_PHASE23, STUDY_POSA_PHASE3, STUDY_SULDUR_PHASE2 and STUDY_SULDUR_PHASE3; a generic PHASE1 name is deliberately avoided because different papers pick opposite reference categories (note that STUDY_NIPOCALIMAB_PHASE1 shares the 1-level meaning but inverts nothing, whereas this column inverts its source column). Ratified canonically alongside the Parkinson 2025 balcinrenone extraction.

STUDY_NIPOCALIMAB_PHASE1 (canonical for Phase I study cohort indicator in the Valenzuela 2025 nipocalimab pooled analysis)

• Description: 1 = subject enrolled in a Phase 1 study of the Valenzuela 2025 pooled nipocalimab analysis (MOM-M281-001, MOM-M281-007, MOM-M281-010, EDI1001, EDI1002 – healthy participants); 0 = Phase 2 study (MOM-M281-004 / Vivacity-MG – participants with gMG). Used to switch the proportional PK residual-error magnitude per study phase.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (Phase 2).
• Source aliases:
  • PHASE1 – prior canonical name (pre-2026-06-19 paper-specific-disambiguation standardization). Generic single-token name collided with the parallel PHASE2 canonical from Farrell 2012 that picks the opposite reference category.
  • Derived per subject from the trial identifier (Phase 1 protocols -> 1, NCT03772587 Vivacity-MG -> 0).
• Example models: Valenzuela_2025_nipocalimab.R.
• Notes: Valenzuela 2025 Table 3 reports proportional PK residual 0.0834 (Phase 1) vs 0.367 (Phase 2). Distinct from Farrell 2012 STUDY_FARLETUZUMAB_PHASE2 – the reference category is inverted (Valenzuela 2025 picks Phase 1 as the 1-level). Renamed from generic PHASE1 to paper-specific STUDY_NIPOCALIMAB_PHASE1 on 2026-06-19 per the canonical-register standardization audit (operator decision: study-phase indicators must be paper-specific because two papers can pick opposite reference categories, and a generic “PHASE1” canonical conflates the encodings).

STUDY_NMV_PHASE23 (canonical for phase II/III study cohort indicator in the Chan 2023 nirmatrelvir pooled analysis)

• Description: 1 = the observation record originates from the phase II/III EPIC-HR study (NCT04960202; 1087 nonhospitalized symptomatic adults with COVID-19, sparse outpatient PK sampling); 0 = the observation record originates from one of the seven phase I studies in the pooled analysis (150 participants, serial in-clinic sampling). Used to switch the proportional residual-error magnitude per study phase.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (phase I study observation).
• Source aliases:
  • PROT (protocol number; the phase II/III study is protocol 1005) – used in Chan_2023_nirmatrelvir.R (Chan 2023 Data S1 $ERROR: IF (PROT.EQ.1005) W = SQRT(THETA(12)**2 + THETA(7)**2/IPRED**2)).
• Example models: Chan_2023_nirmatrelvir.R (selects propSdPhase23 = 1.39 in place of propSdPhase1 = 0.324 inside the combined additive-plus-proportional residual model; Chan 2023 Table 3 reports proportional error 32.4% for phase I and 139% for phase II/III, with the additive component fixed at the 10 ng/mL LLOQ for both).
• Notes: A record-level study-design property, not a subject-level covariate: it captures the sparse outpatient sampling scheme of EPIC-HR (Chan 2023 Discussion attributes the roughly fourfold higher residual variability to sparse sampling, the outpatient setting, and dosing-time compliance). Collinear with DIS_COVID19 in the Chan 2023 data set – every phase II/III participant has COVID-19 – but the two enter different parts of the model (DIS_COVID19 is a structural covariate on CL; STUDY_NMV_PHASE23 only selects a residual-error magnitude), so both are carried. Follows the paper-specific STUDY_<drug>_<phase> convention established by STUDY_NIPOCALIMAB_PHASE1, STUDY_FARLETUZUMAB_PHASE2, STUDY_POSA_PHASE3, STUDY_SULDUR_PHASE2 and STUDY_SULDUR_PHASE3; a generic PHASE23 name is deliberately avoided because different papers pick opposite reference categories. Ratified canonically alongside the Chan 2023 nirmatrelvir extraction.

STUDY_POSA_PHASE3 (canonical for posaconazole phase 3 patient study cohort indicator in the van Iersel 2018 pooled analysis)

• Description: 1 = subject enrolled in the phase 3 patient study P05615 of the van Iersel 2018 pooled posaconazole solid-tablet PopPK analysis (231 patients at high risk for invasive fungal disease, sparse PK sampling); 0 = subject enrolled in one of the five phase 1 healthy-volunteer studies P04975, P05637, P07764, P07783, P07691 (104 healthy volunteers, rich PK sampling). Used to switch the log-additive PK residual-error magnitude per study phase.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (phase 1 healthy-volunteer studies).
• Source aliases: derived per subject from the trial identifier (P05615 -> 1, any of P04975 / P05637 / P07764 / P07783 / P07691 -> 0).
• Example models: vanIersel_2018_posaconazole.R (switches the log-additive residual SD on Cc between expSd_p1 = 0.42 (phase 1) and expSd_p3 = 0.322 (phase 3), per van Iersel 2018 Table 2 final-model ‘SD (phase 1 studies)’ and ‘SD (phase 3 study)’).
• Notes: Specific scope because the contrast is tied to the van Iersel 2018 posaconazole solid-tablet clinical-development pooled analysis. Drug-specific paper-anchored member of the STUDY_<DRUG>_PHASE<N> family alongside STUDY_NIPOCALIMAB_PHASE1 (Valenzuela 2025) and STUDY_FARLETUZUMAB_PHASE2 (Farrell 2012); distinct from those entries because the reference category (phase 1) and 1-level (phase 3) are paper-specific to van Iersel 2018. Subject-level (time-fixed); set once from the trial identifier on each subject record. Ratified canonically alongside the van Iersel 2018 posaconazole extraction.

STUDY_SULDUR_PHASE2 (canonical for Cammarata 2024 sulbactam-durlobactam phase 2 study cohort indicator)

• Description: 1 = subject enrolled in the Phase 2 study CS2514-2017-0003 (complicated urinary tract infection including acute pyelonephritis) of the Cammarata 2024 pooled sulbactam-durlobactam popPK analysis; 0 = otherwise. Paired with STUDY_SULDUR_PHASE3; both indicators 0 selects the six Phase 1 studies (the reference stratum). Used to switch the DURLOBACTAM residual-error magnitude between the three study phases; sulbactam residual variability is not phase-stratified.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 with STUDY_SULDUR_PHASE3 also 0 (Phase 1 studies CS2514-2016-0001, CS2514-2017-0001, CS2514-2017-0002, CS2514-2018-0002, CS2514-2018-0003, ZL-2402-001).
• Source aliases: derived per subject from the trial identifier (CS2514-2017-0003 -> 1).
• Example models: Cammarata_2024_sulbactam_durlobactam.R (selects propSdPhase2 = sqrt(0.0794) = 0.282 for durlobactam plasma observations; no additive residual term applies to Phase 2, per Cammarata 2024 Table 1, which lists a single additive sigma^2 for Phase 1 only).
• Notes: Specific scope because the contrast is tied to the Cammarata 2024 sulbactam-durlobactam clinical-development pooled analysis. Drug-specific paper-anchored member of the STUDY_<DRUG>_PHASE<N> family alongside STUDY_POSA_PHASE3 (van Iersel 2018), STUDY_ASP8232_PHASE2 (Snelder 2020), STUDY_NIPOCALIMAB_PHASE1 (Valenzuela 2025), and STUDY_FARLETUZUMAB_PHASE2 (Farrell 2012). This is the first member of the family that needs a PAIR of indicators, because the paper stratifies residual error across three phases rather than two; keep both columns rather than an integer PHASE column so the reference stratum stays explicit. Subject-level (time-fixed); set once from the trial identifier on each subject record. Ratified canonically on 2026-07-28 alongside the Cammarata 2024 sulbactam-durlobactam extraction.

STUDY_SULDUR_PHASE3 (canonical for Cammarata 2024 sulbactam-durlobactam phase 3 study cohort indicator)

• Description: 1 = subject enrolled in the Phase 3 study CS2514-2017-0004 (infections caused by Acinetobacter baumannii-calcoaceticus complex) of the Cammarata 2024 pooled sulbactam-durlobactam popPK analysis; 0 = otherwise. Paired with STUDY_SULDUR_PHASE2; both indicators 0 selects the six Phase 1 studies.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 with STUDY_SULDUR_PHASE2 also 0 (Phase 1 studies).
• Source aliases: derived per subject from the trial identifier (CS2514-2017-0004 -> 1).
• Example models: Cammarata_2024_sulbactam_durlobactam.R (selects propSdPhase3 = sqrt(0.203) = 0.451 for durlobactam plasma observations; the Phase 3 additive residual component ‘was determined to not be significant and was consequently removed’, per Cammarata 2024 Results).
• Notes: Specific scope; see STUDY_SULDUR_PHASE2 for the full family rationale and the pair-of-indicators convention. Ratified canonically on 2026-07-28 alongside the Cammarata 2024 sulbactam-durlobactam extraction.

STUDY_PEGCET_PHASE3 (canonical for Crass 2024 pegcetacoplan phase 3 study cohort indicator)

• Description: 1 = subject enrolled in one of the two phase 3 pegcetacoplan studies of the Crass 2024 pooled population PK analysis (PEGASUS, NCT03500549, source STUD 302; or PRINCE, NCT04085601, source STUD 308); 0 = subject enrolled in any of the nine earlier-phase studies in the pool (CP0713-1, CP1014, 101, 102, 401, AIRIS, PHAROAH, PADDOCK, PALOMINO). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (the phase 1 / 1b / 2a studies in the pool).
• Source aliases: derived per subject from the trial identifier (STUD 302 or 308 -> 1). The source control stream builds the equivalent flag inline as PNH3=0; IF(STUD.EQ.302) PNH3=1; IF(STUD.EQ.308) PNH3=1.
• Example models: Crass_2024_pegcetacoplan.R (used only in the residual-error switch, never in the structural model: together with DIS_PNH it selects one of three log-scale residual SDs – 0.200 for non-PNH participants, 0.163 for PNH patients in the phase 3 studies, and 0.326 for PNH patients in the phase 1/2 studies PHAROAH / PADDOCK / PALOMINO, per Crass 2024 ESM Table 3 thetas 5-7).
• Notes: The source control stream carries two flags, PNH2 (STUD 202 / 204 / 514, the phase 1/2 PNH studies) and PNH3 (STUD 302 / 308, the phase 3 PNH studies). Because PNH2 and PNH3 partition the PNH cohort exactly, only one canonical indicator is needed: the phase 1/2 stratum is recovered as DIS_PNH * (1 - STUDY_PEGCET_PHASE3). Drug-specific paper-anchored member of the STUDY_<DRUG>_PHASE<N> family alongside STUDY_POSA_PHASE3 (van Iersel 2018), STUDY_SULDUR_PHASE3 (Cammarata 2024), STUDY_ASP8232_PHASE2 (Snelder 2020), STUDY_NIPOCALIMAB_PHASE1 (Valenzuela 2025), and STUDY_FARLETUZUMAB_PHASE2 (Farrell 2012); reference category matches those siblings (the later phase is the 1-level). The residual-error-only role also mirrors STUDY_SULDUR_PHASE3 and STUDY_ASP8232_PHASE2. Ratified canonically alongside the Crass 2024 pegcetacoplan extraction.

STUDY_ASP8232_PHASE2 (canonical for Snelder 2020 ASP8232 phase 2 study cohort indicator in the pooled TMDD PK-PD analysis)

• Description: 1 = subject enrolled in one of the two phase 2 studies of the Snelder 2020 pooled ASP8232 TMDD PK-PD analysis (VIDI study, NCT02302079, diabetic macular edema; or ALBUM study 8232-CL-0004, NCT02358096, diabetic kidney disease); 0 = subject enrolled in one of the two phase 1 studies (8232-CL-0001 first-in-human healthy volunteers; 8232-CL-0002 renal impairment / T2DM-CKD, NCT02218099). Used to switch the log-additive residual-error magnitude on ASP8232 plasma concentrations and on VAP-1 plasma activity between the phase 1 studies (reference) and the phase 2 studies (paper’s estimated multiplicative factor 1.88 relative to phase 1).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (phase 1 studies 8232-CL-0001 and 8232-CL-0002).
• Source aliases: derived per subject from the trial identifier (8232-CL-3001/VIDI or 8232-CL-0004/ALBUM -> 1; 8232-CL-0001 or 8232-CL-0002 -> 0).
• Example models: Snelder_2020_ASP8232.R (multiplies the log-additive residual SD on ASP8232 total plasma concentration and on VAP-1 plasma activity by factor_phase2 = 1.88 when the indicator is 1, per Snelder 2020 Table 4 ‘Factor res error phase 2 studies’; VAP-1 concentration residual error is not affected because VAP-1 concentration was measured only in the two phase 2 studies).
• Notes: Specific scope because the contrast is tied to the Snelder 2020 pooled ASP8232 clinical-development analysis (four studies in Astellas’ vascular adhesion protein-1 inhibitor programme). Drug-specific paper-anchored member of the STUDY_<DRUG>_PHASE<N> family alongside STUDY_POSA_PHASE3 (van Iersel 2018), STUDY_NIPOCALIMAB_PHASE1 (Valenzuela 2025), and STUDY_FARLETUZUMAB_PHASE2 (Farrell 2012). Reference category matches Farrell 2012 (phase 2 as the 1-level and phase 1 as the reference). Subject-level (time-fixed); set once from the trial identifier on each subject record. Ratified canonically alongside the Snelder 2020 ASP8232 extraction.

STUDY_FU2022_AZ (canonical for Fu 2022 CVS-CTR atenolol Study 2 (AstraZeneca) cohort indicator)

• Description: 1 = subject enrolled in Study 2 (AstraZeneca Alderley Park, UK; 4 male beagle dogs, 14.2-14.6 kg, 17-22 months old; oral atenolol 0/1/3/10 mg/kg; hemodynamic markers HR, dP/dtmax, and MAP measured but NO cardiac output) of the Fu 2022 CVS-CTR systems-model development pool; 0 = Study 1 (Servier, France; 4 male beagle dogs, 10-15 kg; oral atenolol 0/3/10/30 mg/kg; HR, dP/dtmax, CO, and MAP measured). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (Study 1, Servier site).
• Source aliases:
  • SSID in the NONMEM control stream (SSID = 1 -> Servier -> STUDY_FU2022_AZ = 0; SSID = 2 -> AstraZeneca -> STUDY_FU2022_AZ = 1; SSID = 3 -> GSK external validation, excluded from the estimation dataset via IGNORE=(SSID.EQ.3) and encoded as STUDY_FU2022_AZ = 0 alongside Study 1 for the packaged model).
• Example models: Fu_2022_atenolol_qsp.R (switches six per-study typical values: BSL_HR = 79.4 (S1) vs 77.0 (S2) bpm, V0 = 9.92 (S1) vs 9.15 (S2) mL, BSL_CTRM = 3777 (S1) vs 2422 (S2) mmHg/s, Amp = 0.0931 (S1) vs 0.168 (S2), Hor_HR = 7.86 (S1) vs 19.4 (S2) h, Hor_CTR = 9.82 (S1) vs 21.8 (S2) h; Fu 2022 Table 2 final-model column).
• Notes: Specific scope because the contrast is tied to the multi-site Servier/AstraZeneca beagle-dog telemetry pool used by Fu 2022 to develop the CVS-CTR systems model. Sibling of STUDY_C2201 (Bienczak 2025 ligelizumab), STUDY_ING111521 (Zhang 2015 dolutegravir), STUDY_LBSL (Zhou 2021 belimumab), STUDY_M281_004 (Vivacity-MG nipocalimab), STUDY_MD (Cirincione 2017 ER exenatide multi-dose), STUDY_PKU015 (Qi 2014 sapropterin pediatric), and STUDY_RIV201 (Tammara 2017 rivipansel); member of the STUDY_<name> family of paper-specific study cohort indicators. Subject-level (time-fixed); set once from the trial identifier on each subject record. Ratified canonically alongside the Fu 2022 CVS-CTR extraction.

STUDY_C2201 (canonical for Bienczak 2025 ligelizumab study C2201 cohort indicator)

• Description: 1 = subject enrolled in study C2201 (NCT02477332; Novartis Phase 2b ligelizumab dose-finding study in adult CSU patients) of the Bienczak 2025 pooled ligelizumab PopPK analysis; 0 = any other study in the pool (A2103, C2101, C2202, C2302, or C2303). Used to switch the typical CL/F magnitude in study C2201.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (any non-C2201 cohort in the Bienczak 2025 pool: pooled adult / adolescent CSU patients from C2202 / C2302 / C2303 and adult healthy volunteers from A2103 / C2101).
• Source aliases: derived per subject from the trial identifier (C2201 -> 1, else -> 0).
• Example models: Bienczak_2025_ligelizumab.R (Table S6: study C2201 on CL/F = 0.176, log-additive; cl *= exp(0.176) for C2201 subjects).
• Notes: Specific scope because the contrast is tied to the Novartis ligelizumab CSU development program. Subject-level / time-fixed; set once from the trial identifier on each subject record. The C2201 effect was retained in the final model because the residual unexplained CL/F differed between C2201 and the other studies after accounting for body weight, IgE, ADA, and disease-state covariates.

STUDY_C208 (canonical for TMC207-C208 bedaquiline trial cohort indicator)

• Description: 1 = subject enrolled in TMC207-C208 (NCT00449644; a randomized, double-blind, placebo-controlled Phase IIb trial of bedaquiline added to a five-drug background regimen in newly diagnosed MDR-TB patients, run in two stages with 8-week and 24-week treatment durations); 0 = subject enrolled in TMC207-C209 (NCT00910871; an open-label, single-arm Phase IIb trial of 24-week bedaquiline plus an individualized background regimen, enrolling both newly diagnosed and treatment-experienced MDR-TB and XDR-TB patients). Used in pooled analyses of the two bedaquiline Phase IIb trials to carry residual between-study differences in trial design.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (TMC207-C209).
• Source aliases: derived per subject from the trial identifier (C208 -> 1, C209 -> 0); the Lin 2024 NONMEM data column is literally named C208.
• Example models: Lin_2024_TB_multistate.R (log-hazard effect shared by all three dropout transitions, lambda14 / lambda24 / lambda34 *= exp(e_study_lambda1424 * STUDY_C208) with e_study_lambda1424 = 0.909188, a hazard ratio of 2.48 for C208 relative to C209).
• Notes: Specific scope because the contrast is tied to the Janssen bedaquiline Phase IIb development program. Subject-level / time-fixed; set once from the trial identifier on each subject record. Lin 2024’s Discussion attributes the higher C208 dropout hazard to trial design and calendar time rather than to any pharmacological difference: patients in a double-blind placebo-controlled trial “might be less willing to return for scheduled follow-ups with few signs of improvement”, and C209 ran after positive C208 results had been reported, so C209 patients “might therefore have higher confidence in drug efficacy”. Cohort split in Lin 2024: C208 n = 195 (49%), C209 n = 207 (51%). Note the studies also differ in what they enrolled – C208 excluded prior anti-TB treatment and enrolled MDR-TB only, while C209 admitted treatment-experienced patients and XDR-TB – so a model carrying this indicator alongside DIS_TB_XDR_STRICT is partially confounding study with resistance stratum by construction.

STUDY_1 (canonical for Duong 2017 Study-1 (newly diagnosed obese T2DM) cohort indicator)

• Description: 1 = subject enrolled in Study 1 (NCT00236600) of the Duong 2017 pooled placebo T2DM analysis (newly diagnosed, treatment-naive, obese T2DM subjects; 6-week placebo run-in followed by 60-week placebo treatment phase with an ancillary weight-loss diet and exercise counselling arm); 0 = subject enrolled in Study 2 (NCT01071850) or Study 3 (NCT01117584) of the same pool (advanced T2DM cohorts on stable diet and exercise, no weight-loss counselling).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (Studies 2 and 3 pooled – the “advanced T2DM” reference: baseline b-cell function logit b0 = 0.677 and no treatment-phase placebo effect on weight (EFPL)).
• Source aliases:
  • STUDY = 1 (character-valued or integer study identifier; STUDY_1 = as.integer(STUDY == 1)).
• Example models: Duong_2016_WHIG_T2DM.R (switches the baseline b-cell function logit b0 = -0.298 for Study 1 vs 0.677 for Studies 2 and 3, and activates the treatment-phase placebo weight effect EFPL only for Study 1 – the “additional placebo effect during the treatment phase for Studies 2 and 3 was not significant” per Duong 2017 Results).
• Notes: Follows the auto-approved STUDY_<id> canonical family. Distinct from the Cirincione 2017 STUDY1 (no underscore) which selects a different study in a different pooled analysis. Subject-level (time-fixed).

STUDY_CLO_BE2 (canonical for Pejcic 2024 clopidogrel bioequivalence Study-2 cohort indicator)

• Description: 1 = subject enrolled in Study 2 of the Pejcic 2024 pooled clopidogrel / clopidogrel-carboxylic-acid analysis (n = 26; sampling to 36 h post-dose, 17 samples per subject per period); 0 = Study 1 (n = 24; sampling to 48 h post-dose, 14 samples per subject per period). Both were 2-treatment, 2-period, 2-sequence crossover bioequivalence studies of a generic 75 mg film-coated clopidogrel tablet against Plavix, dosed as 150 mg (2 tablets) under fasting conditions.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (Study 1).
• Source aliases: derived per subject from the study identifier (Study 2 -> 1, Study 1 -> 0).
• Example models: Pejcic_2024_clopidogrel.R (switches every study-specific quantity in the joint semi-physiological model: mean transit time MTT 0.470 vs 0.410 h, generic relative bioavailability Fgen 1.08 vs 0.960, the first-pass fraction parameter FR1 119 vs 76.8, the IIV magnitudes on F and FR1, the IOV magnitudes on F and MTT, and the proportional residual error 41.95% vs 29.39%).
• Notes: Follows the auto-approved STUDY_<id> canonical family. Subject-level (time-fixed) – each subject participated in exactly one of the two studies. Unusually broad in scope for a study indicator: rather than switching a single parameter, it selects an entire study-specific parameter block, because Pejcic 2024 pooled two datasets collected under different study conditions and found that estimating the absorption / bioavailability / first-pass parameters separately per study lowered OFV and stabilised the model (Discussion paragraph 3). Distinct from the Cirincione 2017 STUDY1 and the Duong 2017 STUDY_1, which index studies in unrelated pooled analyses. Pair with FORM_CLO_GENERIC, which distinguishes the two products within each subject’s crossover, and with OCC, which indexes the two periods.

STUDY_ING111521 (canonical for Zhang 2015 dolutegravir proof-of-concept study cohort indicator)

• Description: 1 = subject enrolled in study ING111521 (phase 2a, proof-of-concept dose-ranging study of dolutegravir monotherapy in HIV-1-infected adults; n = 19 in the Zhang 2015 pooled analysis) of the Zhang 2015 dolutegravir PopPK; 0 = SPRING-1 (phase 2b, n = 141) or SPRING-2 (phase 3, n = 403). Used to switch the typical CL/F magnitude in study ING111521.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (SPRING-1 / SPRING-2 combined adult treatment-naive HIV-1 cohort).
• Source aliases:
  • POC (proof-of-concept) – used in Zhang_2015_dolutegravir.R (= 1 for ING111521, 0 otherwise; per Zhang 2015 Table 3 footnote CL/F = 0.901 * 1.16^SMOK * 1.35^POC * (WT/70)^0.438 * (AGE/40)^0.193 * (BILI/9)^-0.211).
• Example models: Zhang_2015_dolutegravir.R (exponential effect on CL/F: cl *= exp(e_ing111521_cl * STUDY_ING111521) with e_ing111521_cl = log(1.35) = 0.300, so CL/F is 35% higher in study ING111521 than in the SPRING-1 / SPRING-2 reference).
• Notes: Sibling of STUDY_C2201 (Bienczak 2025 ligelizumab), STUDY_LBSL (Zhou 2021 belimumab), STUDY_M281_004 (Vivacity-MG nipocalimab), STUDY_MD (Cirincione 2017 ER exenatide multi-dose), STUDY_PKU015 (Qi 2014 sapropterin pediatric), and STUDY_RIV201 (Tammara 2017 rivipansel); member of the STUDY_<name> family of paper-specific study cohort indicators. The 35% higher CL/F in ING111521 vs SPRING-1 / SPRING-2 is unexplained by available covariates; Zhang 2015 attributes it plausibly to the smaller sample size and less diverse patient population in ING111521 (Zhang 2015 Discussion p. 506). Subject-level (time-fixed); set once from the trial identifier on each subject record. Ratified canonically alongside the Zhang 2015 dolutegravir extraction.

STUDY_HARROLD_PEG (canonical for Harrold 2020 pegfilgrastim-vs-filgrastim pivotal NHP study indicator)

• Description: 1 = subject enrolled in the pegfilgrastim pivotal NHP study (Harrold 2020 reference 13; pegfilgrastim 300 ug/kg SC on days 1 and 8); 0 = subject enrolled in the filgrastim pivotal NHP study (Harrold 2020 reference 14; filgrastim 10 ug/kg QD SC starting day 1). Used by the Harrold 2020 OS time-to-event sub-model to select between the two study-specific parameter sets in Table III (lambda_ANC, lambda_BC, k_e0); the ANC response sub-model (Table II) is fit on the combined placebo cohorts and does not depend on this indicator.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (filgrastim pivotal NHP study).
• Source aliases:
  • STUDY – the paper-text label for the binary study identifier; derived per subject from the trial-of-origin column in the source dataset.
• Example models: Harrold_2020_radiation_neutropenia.R (selects between the two OS parameter sets in Table III: lambda_ANC -2.15 vs -0.229, lambda_BC -0.347 vs 0.300, k_e0 0.668 vs 0.156 1/day).
• Notes: Specific scope; the indicator is tied to the two Amgen NHP pivotal studies used by Harrold 2020 (filgrastim reference 14 and pegfilgrastim reference 13) and is required because the paper documents an unexplained between-study OS difference that no observed covariate could resolve. Subject-level (time-fixed). Follows the auto-approved STUDY_<id> canonical family.

STUDY_SPZCH (canonical for sporozoite-challenge (SpzCh) study cohort indicator)

• Description: 1 = participant enrolled in a controlled sporozoite challenge (SpzCh) malaria study, in which infection is initiated at the liver stage by inoculating Plasmodium falciparum sporozoites; 0 = participant enrolled in an induced blood stage malaria (IBSM) challenge study, in which infection is initiated directly at the blood stage by inoculating parasitised erythrocytes. Subject-level (time-fixed). In the founding model the indicator does double duty: it scales blood-stage drug potency, EC50,b,SpzCh = EC50,b,IBSM * (1 - theta * STUDY_SPZCH) with theta = 0.83 (Courlet 2023 Table 1 footnote c), and it selects which life-cycle stage is inoculated, so a single model file reproduces both trial designs (liver state seeded with Finc * inoculum when 1, blood state seeded with P0 when 0).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (induced blood stage malaria, IBSM, challenge).
• Source aliases:
  • SpzCh – Courlet 2023 Table 1 footnote c notation (“with SpzCh being equal to 0 or 1 for IBSM and SpzCh populations, respectively”).
• Example models: Courlet_2023_cabamiquine.R (blood-stage EC50 falls from 7.60 ng/mL in the IBSM population to 1.29 ng/mL in the SpzCh population, and the inoculation route switches from blood to liver).
• Notes: Follows the auto-approved STUDY_<id> canonical family, but names a challenge-model design rather than a single named trial, because the IBSM-vs-SpzCh distinction is a standard, reusable split in controlled human malaria infection (CHMI) research and any future CHMI extraction pooling the two designs will need exactly this indicator. The founding paper hypothesises the potency difference is mechanistic rather than a study artefact – cabamiquine damages parasites during the liver stage, so the merozoites subsequently released into blood are less viable and are killed at lower concentrations – which is a further reason to name the covariate after the challenge model rather than after Courlet 2023’s specific protocol numbers. Ratified canonically alongside the Courlet 2023 cabamiquine extraction.

STUDY_SPR994_104 (canonical for tebipenem pivoxil hydrobromide study SPR994-104 (thorough-QT crossover) cohort indicator)

• Description: 1 = subject enrolled in study SPR994-104 (NCT04238195), the phase 1 randomized, double-blind, placebo- and active-controlled four-way crossover thorough-QT study in which 24 healthy adults received single oral doses of 600 mg and 1200 mg tebipenem pivoxil hydrobromide (TBP-PI-HBr) in crossover fashion; 0 = subject enrolled in any other study of the Ganesan 2023 pooled analysis (SPR994-101, SPR994-102, or the phase 3 SPR994-301 / ADAPT-PO trial). Subject-level (time-fixed).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (studies SPR994-101, SPR994-102, and SPR994-301 / ADAPT-PO).
• Source aliases:
  • S104 – the flag variable named in Ganesan 2023 Eq. 6 (“where S104 is a flag variable to indicate if subject was enrolled in study 104”).
• Example models: Ganesan_2023_tebipenem.R (gates the dose effect on the absorption rate constant: ka * (1 + e_dose_ka * (DOSE / 1200) * STUDY_SPR994_104) with e_dose_ka = -0.478, so Ka is 47.8% lower at 1200 mg and 23.9% lower at 600 mg relative to the same subject’s non-study-104 value, and the term is inert for every other study).
• Notes: Conceptually the same per-study switch family as STUDY1 / STUDY5 / STUDY_LBSL / STUDY_FARLETUZUMAB_PHASE2, but the switch here gates a covariate effect (dose on Ka) rather than a residual-error magnitude or a structural scale factor. Ganesan 2023 restricts the dose effect to this study because it was the only crossover design in the pooled analysis: the same 24 subjects received both dose levels, which limits the between-subject variability that otherwise masks the modest dose-on-absorption-rate signal (the effect was not detectable across the 100-900 mg single doses of study SPR994-101). Must be paired with a DOSE column carrying the administered milligram amount; the product (DOSE / 1200) * STUDY_SPR994_104 is zero for every subject outside study 104.

STUDY_LBSL (canonical for early-phase belimumab LBSL01 / LBSL02 study indicator)

• Description: 1 = subject enrolled in study LBSL01 (NCT00657007) or LBSL02 (NCT00071487) – the two early-phase belimumab studies that used a different ELISA-based bioanalytical assay; 0 = any other belimumab study in the Zhou 2021 pooled analysis. Used to switch CL and V1 magnitudes per study group (effectively an assay / early-development PK adjustment).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (later-phase studies using the electrochemiluminescence assay).
• Source aliases:
  • INDR – used in Zhou_2021_belimumab.R (Zhou 2021 Table 2 footnote: study indicator).
• Example models: Zhou_2021_belimumab.R (multiplicative factors 1.63 on CL and 1.26 on V1 when STUDY_LBSL = 1).
• Notes: Conceptually similar to STUDY1 / STUDY_FARLETUZUMAB_PHASE2 / ELISA / STUDY_NIPOCALIMAB_PHASE1 (per-study switches) but specific to the belimumab program. Subject-level (time-fixed); set from the trial identifier on each subject record.

STUDY_VORI (canonical for Friberg 2012 voriconazole pooled-analysis study indicator)

• Description: Integer-valued (1-5) subject-level identifier of which of the five pooled PK studies of the Friberg 2012 voriconazole integrated population PK analysis a subject belongs to. 1 / 2 / 3 = immunocompromised children (2 to <12 years; Friberg 2012 Table 1 studies 1-3); 4 = immunocompromised adolescents (12 to <17 years; study 4); 5 = healthy adults (22-55 years; study 5). Time-fixed per subject.
• Units: (integer 1-5)
• Type: categorical
• Scope: specific
• Reference category: 5 (healthy adult study; the typical-value reference for ka, Alag, Q, F1 IIV, CL IIV, and residual error).
• Source aliases:
  • STDY_VORI – prior canonical name (pre-2026-06-19 typo correction); a missing-vowel abbreviation of STUDY_VORI.
  • Derived per subject from the Friberg 2012 dataset’s STUDY / STDY identifier column.
• Example models: Friberg_2012_voriconazole.R (drives several effects: -0.382 Study-1 pediatric modifier on Km and Vmax,1; non-adult uplift on Q (+0.637); adolescent ka modifier; non-adult CL IIV scaling (+1.70); F1 IIV magnitude switching between adult and non-adult; per-study residual-error switching across the four levels Study 1, Study 2, Studies 3+4, Study 5).
• Notes: Departs from the binary STUDY1 / STUDY5 / STUDY_PKU015 precedent because the Friberg 2012 analysis uses five distinct studies and four of them carry distinct typical-value or residual-error coefficients (Studies 3 and 4 share one residual-error magnitude). Encoding as a single integer column avoids registering five paired binary indicators; the model file derives (STUDY_VORI == 1) style indicators inline. Renamed from STDY_VORI to STUDY_VORI on 2026-06-19 per the canonical-register standardization audit (operator decision: typo correction - STDY was a missing-vowel abbreviation of STUDY).

STUDY_SALEM (canonical for Derippe 2024 mouse venetoclax Salem 2021 ABBV-167 prodrug study indicator)

• Description: 1 = mouse from Salem 2021 (Mol Cancer Ther 20(6):999-1008), which gave a single 5 mg/kg intravenous dose of the venetoclax prodrug ABBV-167 and measured the venetoclax that appeared from it; 0 = mouse from Eisenmann 2020 (J Chromatogr B 1152:122176), which gave a single 10 mg/kg oral dose of venetoclax itself. Subject-level (time-fixed). The indicator is load-bearing beyond a scale factor because the two structural parameters change physical meaning across its levels: in the Eisenmann cohort ka is a first-order oral absorption rate constant and vc is a volume of distribution, whereas in the Salem cohort ka is the prodrug-to-venetoclax biotransformation rate constant and vc additionally absorbs the unknown fraction biotransformed.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (Eisenmann 2020 oral venetoclax study).
• Source aliases:
  • Study – the column header of the Supplement’s “Mice PK modeling” parameter table, whose levels are printed as “Eisenman et al.” and “Salem”.
• Example models: Derippe_2024_venetoclax_mouse.R (carries both a ka effect, 1.65 vs 0.856 1/h, and a volume effect, 3.56 vs 6.54 L/kg).
• Notes: Follows the auto-approved STUDY_<id> canonical family. Must be paired with SEXF: the Derippe 2024 supplement treats sex as a three-level covariate on volume (female, male, and “unknown / second experiment”), where the third level is exactly the Salem cohort. The model file multiplies the male volume factor by (1 - STUDY_SALEM) so the sex term is inert for Salem rows regardless of what SEXF holds there; set SEXF = 0 for Salem records.

ORAL_VORI (canonical for Friberg 2012 voriconazole observation-during-oral-dose-phase indicator)

• Description: 1 = observation collected when the most recent administered voriconazole dose was oral (powder for oral suspension or tablet); 0 = observation collected when the most recent administered dose was IV. Per-observation (record-level) indicator.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (IV-phase observation).
• Source aliases: derived per observation from the most recent administered dose’s route (POS or tablet -> 1; IV infusion -> 0).
• Example models: Friberg_2012_voriconazole.R (combines with STDY_VORI == 5 to switch the adult residual-error magnitude between IV-only expSdStdy5Iv = 0.0912 and oral sqrt(0.0912^2 + 0.132^2) = 0.160 per Table 3 footnote on the residual-error structure W).
• Notes: Conceptually similar to SAMPLE_INTENSIVE (a generic per-observation switch between estimated residual-error magnitudes); the contrast here is dosing route (oral vs IV) within the same subject’s crossover protocol rather than sampling design. Specific scope because the route-vs-residual-error switch is paper-specific to the Friberg 2012 voriconazole analysis.

SAMPLE_INTENSIVE (canonical for per-observation sampling-intensity indicator)

• Description: 1 = observation belongs to an intensive (rich, post-dose) PK sampling window; 0 = sparse (pre-dose / steady-state trough) sampling. Per-observation (record-level) indicator used to switch the proportional residual-error magnitude when a source paper estimates separate residual errors for intensive vs sparse sampling phases of the same dataset.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (sparse sampling).
• Source aliases: derived per observation from the sampling-design label in the source dataset (intensive 24-h profiles / dense post-dose schedules -> 1; pre-dose troughs and steady-state population samples -> 0).
• Example models: Macpherson_2015_rosuvastatin.R (Table 2 final model: 39.4% CV intensive vs 59.5% CV sparse residual error; switched per observation as propSd <- propSdIntensive * SAMPLE_INTENSIVE + propSdSparse * (1 - SAMPLE_INTENSIVE)).
• Notes: Conceptually similar to STUDY1 / STUDY5 / PHASE1 / ELISA (per-record switches that select between estimated residual-error magnitudes) but the contrast is sampling design rather than study cohort or bioanalytical assay. The indicator is generally applicable: any pediatric / dense-vs-sparse pooled popPK design that estimates two residual errors can carry it. Within-subject variation is permitted (a single subject can have both intensive and sparse observations, as in the Macpherson 2015 CHARON PK-pilot cohort where 12 subjects had a Day-0 intensive profile followed by 2 years of sparse troughs).

SAMPLE_CAPILLARY (canonical for per-observation capillary-vs-venous blood sampling-site indicator)

• Description: 1 = the observation was drawn as a capillary blood sample (finger-prick / heel-prick); 0 = venous blood sample. Per-observation (record-level) indicator used to switch the proportional residual-error magnitude when a source paper estimates separate residual errors for capillary and venous sampling within the same dataset.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (venous sample).
• Source aliases: derived per observation from the sample-matrix / collection-site label in the source dataset (capillary, finger-prick, heel-prick, dried-blood-spot-style microsampling -> 1; venous plasma draws -> 0).
• Example models: Cleary_2023_risdiplam.R (Table 2 final PPK model: 23.4% CV venous vs 34.2% CV capillary proportional residual error, capillary being 3% of the 10,205-record dataset; switched per observation as propSd <- propSdCapillary * SAMPLE_CAPILLARY + propSdVenous * (1 - SAMPLE_CAPILLARY)).
• Notes: Third axis of the per-record residual-switch family, alongside SAMPLE_INTENSIVE (sampling design: rich vs sparse) and ELISA / RIA_ASSAY / IMMUNOASSAY / ASSAY_OSA (bioanalytical assay method). This entry covers the sample matrix / collection site, which none of those capture: in Cleary 2023 both matrices were quantified by the same validated LC-MS/MS method (LLOQ 0.25 ng/mL), so the extra residual variability is attributable to the capillary collection itself rather than to a different assay. Distinct from HCT, RBC, BLOOD_GROUP_O and the other blood-named entries, which are physiological quantities rather than sampling attributes. General scope because capillary microsampling is a standard paediatric-trial technique and any pooled paediatric popPK that estimates a separate capillary residual error can reuse the column. Within-subject variation is permitted (a subject may contribute both capillary and venous observations). Ratified canonically on 2026-07-30 alongside the Cleary 2023 risdiplam extraction (operator decision in sidecar request-002 q1, option A).

ELISA (canonical for ELISA-vs-electrochemiluminescence bioanalytical assay indicator)

• Description: 1 = the concentration was measured by an enzyme-linked immunosorbent assay (ELISA); 0 = measured by an electrochemiluminescence assay (ECL / ECLIA). Per-observation indicator used to switch the residual-error magnitude (additive, proportional, or log-scale) between the two immunoassay platforms, which typically differ in LLOQ and precision. The two platforms and their LLOQs are paper-specific and MUST be documented in covariateData[[ELISA]]$notes.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (the electrochemiluminescence assay – in both ratifying papers the ECL platform is the newer, lower-LLOQ method used in the later-phase studies, which makes it the natural reference).
• Source aliases: derived per study from the bioanalytical method (Text S2 of Valenzuela 2025; Qi 2024 Sect. 2.3).
• Example models: Valenzuela_2025_nipocalimab.R (Valenzuela 2025 Table 3: additive PK residual 0.445 nmol/L for ELISA, LLOQ 0.150 ug/mL, studies MOM-M281-001 / MOM-M281-007 / MOM-M281-010, vs 0.0342 nmol/L for ECLIA, LLOQ 0.010 ug/mL, studies EDI1001 / EDI1002 / MOM-M281-004), Qi_2024_vosoritide.R (Qi 2024 Table 5: log-scale residual SD 0.665 for the validated ELISA used in studies 111-202 and 111-205, LLOQ 0.391 ug/L, vs 0.610 for the optimized ECL assay used in studies 111-206, 111-301 and 111-302, LLOQ 0.137 ug/L; encoded as expSd <- expSdElisa * ELISA + expSdEcl * (1 - ELISA)).
• Notes: Assay choice is study-fixed in both ratifying papers, but the column is carried per observation so pooled datasets apply the correct residual to each row. Distinct from IMMUNOASSAY (immunoassay of any kind vs an LC-MS/MS reference) and RIA_ASSAY (radioimmunoassay vs LC-MS/MS): those two contrast an immunoassay against chromatography, whereas ELISA contrasts two immunoassay platforms with each other and has no LC-MS/MS arm. Promoted from specific to general scope when the Qi 2024 vosoritide extraction ratified the same ELISA-vs-ECL encoding in an unrelated therapeutic area.

SIDN (canonical for the opaque study-level nesting column)

• Description: Integer-valued grouping column identifying the clinical trial (or trial group) a subject’s records belong to, used as the nesting level for a second hierarchical level of random effects on top of the per-subject etas. Values 1, 2, …, N. Unlike the STUDY_<id> family, SIDN does not enter any typical-value covariate equation and has no reference category – it only indexes which study-level random-effect draw applies to a record. Use it when the source paper fits a between-study random effect (rather than a fixed per-study shift), whether or not the paper publishes a mapping from trial identifier to level.
• Units: (count)
• Type: categorical
• Scope: general
• Reference category: n/a – SIDN is a nesting level, not a covariate with a reference category.
• Source aliases:
  • SIDN (“secondary study identity number”) – used in Qi_2024_vosoritide.R (Qi 2024 Sect. 3.2).
  • STUD – Rosenborg 2025 supplement Fig. 1, $LEVEL STUD=(9[1]) ; interstudy variability. Here the mapping is published (levels 1, 2 and 3 are the paper’s studies 1, 2 and 3), so the column is opaque only in Qi 2024’s case; the canonical name is still SIDN because the role – indexing which study-level random-effect draw applies – is identical, and the level index never enters a typical-value equation.
• Example models: Rosenborg_2025_salmeterol.R (Rosenborg 2025 Table 2 row F4_rel_OMEGA_ISV = 0.0293, the between-study SD of relative bioavailability, encoded as etalfdepot_study ~ 0.00085849 | SIDN on top of the subject-level etalfdepot), Qi_2024_vosoritide.R (Qi 2024 Sect. 2.4 and 3.2: the additional hierarchical level eta6 on CL/F and eta7 on V/F, encoded with rxode2/nlmixr2 native nested random effects as etalcl_study ~ 0.066049 | SIDN and etalvc_study ~ 0.000144 | SIDN; Qi 2024 states only three SIDN values were present in the database and does not enumerate which trials map to which value, so the column is deliberately opaque).
• Notes: Distinct from the STUDY_<id> family (binary per-trial indicators that switch a typical value, a residual-error magnitude, or a covariate coefficient) and from OCC (integer occasion / period index for inter-occasion variability within a subject). SIDN groups across subjects: a subject belongs to one study level and all of that subject’s records share the study-level draw. Because it is consumed by the ini() nesting syntax rather than by a model() expression, covariateData[['SIDN']] will not be referenced anywhere in model(). Simulation caveat: rxode2 expands nested random effects into per-level terms, so an event table must contain at least two distinct SIDN values or rxSolve() fails with The following parameter(s) are required for solving: THETA[1], and omega must be passed explicitly (omega = mod$omega) because the nested omega is a list of matrices keyed by level. Ratified canonically alongside the Qi 2024 vosoritide extraction, the first nlmixr2lib model to use nested random effects.

STUDY_M281_004 (canonical for MOM-M281-004 (Vivacity-MG) study indicator)

• Description: 1 = subject enrolled in study MOM-M281-004 (Vivacity-MG; NCT03772587; phase 2 in generalized myasthenia gravis); 0 = any other study in the Valenzuela 2025 pooled analysis. Used to switch the IgG-baseline scaling factor.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-Vivacity-MG studies).
• Source aliases: derived per subject from the trial identifier (NCT03772587 -> 1, else -> 0).
• Example models: Valenzuela_2025_nipocalimab.R.
• Notes: Valenzuela 2025 estimated a slightly lower IgG baseline in MOM-M281-004 participants (baseline scaled by FRIgG0_M281_004 = 0.777 vs. 1 in other studies). Distinct from the disease-state indicator implied by gMG – it is specifically the Vivacity-MG study flag because the IgG baseline factor was only estimated for that study.

STUDY_ABA2_HLA78 (canonical for ABA2 trial HLA 7/8-matched cohort indicator)

• Description: 1 = subject enrolled in the ABA2 hematopoietic-cell-transplant trial (IM101311; NCT01743131) HLA 7/8 (one-allele-mismatched donor) cohort, 0 = any other study in the Takahashi 2023 pooled abatacept population PK analysis (RA/JIA reference and ABA2 8/8 cohort).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-ABA2-7/8 – pooled adult RA / pediatric JIA cohort, plus ABA2 HLA 8/8 cohort which is flagged separately by STUDY_ABA2_HLA88).
• Source aliases: derived per subject from the trial-cohort identifier (Cohort = ABA2 7/8 -> 1, else -> 0). Takahashi 2023 Supplemental Table 4 names the corresponding theta thetaCohort_CL / thetaCohort_Vc.
• Example models: Takahashi_2023_abatacept.R (multiplicative Ratio factors on CL = 0.70 and on Vc = 0.99 vs the RA/JIA reference; values from Takahashi 2023 Supplemental Table 4).
• Notes: Pairs with STUDY_ABA2_HLA88 to reproduce the three-level cohort categorical (RA/JIA, ABA2 7/8, ABA2 8/8) the paper reports as the only retained categorical PK covariate. At most one of STUDY_ABA2_HLA78 and STUDY_ABA2_HLA88 is 1 per subject; both 0 reproduces the RA/JIA reference. Scope: specific because the contrast is tied to the ABA2-vs-RA/JIA pooling design.

STUDY_ABA2_HLA88 (canonical for ABA2 trial HLA 8/8-matched cohort indicator)

• Description: 1 = subject enrolled in the ABA2 hematopoietic-cell-transplant trial (IM101311; NCT01743131) HLA 8/8 (allele-matched donor) cohort, 0 = any other study in the Takahashi 2023 pooled abatacept population PK analysis (RA/JIA reference and ABA2 7/8 cohort).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-ABA2-8/8 – pooled adult RA / pediatric JIA cohort, plus ABA2 HLA 7/8 cohort which is flagged separately by STUDY_ABA2_HLA78).
• Source aliases: derived per subject from the trial-cohort identifier (Cohort = ABA2 8/8 -> 1, else -> 0). Takahashi 2023 Supplemental Table 4 names the corresponding theta thetaCohort_CL / thetaCohort_Vc.
• Example models: Takahashi_2023_abatacept.R (multiplicative Ratio factors on CL = 0.91 and on Vc = 1.32 vs the RA/JIA reference; values from Takahashi 2023 Supplemental Table 4).
• Notes: Pairs with STUDY_ABA2_HLA78. At most one of the two indicators is 1 per subject; both 0 reproduces the RA/JIA reference. Scope: specific.

STUDY_d2eGFP (canonical for Frohlich 2018 multi-experiment NLME reporter-construct cohort indicator)

• Description: 1 = cell transfected with destabilized eGFP (d2eGFP, mRNA construct pVAXA120-d2EGFP, ~6.6 h protein half-life via C-terminal PEST sequence); 0 = cell transfected with eGFP (mRNA construct pVAXA120-eGFP, ~22.8 h protein half-life). Cohort indicator in the Frohlich 2018 multi-experiment NLME single-cell translation-kinetics analysis used to select between the two reporter-construct cohorts (Experiment 1 eGFP vs Experiment 2 d2eGFP). All structural and ribosomal-binding parameters (mRNA degradation, ribosome-mRNA binding rate x m0, ribosome catalytic rate, R0/m0, fluorescence offset, transfection-onset time, scale x m0) are shared between cohorts; only the protein degradation rate (gamma_eGFP for STUDY_d2eGFP = 0 vs gamma_d2eGFP for STUDY_d2eGFP = 1) and its IIV variance differ.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (eGFP cohort – the stable reporter; this is the cohort with the slower protein degradation rate ~0.030 1/h and the larger between-cell IIV variance on it).
• Source aliases: derived per cell from the experiment identifier in the source dataset (Experiment 1 / eGFP -> 0, Experiment 2 / d2eGFP -> 1; see code.zip project/models/experiments_transfection_ribo.m in the Frohlich 2018 Zenodo deposit doi:10.5281/zenodo.1228899).
• Example models: Frohlich_2018_mRNA_translation.R.
• Notes: Specific scope because the cohort is tied to the Frohlich 2018 in-vitro reporter-mRNA assay (eGFP vs d2eGFP). Cell-level (time-fixed) indicator; set once per cell from the transfection-construct identifier. Conceptually analogous to a single-study two-arm cohort indicator (e.g. STUDY_FARLETUZUMAB_PHASE2, STUDY_NIPOCALIMAB_PHASE1), but the contrast is between two reporter mRNA constructs measured under the same physical / imaging protocol rather than between trial phases.

STUDY_RIV201 (canonical for Tammara 2017 rivipansel phase II SCD study indicator)

• Description: 1 = subject enrolled in the phase II rivipansel study (NCT01119833; “study 201” in the Tammara 2017 integrated population PK analysis; Telen 2015 Blood 125:2656-2664 reports the trial results); 0 = healthy adult volunteers from the three rivipansel phase I studies (studies 101, 102, and 103) pooled into the integrated dataset. Used both as an additive shift on typical clearance (interpreted by the authors as the SCD-hyperfiltration component, ~23%) and to switch the additive / proportional residual-error magnitudes between the two cohorts (Table 1).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (rivipansel phase I healthy-adult studies 101, 102, and study 103 healthy adults with SCD pooled with phase I per Tammara 2017; the paper labels these as “phase I studies” in the residual-error rows of Table 1).
• Source aliases: derived per subject from the trial identifier (NCT01119833 -> 1, else -> 0); the source STUD column in the NONMEM dataset described in Tammara 2017 Table 1 footnote b.
• Example models: Tammara_2017_rivipansel.R (Table 1: additive effect 0.234 on CL via 1 + 0.234 * STUDY_RIV201; selects the cohort-specific additive and proportional residual SDs in model()).
• Notes: Specific scope because the contrast is tied to the rivipansel development program. The Tammara 2017 paper interprets the 23% CL increment as a putative hyperfiltration effect of SCD; in simulation use cases targeting the SCD population (the paper’s stated goal) set STUDY_RIV201 = 1 for every subject. Subject-level / time-fixed; set once from the trial identifier on each subject record.

STUDY_LPS30M (canonical for Kutumova 2024 ANP-30-min-after-LPS cohort indicator)

• Description: 1 = albumin nanoparticles administered intravenously 30 min after the intraperitoneal lipopolysaccharide challenge (the paper’s “LPS 30 min” arm, exp 2); 0 otherwise. Cohort indicator in the Kutumova 2024 four-experiment nanoparticle-biodistribution PBPK analysis, used together with STUDY_LPS6H and STUDY_LPS24H to select the arm-specific permeability (PAC) and distribution (P) coefficients of every organ from Table 1. All other fitted parameters (the Hill-function endocytic uptake constants KRESmax / KRES50 / KRESn, the exocytic release constant KRESrelease, the biliary and urinary excretion coefficients, and the radiant-efficiency scale factor k) are shared across all four arms.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (LPS-naive control cohort, exp 1 – mice given nanoparticles only, no LPS).
• Source aliases: derived per animal from the “LPS 30 min” column heading of Kutumova 2024 Table 1 / the ANP-administration time point of 0.5 h after LPS in section 2.4.
• Example models: Kutumova_2024_albuminNanoparticles_mouse_pbpk.R.
• Notes: Specific scope because the cohort is tied to the Kutumova 2024 murine LPS acute-lung-injury protocol. Animal-level (time-fixed) indicator. The three STUDY_LPS* indicators are mutually exclusive; all three zero selects the control arm via f_ctrl <- 1 - STUDY_LPS30M - STUDY_LPS6H - STUDY_LPS24H in model(). This encodes an experimental-arm contrast rather than a separate trial, in the same sense as STUDY_d2eGFP (reporter-construct cohorts measured under one imaging protocol).

STUDY_LPS6H (canonical for Kutumova 2024 ANP-6-h-after-LPS cohort indicator)

• Description: 1 = albumin nanoparticles administered intravenously 6 h after the intraperitoneal lipopolysaccharide challenge (the paper’s “LPS 6 h” arm, exp 3); 0 otherwise. Cohort indicator in the Kutumova 2024 four-experiment nanoparticle-biodistribution PBPK analysis, selecting the LPS-6-h column of Table 1 for every organ’s permeability (PAC) and distribution (P) coefficient. This is the arm with peak pulmonary nanoparticle accumulation (lung P = 0.89661, the maximum across all four arms).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (LPS-naive control cohort, exp 1 – mice given nanoparticles only, no LPS).
• Source aliases: derived per animal from the “LPS 6 h” column heading of Kutumova 2024 Table 1 / the ANP-administration time point of 6 h after LPS in section 2.4.
• Example models: Kutumova_2024_albuminNanoparticles_mouse_pbpk.R.
• Notes: Specific scope because the cohort is tied to the Kutumova 2024 murine LPS acute-lung-injury protocol. Animal-level (time-fixed) indicator; mutually exclusive with STUDY_LPS30M and STUDY_LPS24H.

STUDY_LPS24H (canonical for Kutumova 2024 ANP-24-h-after-LPS cohort indicator)

• Description: 1 = albumin nanoparticles administered intravenously 24 h after the intraperitoneal lipopolysaccharide challenge (the paper’s “LPS 24 h” arm, exp 4); 0 otherwise. Cohort indicator in the Kutumova 2024 four-experiment nanoparticle-biodistribution PBPK analysis, selecting the LPS-24-h column of Table 1 for every organ’s permeability (PAC) and distribution (P) coefficient. The paper interprets this arm as partial pulmonary recovery with persisting injury elsewhere (lung P falls from its 6-h peak while splenic PAC keeps rising).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (LPS-naive control cohort, exp 1 – mice given nanoparticles only, no LPS).
• Source aliases: derived per animal from the “LPS 24 h” column heading of Kutumova 2024 Table 1 / the ANP-administration time point of 24 h after LPS in section 2.4.
• Example models: Kutumova_2024_albuminNanoparticles_mouse_pbpk.R.
• Notes: Specific scope because the cohort is tied to the Kutumova 2024 murine LPS acute-lung-injury protocol. Animal-level (time-fixed) indicator; mutually exclusive with STUDY_LPS30M and STUDY_LPS6H. The optimisation constrained PAC to be non-decreasing from control through this arm for the lungs, liver, spleen, and kidneys (section 2.8 equation 9), so a monotone PAC trend across the four indicators is a fitted constraint rather than an emergent result.

STUDY_WAGH_2 (canonical for Wagh 2021 spectinamide 1810 mouse TB study 2 indicator)

• Description: 1 = BALB/c mouse cohort enrolled in study 2 of the Wagh 2021 spectinamide 1810 dose ranging / dose fractionation efficacy program (Wagh 2021 Table 5: study 2 used 4-arm dosing regimens of 50 BID, 100 QD, 166 TIW, 200 BID, and 333 TIW with a 5.37 log CFU baseline at start of treatment); 0 = study 1 (Wagh 2021 Table 4: 24-arm dose-fractionation schedule with a 7.08 log CFU baseline at start of treatment). Used to switch K_kill_max from the study 1 typical value to the 1.15-fold higher study 2 value in the integrated PK/PD model.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (Wagh 2021 study 1: 7.08 log CFU initial bacterial load; the reference K_kill_max = 0.0374 1/h applies).
• Source aliases:
  • STUDY – the Wagh 2021 NONMEM dataset’s study identifier column (1 / 2 in the source; mapped to 0 / 1 in this binary indicator).
• Example models: Wagh_2021_spectinamide_1810_mouse.R (Table 3: study-specific coefficient for K_kill_max = 1.15, RSE 4.0%; applied multiplicatively as killmax = killmax_base * (1 + STUDY_WAGH_2 * (1.15 - 1))).
• Notes: Specific scope because the contrast is tied to the Wagh 2021 study 1 vs study 2 difference in inoculum-induced baseline bacterial load and a downstream effect on observed efficacy. Subject-level / time-fixed; set once per mouse from the source-study identifier on each animal record. Per the paper Discussion, the higher K_kill_max in study 2 (1.15x) is plausibly explained by the lower baseline bacterial load (5.37 vs 7.08 log CFU); see vignette Assumptions and deviations for the per-study residual-error treatment.

STUDY_CT_USA (canonical for the Wang 2024 risperidone LAI US relative-bioavailability trial-group indicator)

• Description: 1 = subject enrolled in one of the two US relative-bioavailability trials of the Wang 2024 RYKINDO (LY03004) / RISPERDAL CONSTA programme – CT-USA-104 (NCT02186769; open-label parallel single-dose 25 and 50 mg Rykindo vs Consta) or CT-USA-102 (NCT02091388; open-label parallel 25 mg every 2 weeks for five injections); 0 = subject enrolled in CT-1S01 (NCT02055287), the open-label single-ascending-dose Rykindo trial (12.5 / 25 / 37.5 / 50 mg). Used to switch the apparent clearance of the risperidone active moiety between the two trial groups.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (CT-1S01, the single-ascending-dose trial, which anchors the reference apparent clearance of 186.7 L/day in males and 153.4 L/day in females).
• Source aliases: derived per subject from the trial identifier (CT-USA-104 or CT-USA-102 -> 1; CT-1S01 -> 0). Wang 2024 Table 2 names the effect in the row label “CL ratio for CT-USA-104 and 102 to CT-1S01”.
• Example models: Wang_2024_risperidone_rykindo.R (log-multiplicative effect on apparent clearance: exp(log(0.675) * STUDY_CT_USA), i.e. a 0.675-fold, 32.5% lower, apparent clearance in the two US trials; Wang 2024 Table 2, %RSE 6.03).
• Notes: Specific scope; tied to the three-trial Luye Pharma risperidone LAI programme. Subject-level (time-fixed); set once from the trial identifier. A group-level indicator spanning two trials, directly analogous to STUDY_DORZA_EARLY and STUDY_LBSL. Applies to the Rykindo model only – the companion Wang_2024_risperidone_consta.R has a single apparent clearance across trials and sexes. Beware a text-versus-table conflict in the source: Wang 2024 Results (“Population Pharmacokinetic Modeling of Rykindo”) describes the reduced clearance as applying to “the multiple-dose study”, i.e. CT-USA-102 alone, whereas the Table 2 row label names both CT-USA-104 and CT-USA-102. The table definition is the one encoded, because it is the final-model parameter definition and because it reproduces the observed CT-USA-104 single-dose exposures (AUC0-t 227 and 399 day*ng/mL at 25 and 50 mg) far better than the text reading does. Distinct from STUDY_CT_USA_102, which is nested inside this group and switches the absorption rate constant rather than clearance; a CT-USA-102 subject has both indicators set to 1. Follows the auto-approved STUDY_<id> canonical family.

STUDY_CT_USA_102 (canonical for the Wang 2024 risperidone LAI multiple-dose trial indicator)

• Description: 1 = subject enrolled in trial CT-USA-102 (NCT02091388), the multiple-dose relative-bioavailability trial of the Wang 2024 RYKINDO (LY03004) / RISPERDAL CONSTA programme, in which 25 mg of either formulation was given intramuscularly every 2 weeks for five injections; 0 = subject enrolled in one of the single-dose trials CT-1S01 (NCT02055287) or CT-USA-104 (NCT02186769). Used to switch the first-order absorption rate constant of the main (third) release phase.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (the single-dose trials; for Rykindo this is CT-1S01 and CT-USA-104 pooled, for Consta it is CT-USA-104 alone since Consta was not studied in CT-1S01).
• Source aliases: derived per subject from the trial identifier (CT-USA-102 -> 1, else -> 0). Wang 2024 Table 2 names the two levels in the row labels “KA_CT-1S01 and CT-USA-104” and “KA_CT-USA-102”, with the footnote “KA_CT-USA-104 for Consta”.
• Example models: Wang_2024_risperidone_rykindo.R (log-multiplicative effect on the main-release KA: 0.288 -> 0.380 /day, a 1.319-fold increase), Wang_2024_risperidone_consta.R (0.179 -> 0.271 /day, a 1.514-fold increase).
• Notes: Specific scope; tied to the three-trial Luye Pharma risperidone LAI programme. Subject-level (time-fixed); set once from the trial identifier. Wang 2024 Discussion attributes the faster apparent absorption in the multiple-dose trial to its much sparser PK sampling scheme (trough-focused after the first injection) rather than to any formulation or population difference, so users simulating a clinical dosing scenario should generally leave this at the single-dose reference value of 0. Note that both Wang 2024 models set the elimination rate constant equal to KA (flip-flop kinetics) and derive the apparent central volume as V = CL/KA, so this indicator also shifts the apparent volume. Nested inside STUDY_CT_USA: a CT-USA-102 subject has both indicators set to 1. Follows the auto-approved STUDY_<id> canonical family.

DLVL (canonical for source-protocol dose-level integer indicator)

• Description: Integer dose-level / protocol-arm indicator carried per subject in the DDMODEL00000281 lidocaine bundle’s simulated dataset (Simulated_Lid_B04_ddmore.csv). Values 1-4 (or higher) flag distinct study-protocol dose / regimen tiers; the NA_NA_lidocaine.R model binarises as DLVL > 2 to switch the typical-value baselines for both the GX elimination rate constant K30 and the lidocaine apparent central volume V1 between a “low” (DLVL <= 2) and a “high” (DLVL > 2) regimen.
• Units: (integer-coded categorical)
• Type: categorical
• Scope: specific
• Reference category: the binary form DLVL <= 2 is the reference (THETA(4) for K30 and THETA(14) for V1 in the source .ctl); DLVL > 2 selects the higher-exposure regimen (THETA(5) and THETA(15) respectively).
• Source aliases: DLVL – the column header used in the DDMORE bundle’s .ctl $INPUT and the Simulated_Lid_B04_ddmore.csv data file.
• Example models: NA_NA_lidocaine.R (DDMODEL00000281; binary derivation DLVL_HIGH = as.integer(DLVL > 2) on K30 base and V1 base).
• Notes: Specific scope because the integer-coded dose / regimen tiers are paper-specific to the lidocaine BAST.dat (“4-cRUN249”) study and the linked publication is not on disk for this extraction. The binary threshold > 2 reproduces the source .ctl line IF(DLVL.GT.2)P1=0. If a future model needs a different dose-level binarisation or a continuous treatment, register a distinct canonical name rather than overloading DLVL.

S1A2 (canonical for source-protocol CYP1A2 substrate / co-medication categorical indicator)

• Description: Categorical CYP1A2-modifying co-medication / phenotype indicator carried per subject in the DDMODEL00000281 lidocaine bundle’s simulated dataset. Integer code; in the NA_NA_lidocaine.R model the value S1A2 == 3 selects the “CYP1A2 inducer present” sub-cohort (lidocaine N-deethylation to MEGX is CYP1A2-mediated, so the modifier acts on the GX elimination rate constant K30 in the source’s parameterisation). Other integer codes (0, 1, 2) are pooled into the reference.
• Units: (integer-coded categorical)
• Type: categorical
• Scope: specific
• Reference category: S1A2 != 3 (i.e., values 0, 1, 2) – pooled into the reference.
• Source aliases: S1A2 – the column header used in the DDMORE bundle’s .ctl $INPUT and the simulated dataset, with sibling columns D1A2 and H1A2 carried in the data file but dropped via =DROP in the source .ctl.
• Example models: NA_NA_lidocaine.R (DDMODEL00000281; binary derivation S1A2_IND = as.integer(S1A2 == 3) on the GX elimination rate constant K30).
• Notes: Specific scope because the integer codes for S1A2 are paper-specific to the lidocaine BAST.dat study and the linked publication is not on disk for this extraction. The exact biological meaning of each integer level (0/1/2/3) is not fully reconstructable from the bundle alone – the natural interpretation, given the column name encodes “CYP1A2” and the model attaches a sizeable positive K30 modifier of +0.853 to the level-3 cohort, is a CYP1A2-induction or smoking / inducer co-medication indicator. Sibling columns D1A2 (donor / inhibitor?) and H1A2 (host / inhibitor?) are dropped in the source .ctl so only the level-3 indicator is structurally identifiable from the surviving model code. If a future model needs a richer encoding of CYP1A2 modulation, register a separate canonical (e.g., CYP1A2_IND) rather than overloading S1A2.

DOSE_HIGH_EFL (canonical for high-dose eflornithine indicator)

• Description: 1 = dose record is at the highest oral eflornithine dose level (>= 3000 mg/kg in the Jansson 2008 Sprague-Dawley rat single-dose study), 0 = oral dose at the lower levels (750, 1500, 2000 mg/kg) or any IV dose.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (750-2000 mg/kg oral, or IV at 375 / 1000 mg/kg).
• Source aliases: derived per dose record from the administered amount and route. Jansson 2008 modeled it as a categorical indicator on bioavailability for both enantiomers (Results paragraph; OFV drop -11.4).
• Example models: Jansson_2008_eflornithine_rat.R.
• Notes: Specific scope because the threshold (3000 mg/kg of body weight) and the bioavailability shifts (+14.6% for L-eflornithine, +32.8% for D-eflornithine relative to the 750-2000 mg/kg reference) are intrinsically tied to the Jansson 2008 dose-design and are not transferable to other drugs. Encoded as a binary because the paper’s prose explicitly states linear and power dose-F relationships did not improve the fit. Per-subject indicator in the source data (each rat received exactly one oral dose); for multi-dose simulation, set the indicator per dose record.

DOSE_PEG_300UGKG (canonical for pegfilgrastim 300 ug/kg dose-cohort indicator)

• Description: 1 = subject received pegfilgrastim at 300 ug/kg (the highest pegfilgrastim dose evaluated in Melhem 2018; n = 12 subjects: 8 healthy adult volunteers and 4 adult cancer patients on chemotherapy), 0 = filgrastim or pegfilgrastim at any dose other than 300 ug/kg. Encodes the empirically-observed low-clearance behaviour of the pegfilgrastim 300 ug/kg cohort as a subject-level cohort indicator on CLD (Melhem 2018 Results ‘Model development’ and Table 2 row CLD PEG 300 ug/kg = 0.107 L/h vs CLD PEG = 0.362 L/h). Subject-level (each subject enrolled in one dose-cohort per study); coexists with FORM_GCSF_PEG in the covariate frame and only takes non-zero effect when FORM_GCSF_PEG = 1 (a filgrastim subject with DOSE_PEG_300UGKG = 1 is nonsensical per the paper design).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (filgrastim, or pegfilgrastim at any dose other than 300 ug/kg).
• Source aliases:
  • DOSE_PEG_300 – paper-narrative descriptor for the pegfilgrastim 300 ug/kg cohort in Melhem_2018_g_csf.R.
• Example models: Melhem_2018_g_csf.R.
• Notes: Sibling of DOSE_HIGH_EFL (Jansson 2008 eflornithine highest-dose indicator) and DOSE_400MG / DOSE_50MG / DOSE_70MG / DOSE_130MG / DOSE_260MG (Jorga 2000 / other dose-level indicators). Specific scope because the low-clearance shift (0.107 / 0.362 = fold-decrease 0.295) is intrinsically tied to the Melhem 2018 pooled analysis. Melhem 2018 introduced the effect after graphical analysis of IIV suggested distinct clearance behaviour for the 300 ug/kg pegfilgrastim cohort; the paper notes the small sample size (n = 12) and physiological plausibility of the finding. Encoded as an additive log-scale shift on CLD that is meaningful only when FORM_GCSF_PEG = 1; a study of pegfilgrastim at other dose levels should set DOSE_PEG_300UGKG = 0. Ratified canonically alongside the Melhem 2018 g_csf extraction. Auto-approved sibling of the DOSE_HIGH_<drug> / DOSE_<N>MG cohort-indicator family.

DOSE_HIGH_RIV (canonical for high-dose rivaroxaban indicator)

• Description: 1 = dose record is at the 20 mg-equivalent body-weight-adjusted rivaroxaban dose, 0 = the 10 mg-equivalent body-weight-adjusted rivaroxaban dose. The Willmann 2018 EINSTEIN-Jr phase I paediatric study (NCT01145859) dosed children with body-weight-adjusted amounts targeting adult exposures of either rivaroxaban 10 mg or 20 mg; the popPK model estimates a relative bioavailability F1 = 0.648 for the 20 mg-equivalent dose, with the 10 mg-equivalent dose anchored at F1 = 1 by definition.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (10 mg-equivalent body-weight-adjusted dose; relative bioavailability fixed to 1 in the source paper).
• Source aliases: derived per dose record from the EINSTEIN-Jr phase I dose level assigned to the cohort; the paper’s Methods describe the dose stratification as “two dose levels, equivalent to adult doses of rivaroxaban 10 mg and 20 mg” (Willmann 2018 Methods / Modelling strategy).
• Example models: Willmann_2018_rivaroxaban.R (multiplicative effect on the depot bioavailability: fdepot <- exp(lfdepot * DOSE_HIGH_RIV) with lfdepot = log(0.648) per Willmann 2018 Table 1, RSE 9.03%; the dose-dependent reduction is consistent with the saturable-solubility behaviour of rivaroxaban reported in the adult patient popPK [reference 21 of Willmann 2018]).
• Notes: Specific scope because the 20 mg-equivalent vs 10 mg-equivalent dose stratification and the 0.648 relative-bioavailability estimate are intrinsically tied to the Willmann 2018 EINSTEIN-Jr phase I paediatric study. Drug-specific member of the DOSE_HIGH_* family alongside DOSE_HIGH_EFL (Jansson 2008 eflornithine high-dose indicator). Per-dose-occasion indicator in principle, although in the Willmann 2018 single-dose study each subject received exactly one rivaroxaban dose.

DOSE_LOW_AMG221 (canonical for low-dose AMG 221 indicator)

• Description: 1 = dose record is the 3 mg oral AMG 221 dose in the Gibbs 2011 phase 1 study; 0 = the 30 or 100 mg oral AMG 221 dose. Sibling of DOSE_HIGH_EFL: same drug-suffixed dose-level indicator family, opposite direction of effect (the reduced-bioavailability tier is the flagged tier).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (30 or 100 mg oral AMG 221, where F1 = 1).
• Source aliases: derived per dose record from the administered amount. Gibbs 2011 Table III + footnote a: F1 = 0.546 at 3 mg vs F1 = 1 fixed at 30 or 100 mg.
• Example models: Gibbs_2011_amg221.R.
• Notes: Specific scope because the threshold (3 mg vs 30/100 mg oral AMG 221 as a suspension in healthy obese adults) and the -45.4% bioavailability shift are intrinsically tied to the Gibbs 2011 dose-design and are not transferable to other drugs. Gibbs 2011 Discussion attributes the reduced 3 mg bioavailability to a possible high-affinity intestinal-metabolism / transport process saturating at higher doses (Caco-2 permeability plus in vitro CYP3A metabolism with Km > 100 uM, so a 30 mg dose is expected to produce intestinal concentrations high enough to saturate intestinal metabolism); the paper flags an alternative Michaelis-Menten dose-F structure that was not fit because only three discrete dose levels were tested. Per-dose-record indicator; observation rows inherit the indicator from the preceding dose.

MEAL_A (canonical for Zvada 2010 meal-A high-fat English breakfast indicator)

• Description: 1 = oral dose administered 30 min after Zvada 2010 meal A (a high-fat English breakfast); 0 = otherwise. Per Zvada 2010 Table 1 meal A consists of 2 rashers of bacon (20 g), 1 fried egg (50 g), 1 slice white toast (30 g) with butter (7 g) and marmalade (10 g), 2 cups decaffeinated coffee (400 ml) with full-cream milk (100 ml) and 2 teaspoons sugar (10 g); 18.9 g protein, 27 g fat, 38 g carbohydrate, 1,966 kJ, 627 g total weight. Distinct from the general FED_HIGHFAT because Zvada 2010 isolates four operationally-distinct meal compositions (A/B/C/D) each with its own bioavailability effect estimate and because the paper’s Discussion explicitly attributes part of meal A’s effect to eggs rather than total fat.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (fasted dosing, equivalent to Zvada 2010 meal E = 200 ml of water; the all-zero state of MEAL_A / MEAL_B / MEAL_C / MEAL_D).
• Source aliases: paper narrative “meal A” (Zvada 2010 Table 1 / Table 3).
• Example models: Zvada_2010_rifapentine.R (additive contribution to TVF = 1 * (1 + sum of per-meal fractional changes); RXF_A = +0.857, RSE 20.1%; Zvada 2010 Table 3).
• Notes: Specific scope because the operational meal definition is paper-specific. Mutually exclusive with MEAL_B, MEAL_C, MEAL_D - at most one of the four indicators can be 1 on a given dosing record; the fasted reference is all four = 0. Future rifapentine / antimycobacterial food-effect studies that reuse the same meal compositions (uncommon - meal compositions are typically protocol-specific) can extend this entry’s example list. Distinct from MEAL_FLAG / SNACK_FLAG (time-varying intra-day flags) because MEAL_A is a per-dose-record indicator that selects between five mutually exclusive meal states.

MEAL_B (canonical for Zvada 2010 meal-B low-fat bulky maize-meal porridge indicator)

• Description: 1 = oral dose administered 30 min after Zvada 2010 meal B (a low-fat bulky maize-meal porridge breakfast); 0 = otherwise. Per Zvada 2010 Table 1 meal B consists of 1.5 cups soft maize meal porridge (375 g cooked) with 3 teaspoons sugar (15 g), 1 cup decaffeinated coffee (200 ml) with full-cream milk (50 ml) and 1 teaspoon sugar (5 g); 6 g protein, 3 g fat, 66 g carbohydrate, 1,285 kJ, 645 g total weight.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (fasted dosing, equivalent to Zvada 2010 meal E).
• Source aliases: paper narrative “meal B” (Zvada 2010 Table 1 / Table 3).
• Example models: Zvada_2010_rifapentine.R (additive contribution to TVF; RXF_B = +0.327, RSE 40.4%; Zvada 2010 Table 3).
• Notes: Specific scope; mutually exclusive with MEAL_A, MEAL_C, MEAL_D. See MEAL_A notes for the per-dose-record indicator pattern.

MEAL_C (canonical for Zvada 2010 meal-C high-fat bulky maize-meal porridge with lard indicator)

• Description: 1 = oral dose administered 30 min after Zvada 2010 meal C (a high-fat bulky maize-meal porridge breakfast with lard); 0 = otherwise. Per Zvada 2010 Table 1 meal C consists of 1.5 cups soft maize meal porridge (375 g cooked) with 3 teaspoons sugar (15 g) and 5 teaspoons of lard (25 g), 1 cup decaffeinated coffee (200 ml) with full-cream milk (50 ml) and 1 teaspoon sugar (5 g); 6 g protein, 28 g fat, 66 g carbohydrate, 2,229 kJ, 670 g total weight.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (fasted dosing, equivalent to Zvada 2010 meal E).
• Source aliases: paper narrative “meal C” (Zvada 2010 Table 1 / Table 3).
• Example models: Zvada_2010_rifapentine.R (additive contribution to TVF; RXF_C = +0.457, RSE 29.3%; Zvada 2010 Table 3).
• Notes: Specific scope; mutually exclusive with MEAL_A, MEAL_B, MEAL_D. Meal C and meal A have similar total fat content but the Zvada 2010 Discussion attributes the substantially smaller effect of meal C (+45.7%) versus meal A (+85.7%) to the absence of eggs in C - supporting the authors’ egg-specific bioavailability hypothesis.

MEAL_D (canonical for Zvada 2010 meal-D low-fat high-fluid chicken noodle soup indicator)

• Description: 1 = oral dose administered 30 min after Zvada 2010 meal D (a low-fat high-fluid chicken noodle soup breakfast); 0 = otherwise. Per Zvada 2010 Table 1 meal D consists of 2 cups reconstituted powdered chicken noodle soup (400 ml), 1 cup decaffeinated coffee (200 ml) with skim milk (50 ml) and 1 teaspoon sugar (5 g); 9 g protein, 4 g fat, 28 g carbohydrate, 774 kJ, 660 g total weight.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (fasted dosing, equivalent to Zvada 2010 meal E).
• Source aliases: paper narrative “meal D” (Zvada 2010 Table 1 / Table 3).
• Example models: Zvada_2010_rifapentine.R (additive contribution to TVF; RXF_D = +0.489, RSE 30.7%; Zvada 2010 Table 3).
• Notes: Specific scope; mutually exclusive with MEAL_A, MEAL_B, MEAL_C. Zvada 2010 Discussion notes that monosodium glutamate (MSG) in the chicken noodle soup may contribute to the effect by accelerating gastric emptying (per Boutry et al., Am J Physiol Endocrinol Metab 2011, on MSG and gastric emptying).

CONMED_ATORVASTATIN_DOSE, CONMED_FLV_DOSE, CONMED_LOV_DOSE, CONMED_PRV_DOSE, CONMED_RSV_DOSE, CONMED_SMV_DOSE, CONMED_EZT_DOSE, CONMED_INH_DOSE, CONMED_TRAMADOL_DOSE (canonical for daily dose of a co-administered named drug)

• Description: Daily dose of the named drug (suffix = INN abbreviation: ATORVASTATIN atorvastatin (spelled out to avoid an ATV collision with atazanavir; see CONMED_ATAZANAVIR), flv fluvastatin, lov lovastatin, prv pravastatin, rsv rosuvastatin, smv simvastatin, ezt ezetimibe, inh isoniazid, TRAMADOL tramadol (spelled out to keep drug names unambiguous in the MBMA opioid-pain family)). 0 = the named drug is not part of the regimen for this study arm / subject; positive value = total daily dose. Captures the dose-response amplitude in MBMA or co-administered-drug PK/PD models where each drug arm contributes its own dose-effect curve.
• Units: mg/day for the statin / ezetimibe / tramadol series; mg/kg for CONMED_INH_DOSE (paper-specific unit, documented in covariateData[[CONMED_INH_DOSE]]$units).
• Type: continuous
• Scope: specific
• Reference category: 0 (the drug is not given in this arm; equivalent to “this drug-arm contributes 0 to its dose-effect term”).
• Source aliases: DOSE_<drug> legacy form (used in Vargo 2014 statins / ezetimibe MBMA pre-rename, Chen 2017 TB mouse pre-rename); aliases of the canonical CONMED_<drug>_DOSE form per the 2026-05-28 naming audit rename. CONMED_ATV_DOSE (legacy abbreviated form used briefly between the 2026-05-28 naming audit and the atazanavir-collision rename to CONMED_ATORVASTATIN_DOSE).
• Example models: Vargo_2014_statins_ezetimibe_mbma.R (each CONMED_<statin>_DOSE drives the corresponding statin’s Hill / Emax dose-response curve; per-arm dose level in mg/day, default 0 outside the named statin arm; ezetimibe and statin arms combine via the sub-additive gamma_int term), Chen_2017_TB_MTP_GPDI_mouse.R (CONMED_INH_DOSE drives the isoniazid CL adjustment: cl_inh = cl_inh_lowdose * (1 - slope_inh * (CONMED_INH_DOSE - 12.5))), Mercier_2014_tramadol_tapentadol_mbma.R (CONMED_TRAMADOL_DOSE drives the tramadol Emax-in-dose term in the extent-of-reduction R with ED50 = 184 mg/day).
• Notes: The CONMED_<drug>_DOSE shape replaces the earlier DOSE_<drug> / DOSE_<drug>_<unit> names (which conflated a covariate with a dose-amount column). The atorvastatin slot is spelled out as CONMED_ATORVASTATIN_DOSE rather than CONMED_ATV_DOSE to keep the ATV abbreviation reserved for the binary atazanavir coadministration indicator (CONMED_ATAZANAVIR, source alias ATV) used in HIV-PK extractions like ArabAlameddine 2012 raltegravir. Tramadol is spelled out for the same reason (no established TRM abbreviation collision, but the full drug name keeps the MBMA opioid-pain family unambiguous alongside the sibling TAPENTADOL treatment-arm indicator). New co-medication dose-effect MBMA models should reuse this family and add the appropriate <drug> INN abbreviation; spell out drug names whenever the natural 3-4 letter abbreviation would collide with another CONMED_ entry. The units field is per-paper.

ON_TREATMENT (canonical for generic on-treatment (active-vs-placebo) indicator)

• Description: Generic binary treatment-arm indicator. 1 = subject is in the active-drug arm (or, for time-varying use, the subject is currently on treatment at this record), 0 = subject is in the placebo arm (or, for time-varying use, currently off treatment / pre-treatment / placebo-controlled run-in). Per-subject and time-fixed by default in randomized parallel-group trials; admits a time-varying interpretation for cross-over, run-in, or treatment-suspension designs where the indicator switches at well-defined per-record times. Used in non-PK / non-exposure disease-progression and PD models where the “drug effect” enters the structural equations as a categorical on/off switch rather than as an exposure-driven response, and where the trial design pools or abstracts away dose-level differences within the active arm.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (placebo arm / off treatment).
• Source aliases:
  • Trt (Lee 2011 paper notation: Trt = (0, 1, ...), where the source paper notes the indicator could in principle take more than two levels but only the binary placebo-vs-active contrast is used in the actual analyses) – used in Lee_2011_parkinson_progression.R.
• Example models: Lee_2011_parkinson_progression.R (additive shifts on the placebo disease-progression slope and on the placebo short-term symptomatic effect: slope = beta0 + beta1 * ON_TREATMENT + etaslope and symeff = gamma0 + gamma1 * ON_TREATMENT + etasymeff; default ini() parameter values are from the TEMPO study rasagiline arm, so ON_TREATMENT = 1 predicts the rasagiline-active disease trajectory and ON_TREATMENT = 0 predicts the placebo trajectory).
• Notes: Distinct from TRT (which is the Novakovic 2017 cladribine-cohort categorical indicator with three levels and a paper-specific 1-vs-2 cohort labelling) and from DRUG_ORMU / DRUG_PRED / DRUG_OBI (which are head-to-head drug-product comparator indicators carrying the contrast between two named active drugs in the same trial). Use ON_TREATMENT only when the structural model has no drug-specific PK / exposure and the active arm enters as a pooled on/off switch. When extracting a head-to-head comparator paper (drug A vs drug B) register a sibling DRUG_<X> canonical instead. When the source paper differentiates multiple active dose levels with distinct parameters, register a categorical indicator with paper-specific level labelling (e.g., TRT for the cladribine cohorts) rather than overloading ON_TREATMENT. Per-model covariateData[[ON_TREATMENT]]$notes should document which drug the active arm corresponds to in that file’s default ini() values, the trial design (parallel-group vs cross-over), and any time-varying interpretation. Ratified canonically alongside the Lee 2011 Parkinson’s disease-progression extraction (the existing TRT canonical’s Notes already directed future generic on-treatment indicators to register this canonical rather than reusing TRT).

PLACEBO (canonical for placebo-arm membership indicator)

• Description: Binary indicator of randomized placebo-arm membership: 1 = subject is in the placebo arm, 0 = active-treatment arm. Time-fixed per subject. Used to select placebo-arm-specific structural parameters (e.g., a separately estimated baseline) in models that ALSO carry an exposure-driven active-drug effect, so the placebo indicator is a covariate on a structural parameter rather than the drug-effect switch itself.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (active-treatment arm)
• Source aliases: none.
• Example models: Hansson_2013_sunitinib_dbp.R (switches the typical baseline diastolic blood pressure dbp0 from 71.8 mmHg in the active arm to 77.6 mmHg in the placebo run-in arm via exp(ldbp0 + e_placebo_dbp0 * PLACEBO); the sunitinib AUC drug effect on kin is modelled separately and exposure-driven).
• Notes: Ratified 2026-06-28. Distinct from ON_TREATMENT (the generic pooled active-vs-placebo on/off switch used when the active arm REPLACES exposure in the structural model, reference 0 = placebo): PLACEBO has the opposite polarity (1 = placebo) and is used alongside, not instead of, an exposure-driven drug effect, marking the placebo subpopulation for an arm-specific structural parameter. If a model needs the pooled on/off drug switch, use ON_TREATMENT (= 1 - PLACEBO) instead.

SEMESTER (canonical for paired-season indicator (winter or spring vs summer or fall))

• Description: Calendar paired-season indicator: 1 = winter or spring (December 21 to June 19), 0 = summer or fall (June 20 to December 20). Time-fixed per subject (or per simulation) based on the calendar date at which the observation occurred.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (summer or fall)
• Source aliases:
  • SEMESTER1 – used in Gonzalez-Sales 2015 testosterone circadian model with the same encoding (winter or spring = 1, summer or fall = 0).
• Example models: GonzalezSales_2015_testosterone.R (+8.09% multiplicative effect on Base; Table III).
• Notes: Gonzalez-Sales 2015 split season into a paired SEMESTER1 / SEMESTER2 covariate (SEMESTER1 = 1 if winter or spring; SEMESTER2 = 1 if fall or winter), keeping only SEMESTER1 in the final model. The canonical column omits the 1 suffix because the second indicator was not retained; the source-paper naming is recorded as a source alias. Calendar boundaries are reported verbatim in the paper Methods (winter: Dec 21 - Mar 19; spring: Mar 20 - Jun 19; summer: Jun 20 - Sep 21; fall: Sep 22 - Dec 20). Scope: specific until a second endogenous-rhythm model adopts the same encoding; at that point promote to general. Distinct from SEASON2 (RSV-second-exposure indicator) which has different semantics.

CEN (canonical for Wahlby 2004 pefloxacin study-centre indicator)

• Description: Binary study-centre indicator carried over from the Karlsson MO, Sheiner LB 1993 (J Pharmacokin Biopharm 21(6):735-750) pefloxacin analysis that Wahlby 2004 re-fit. The source paper does not specify which of the two recruiting centres corresponds to CEN = 1 vs CEN = 0; the indicator captures an unexplained between-centre shift in clearance.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (one of the two recruiting centres; the source paper does not state which).
• Source aliases:
  • CEN – Wahlby 2004 source-column convention; used in Wahlby_2004_pefloxacin.R.
• Example models: Wahlby_2004_pefloxacin.R (multiplicative effect on CL via exp[0.19 * CEN]; CEN = 1 increases CL by ~21 percent relative to CEN = 0).
• Notes: Specific scope; the underlying centre identity is paper-defined and not generalisable. Users assembling a virtual cohort should treat CEN as a sensitivity covariate or fix it at 0 (the reference centre) when between-centre exploration is not relevant. Ratified canonically alongside the Wahlby 2004 extraction.

FORM_AXI_XLI (canonical for axitinib Form XLI vs Form IV crystal polymorph indicator)

• Description: 1 = subject received axitinib as crystal polymorph Form XLI (the marketed commercial polymorph used in the approved tablet); 0 = subject received axitinib as crystal polymorph Form IV (the earlier Phase I polymorph and the typical-value bioavailability reference). Per-dose-occasion categorical indicator: a single subject may carry both indicator values across the within-subject crossover formulation arms in studies 7 and 9.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (Form IV; the typical-value F reference in Garrett 2014 Table 3, F = 0.465 in the fed state).
• Source aliases:
  • FORM – used in Garrett_2014_axitinib.R (Garrett 2014 Methods ‘Statistical analysis’ and Results ‘Full model and final model’ describe the formulation as a categorical {Form IV, Form XLI} with Form XLI = 1 as the test polymorph and Form IV = 0 as the typical-value reference).
• Example models: Garrett_2014_axitinib.R (linear-proportional effect on bioavailability per Garrett 2014 Table 3 final estimates: f = exp(lfdepot) * (1 + e_fast_f * (1 - FED)) * (1 + e_xli_f * FORM_AXI_XLI) with e_xli_f = -0.150 (15% reduction in F for Form XLI relative to Form IV in the fed state); Form XLI has no retained effect on ka, CL, Vc, Q, or Vp).
• Notes: Specific scope because the Form XLI vs Form IV crystal polymorph contrast is tied to the axitinib drug-product-development comparison in Garrett 2014. Crystal polymorph difference (same dosage form, same drug substance, different crystal lattice with different solubility / dissolution behaviour) rather than a dosage-form difference (tablet vs capsule vs solution) – distinct from FORM_TABLET, FORM_CAPSULE, FORM_SOLUTION, and the rest of the dosage-form FORM_* family. Mirrors the existing FORM_SAR_DP2 (sarilumab drug-product 2), FORM_ISA_P2F2 (isatuximab P2F2 drug material), FORM_LINAG_TAB1 (linagliptin tablet 1), FORM_VISMO_PHASEI (vismodegib Phase I capsule), FORM_ITR_SUBA (SUBA-itraconazole vs Sporanox capsule), FORM_TAC_IR (tacrolimus IR vs prolonged release), FORM_DOX_DORYX_MPC (Doryx MPC tablet), and FORM_THEO_APNECUT (theophylline Apnecut) entries under the FORM_* family of drug-specific drug-product-version / polymorph indicators. Set to 0 to simulate the Form IV early-phase reference; set to 1 to simulate the Form XLI marketed product (the routine clinical-use default). Ratified canonically on 2026-06-16 alongside the Garrett 2014 axitinib extraction.

STUDY_B1831090 (canonical for Abrantes 2017 moroctocog study B1831090 cohort indicator)

• Description: 1 = subject enrolled in study B1831090 (phase I bioequivalence study of Refacto; n = 18, severe and moderately severe hemophilia A, rich sampling, CSA assay) of the Abrantes 2017 pooled population PK analysis; 0 = any other study in the 13-study pool (B1831003, B1831004, B1831015, B1831053, B1831054, B1831061, B1831066, B1831067, B1831068, B1831070, B1831071, B1831077). Used to switch the typical CL magnitude downward for the B1831090 cohort while keeping the rest of the pooled-population PK parameters applicable to the remaining 12 studies.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (any non-B1831090 study in the Abrantes 2017 pool).
• Source aliases:
  • STUD – used in Abrantes_2017_moroctocog.R (Abrantes 2017 Table 2 footnote b: study indicator on CL).
• Example models: Abrantes_2017_moroctocog.R (multiplicative effect on CL: (1 - 0.347 * STUDY_B1831090) so B1831090 subjects have ~34.7% lower CL than the other 12 studies; Abrantes 2017 Table 2 also reports a 95% CI of -40.7% to -24.2%).
• Notes: Specific scope because the contrast is tied to the Abrantes 2017 pooled-analysis design. Subject-level / time-fixed; set once from the trial identifier on each subject record. Inclusion in the model allows the typical PK parameters to describe the remaining 12 studies, with B1831090 captured by the indicator (Abrantes 2017 Discussion). Ratified canonically on 2026-06-21 alongside the Abrantes 2017 moroctocog extraction.

TINF (canonical for the duration of an intravenous infusion)

• Description: Duration of the intravenous infusion of the dose record, in hours. Continuous. Used as a covariate when a source analysis estimates an effect of the administration protocol on PK parameters; this is separate from, and in addition to, the physical infusion duration carried on the dose record itself.
• Units: h
• Type: continuous
• Scope: general
• Reference category: none (continuous). Source analyses typically enter it as a linear deviation from a paper-specific reference duration, so record the reference in the model’s covariateData[["TINF"]]$notes rather than assuming a library-wide default.
• Source aliases:
  • Tinf – used in Schreib_2024_busulfan.R (Table 3 covariates theta_V3 and theta_k6, both entering as (Tinf - 3 h)).
• Example models: Schreib_2024_busulfan.R (exponential effects on both the central volume, exp(0.226 * (TINF - 3)), and the elimination rate constant, exp(-0.161 * (TINF - 3)); the center changed its standard busulfan infusion from 3 h to 4 h in October 2014, so only two values occur in that cohort and the covariate term is 0 or 1).
• Notes: Matches the near-universal pharmacometric symbol T_inf. Supply this as an explicit data column even though the event table already encodes the infusion duration – rxode2 takes the physical infusion duration from the dur / rate fields of the dose record and does not expose it to model(), so a covariate effect on infusion duration needs its own column. Registered with general scope: the concept is protocol-independent even though any particular reference duration is paper-specific. Opposite-signed effects on volume and on the elimination rate constant (as in Schreib 2024) are a recognised signature of a one-compartment model absorbing a distribution phase that a shorter infusion exposes – clearance is then nearly independent of infusion duration; note that interpretation when it applies rather than reading the two effects as independent physiology. Ratified canonically on 2026-08-05 (task oare_PMC11154452 sidecar question q2, answer A) alongside the Schreib 2024 busulfan extraction.

T_PUMP (canonical for the duration of a breast-milk pumping (expression) session)

• Description: Time the lactating subject spent expressing milk with a breast pump to produce the milk sample, i.e. the duration of the collection procedure itself. Continuous. In lactation PK the covariate is not a nuisance sampling detail: milk fat content rises through a single expression as hind-milk is released, so a longer pumping session yields a fattier, more lipid-rich sample and a correspondingly different concentration of the analyte, water-soluble drugs being diluted and lipophilic drugs concentrated. Recorded per sample, so it is time-varying within subject in principle; the founding model treats it as a per-subject constant because each woman’s sessions were of similar length and only one duration is tabulated per individual.
• Units: h
• Type: continuous
• Scope: general
• Reference category: none (continuous). Source analyses centre it at a cohort-typical duration and enter the deviation linearly, so record the centring value in the model’s covariateData[["T_PUMP"]]$notes rather than assuming a library-wide default. Melander 2025 centres at 0.22 h (13.2 min).
• Source aliases:
  • TPUMP – used in Melander_2025_cetirizine.R (the NONMEM covariate symbol carried in the published covariate equation, VTpump = 1 + VTpump1 * (TPUMP - 0.22); same quantity in the same units, no value transformation).
• Example models: Melander_2025_cetirizine.R (linear centred effect on the apparent milk volume of distribution, vc = 19.9 * (1 + 3.47 * (T_PUMP - 0.22)); longer pumping increases the apparent milk volume, which the authors attribute to the higher fat content of milk expressed later in a session and cetirizine’s water solubility. Sole covariate retained by a stepwise search that also screened maternal age, maternal weight, BMI, breastfeeding exclusivity and infant age).
• Notes: Units are hours, matching the general library convention and the equation text of the founding paper, even though lactation studies conventionally quote pumping durations in minutes (Melander 2025 reports a cohort mean of 14.9 min, range 2-50 min, i.e. 0.033-0.833 h); convert minutes to hours when building a covariate column rather than registering a minute-scaled sibling. Per the T_<event> canonical family the T_ prefix denotes a procedure-related time covariate and, as with T_CPB (cardiopulmonary-bypass run duration), the “event” here is the pumping session itself and the column carries its duration – not a time since or until it. Distinct from TINF (duration of an intravenous infusion, a dose-administration rather than a sample-collection procedure) and from TPP (time postpartum). A study that instead records which milk fraction was sampled (fore-milk vs hind-milk vs whole-breast) is measuring a related construct through a categorical lens and should register its own indicator rather than being coerced into this continuous column. Ratified canonically alongside the Melander 2025 cetirizine breast-milk extraction, as a well-formed member of the auto-approved T_<event> family.

STUDY_LEFAMULIN_PHASE1 (canonical for Bian 2024 lefamulin phase 1 study cohort indicator)

• Description: 1 = subject enrolled in one of the Phase 1 studies (n = 98 healthy adults) of the pooled lefamulin popPK model-building dataset; 0 = otherwise. Paired with STUDY_LEFAMULIN_PHASE2; both indicators 0 selects the Phase 3 CABP studies (n = 622), which are the reference stratum. Shifts clearance, distributional clearance to the first peripheral compartment, and the first peripheral volume.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 with STUDY_LEFAMULIN_PHASE2 also 0 (Phase 3 community-acquired bacterial pneumonia studies). Bian 2024 Equations 1, 3 and 4 all state it explicitly: “When PHASE = 3, CLPHASE = 1”.
• Source aliases: derived per subject from the integer PHASE column of the pooled dataset (PHASE == 1 -> 1).
• Example models: Bian_2024_lefamulin_original_ppb.R, Bian_2024_lefamulin_higher_ppb.R (multiplies CL by 1.766 / 1.710, CLd1 by 2.12 / 1.788, and Vp1 by 2.75 / 1.889 in the original- and higher-plasma-protein-binding fits respectively).
• Notes: Specific scope because the contrast is tied to the lefamulin clinical-development pooled analysis. Drug-specific paper-anchored member of the STUDY_<DRUG>_PHASE<N> family alongside STUDY_SULDUR_PHASE2 / STUDY_SULDUR_PHASE3 (Cammarata 2024), STUDY_POSA_PHASE3 (van Iersel 2018), STUDY_ASP8232_PHASE2 (Snelder 2020), STUDY_NIPOCALIMAB_PHASE1 (Valenzuela 2025) and STUDY_FARLETUZUMAB_PHASE2 (Farrell 2012). Like the sulbactam-durlobactam pair this needs TWO indicators because the source stratifies across three phases; keeping both columns rather than an integer PHASE column keeps the reference stratum explicit. Subject-level (time-fixed). The underlying source is the pooled foreign dataset also used by Zhang 2019 (doi:10.1093/jac/dkz088) and the FDA XENLETA review (NDA 211672/211673), so the same two columns apply to any further extraction from that lineage. Ratified canonically on 2026-08-09 alongside the Bian 2024 lefamulin extraction.

STUDY_LEFAMULIN_PHASE2 (canonical for Bian 2024 lefamulin phase 2 study cohort indicator)

• Description: 1 = subject enrolled in the Phase 2 acute bacterial skin and skin structure infection study (n = 129) of the pooled lefamulin popPK model-building dataset; 0 = otherwise. Paired with STUDY_LEFAMULIN_PHASE1; both indicators 0 selects the Phase 3 CABP studies. Shifts clearance, distributional clearance to the first peripheral compartment, and the first peripheral volume.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 with STUDY_LEFAMULIN_PHASE1 also 0 (Phase 3 community-acquired bacterial pneumonia studies).
• Source aliases: derived per subject from the integer PHASE column of the pooled dataset (PHASE == 2 -> 1).
• Example models: Bian_2024_lefamulin_original_ppb.R, Bian_2024_lefamulin_higher_ppb.R (multiplies CL by 1.827 / 1.707, CLd1 by 1.44 / 1.192, and Vp1 by 1.985 / 1.28 in the original- and higher-plasma-protein-binding fits respectively).
• Notes: Specific scope; see STUDY_LEFAMULIN_PHASE1 for the full family rationale and the pair-of-indicators convention. Ratified canonically on 2026-08-09 alongside the Bian 2024 lefamulin extraction.

INJSITE_BUTTOCK (canonical for SC injection-site = buttock indicator)

• Description: 1 = subject’s SC dose injected into the buttock, 0 = abdomen (the universal SC reference site across the popPK literature) or a different non-buttock injection site. Per-dose-record OR per-subject covariate flagging the SC injection site when a population analysis estimates site-specific absorption parameters.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (abdomen).
• Source aliases: paper narrative “buttock” / “abdomen” injection-site labels driving site-specific absorption parameters.
• Example models: Glatard_2025_octreotide.R (fractional change -0.527 on the fast-release mean absorption time MAT_fast of the CAM2029 octreotide depot relative to abdominal injection, per Table 3 / Eq 11; supplied per dose record alongside INJSITE_THIGH, both 0 meaning abdomen).
• Notes: Specific scope because the buttock-vs-abdomen contrast is paper-specific. Third sibling of the INJSITE_<site> family alongside INJSITE_ARM (arm-vs-abdomen) and INJSITE_THIGH (thigh-vs-abdomen); the three indicators are mutually exclusive and all-zero denotes the abdomen reference. Distinct from ROUTE_IV (IV vs SC route, not within-SC site) and from DEVICE_AI (autoinjector vs prefilled syringe, device rather than anatomical site). In Glatard 2025 the buttock effect rests on only 1.5% of observations (from 5.1% of participants) and the authors explicitly advise caution in interpreting it – carry that caveat forward when reusing the estimate.

FORM_OCTREOTIDE_IR (canonical for immediate-release octreotide formulation indicator)

• Description: 1 = record belongs to immediate-release (IR) subcutaneous octreotide, 0 = the CAM2029 sustained-release subcutaneous octreotide depot (FluidCrystal injection depot).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (CAM2029 sustained-release depot).
• Source aliases: “octreotide IR” / “CAM2029” treatment-group labels in Glatard 2025.
• Example models: Glatard_2025_octreotide.R (selects between the two formulation-specific log-scale residual error terms of Table 3 – 0.393 for CAM2029 vs 0.204 for octreotide IR, each carrying its own exponential IIV).
• Notes: Member of the FORM_<drug>_<formulation> family. In the founding model this indicator does not route the dose – the dosing compartment does that (depot / depot2 for CAM2029’s two parallel release processes, depot3 for octreotide IR) – it selects the residual-error stratum. Time-varying within a participant: in trial HS-19-664 healthy volunteers received four octreotide IR doses, then after a washout of at least 6 days received CAM2029, so the same subject contributes records under both values.

PRIOR_OCTREOTIDE (canonical for prior/ongoing octreotide (or lanreotide) therapy at a pre-first-dose baseline record)

• Description: 1 = the record is a pre-first-study-dose baseline sample from a participant already receiving octreotide LAR or lanreotide autogel therapy, 0 = octreotide-naive participant, or any record at or after the first study dose.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (octreotide-naive, or any on-treatment record).
• Source aliases: “Pre-treatment” treatment-group label in Glatard 2025 Online Resource 2; “pre-treated participants with octreotide” in Sect. 2.3.1.
• Example models: Glatard_2025_octreotide.R (gates the estimated additive baseline rbase = 0.433 ng/mL, the authors’ “fudge factor” for the octreotide concentration present before the first CAM2029 dose in participants entering the phase 3 trials on stable somatostatin-receptor-ligand therapy).
• Notes: Member of the registered PRIOR_<drug or class> family (cf. PRIOR_STATIN, PRIOR_EZE, PRIOR_TNF, PRIOR_IPI, PRIOR_TAXANE), but unlike those per-subject indicators this one is supplied per record, because the parameter it gates describes a specific baseline observation rather than a subject-level exposure history. Online Resource 2 of the founding paper reports exactly 1.0 observation per participant in the “Pre-treatment” group (22 of 46 participants in HS-18-633; 49 of 95 in HS-19-647), i.e. the single pre-dose baseline sample. At such a record the model prediction arising from study dosing is identically zero, so adding the baseline to the prediction and substituting it for the prediction are equivalent; set the indicator to 0 for ordinary forward simulation. Distinct from CONMED_<INN> (a genuinely concomitant medication continued during the study) because the prior therapy is stopped when study treatment begins.

STUDY_TLV_PHASE2 (canonical for Van Wart 2025 telavancin phase 2 study cohort indicator)

• Description: 1 = subject enrolled in a Phase 2 study of the Van Wart 2025 pooled telavancin popPK analysis (the Phase 2 cSSSI studies and the Phase 2 study in patients with uncomplicated Staphylococcus aureus bacteremia); 0 = otherwise. Paired with STUDY_TLV_PHASE3; both indicators 0 selects the pooled Phase 1 and Phase 4 stratum (the reference). Used to switch the proportional (constant-coefficient-of-variation) residual-error magnitude between study phases; the additive residual component is shared across all phases.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 with STUDY_TLV_PHASE3 also 0 (the twelve Phase 1 studies in healthy subjects, the Phase 1 study in obese subjects, and the Phase 4 study TLV-2014-020 in otherwise healthy subjects with renal impairment).
• Source aliases: derived per subject from the study identifier and its phase (Van Wart 2025 Table S1 lists the phase of each of the 21 studies).
• Example models: VanWart_2025_telavancin.R (selects propSdPhase2 = sqrt(0.0169) = 0.130 for plasma observations).
• Notes: Specific scope because the contrast is tied to the Van Wart 2025 telavancin clinical-development pooled analysis. Drug-specific paper-anchored member of the STUDY_<DRUG>_PHASE<N> family alongside STUDY_SULDUR_PHASE2 / STUDY_SULDUR_PHASE3 (Cammarata 2024), STUDY_POSA_PHASE3 (van Iersel 2018), STUDY_ASP8232_PHASE2 (Snelder 2020), STUDY_NIPOCALIMAB_PHASE1 (Valenzuela 2025) and STUDY_FARLETUZUMAB_PHASE2 (Farrell 2012). Second member of the family needing a PAIR of indicators for a three-way phase stratification, after STUDY_SULDUR_PHASE2/3; keep both columns rather than an integer PHASE column so the reference stratum stays explicit. Note the wrinkle that distinguishes this pair from the Cammarata pair: here the reference stratum pools two phases (1 and 4), because Van Wart 2025 states “Phases 1 and 4 were combined into a single value” – so a Phase 4 subject sets both indicators to 0 and there is deliberately no STUDY_TLV_PHASE4 column. Subject-level (time-fixed); set once from the study identifier on each subject record. Ratified canonically on 2026-08-17 alongside the Van Wart 2025 telavancin extraction.

STUDY_TLV_PHASE3 (canonical for Van Wart 2025 telavancin phase 3 study cohort indicator)

• Description: 1 = subject enrolled in a Phase 3 study of the Van Wart 2025 pooled telavancin popPK analysis (the Phase 3 cSSSI studies and the two Phase 3 studies in patients with hospital-acquired or ventilator-associated bacterial pneumonia); 0 = otherwise. Paired with STUDY_TLV_PHASE2; both indicators 0 selects the pooled Phase 1 and Phase 4 stratum.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 with STUDY_TLV_PHASE2 also 0 (pooled Phase 1 and Phase 4 studies).
• Source aliases: derived per subject from the study identifier and its phase (Van Wart 2025 Table S1).
• Example models: VanWart_2025_telavancin.R (selects propSdPhase3 = sqrt(0.0441) = 0.210 for plasma observations – the largest of the three phase magnitudes, consistent with the sparser and more variable sampling in the Phase 3 patient studies).
• Notes: Specific scope; see STUDY_TLV_PHASE2 for the full family rationale, the pair-of-indicators convention, and why the reference stratum pools Phases 1 and 4. Ratified canonically on 2026-08-17 alongside the Van Wart 2025 telavancin extraction.

STUDY_AZTAVI_PHASE2 (canonical for Xie 2025 aztreonam-avibactam phase 2 residual-error stratum indicator)

• Description: 1 = the observation belongs to the Phase 2 stratum of the Xie 2025 pooled aztreonam-avibactam popPK analysis; 0 = otherwise. One of a trio (STUDY_AZTAVI_PHASE2, STUDY_AZTAVI_PHASE3, STUDY_AZTAVI_PHASE23); all three 0 selects the Phase 1 stratum, which is the reference. Used to switch the AZTREONAM proportional residual-error magnitude between study strata; avibactam residual variability is not stratified.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 with STUDY_AZTAVI_PHASE3 and STUDY_AZTAVI_PHASE23 also 0 (Phase 1 stratum).
• Source aliases: derived per subject / record from the study phase.
• Example models: Xie_2025_aztreonam_avibactam.R (selects propSdPhase2 = 0.224 for aztreonam observations; Xie 2025 Table S3 row Prop RSV_ATM phase 2 = 22.4%).
• Notes: Specific scope because the contrast is tied to the Xie 2025 aztreonam-avibactam pooled analysis. Member of the STUDY_<DRUG>_PHASE<N> family alongside STUDY_SULDUR_PHASE2 / STUDY_SULDUR_PHASE3 (Cammarata 2024), STUDY_POSA_PHASE3 (van Iersel 2018), STUDY_ASP8232_PHASE2 (Snelder 2020), STUDY_NIPOCALIMAB_PHASE1 (Valenzuela 2025) and STUDY_FARLETUZUMAB_PHASE2 (Farrell 2012). This is the first member needing a TRIO of indicators, because Xie 2025 Table S3 reports four aztreonam proportional magnitudes; keep the separate columns rather than an integer PHASE column so the reference stratum stays explicit. Ratified canonically on 2026-08-17 alongside the Xie 2025 aztreonam-avibactam extraction.

STUDY_AZTAVI_PHASE3 (canonical for Xie 2025 aztreonam-avibactam phase 3 residual-error stratum indicator)

• Description: 1 = the observation belongs to the Phase 3 stratum of the Xie 2025 pooled aztreonam-avibactam popPK analysis (studies REVISIT / NCT03329092 and ASSEMBLE / NCT03580044); 0 = otherwise. Paired with STUDY_AZTAVI_PHASE2 and STUDY_AZTAVI_PHASE23.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 with the other two phase indicators also 0 (Phase 1 stratum).
• Source aliases: derived per subject / record from the study phase.
• Example models: Xie_2025_aztreonam_avibactam.R (selects propSdPhase3 = 0.403 for aztreonam observations; Xie 2025 Table S3 row Prop RSV_ATM phase 3 = 40.3%).
• Notes: Specific scope; see STUDY_AZTAVI_PHASE2 for the full family rationale. Ratified canonically on 2026-08-17 alongside the Xie 2025 aztreonam-avibactam extraction.

STUDY_AZTAVI_PHASE23 (canonical for Xie 2025 aztreonam-avibactam pooled phase-2/3 residual-error stratum indicator)

• Description: 1 = the observation belongs to the pooled phase-2/3 stratum of the Xie 2025 aztreonam residual-error model; 0 = otherwise. Paired with STUDY_AZTAVI_PHASE2 and STUDY_AZTAVI_PHASE3.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 with the other two phase indicators also 0 (Phase 1 stratum).
• Source aliases: Prop RSV_ATM phase 2/3 – the Xie 2025 Table S3 row label, which is the only place the stratum is named.
• Example models: Xie_2025_aztreonam_avibactam.R (selects propSdPhase23 = 0.533; Xie 2025 Table S3 row Prop RSV_ATM phase 2/3 = 53.3%).
• Notes: Specific scope; see STUDY_AZTAVI_PHASE2 for the family rationale. Stratum membership is not defined by any source on disk. Xie 2025 Table S3 reports this fourth aztreonam proportional magnitude alongside separate phase 2 (theta21) and phase 3 (theta22) magnitudes, and neither the paper, its supplement, nor the predecessor Das 2024 says which records make up the pooled phase-2/3 stratum as distinct from the other two. The column is registered so the published parameter set can be carried complete rather than silently dropping theta23; assigning records to it is left to the user, and the founding model’s vignette records the gap in its Errata. Because this selects only a residual-error magnitude, it affects the simulated observation and not the typical-value or individual-prediction profile. Ratified canonically on 2026-08-17 alongside the Xie 2025 aztreonam-avibactam extraction.

STUDY_CIAI_PH2 (canonical for the phase 2 cIAI study cohort indicator in the ceftazidime-avibactam / aztreonam-avibactam model lineage)

• Description: 1 = subject enrolled in the Phase 2 complicated-intra-abdominal-infection study of the ceftazidime-avibactam development program (labelled “Study2002” in Xie 2025 Table S3); 0 = otherwise. A study-cohort effect that is retained across successive models in this lineage and stacks ON TOP OF the infection-type covariate DIS_CIAI for those subjects rather than replacing it.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (any other study).
• Source aliases:
  • Study2002 – printed row label in Xie_2025_aztreonam_avibactam.R (Table S3 rows Study2002 on CL_AVI, Study2002 on Vc_AVI).
  • Population effect on CL / Vc (cIAI, phase II) – the corresponding row labels in Das 2024 supplementary Table 5, which is where the proportional X*(1 + theta) form comes from.
• Example models: Xie_2025_aztreonam_avibactam.R (proportional shifts of +0.89 on avibactam CL and +1.64 on avibactam Vc; aztreonam carries no term, and the indicator is 0 for every aztreonam-avibactam subject and for the whole simulated population of the paper).
• Notes: Specific scope because the cohort is a single named trial in one development program. Registered as a STUDY_<cohort> indicator rather than a DIS_ indicator because the effect is attached to a study, not to a disease state: Xie 2025 Results lists it among effects “identified in prior modeling” and carried forward, and the model separately carries DIS_CIAI for the infection type itself. A subject in this cohort therefore receives both shifts. Ratified canonically on 2026-08-17 alongside the Xie 2025 aztreonam-avibactam extraction.

STUDY_TACRO_FRANCKE (canonical for Francke 2025 tacrolimus transplant-centre x bioanalytical-method residual-error stratum)

• Description: Integer-valued (1-7) per-observation identifier of the combination of transplant centre and tacrolimus measurement method to which a whole-blood concentration record belongs, in the Francke 2025 international multicentre tacrolimus population PK analysis. The seven strata pair the four contributing centres (Erasmus MC Rotterdam, LUMC Leiden, Bellvitge University Hospital Barcelona, Cliniques Universitaires St Luc Brussels) with the two bioanalytical methods (immunoassay, LC-MS/MS). Record-level, not subject-level: a single recipient can contribute records under more than one code if their centre changed analytical method during follow-up.
• Units: (integer 1-7)
• Type: categorical
• Scope: specific
• Reference category: None. Every observation belongs to exactly one stratum, and each stratum selects one of five estimated residual-error magnitudes; no stratum is a typical-value reference.
• Source aliases:
  • DATA – the column name used in the Francke 2025 Supplementary Data S3 $ERROR block (IF (DATA.EQ.1.OR.DATA.EQ.2) Y=IPRED + ERR1*EPS(1), etc.).
  • CENTER – the column name listed in the same control stream’s $INPUT record. The published stream does not reconcile the two names; they refer to the same stratification.
• Example models: Francke_2025_tacrolimus.R, Francke_2025_tacrolimus_startingdose.R (both select the log-scale residual SD per observation across five levels: codes 1-2 -> Rotterdam immunoassays, codes 3-4 -> Rotterdam and Leiden LC-MS/MS, code 5 -> Barcelona immunoassay, code 6 -> Barcelona LC-MS/MS, code 7 -> Brussels immunoassay).
• Notes: Follows the STUDY_VORI precedent (a single integer column rather than a family of paired binary indicators) because the stratification has seven levels and the model file can derive (STUDY_TACRO_FRANCKE == n) indicators inline. Francke 2025 Section 2.2.1 states that seven centre-by-method combinations were formed and that “residual errors were combined where possible based on analytical method and fit of the model”, which is why the seven codes collapse onto five estimated magnitudes. The source paper does not state what distinguishes code 1 from code 2, or code 3 from code 4; the most plausible reading is two immunoassay platforms within Rotterdam and the Rotterdam-versus-Leiden split of a shared LC-MS/MS magnitude. Because each member of those pairs selects the same estimated SD, the ambiguity has no effect on model predictions and a user may code either member. For a new single-centre LC-MS/MS dataset, code 3 (or 4) is the closest analogue. Distinct from the general IMMUNOASSAY canonical, which captures only the bioanalytical method and cannot express the centre dimension that Francke 2025 stratifies jointly on. Auto-approved member of the STUDY_<id> canonical family. Ratified 2026-08-18 alongside the Francke 2025 tacrolimus extraction.

DOSE_RIMEGEPANT_MG (canonical for administered rimegepant per-administration dose amount)

• Description: Continuous per-dose-record covariate carrying the administered rimegepant dose amount in mg. Drives the nonlinear relative bioavailability of oral rimegepant, which increases greater-than-dose-proportionally over the studied 10 to 150 mg range – attributed to dose-dependent autoinhibition of CYP3A-mediated first-pass metabolism, rimegepant being a weak in-vitro CYP3A4 inhibitor.
• Units: mg
• Type: continuous
• Reference category: n/a – used with power scaling (DOSE_RIMEGEPANT_MG / 10)^exponent; the 10 mg reference is the lowest studied dose level.
• Source aliases:
  • Dose – used in Comisar_2025_rimegepant.R (Comisar 2025 Table 2 row label ‘Dose effect on F 1’).
• Example models: Comisar_2025_rimegepant.R (founding example; fdepot <- 1 * (DOSE_RIMEGEPANT_MG / 10)^0.192 * (1 + (-0.331) * FED). The power form and the 10 mg reference are printed verbatim on the structural schematic of the upstream adult-only model, Comisar 2025 CPT:PSP Figure 2, as F1 = (Dose/10)^0.191; relative bioavailability at the marketed 75 mg dose is therefore 1.47-fold that at 10 mg. Paired with the separate binary DOSE_LOW indicator, which carries the step change in the transit rate constant at the 10 and 25 mg levels).
• Notes: Auto-approved member of the DOSE_<drug>_<units> family. Use this rather than the bare [[DOSE]] canonical: rxode2’s event-table translator etTrans() consumes a covariate column literally named DOSE (in any casing) and never exposes it to model(), so a model reading the bare name fails at solve time with The following parameter(s) are required for solving: DOSE. The drug-suffixed name sidesteps that entirely. Distinct from [[DOSE_LOW]] and the DOSE_<N>MG indicator family, which are binary dose-level flags rather than the numeric dose amount.

Occasion / period (IOV)

OCC (canonical for the integer-valued occasion / period column)

• Description: Integer-valued occasion / period indicator for inter-occasion-variability (IOV) modelling. Values 1, 2, …, N identify the occasion to which each observation belongs (typically a dosing visit, study period, or sampling occasion). Time-varying within subject; constant within an occasion.
• Units: (count)
• Type: categorical
• Scope: general
• Reference category: n/a – OCC is decomposed inside model() into mutually-exclusive binary indicators, e.g., oc1 <- (OCC == 1), oc2 <- (OCC == 2), …, that are then multiplied against per-occasion eta* slots.
• Source aliases:
  • OCC – used in Jonsson_2011_ethambutol.R (DDMODEL00000220 NMTRAN $INPUT column; values 1..4).
• Example models: Jonsson_2011_ethambutol.R (4-occasion IOV on log-CL; cl <- exp(lcl + etalcl + oc1 * etalcl_oc1 + oc2 * etalcl_oc2 + oc3 * etalcl_oc3 + oc4 * etalcl_oc4) * (WT/50)^0.75, where each etalcl_oc<k> is a separate ~ fix(0.127) after the first to encode NONMEM $OMEGA BLOCK(1) SAME), Aregbe_2012_alvespimycin.R (5-occasion BOV on Q2 and V1), Oosten_2016_fentanyl.R (10-occasion IOV on transdermal Ka; only OCC >= 1 records carry IOV, sc / non-transdermal records pass OCC = 0 so all indicators zero out), Jonsson_2011_ethambutol_ddmore.R (4-occasion IOV on log-CL; cl <- exp(lcl + etalcl + oc1 * etalcl_oc1 + oc2 * etalcl_oc2 + oc3 * etalcl_oc3 + oc4 * etalcl_oc4) * (WT/50)^0.75, where each etalcl_oc<k> is a separate ~ fix(0.127) after the first to encode NONMEM $OMEGA BLOCK(1) SAME), Chen_2023_nemonoxacin.R (2-occasion IOV on log-CL distinguishing the single-dose occasion (OCC = 1) from steady state 72 h after the first of multiple doses (OCC = 2); iov_cl <- oc1 * etaiov_cl_1 + oc2 * etaiov_cl_2 with etaiov_cl_2 ~ fix(0.017) encoding the shared pi^2 of NONMEM $OMEGA BLOCK(1) SAME), Waterhouse_2024_vedolizumab.R (7-occasion IOV on log-CL across the phase 3 VEDO-3035 vedolizumab dosing schedule; occasions 2-7 ~ fix(0.0315) per NONMEM $OMEGA BLOCK(1) SAME, and records preceding the first dose take OCC = 1), Stoschus_2025_phenobarbital.R (8-occasion IOV on log-CL; the source is a MONOLIX fit that defines an occasion as the interval between consecutive therapeutic-drug-monitoring samples, cohort median 48 h, and reports one shared IOV magnitude without stating an occasion count, so eight 48-h occasions are encoded to span 0-384 h and cover the paper’s 336-h steady-state assessment time, with occasions 2-8 ~ fixed(0.127331)).
• Notes: OCC is the recommended canonical for new IOV-using models – the binary ooc1..oocN indicators below remain canonical for legacy / pre-existing models that ship the data already-decomposed. Ratified canonically on 2026-05-06.

ooc1, ooc2, ooc3, ooc4 (canonical for mutually-exclusive crossover occasion indicators)

• Description: Mutually exclusive occasion indicators for a crossover / multi-period design. Exactly one is 1 per observation.
• Units: (binary)
• Type: binary
• Scope: specific
• Example models: Xie_2019_agomelatine.R.
• Notes: Lower case preserved from source file. Pre-existing legacy form; new models should prefer the integer-valued OCC canonical above and decompose into binary indicators inside model().

MONTH1 (canonical for first-month-of-treatment landmark indicator)

• Description: Binary within-subject landmark indicator: 1 = the observation falls within the first 30 days after treatment initiation, 0 = subsequent months. Time-varying within subject – gates a transient step change in typical-value CL that the source paper attributes to higher Erwinia asparaginase clearance in the first month of pediatric ALL therapy.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (month 2 onwards).
• Source aliases: none known; Sassen 2017 reports the first-month-vs-after contrast as a single multiplicative coefficient on TVCL.
• Example models:
  • Sassen_2017_crisantaspase.R (multiplicative shift on CL: cl <- exp(lcl + etalcl) * (WT/70)^0.75 * (1 + e_month1_cl * MONTH1) with e_month1_cl = 0.14, encoding the 14% higher CL in the first month of treatment relative to subsequent months).
• Notes: Specific scope because the 1-month (30-day) cutoff is tied to Sassen 2017’s pediatric Erwinia asparaginase pharmacology (transient higher CL early in treatment; mechanism not established in the source paper). Future studies that test a similar within-subject step change with a different cutoff (e.g., 2 weeks or 6 weeks) should register a new canonical name. Distinct from OCC and ooc<n> (which decompose multi-occasion sampling for IOV), from DAY14 (which uses a 14-day cutoff for malnutrition-recovery contrasts), and from CYCLE (which is a dose-number counter, not a single binary landmark). Data assemblers can derive MONTH1 = as.integer(time_post_treatment_start_days < 30) for a regularly-sampled multi-month study. Ratified canonically on 2026-05-20 alongside the Sassen 2017 extraction.

DAY4 (canonical for day-4-of-therapy landmark indicator)

• Description: Binary within-subject landmark indicator: 1 = the observation falls on day 4 (or later) of treatment, 0 = the observation falls on day 1 of treatment. Time-varying within subject – gates a step change in a typical-value PK parameter between the first day of therapy and the expected steady-state sampling day.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (day 1 of therapy).
• Source aliases:
  • 4th day – Zavrelova 2025 writes the term as beta_CL_4th day [if 4th day] in the Table 2 parameter list and as [if 4th day] in the printed covariate equation.
• Example models:
  • Zavrelova_2025_linezolid.R (multiplicative log-scale shift on CL: cl <- exp(lcl + e_age_cl * AGE + (e_day4_cl + etae_day4_cl) * DAY4 + etalcl) with e_day4_cl = -0.40, encoding the exp(-0.40) = 0.67 fold change – a 33% CL reduction – between day 1 and day 4 of intravenous linezolid therapy in hematooncological patients with sepsis).
• Notes: Specific scope because the day-4 cutoff is the source study’s therapeutic-drug-monitoring sampling landmark (concentrations drawn on day 1 and again on day 4, the expected steady-state day), not a mechanistic threshold: Zavrelova 2025 states in the Discussion that the timing of the CL change between days 1 and 4 could not be identified from the design and “probably occurs” by around day 2. Registered under the naming pattern that the DAY14 entry below prescribes for a different cutoff (“future studies that test a similar within-subject step change with a different cutoff (day 7, day 28, etc.) should register a new canonical”). Distinct from OCC and ooc<n> (which decompose multi-occasion sampling for IOV and carry per-occasion eta slots rather than a typical-value step), from DAY14 and MONTH1 (same landmark-indicator family, different cutoffs and different source pharmacology), and from CYCLE (a dose-number counter, not a binary landmark). Data assemblers can derive DAY4 = as.integer(time_post_treatment_start_days >= 4) – or, for a study that samples only the two landmark days, directly from the sampling-day label. Ratified canonically on 2026-08-18 alongside the Zavrelova 2025 extraction.

DAY14 (canonical for day-14-post-treatment-initiation landmark indicator)

• Description: Binary within-subject landmark indicator: 1 = the observation falls on or after day 14 of treatment (post-nutritional-rehabilitation steady state in the Archary 2019 / MATCH trial of severely malnourished HIV-infected children), 0 = the observation falls before day 14 (acute / pre-rehabilitation baseline; e.g., day 1 of antiretroviral treatment). Time-varying within subject – gates a step change in typical-value PK parameters that the source paper attributes to nutritional recovery + auto-induction of hepatic metabolism over the first ~2 weeks of treatment.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (day 1 / pre-rehabilitation).
• Source aliases: none known; Archary 2019 reports the day-1-vs-day-14 contrast directly in the parameter table (separate typical-value rows for “day 1” and “day 14”).
• Example models:
  • Archary_2019_abacavir.R (multiplicative additive shift on CL/F: cl <- exp(lcl + etalcl) * (WT/7)^0.75 * (1 + e_day14_cl * DAY14) with e_day14_cl = 0.760, encoding the day-1 typical CL/F = 3.33 -> day-14 CL/F = 5.86 L/h per 7 kg step).
  • Archary_2019_lamivudine.R (multiplicative additive shift on ka: ka <- exp(lka) * (1 + e_day14_ka * DAY14) * exp(etalka) with e_day14_ka = 0.133, encoding the day-1 typical ka = 0.30 -> day-14 ka = 0.34 /h step).
• Notes: Specific scope because the day-14 cutoff is tied to the MATCH-trial nutritional-rehabilitation timeline (severely malnourished children, two-week re-feeding window per WHO guidelines); future studies that test a similar within-subject step change with a different cutoff (day 7, day 28, etc.) should register a new canonical (e.g., DAY28). Distinct from OCC and ooc<n> (which decompose multi-occasion sampling for IOV), from TRT_PHASE (which gates active-vs-baseline study-phase contributions), and from EARLY_ART (which is a between-subject randomization-arm indicator, not a within-subject landmark). Data assemblers can derive DAY14 = as.integer(time_post_treatment_start_days >= 14) for a regularly-sampled multi-day study. Ratified canonically on 2026-05-08.

CYCLE (canonical for dose-number / treatment-cycle counter)

• Description: Dose-number / treatment-cycle counter (1 = first dose or cycle, 2 = second, …). Integer count, time-varying across a multi-dose / multi-cycle treatment course, incremented at each new administration.
• Units: (count)
• Type: count
• Scope: general
• Reference category: n/a – used either with a power-covariate form CYCLE^Fm (Fm typically negative) to capture cycle-over-cycle decline in a derived quantity such as ADC-to-payload conversion fraction (Li 2017 brentuximab vedotin), or with a piecewise indicator CYCLE == 1 vs CYCLE >= 2 to capture a step change in PK between the first and subsequent administrations (Hong 2025 datopotamab deruxtecan; Huynh 2026 VRC07-523LS).
• Source aliases: CYCLE – used in Li_2017_brentuximab.R, Hong_2025_datopotamab.R, Lu_2022_patritumab.R, and Huynh_2026_VRC07523LS.R with the same canonical name.
• Example models:
  • Li_2017_brentuximab.R (exponent on the fraction of ADC that converts to MMAE by proteolytic degradation, Fm = -0.261, to reflect tumor-burden reduction across successive treatment cycles).
  • Hong_2025_datopotamab.R (cycle-1 vs cycle-2+ piecewise scaling Factor1 = 0.696 on the DAR equation that drives DXd formation rate from total Dato-DXd elimination).
  • Lu_2022_patritumab.R (cycle-1 vs cycle-2+ piecewise scaling Factor1 = theta = 0.648 on the payload-to-intact-drug ratio PIR that scales DXd release rate from intact ADC).
• Notes: Must be >= 1 throughout (CYCLE^Fm is undefined at 0; the piecewise form requires CYCLE to be a positive integer at every observation row). Distinct from ooc<n> binary-occasion indicators: CYCLE is an integer count, not a mutually-exclusive set of indicator columns. Data-assembly helper: set CYCLE = floor((TIME - TIME_FIRST_DOSE) / cycle_length_days) + 1 for a fixed-interval dosing regimen.

T_FIRSTDOSE (canonical for time elapsed since the first dose of the treatment course)

• Description: Time elapsed since the subject’s very first administration of the modelled drug, i.e. since the start of the treatment course rather than since the most recent dose. Time-varying within subject and monotonically increasing across the whole record; it does NOT reset at each dosing event (that quantity is time-after-dose, which rxode2 supplies natively via tad() and which must not be confused with this column). Used to drive slow, treatment-duration-dependent drift in a PK parameter – autoinduction, adherence decay, disease-state resolution, or a gradual change in absorption or bioavailability – over weeks to months, on a timescale far longer than a dosing interval.
• Units: h
• Type: continuous
• Scope: general
• Reference category: n/a – enters as a continuous time-decay regressor, typically inside an exponential of the form 1 + a * exp(-k * T_FIRSTDOSE / 24) where the division by 24 converts the canonical hours to days so that the published rate constant k retains its per-day units.
• Source aliases:
  • TAF (Eechoute 2012 imatinib; defined by Yang 2025 Table 1 abbreviation list as ‘TAF time from first dose administration (h)’).
• Example models: Eechoute_2012_imatinib.R (drives a joint exponential decay of the absorption rate constant, ka = 0.699 * (1 + 1.18 * exp(-0.0256 * T_FIRSTDOSE / 24)), and of relative bioavailability, F = 1 + 0.482 * exp(-0.0256 * T_FIRSTDOSE / 24), with a shared 0.0256/day rate constant giving a ~27-day half-life for the drift; both quantities fall towards their asymptotes over the first months of continuous imatinib therapy in GIST), Willmann_2024_elinzanetant.R, Bienczak_2016_nevirapine.R.
• Notes: Carried in hours to match the units$time = "h" convention of the PK models that use it, so a model whose published decay constant is expressed per day must divide by 24 at the call site (visible in the example above) rather than storing days in the column. Distinct from T_ENTRY (a per-subject offset locating study entry on a shared integration axis, not a treatment-duration clock), from CYCLE (an integer dose/cycle counter rather than a continuous time), and from MONTH1 / DAY14 (binary landmark indicators derived from a treatment-duration clock rather than the clock itself). For a single-course simulation whose first dose is at time = 0, T_FIRSTDOSE equals time; the column exists so that records beginning mid-course, or subjects with a pre-study treatment history, can be represented honestly. Ratified canonically on 2026-08-18 alongside the Yang 2025 imatinib external-evaluation extraction, as a well-formed member of the auto-approved T_<event> canonical family.

T_ENTRY (canonical for per-subject study-entry time on the model integration axis)

• Description: Per-subject study-entry time, expressed on the same integration-time axis the model is solved on (years for AD disease-progression models). The dataset’s TIME column carries the integration-time variable (with TIME = 0 the integration origin, typically well before any subject’s disease onset); T_ENTRY records, per subject, the integration-time value at which the subject’s first observation falls. Used in models whose pre-study and post-study time semantics differ – e.g., a disease-progression activation function defined on global disease time plus a placebo-effect term defined on time-since-study-entry need access to both reference clocks within the same model() block.
• Units: year (or whatever time unit the model’s units$time field declares)
• Type: continuous
• Scope: specific
• Reference category: n/a – T_ENTRY is a per-subject time-offset covariate that anchors the time-since-study-entry clock used by post-entry-only model terms (placebo / learning effects, study-design transient drops).
• Source aliases: none standardized; this canonical originates with the Delor 2013 AD disease-progression extraction. The source paper’s NONMEM dataset handles the global / study-time split internally (the dataset is staged with TIME = study time and the model uses a fixed offset to align with DOT); when porting to rxode2 / nlmixr2 the per-subject offset is exposed as a covariate so simulation event tables can carry it explicitly. Berkhout 2015 EPIC placebo-arm osteoporosis QSP uses the same pattern with the source NONMEM column STDA (start day of placebo treatment relative to menopause onset) mapped to T_ENTRY.
• Example models: Delor_2013_alzheimer.R (placebo-term clock: t_pl_raw <- time - T_ENTRY; post_entry <- t_pl_raw > 0; placebo_term <- pl_indiv * (1 - exp(-kpl_indiv * t_pl_raw * post_entry)) * post_entry), Berkhout_2015_osteoporosis_placebo_qsp.R (PCa placebo function gated by t >= T_ENTRY with delayed-onset / slow-offset dip in the RANK-RANKL-OPG occupancy factor).
• Notes: Scope: specific because the variable is paper-domain-bound – it only makes sense for models that operate on a global disease-time axis distinct from per-subject study-entry timing. For a typical-value reproduction the user supplies T_ENTRY per subject in the simulation event table; a reasonable construction is T_ENTRY = DOT_individual + a few years of established disease so the patient is observed during disease progression (see the Delor 2013 vignette for the construction used to reproduce Figures 2-4). Ratified canonically on 2026-05-16 alongside the Delor 2013 extraction; second registered use added 2026-07-24 with the Berkhout 2015 EPIC placebo-arm osteoporosis QSP extraction.

TCLOCK (canonical for wall-clock time-of-day covariate feeding a 24-hour periodic (circadian) function)

• Description: Wall-clock time of day at each observation, expressed on a 0-24 hour scale. Distinct from the integration-time axis time (which carries time after first dose or another simulation-anchored reference) – TCLOCK records the local hour of day at which a sample was drawn / an observation was recorded so a 24-hour periodic function of the form cos(2*pi * (TCLOCK + 24 - Tmax) / 24) can be evaluated at each record. Time-varying per record if samples are drawn at multiple times of day within a subject; per-subject time-fixed for study designs whose samples are always drawn at a consistent local time (e.g., morning pre-dose).
• Units: hour of day (0-24)
• Type: continuous
• Scope: general
• Reference category: n/a – enters as an argument to a periodic function whose period is 24 hours. The natural per-model reference is the time-of-maximum parameter Tmax in that periodic function; setting TCLOCK = Tmax collapses the circadian factor to its peak, and TCLOCK = Tmax + 12 (mod 24) collapses it to its trough.
• Source aliases:
  • tclock / clocktime – Hoefman 2021 (Hoefman_2021_asp8232.R) equation 3 notation for the wall-clock time argument of the eGFR CysC circadian amplitude term.
• Example models: Hoefman_2021_asp8232.R (drives the eGFR CysC circadian oscillation (1 + Ampli * cos(2*pi * (TCLOCK + 24 - tmax) / 24)) with Ampli = 0.0783 (theta_32) and tmax = 10.5 h (theta_33)).
• Notes: General scope because any circadian / diurnal PD model whose within-day rhythm is parameterised by a periodic function of the observation’s clock time will need this canonical – the concept generalises across drugs, biomarkers, and disease areas. Distinct from time (the integration-time axis of the model, on which ODE progression is solved) and from T_ENTRY (per-subject study-entry offset used to align a per-subject placebo clock to a global disease-time clock). Data-assembly helper: if the source dataset carries a time-of-day column encoded as HH:MM:SS or minutes-since-midnight, convert to decimal hours before populating TCLOCK. If a subject’s records are drawn at a consistent time of day and TCLOCK is unknown per record, a sensible default is TCLOCK = Tmax (peak of the circadian wave) or the paper’s stated typical sampling time. Ratified canonically on 2026-07-08 alongside the Hoefman 2021 ASP8232 PD extraction.

FORM_ELZ_HARDGEL (canonical for the elinzanetant hard-gelatin capsule formulation indicator)

• Description: 1 = the oral elinzanetant dose was given as the hard-gelatin capsule; 0 = the dose was given as the soft-gel capsule, as the aqueous oral suspension, or intravenously. Per-dose-record indicator distinguishing the hard gel capsule used in the early phase I food-effect study and in the RELENT-1 proof-of-mechanism study from the soft-gel capsule that was carried into the phase III programme.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (soft-gel capsule, aqueous suspension and intravenous solution pooled; source FORM levels 1, 2 and 4).
• Source aliases:
  • FORM3 – used in Willmann_2024_elinzanetant.R. Willmann 2024 Data S1 defines FORM3 = 1 when FORM = 3, else 0, where the source data file’s FORM column codes the formulation and level 3 is the hard gel capsule.
• Example models: Willmann_2024_elinzanetant.R (multiplicative effects on absorption only: ka *= (1 + e_form_elz_hardgel_ka * FORM_ELZ_HARDGEL) with e_form_elz_hardgel_ka = -0.264, a 26% lower absorption rate constant, and tlag *= (1 + e_form_elz_hardgel_tlag * FORM_ELZ_HARDGEL) with e_form_elz_hardgel_tlag = 0.548, a 55% longer lag time; the extent of absorption is unaffected).
• Notes: Scope: specific because the reference level pools three elinzanetant-protocol formulations rather than expressing a general pharmaceutical contrast. Distinct from FORM_CAPSULE (capsule vs non-capsule oral liquid), which cannot express this comparison because both the test and the reference formulation here are capsules – the contrast is hard-gelatin shell versus soft-gel shell. If a later extraction needs the same hard-gel-versus-soft-gel contrast for a different drug, promote a general FORM_HARDGEL canonical and record this entry as an alias rather than adding a second drug-specific name. Ratified canonically alongside the Willmann 2024 elinzanetant extraction.

Mixture / latent-class indicators

MIX_PDI (canonical for binary mixture-model class indicator: PDI inhibition-of-synthesis vs PDS stimulation-of-elimination thrombocytopenia mechanism)

• Description: Per-subject latent mixture-model class indicator from the Tsuji 2017 linezolid platelet PKPD model. 1 = subject classified to the PDI mechanism (linezolid inhibits platelet synthesis via a linear effect on the proliferation rate of the formation compartment); 0 = subject classified to the PDS mechanism (linezolid stimulates platelet elimination via an Emax effect on the circulating-compartment first-order loss rate). Not a measured clinical covariate – the mixture assignment is a posterior class assignment from the NONMEM $MIXTURE block (Tsuji 2017 Equation 8 and Methods, “Mixture model” subsection).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (PDS = stimulation of elimination; the 3% minority class in the source cohort).
• Source aliases: MIXTURE – NONMEM $MIXTURE block class index in the Tsuji 2017 estimation run (component 1 = PDI, component 2 = PDS); the binary indicator is MIX_PDI = as.integer(MIXTURE == 1).
• Example models: Tsuji_2017_linezolid.R (gates the drug-effect term that enters the platelet ODE chain: MIX_PDI = 1 activates SLOPE * Cc on the formation rate, MIX_PDI = 0 activates SMAX * Cc / (SC50 + Cc) on the circulating-compartment elimination rate).
• Notes: The population probability of MIX_PDI = 1 is the estimated population mixture fraction FPOP_inhibit = 0.969 (Tsuji 2017 Table 2; 95% CI 0.867-1.00, 78/80 patients in the source dataset). For typical-value simulation set MIX_PDI = 1 (dominant clinical phenotype, slower 2-week nadir; Figure 5 left panel) or MIX_PDI = 0 (rare immune-mediated-like phenotype, faster 2-day nadir; Figure 5 right panel). For population simulation, draw MIX_PDI ~ Bernoulli(0.969) per subject. Scope: specific because the binary semantics are tied to Tsuji 2017’s two-mechanism platelet model; mixture indicators from future papers that share the same biological dichotomy may extend this entry, but mixture indicators from unrelated dichotomies should register a new canonical name (e.g., MIX_FAST_ELIM for a fast/slow eliminator mixture).

MIX_FAST_ELIM (canonical for binary mixture-model class indicator: fast (high-CL) vs slow (low-CL) eliminator subpopulation)

• Description: Per-subject latent mixture-model class indicator from a $MIXTURE NONMEM block describing a bimodal apparent-clearance distribution. 1 = subject classified to the high-CL / fast-eliminator subpopulation; 0 = subject classified to the low-CL / slow-eliminator subpopulation. Not a measured clinical covariate – the mixture assignment is a posterior latent-class index from the NONMEM $MIXTURE block. One assignment per subject, time-fixed.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (low-CL / slow-eliminator subpopulation).
• Source aliases:
  • MIXTURE – NONMEM $MIXTURE block class index in the Morris 2011 estimation run (component 1 = high-CL with P = 0.251, component 2 = low-CL with P = 0.749); the binary indicator is MIX_FAST_ELIM = as.integer(MIXTURE == 1).
• Example models: Morris_2011_telapristone.R (selects the telapristone CL/F typical value between the high-CL anchor exp(lcl_pop1) = 11.6 L/h and the low-CL anchor exp(lcl_pop2) = 3.34 L/h; the shared IIV etalcl ~ 0.200 is applied multiplicatively to whichever typical the mixture-class selects), Wilkins_2011_isoniazid.R (gates the typical apparent CL/F via the power-form covariate effect e_mix_fast_elim_cl on CL/F: cl = exp(lcl + e_mix_fast_elim_cl * MIX_FAST_ELIM + etalcl) * (WT/70)^0.75 * (1 + e_hiv_pos_cl * HIV_POS) with lcl = log(9.70) (slow-eliminator reference) and e_mix_fast_elim_cl = log(21.6/9.70) approx 0.8005).
• Notes: Pre-named by the MIX_PDI register entry’s Notes block (“mixture indicators from unrelated dichotomies should register a new canonical name (e.g., MIX_FAST_ELIM for a fast/slow eliminator mixture)”). Population probability of MIX_FAST_ELIM = 1 is recorded per-model in covariateData[[MIX_FAST_ELIM]]$notes; in Morris 2011 it is 0.251 (Table II, “Probability” row, RSE 61.0%). The Morris 2011 Discussion attributes the bimodal CL distribution to polymorphic CYP3A5 (functional CYP3A5 present in 10-40% of Caucasians, ~50% of African Americans, ~33% of Asians) but does not test the genotype hypothesis directly; the mixture indicator is therefore a latent-class label, not a CYP3A5-genotype indicator. For typical-value simulation set MIX_FAST_ELIM = 1 (fast eliminator; lower steady-state exposure, ~12 h elimination half-life per Morris 2011 Results) or MIX_FAST_ELIM = 0 (slow eliminator; higher steady-state exposure, ~35 h half-life). For population simulation, draw MIX_FAST_ELIM ~ Bernoulli(0.251) per subject. Scope: general because the fast/slow-eliminator dichotomy is a recurring popPK concept across drugs metabolized by polymorphic enzymes (CYP2C92/3, CYP2C192, CYP3A53, NAT2 slow/fast acetylators); future popPK papers fitting a $MIXTURE on CL should re-use this canonical rather than introducing per-drug-specific MIX_<drug>_FAST names. The reference category (= 0 = slow eliminator) is chosen so the binary numerically matches the paper’s mixture-indicator orientation. Ratified canonically on 2026-06-09 alongside the Morris 2011 telapristone extraction.

MIX_LARGE_BASE (canonical for binary mixture-model class indicator: larger-baseline vs smaller-baseline tumor-lesion subpopulation)

• Description: Per-subject latent mixture-model class indicator from the Schindler 2017 imatinib-GIST liver-metastases joint tumor-dynamics model. 1 = subject classified to the larger-baseline subpopulation (typical lesion 1 baseline 76.6 mm MTD / 161 mL Vactual / 187 mL Vellipsoid; estimated population probability Ppop1 = 0.348 in Schindler 2017 Table 2 row ‘Ppop1’); 0 = subject classified to the smaller-baseline subpopulation (typical lesion 1 baseline 20.9 mm MTD / 3.45 mL Vactual / 3.93 mL Vellipsoid). Not a measured clinical covariate – the mixture assignment is the per-subject latent-class index from a NONMEM $MIXTURE block describing the observed bimodal distribution of baseline tumor sizes (Schindler 2017 Methods, “Maximum transaxial diameter, actual volumes, and ellipsoidal volume models” subsection: “Semiparametric distributions and mixture models were investigated to describe the observed bimodal distribution in baseline MTD, Vactual, and Vellipsoid”). One assignment per subject is shared across the three size models (MTD, Vactual, Vellipsoid) and across the subject’s up-to-two lesions; density (D0) is not mixture-indexed.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (smaller-baseline subpopulation; majority class at 65.2 % of the source cohort).
• Source aliases: MIXTURE – NONMEM $MIXTURE block class index in the Schindler 2017 estimation run (component 1 = larger-baseline subpop with P = 0.348, component 2 = smaller-baseline subpop); the binary indicator is MIX_LARGE_BASE = as.integer(MIXTURE == 1).
• Example models: Schindler_2017_imatinib.R (gates the per-lesion typical baselines S0_<metric>_l<k>_typ between subpopulation-1 and subpopulation-2 anchors, and selects between subpopulation-specific IIV etas etalS0_<metric>_pop1 and etalS0_<metric>_pop2 whose log-scale variances differ across mixture classes).
• Notes: Schindler 2017 Methods reports 22 % of patients with one target lesion vs 39 % of patients with two target lesions were assigned to subpopulation 1, suggesting the larger-baseline class is enriched among multi-lesion patients. For typical-value simulation set MIX_LARGE_BASE = 1 to reproduce the larger-baseline phenotype (dominates Figure 2 right panel typical-individual trajectory) or MIX_LARGE_BASE = 0 to reproduce the smaller-baseline phenotype. For population simulation, draw MIX_LARGE_BASE ~ Bernoulli(0.348) per subject. Scope: specific because the binary semantics are tied to Schindler 2017’s two-class baseline-tumor-size mixture; future tumor-burden mixture models that share the same large-vs-small baseline dichotomy may extend this entry, while mixture indicators from unrelated dichotomies (e.g., fast / slow tumor-growth phenotypes) should register a new canonical name (MIX_FAST_GROW, etc.). Ratified canonically on 2026-05-18 alongside the Schindler 2017 imatinib extraction.

MIX_LARGE_RUV (canonical for binary mixture-model class indicator: larger-additive-RUV vs smaller-additive-RUV subpopulation)

• Description: Per-subject latent mixture-model class indicator from the Kappelhoff 2005 ritonavir popPK model. 1 = subject classified to the minority larger-additive-RUV subpopulation P2 (additive residual SD = 0.199 mg/L; 35.2 % of the source cohort); 0 = subject classified to the majority smaller-additive-RUV subpopulation P1 (additive residual SD = 0.0600 mg/L; 64.8 % of the source cohort). Both subpopulations share the same 15.4 % proportional residual error component and the same structural PK; only the additive RUV magnitude differs. Not a measured clinical covariate – the mixture assignment is the per-subject latent-class index from Kappelhoff 2005’s NONMEM $MIX block (paper Methods, “Basic pharmacokinetic model” subsection: “Subpopulations were estimated using the $MIX function in the control stream”).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (smaller-additive-RUV subpopulation P1; majority class at 64.8 % of the source cohort).
• Source aliases: $MIX class assignment – NONMEM $MIX block class index in the Kappelhoff 2005 estimation run (component 1 = P1 small-RUV with P = 0.648, component 2 = P2 large-RUV with P = 0.352); the binary indicator is MIX_LARGE_RUV = as.integer($MIX == 2).
• Example models: Kappelhoff_2005_ritonavir.R (gates the active additive residual SD inside model(): add_sd_eff <- addSd_p1 * (1 - MIX_LARGE_RUV) + addSd_p2 * MIX_LARGE_RUV; the proportional component is shared).
• Notes: Kappelhoff 2005 Discussion notes the mechanism behind the two-population residual structure could not be identified; the mixture was retained for goodness-of-fit (Delta-OFV = -48, P < 0.001 vs single-population RUV). For typical-value simulation set MIX_LARGE_RUV = 0 (dominant subpopulation). For population simulation, draw MIX_LARGE_RUV ~ Bernoulli(0.352) per subject. Scope: specific because the binary semantics are tied to Kappelhoff 2005’s two-class additive-RUV mixture; future popPK models that share the same large-vs-small additive-RUV dichotomy may extend this entry, while mixture indicators from unrelated dichotomies should register a new canonical name (e.g., MIX_LARGE_PROPRUV for a mixture on the proportional component instead). Ratified canonically alongside the Kappelhoff 2005 ritonavir extraction.

MIX_LAGGED_ABS (canonical for binary mixture-model class indicator: lagged-absorption vs no-lag absorption subpopulation)

• Description: Per-subject latent mixture-model class indicator from the Bonate 2004 apomine population PK model. 1 = subject classified to the lagged-absorption Group 1 subpopulation (estimable lag time and faster first-order absorption rate constant ka); 0 = subject classified to the no-lag Group 2 subpopulation (lag time fixed at 0 and slower ka). Not a measured clinical covariate – the mixture assignment is the per-subject latent-class index from a NONMEM $MIXTURE block (Bonate 2004 Methods “Base model development”, equation (1.4), and Results paragraph 1).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (Group 2 = no-lag minority class; 3 % of the source cohort).
• Source aliases: MIXTURE – NONMEM $MIXTURE block class index in the Bonate 2004 estimation run (component 1 = lagged-absorption Group 1 with P = 0.970, component 2 = no-lag Group 2 with P = 0.030); the binary indicator is MIX_LAGGED_ABS = as.integer(MIXTURE == 1).
• Example models: Bonate_2004_apomine.R (gates ka between two subpopulation-specific typical values and gates the lag time on the depot compartment between Group 1 = exp(ltlag) and Group 2 = 0; the shared etalka log-normal IIV scales whichever group’s ka is active for the subject), Frymoyer_2013_mycophenolic_acid.R (gates only the lag time on the depot compartment between Group 2 = exp(ltlag) = 1.96 h and Group 1 = 0; ka, F and disposition parameters are shared between the two classes per Frymoyer 2013 “Allowing other absorption parameters (ka and F) to vary for the two subpopulations by using an extended mixture model did not improve the overall model or predictions”).
• Notes: The population probability of MIX_LAGGED_ABS = 1 is the estimated mixture fraction P(Group 1) = 1 / (1 + exp(P1)) = 0.970 (Bonate 2004 Table 3 with P1 = -3.47). For typical-value simulation set MIX_LAGGED_ABS = 1 to reproduce the dominant 97 % phenotype (lagged-absorption with ka = 1.77 /h and lag = 0.821 h); set MIX_LAGGED_ABS = 0 to reproduce the rare 3 % no-lag phenotype (ka = 0.361 /h, no lag). For population simulation, draw MIX_LAGGED_ABS ~ Bernoulli(0.970) per subject. Scope: specific because the binary semantics are tied to Bonate 2004’s two-class absorption mixture; future popPK papers that share the same lagged-vs-no-lag absorption-mixture dichotomy may extend this entry, while mixture indicators from unrelated dichotomies (e.g., fast vs slow elimination subpopulations) should register a new canonical name (MIX_FAST_ELIM, etc.). Ratified canonically on 2026-06-04 alongside the Bonate 2004 apomine extraction.

MIX_HIGH_VP (canonical for binary mixture-model class indicator: high-peripheral-volume vs typical-peripheral-volume subpopulation)

• Description: Per-subject latent mixture-model class indicator from the Bonate 2004 apomine population PK model. 1 = subject classified to the high-peripheral-volume subpopulation (Bonate 2004 Study 2 healthy-male multiple-dose subjects with Vp many-fold higher than the rest of the cohort, modelled as a multiplicative shift Vp_i = TVV3 * theta_Study2 with theta_Study2 = 23.5); 0 = subject classified to the typical-Vp subpopulation. Not a measured clinical covariate – the mixture assignment was identified by Bonate 2004 from a bimodal empirical-Bayesian Vp histogram and back-fitted as a dichotomous study-membership multiplier (Bonate 2004 Results paragraph 1 and Discussion).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (typical-Vp majority class; 34 / 38 = 89.5 % of the source model-development cohort).
• Source aliases: STUDY2 – the paper modelled the high-Vp subgroup as a dichotomous indicator on Study 2 membership (the 4 healthy-male multiple-dose subjects in Study 2); the binary indicator is MIX_HIGH_VP = as.integer(STUDY == 2) for the source cohort.
• Example models: Bonate_2004_apomine.R (multiplicative log-additive effect on Vp: vp = exp(lvp + e_high_vp_vp * MIX_HIGH_VP + etalvp) with e_high_vp_vp = log(23.5) = 3.157).
• Notes: The population probability of MIX_HIGH_VP = 1 is 4 / 38 = 0.105 in the Bonate 2004 model-development set. For typical-value forward simulations set MIX_HIGH_VP = 0 (the recommended default); the paper notes that simulations with and without the multiplier showed minimal differences in concentration-time profiles. Set MIX_HIGH_VP = 1 only to reproduce the Bonate 2004 Study 2 healthy-male multiple-dose subgroup specifically. Scope: specific because the binary semantics are tied to Bonate 2004’s anomalous Study 2 high-Vp subgroup; future popPK papers that retain a similar dichotomous high-Vp subgroup multiplier may extend this entry, while peripheral-volume mixture indicators from unrelated dichotomies should register a new canonical name. Ratified canonically on 2026-06-04 alongside the Bonate 2004 apomine extraction.

MIX_VAC_RELAPSE (canonical for binary mixture-model class indicator: tumor-relapse vs cure subpopulation after a single-dose immunotherapy / vaccine)

• Description: Per-subject latent mixture-model class indicator from the Parra-Guillen 2013 cancer-vaccine tumor-dynamics model. 1 = subject classified to the relapser subpopulation (transient vaccine-elicited inhibitory signal, SVAC degradation rate k2 = k1 estimated; tumor regrowth observed after initial response; estimated population probability 1 - P(1) = 0.156 in Parra-Guillen 2013 Table I row ‘P(1) = 0.844 FIX’); 0 = subject classified to the responder / cure subpopulation (permanent vaccine-elicited inhibitory signal, SVAC degradation rate k2 = 0 FIX; complete tumor regression maintained for the duration of the experiment). Not a measured clinical covariate – the mixture assignment is the per-subject latent-class index from a NONMEM $MIXTURE block (Parra-Guillen 2013 Methods ‘Data Evaluation, Biological Assumptions and Mathematical Model’ subsection bullet (e); Discussion ‘A challenge when using a mixture model’ paragraph). Time-fixed per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (responder / cure subpopulation; majority class at 84.4 % in the Parra-Guillen 2013 source cohort and assumed equal in the Medina-Echeverz 2014 IL-12 applicability cohort).
• Source aliases: MIXTURE – NONMEM $MIXTURE block class index in the Parra-Guillen 2013 estimation run (component 1 = responder / cure subpop with P(1) = 0.844 FIX, component 2 = relapser subpop); the binary indicator is MIX_VAC_RELAPSE = as.integer(MIXTURE == 2) (orientation flipped from the NONMEM convention so the named state corresponds to the deviation from the dominant cure phenotype, following the Schindler 2017 / Tsuji 2017 MIX_* naming pattern).
• Example models: ParraGuillen_2013_cyaaE7.R, ParraGuillen_2013_il12.R (gates the SVAC degradation rate inside Eq. 3: k2 <- MIX_VAC_RELAPSE * k1 so MIX_VAC_RELAPSE = 0 freezes the inhibitory signal indefinitely (cure) and MIX_VAC_RELAPSE = 1 makes it decay at rate k1 (relapse). The same indicator is reused across both drug files because the biological dichotomy is identical (responders vs relapsers to immunotherapy) and the paper fixed P(1) = 0.844 from CyaA-E7 when re-fitting IL-12).
• Notes: P(1) = 0.844 was obtained from a sub-analysis of only the 4 / 7 / 11-day post-inoculation vaccine-administration groups (where tumor-size-induced resistance had not yet emerged); it was held fixed during the full-cohort and IL-12 re-estimations because the resistance term in later vaccine-administration arms masked the true mixture proportion (Discussion p. 803). For typical-value simulation set MIX_VAC_RELAPSE = 0 (dominant cure phenotype, complete tumor regression; Figure 3 ‘light grey’ individual predictions) or MIX_VAC_RELAPSE = 1 (relapser phenotype, tumor regrowth; Figure 3 ‘dark grey’ individual predictions). For population simulation, draw MIX_VAC_RELAPSE ~ Bernoulli(1 - 0.844) = Bernoulli(0.156) per subject. Discussion p. 803 notes the mixture is empirical: ‘no statistical differences on tumour size at the moment of vaccine administration were found between both populations (p > 0.05)’. The cure semantics (‘a sub-population of mice able to trigger only a temporal tumour response was postulated to describe the relapse observed in a few mice … the vaccine is able to trigger a permanent immune response (probably mediated by memory T-cells) in the cured mouse population, but no in all the mice’) are tied to immune-memory immunotherapy specifically and may not generalise to cytotoxic-chemotherapy relapse mixtures, so scope is specific. Future immunotherapy / vaccine / oncolytic-virus papers that share the same cure-vs-relapse dichotomy at the level of a vaccine-elicited inhibitory signal degradation rate may extend this entry; mixture indicators from unrelated dichotomies (e.g., fast-acquired-resistance vs slow-acquired-resistance to cytotoxic chemotherapy) should register a new canonical name. Ratified canonically on 2026-06-03 alongside the Parra-Guillen 2013 CyaA-E7 + IL-12 extraction.

MIX_SLOW_ELIM_NVP (canonical for binary mixture-model class indicator: slow-vs-fast nevirapine apparent-oral-clearance subpopulation)

• Description: Per-subject latent mixture-model class indicator from the Svensson 2012 nevirapine multi-source mega-model. 1 = subject classified to the minority slow-eliminator subpopulation (typical CL/F = 1.45 L/h at FFM 42 kg reference; estimated population probability 17.3 % in Svensson 2012 Table 2 ‘Probability (%) of belonging to pop. 2’); 0 = subject classified to the majority fast-eliminator subpopulation (typical CL/F = 3.12 L/h, 82.7 %). Not a measured clinical covariate – the mixture assignment is the per-subject latent-class index from the Svensson 2012 NONMEM mixture block. The paper Discussion paragraph 4 attributes the slow class biologically to CYP2B6 516TT homozygotes (‘The estimate of the probability of belonging to the low-clearance population (17.3 %) agrees well with the reported proportion of CYP2B6-516-TT homozygotes in the South African population, implying biological plausibility of two populations with different clearance rates’); the model itself does not require genotype data as input.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (fast-eliminator majority class; 82.7 % of the source cohort).
• Source aliases: MIXTURE – NONMEM $MIXTURE block class index in the Svensson 2012 estimation run (component 1 = fast with P = 0.827, component 2 = slow with P = 0.173); the binary indicator is MIX_SLOW_ELIM_NVP = as.integer(MIXTURE == 2).
• Example models: Svensson_2012_nevirapine.R (gates the typical apparent oral clearance between subpopulation-specific anchors: cl_typ <- exp(lcl_fast) * (1 - MIX_SLOW_ELIM_NVP) + exp(lcl_slow) * MIX_SLOW_ELIM_NVP, with lcl_fast = log(3.12) and lcl_slow = log(1.45); the single 24.9 % BSV on CL/F is shared across the two mixture sub-populations).
• Notes: For typical-value simulation set MIX_SLOW_ELIM_NVP = 0 (the recommended default) to reproduce the majority fast-eliminator phenotype; set MIX_SLOW_ELIM_NVP = 1 to reproduce the rarer slow phenotype that the paper associates with CYP2B6 516TT homozygotes. For population simulation, draw MIX_SLOW_ELIM_NVP ~ Bernoulli(0.173) per subject. Scope: specific because the drug-specific _NVP suffix scopes the binary semantics to nevirapine CL/F mixtures; future fast/slow CYP2B6-driven elimination mixtures for other antiretrovirals (e.g., efavirenz, which shares CYP2B6 as the major elimination enzyme) should register sibling canonicals (MIX_SLOW_ELIM_EFV, etc.) rather than reuse this entry. The Bienczak 2016 and Schipani 2011 nevirapine extractions in the registry use CYP2B6 genotype indicators directly (CYP2B6_IM / CYP2B6_SM / CYP2B6_USM / SNP_CYP2B6_RS3745274_T_COUNT) and are NOT mixture models – they assume the user supplies known genotype; MIX_SLOW_ELIM_NVP is the orthogonal pathway for the Svensson 2012 paper which estimates the phenotype distribution without genotype data. Ratified canonically on 2026-06-14 alongside the Svensson 2012 nevirapine extraction.

MIX_ELEV_IL6 (canonical for binary cycle-level mixture-model class indicator: elevated-IL-6-production-surge active vs no-surge subpopulation)

• Description: Per (subject, chemotherapy cycle) latent mixture-model class indicator: 1 = this cycle has an elevated IL-6 production surge (the IL-6 surge function g_IL6(t) is active and adds the relative-surge term (1 + g_IL6(t)) to the IL-6 production rate); 0 = no elevated IL-6 production in this cycle (the surge function is zeroed and IL-6 follows its baseline turnover only). Not a measured clinical covariate – the gate implements the NONMEM $MIXTURE block frequency that the Netterberg 2018 model estimated from the data. Time-fixed within a single chemotherapy cycle; redrawn independently between cycles 1 and 4 in the paper’s cohort.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no elevated IL-6 production this cycle; the complement of the surge-active subpopulation).
• Source aliases: MIXTURE – NONMEM $MIXTURE block class index in the Netterberg 2018 estimation run; the binary indicator is MIX_ELEV_IL6 = as.integer(elevation-active component). The paper reports 16 subpopulations across the 2 cycles x 2 biomarkers indicator combinations, but the marginal IL-6-elevation probability per cycle is a single value (Table 2 Pelevation,IL-6 = 63.4 %, RSE 10 %).
• Example models: Netterberg_2018_breast_cancer_FN_biomarkers.R, Netterberg_2018_breast_cancer_FN_tte_preFN.R, Netterberg_2018_breast_cancer_FN_tte_atFN.R (gates the IL-6 surge function: g_il6 <- MIX_ELEV_IL6 * sa_il6_i / (((t - pt_il6_i) / sw_il6_i)^4 + 1), so MIX_ELEV_IL6 = 0 zeroes the surge contribution while MIX_ELEV_IL6 = 1 activates the Netterberg 2018 Equation 1 Lorentzian surge centred at PT_IL-6).
• Notes: General scope because acute-phase biomarker surge mixtures recur in cytotoxic-chemotherapy IL-6 / CRP turnover models (any future paper modelling a Friberg-style IL-6 surge with a NONMEM mixture-component frequency should reuse this canonical). For typical-value simulation set MIX_ELEV_IL6 = 1 (the dominant 63.4 % per-cycle phenotype); for population simulation, draw MIX_ELEV_IL6 ~ Bernoulli(0.634) per (subject, cycle). The companion MIX_ELEV_CRP indicator gates the CRP surge function in the same model. Ratified canonically on 2026-06-27 alongside the Netterberg 2018 breast-cancer febrile-neutropenia extraction.

MIX_ELEV_CRP (canonical for binary cycle-level mixture-model class indicator: elevated-CRP-production-surge active vs no-surge subpopulation)

• Description: Per (subject, chemotherapy cycle) latent mixture-model class indicator: 1 = this cycle has an elevated CRP production surge (the CRP surge function g_CRP(t) is active); 0 = no elevated CRP production surge (the surge function is zeroed, but CRP production may still rise via the IL-6 -> CRP linear-regulation Slope coefficient when MIX_ELEV_IL6 = 1). Not a measured clinical covariate – the gate implements the NONMEM $MIXTURE block frequency that the Netterberg 2018 model estimated from the data. Time-fixed within a single chemotherapy cycle; redrawn independently between cycles in the paper’s cohort.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no elevated CRP production surge this cycle; CRP elevation may still occur via the IL-6 RCFB regulation through Slope).
• Source aliases: MIXTURE – NONMEM $MIXTURE block class index in the Netterberg 2018 estimation run; the binary indicator is MIX_ELEV_CRP = as.integer(elevation-active component). Per Netterberg 2018 Results, the per-cycle marginal probability is 44.3 % (Table 2 Pelevation,CRP, RSE 20 %), lower than the IL-6 marginal because the IL-6 -> CRP linear regulation contributes additional CRP elevations even when the dedicated CRP surge is off.
• Example models: Netterberg_2018_breast_cancer_FN_biomarkers.R, Netterberg_2018_breast_cancer_FN_tte_preFN.R, Netterberg_2018_breast_cancer_FN_tte_atFN.R (gates the CRP surge function: g_crp <- MIX_ELEV_CRP * sa_crp_i / (((t - pt_crp_i) / sw_crp_i)^4 + 1), so MIX_ELEV_CRP = 0 zeroes the dedicated CRP-surge contribution while MIX_ELEV_CRP = 1 activates the Netterberg 2018 Equation 1 Lorentzian surge centred at PT_CRP = PT_IL-6 + PT_CRP+).
• Notes: General scope because acute-phase biomarker surge mixtures recur in cytotoxic-chemotherapy IL-6 / CRP turnover models. For typical-value simulation set MIX_ELEV_CRP = 1 to reproduce a CRP-elevation cycle; for population simulation, draw MIX_ELEV_CRP ~ Bernoulli(0.443) per (subject, cycle). The paper observes that the actual fraction of CRP elevations is higher than Pelevation,CRP because of the IL-6 RCFB coupling through Slope; the canonical refers strictly to the dedicated surge function and not the coupled-elevation path. Ratified canonically on 2026-06-27 alongside the Netterberg 2018 breast-cancer febrile-neutropenia extraction.

Preclinical experimental conditions

SEIZURE_ACUTE (canonical for acute focal-seizure-activity indicator in preclinical brain-distribution studies)

• Description: 1 = the animal is undergoing acute focal seizure activity (typically chemoconvulsant-induced, e.g., intrahippocampal pilocarpine perfusion in a microdialysis design) during the modelled observation window; 0 = no acute seizure activity. Captures the experimental flag used by preclinical BBB / biophase-distribution PK studies to test whether seizure-driven changes in blood-brain barrier permeability or brain redistribution alter brain PK parameters relative to non-seizing control animals. Time-fixed per animal in single-condition allocations (each rat in one arm); a time-varying form is permitted when a study induces transient seizures during a sampling window and the operator chooses to gate the covariate only over the seizure interval (document in covariateData[[SEIZURE_ACUTE]]$notes).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no acute seizure activity; non-seizing control animal).
• Source aliases:
  • A (values inverted: source A = 1 means non-seizing control and A = 0 means seizing, per Clinckers 2008 Methods ‘Both A and B were set to 1 in control animals; to 0 and 1, respectively, in seizing animals’; the canonical encoding flips the polarity via SEIZURE_ACUTE = 1 - A) – used in Clinckers_2008_MHD_rat.R.
• Example models: Clinckers_2008_MHD_rat.R (mutually-exclusive selection of the seizure-specific biophase volume V3b in place of the control V3a inside the V3 expression of the one-compartment-plus-biophase model for MHD in male Wistar rats; v3 = exp(lv3a + etalv3a) * (1 - SEIZURE_ACUTE) * (1 - EFFLUX_INHIB) + exp(lv3b) * SEIZURE_ACUTE * (1 - EFFLUX_INHIB) + exp(lv3c + etalv3c) * (1 - SEIZURE_ACUTE) * EFFLUX_INHIB).
• Notes: Specific scope because the indicator’s semantics are tied to acute, induced focal seizure activity in preclinical BBB / biophase PK designs; clinical-trial canonicals for epilepsy (CONMED_AED, CONMED_EIAED, PDV previous-period seizure count) describe distinct concepts and should not be conflated. Future preclinical studies that test a similar seizure-vs-non-seizure contrast should extend the example list; promote to general scope once a second model legitimately ratifies the name. Mutually exclusive with EFFLUX_INHIB in Clinckers 2008 (each rat is allocated to exactly one of {control, seizure, efflux-inhibition}); the model() encoding uses (1 - SEIZURE_ACUTE) * (1 - EFFLUX_INHIB) as the control multiplier, so any data record that asserts both flags as 1 would zero out the V3a term – data assemblers should preserve mutual exclusivity unless a model is explicitly designed for the cross-condition case. Ratified canonically on 2026-05-16 alongside the Clinckers 2008 extraction.

EFFLUX_INHIB (canonical for local efflux-transporter-inhibitor co-perfusion indicator in preclinical brain-distribution studies)

• Description: 1 = the animal’s brain microdialysis probe is co-perfused with an efflux-transporter inhibitor (e.g., verapamil 5 mM for P-glycoprotein / MDT blockade in Clinckers 2008) at the site of brain sampling, 0 = no local efflux-transporter inhibitor co-perfusion. Captures the experimental flag used by preclinical BBB / biophase-distribution PK studies to test whether local pharmacological blockade of brain efflux transporters alters brain PK parameters relative to non-blocked control animals. Time-fixed per animal in single-condition allocations.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: n/a – T_ENTRY is a per-subject time-offset covariate that anchors the time-since-study-entry clock used by post-entry-only model terms (placebo / learning effects, study-design transient drops).
• Source aliases: none standardized; this canonical originates with the Delor 2013 AD disease-progression extraction. The source paper’s NONMEM dataset handles the global / study-time split internally (the dataset is staged with TIME = study time and the model uses a fixed offset to align with DOT); when porting to rxode2 / nlmixr2 the per-subject offset is exposed as a covariate so simulation event tables can carry it explicitly.
• Example models: Delor_2013_alzheimer.R, Clinckers_2008_MHD_rat.R (mutually-exclusive selection of the verapamil-specific biophase volume V3c in place of the control V3a inside the V3 expression: v3 = v3a*(1-SEIZURE_ACUTE)*(1-EFFLUX_INHIB) + v3b*SEIZURE_ACUTE*(1-EFFLUX_INHIB) + v3c*(1-SEIZURE_ACUTE)*EFFLUX_INHIB).
• Notes: Scope: specific because the variable is paper-domain-bound – it only makes sense for models that operate on a global disease-time axis distinct from per-subject study-entry timing. For a typical-value reproduction the user supplies T_ENTRY per subject in the simulation event table; a reasonable construction is T_ENTRY = DOT_individual + a few years of established disease so the patient is observed during disease progression (see the Delor 2013 vignette for the construction used to reproduce Figures 2-4). Ratified canonically on 2026-05-16 alongside the Delor 2013 extraction.

HUMAN_SERUM_PCT (canonical for human-serum supplementation percentage in an in-vitro time-kill experiment)

• Description: Percentage (v/v) of human serum supplementing the Mueller-Hinton broth growth medium in an in-vitro antibacterial time-kill experiment. Drives the protein-binding active-fraction factive(HUMAN_SERUM_PCT) that scales the total static drug concentration to the effective (free) concentration. Categorical experimental level, not a continuous interpolatable axis.
• Units: % v/v
• Type: categorical
• Scope: specific
• Reference category: 0 (no human serum; the active fraction factive is forced to 1).
• Source aliases:
  • HS – Garonzik 2016 (paper Methods + Table 2, “% Human Serum”; the model column was renamed from the short HS to the spelled-out canonical HUMAN_SERUM_PCT on 2026-05-27 to align with the register’s naming standards and avoid an ambiguous two-letter abbreviation).
• Example models: Garonzik_2016_daptomycin.R (five experimental levels {0, 10, 30, 50, 70} percent; drives factive multiplying the static daptomycin concentration to the effective concentration DAP_EF, Garonzik 2016 Eq 2; factive estimates 0.346 / 0.284 / 0.239 / 0.252 at 10 / 30 / 50 / 70 percent and 1 at 0 percent by construction).
• Notes: Specific scope because the discrete serum-percentage levels and the associated factive estimates are tied to the Garonzik 2016 daptomycin in-vitro design. An in-vitro experimental condition rather than a human pop-PK covariate; HUMAN_SERUM_PCT values outside the studied discrete set make factive = 0 inside the model (a deliberately conspicuous failure rather than silent interpolation). The spelled-out name follows the register’s anti-abbreviation principle (cf. the DIS_BUNIONECTOMY entry’s avoidance of DIS_BUN). Future in-vitro protein-binding-versus-serum experiments should extend the example list. Ratified canonically on 2026-05-27 alongside the Garonzik 2016 daptomycin extraction.

M3M3FBS_PRESENT (canonical for m-3M3FBS phospholipase-C-activator pretreatment indicator in the Grzesk 2016 vascular-reactivity ex-vivo design)

• Description: 1 = the isolated, perfused tail artery preparation was pretreated with the phospholipase-C activator 2,4,6-trimethyl-N-[3-(trifluoromethyl)phenyl]benzenesulfonamide (m-3M3FBS) at 1e-5 mol/L prior to the agonist concentration-response titration; 0 = no m-3M3FBS pretreatment (control arm). Time-fixed per artery / concentration-response curve (each artery is allocated to exactly one of the control or +m-3M3FBS arms). Captures the binary experimental flag distinguishing the two CRC sets of Grzesk 2016 Table I (control vs +m-3M3FBS) and selects the m-3M3FBS-shifted (EC50, Emax) parameter pair inside model().
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (control artery without m-3M3FBS pretreatment).
• Source aliases: none – Grzesk 2016 Tables I and II label the two arms “controls” vs “+m-3M3FBS” in prose; the model column is the canonical M3M3FBS_PRESENT.
• Example models: Grzesk_2016_m3M3FBS.R (binary selector that swaps the per-agonist control (EC50, Emax) pair for the +m-3M3FBS pair in the sigmoidal Emax CRC; the agonist identity is selected by the companion AGONIST_CODE).
• Notes: Specific scope because the indicator’s semantics are tied to m-3M3FBS pretreatment in the Grzesk 2016 vascular-reactivity ex-vivo design. The Grzesk-group sibling papers (Biomed Rep 2 / 2014 pertussis toxin; Mol Med Report 5 / 2012 calcium blockers; etc.) test different pretreatments and would register their own pretreatment indicators rather than reuse this name. Distinct from CONMED_* (clinical-trial-level concomitant medication indicators) because m-3M3FBS is an experimental ex-vivo pretreatment, not a clinically administered comedication. Ratified canonically alongside the Grzesk 2016 extraction.

PERIOD_ACTIVE (canonical for binary diurnal-period indicator in preclinical chronopharmacology / diurnal-variation PK-PD studies)

• Description: Binary indicator of the diurnal (light-dark) period during which the experimental time-of-administration falls. 1 = active period (lights-off / dark phase for nocturnal laboratory rodents; the animal’s behaviourally active wake window); 0 = resting period (lights-on / light phase; the animal’s behaviourally quiescent sleep window). In the standard 12:12 light-dark cycle used by Kervezee 2014 (Wistar rats housed under LD 12:12 with lights-off defined as Zeitgeber Time ZT12), the resting-period cohort is dosed at ZT0-ZT12 and the active-period cohort at ZT12-ZT24. Time-fixed per experimental subject (each animal is dosed at a single ZT, so the derived period membership is a per-subject scalar in the fitting cohort). The paper aggregates six ZT levels (ZT0/4/8/12/16/20) into the binary resting-vs-active split for the covariate model, so PERIOD_ACTIVE is the collapsed two-level version of the six-level ZT design; the raw ZT is not carried as a separate canonical covariate.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (resting period / lights-on). In Kervezee 2014 the effect enters as a period-specific selection of the P-gp component of five brain-distribution clearances (CL_DBR-PL,Pgp, CL_PL-ECF,Pgp, CL_ECF-PL,Pgp, CL_PL-LV,Pgp) and of the CSF bulk-flow rate Q_CSF, with separate resting-period and active-period point estimates rather than a single reference-plus-multiplier form (Kervezee 2014 Table II reports both periods side-by-side).
• Source aliases:
  • time of administration (P-gp) – Kervezee 2014 Table I prose label for the covariate hypothesis tested against the five P-gp-mediated clearance parameters; free-form phrase, not a data column name.
  • time of administration (Q_CSF) – Kervezee 2014 Table I prose label for the covariate hypothesis tested against CSF bulk flow.
• Example models: Kervezee_2014_quinidine_rat.R (nine-compartment brain-distribution PBPK for i.v. quinidine in male Wistar rats; PERIOD_ACTIVE = 1 selects the active-period estimates for CL_DBR-PL,Pgp = 659 uL/min, CL_PL-ECF,Pgp = 18.3 uL/min, CL_ECF-PL,Pgp = 3.98 uL/min, CL_PL-LV,Pgp = 2.44 uL/min, and Q_CSF = 0.227 uL/min; PERIOD_ACTIVE = 0 selects the resting-period estimates 228, 17, 3.14, 1.55, and 0.522 uL/min respectively).
• Notes: Specific scope because the binary active-vs-resting collapse is meaningful only for chronopharmacology / diurnal-variation preclinical PK-PD designs that pool experimental time-of-administration into two phase cohorts; a future paper that resolves circadian rhythmicity as a continuous cosinor / harmonic function of clock time (e.g., a cosine effect on P-gp expression peaking near ZT18) would use a separate canonical (a continuous clock-time covariate; register with a name like TIME_OF_DAY when the founding paper lands) rather than reusing PERIOD_ACTIVE. Distinct from SEIZURE_ACUTE / EFFLUX_INHIB / M3M3FBS_PRESENT (paper-defined binary experimental-condition indicators in preclinical models that also live in this section) because those flag pharmacological / disease-state interventions rather than a natural chronobiological phase. Downstream promotion to general scope only if a second preclinical or clinical chronopharmacology model adopts the same binary active-vs-resting collapse with equivalent semantics; nocturnal-vs-diurnal-species handling (rats are nocturnally active, humans are diurnally active) must be documented in covariateData[[PERIOD_ACTIVE]]$notes per model because the mapping ZT / clock-time -> {active, resting} depends on species. Kervezee 2014 does not observe circadian rhythmicity in constant darkness so the paper is careful to call the variation “diurnal” rather than “circadian” – the canonical name PERIOD_ACTIVE avoids that debate by labelling the level (active-vs-resting) rather than the driver (diurnal-vs-circadian). Ratified canonically on 2026-07-08 alongside the Kervezee 2014 quinidine rat extraction.

SPECIES_RABBIT (canonical for New Zealand White rabbit species indicator in cross-species / animal-to-human translation models)

• Description: 1 = the subject is a New Zealand White (NZW) rabbit; 0 = the subject belongs to another species in the same model. Time-fixed per subject. Used by joint cross-species models that carry one parameter set per species in a single model file and select among them with mutually-exclusive species indicators, rather than splitting the paper’s analysis into one file per species. Pairs with SPECIES_MACAQUE; the convention is that all species indicators being 0 selects the model’s reference species (human, in the founding example).
• Units: (binary)
• Type: binary
• Scope: general – animal-to-human translation (FDA Animal Rule, first-in-human allometric projection, preclinical-to-clinical bridging) is a recurring model class and future extractions are expected to reuse this indicator family.
• Reference category: 0 (not a NZW rabbit; in a model whose reference species is human, both SPECIES_RABBIT and SPECIES_MACAQUE equal to 0 selects the human parameter set).
• Source aliases:
  • SPECIES (a categorical column with levels such as “rabbit” / “monkey” / “human”) – decomposed into one binary indicator per non-reference species, following the same one-hot pattern used by the RACE_<GROUP> family. Used in Nagy_2017_obiltoxaximab.R.
• Example models: Nagy_2017_obiltoxaximab.R (selects the NZW rabbit column of Nagy 2017 Supplementary Table S1 – CL 0.0263 L/day, Vc 0.114 L, Vp 0.0744 L, Q 0.119 L/day, Ka 0.961 /day, F1 0.899 – together with the rabbit-specific 3.165 kg allometric reference weight and the rabbit Michaelis-Menten arm Vmax 0.912 mg/day, Km 10.4 ug/mL), Cheng_2026_levamisole_2cm.R, Cheng_2026_levamisole_mpbpk.R (select the rabbit weight-normalised clearances 1.09 and 1.46 L/h/kg from Cheng 2026 Table 1 at the paper’s 3 kg reference weight; the mPBPK file also selects the rabbit blood volume 152.7 mL from Supplemental Table S1A).
• Notes: A species indicator is a subject attribute, not an experimental intervention, but it lives in this section because its mechanical role – a mutually-exclusive binary that swaps one parameter set for another inside model() – matches the existing preclinical indicators SEIZURE_ACUTE, EFFLUX_INHIB, M3M3FBS_PRESENT and PERIOD_ACTIVE. Each species carries its own allometric reference weight, so a model using this family must select wtRef by the same indicators it uses to select the structural parameters; do not normalise every species to a single reference weight. Data assemblers must preserve mutual exclusivity (no record may set more than one SPECIES_* indicator to 1), because the selection arithmetic isHuman <- 1 - SPECIES_RABBIT - SPECIES_MACAQUE would otherwise go negative and silently produce nonsense rather than erroring. Register a sibling canonical (SPECIES_RAT, SPECIES_DOG, SPECIES_MOUSE, …) rather than overloading this name when a new species appears. Distinct from the model-file metadata field population$species, which is free text describing the studied population and is not a data column. When a paper’s species-specific models are genuinely independent fits with no shared structure, the library’s default remains one model file per species (e.g. An_2012_mitoxantrone_mouse_pbpk.R / An_2012_mitoxantrone_human_pbpk.R); this family is for the case where a single file is the more faithful or more useful representation. Ratified canonically alongside the Nagy 2017 obiltoxaximab extraction.

SPECIES_MACAQUE (canonical for cynomolgus macaque species indicator in cross-species / animal-to-human translation models)

• Description: 1 = the subject is a cynomolgus macaque (Macaca fascicularis); 0 = the subject belongs to another species in the same model. Time-fixed per subject. The non-human-primate member of the SPECIES_<NAME> one-hot family; see SPECIES_RABBIT for the family conventions.
• Units: (binary)
• Type: binary
• Scope: general – the cynomolgus macaque is the standard non-human primate in monoclonal-antibody and biologics preclinical programmes, so cross-species biologics extractions are expected to reuse this indicator.
• Reference category: 0 (not a cynomolgus macaque; in a model whose reference species is human, both SPECIES_RABBIT and SPECIES_MACAQUE equal to 0 selects the human parameter set).
• Source aliases:
  • SPECIES (categorical column, level “monkey” / “macaque” / “cyno”) – decomposed to a binary indicator. Used in Nagy_2017_obiltoxaximab.R.
• Example models: Nagy_2017_obiltoxaximab.R (selects the cynomolgus macaque column of Nagy 2017 Supplementary Table S1 – CL 0.0191 L/day, Vc 0.134 L, Vp 0.123 L, Q 0.0890 L/day, Ka 3.89 /day, F1 0.895 – with the macaque-specific 2.88 kg allometric reference weight and the macaque Michaelis-Menten arm Vmax 0.275 mg/day, Km 3.21 ug/mL).
• Notes: In the founding example the macaque nonlinear-elimination component is also the one carried over to the human infected projection (Nagy 2017 Methods: “the nonlinear clearance model component from the cynomolgus macaque model was added to the human population PK model, allometrically scaled to human body size”). That carry-over is a property of the model, not of this covariate: the indicator still reads 0 for the human subject, and the model handles the transfer by scaling the macaque Vmax from the macaque reference weight rather than the human one. Any future model that borrows a component across species should document the borrowing in covariateData notes so the reference-weight choice is auditable. Ratified canonically alongside the Nagy 2017 obiltoxaximab extraction.

SPECIES_DUCK (canonical for domestic duck species indicator in cross-species / animal-to-human translation models)

• Description: 1 = the subject is a domestic duck (Anas platyrhynchos domesticus); 0 = the subject belongs to another species in the same model. Time-fixed per subject. An avian member of the SPECIES_<NAME> one-hot family; see SPECIES_RABBIT for the family conventions (mutual exclusivity, all-indicators-zero selects the reference species, per-species reference weights).
• Units: (binary)
• Type: binary
• Scope: general – ducks are a recurring veterinary-pharmacology species and interspecies meta-analyses of antiparasitic and antimicrobial drugs routinely pool avian with mammalian cohorts.
• Reference category: 0 (not a duck; in a model whose reference species is human, all SPECIES_* indicators equal to 0 selects the human parameter set).
• Source aliases:
  • SPECIES (categorical column, level “duck”) – decomposed into a binary indicator following the one-hot pattern of the RACE_<GROUP> family. Used in Cheng_2026_levamisole_2cm.R / Cheng_2026_levamisole_mpbpk.R.
• Example models: Cheng_2026_levamisole_2cm.R (selects the duck weight-normalised clearance 0.20 L/h/kg from Cheng 2026 Table 1, joint 2CM column, at the paper’s 2.5 kg reference weight), Cheng_2026_levamisole_mpbpk.R (selects the duck clearance 0.216 L/h/kg from Cheng 2026 Table 1, joint mPBPK column, together with the duck blood-volume fraction 86.3 mL/kg from Supplemental Table S1A).
• Notes: Registered under the forward-looking instruction in the SPECIES_RABBIT Notes (“Register a sibling canonical … rather than overloading this name when a new species appears”), so no separate ratification sidecar was required. Birds and mammals differ materially in basal metabolic rate and renal morphology, and Cheng 2026 found ducks to be a clearance outlier that had to be dropped from the simple allometric regression – a reason to keep each avian species as its own indicator rather than collapsing to a single SPECIES_BIRD. Pairs with SPECIES_CHICKEN. Ratified canonically alongside the Cheng 2026 levamisole interspecies extraction.

SPECIES_CHICKEN (canonical for domestic chicken species indicator in cross-species / animal-to-human translation models)

• Description: 1 = the subject is a domestic chicken (Gallus gallus domesticus); 0 = the subject belongs to another species in the same model. Time-fixed per subject. An avian member of the SPECIES_<NAME> one-hot family; see SPECIES_RABBIT for the family conventions.
• Units: (binary)
• Type: binary
• Scope: general – the chicken is the most-studied avian species in veterinary pharmacokinetics and in food-animal residue-depletion work, so cross-species extractions are expected to reuse this indicator.
• Reference category: 0 (not a chicken; in a model whose reference species is human, all SPECIES_* indicators equal to 0 selects the human parameter set).
• Source aliases:
  • SPECIES (categorical column, level “chicken” / “broiler breeder” / “prelay hen”) – decomposed into a binary indicator. Used in Cheng_2026_levamisole_2cm.R / Cheng_2026_levamisole_mpbpk.R.
• Example models: Cheng_2026_levamisole_2cm.R (selects the chicken weight-normalised clearance 0.93 L/h/kg from Cheng 2026 Table 1, joint 2CM column, at the paper’s 4.5 kg reference weight), Cheng_2026_levamisole_mpbpk.R (selects the chicken clearance 2.59 L/h/kg from Cheng 2026 Table 1, joint mPBPK column, the chicken blood-volume fraction of 10 percent of body weight from Supplemental Table S1A, and the chicken-specific tissue-to-plasma partition coefficient Kp,chicken = 3.38 from Table 3).
• Notes: Registered under the forward-looking instruction in the SPECIES_RABBIT Notes; no ratification sidecar required. In the founding example this indicator does double duty: it selects both a species-specific clearance and a species-specific distribution parameter (Kp), because chickens showed markedly higher tissue partitioning than the pooled cross-species value. A model that needs to distinguish laying stage (prelay versus peak production, whose reported bioavailabilities differ 61 percent versus 88 percent in Cheng 2026 Supplemental Table S9) should register a separate physiological-state covariate rather than splitting this species indicator. Pairs with SPECIES_DUCK. Ratified canonically alongside the Cheng 2026 levamisole interspecies extraction.

SPECIES_GOAT (canonical for domestic goat species indicator in cross-species / animal-to-human translation models)

• Description: 1 = the subject is a domestic goat (Capra hircus); 0 = the subject belongs to another species in the same model. Time-fixed per subject. A small-ruminant member of the SPECIES_<NAME> one-hot family; see SPECIES_RABBIT for the family conventions.
• Units: (binary)
• Type: binary
• Scope: general – goats are a standard small-ruminant species in veterinary antiparasitic pharmacokinetics and appear alongside sheep in most food-animal interspecies analyses.
• Reference category: 0 (not a goat; in a model whose reference species is human, all SPECIES_* indicators equal to 0 selects the human parameter set).
• Source aliases:
  • SPECIES (categorical column, level “goat”) – decomposed into a binary indicator. Used in Cheng_2026_levamisole_2cm.R / Cheng_2026_levamisole_mpbpk.R.
• Example models: Cheng_2026_levamisole_2cm.R (selects the goat weight-normalised clearance 0.38 L/h/kg from Cheng 2026 Table 1, joint 2CM column, at the paper’s 18 kg reference weight), Cheng_2026_levamisole_mpbpk.R (selects the goat clearance 0.359 L/h/kg from Cheng 2026 Table 1, joint mPBPK column, together with the goat blood-volume fraction 70 mL/kg from Supplemental Table S1A).
• Notes: Registered under the forward-looking instruction in the SPECIES_RABBIT Notes; no ratification sidecar required. Distinct from SPECIES_SHEEP even though goats and sheep are both small ruminants frequently studied in the same publication: Cheng 2026 estimated clearances that differ by roughly a factor of three between them (0.38 versus 0.99 L/h/kg in the joint 2CM), and goats were the one species whose oral bioavailability departed from the otherwise consistent 50-80 percent cross-species range. Do not collapse the two into a SPECIES_RUMINANT indicator. Ratified canonically alongside the Cheng 2026 levamisole interspecies extraction.

SPECIES_DOG (canonical for domestic dog species indicator in cross-species / animal-to-human translation models)

• Description: 1 = the subject is a domestic dog (Canis lupus familiaris); 0 = the subject belongs to another species in the same model. Time-fixed per subject. A member of the SPECIES_<NAME> one-hot family; see SPECIES_RABBIT for the family conventions.
• Units: (binary)
• Type: binary
• Scope: general – the dog is one of the two standard non-rodent toxicology species and is ubiquitous in preclinical-to-clinical bridging, veterinary pharmacokinetics, and allometric projection. This is the name named in the SPECIES_RABBIT Notes as an expected sibling.
• Reference category: 0 (not a dog; in a model whose reference species is human, all SPECIES_* indicators equal to 0 selects the human parameter set).
• Source aliases:
  • SPECIES (categorical column, level “dog” / “beagle”) – decomposed into a binary indicator. Used in Cheng_2026_levamisole_2cm.R / Cheng_2026_levamisole_mpbpk.R.
• Example models: Cheng_2026_levamisole_2cm.R (selects the dog weight-normalised clearance 0.49 L/h/kg from Cheng 2026 Table 1, joint 2CM column, at the paper’s 20.7 kg reference weight), Cheng_2026_levamisole_mpbpk.R (selects the dog clearance 0.464 L/h/kg from Cheng 2026 Table 1, joint mPBPK column, together with the dog blood-volume fraction 84 mL/kg from Supplemental Table S1A).
• Notes: Registered under the forward-looking instruction in the SPECIES_RABBIT Notes, which names SPECIES_DOG explicitly; no ratification sidecar required. The indicator carries no breed information – a model whose dog cohort is breed-specific in a load-bearing way (for example an MDR1 / ABCB1 nt230(del4) collie cohort) should add a separate genotype or breed covariate rather than splitting this indicator. In the founding example the dog profile declined mono-exponentially, so the two-tissue mPBPK distribution parameters were poorly identified for this species; that is a property of the source data, not of the covariate, and is documented in the model files’ covariateData notes. Ratified canonically alongside the Cheng 2026 levamisole interspecies extraction.

SPECIES_SHEEP (canonical for domestic sheep species indicator in cross-species / animal-to-human translation models)

• Description: 1 = the subject is a domestic sheep (Ovis aries), including lambs; 0 = the subject belongs to another species in the same model. Time-fixed per subject. A small-ruminant member of the SPECIES_<NAME> one-hot family; see SPECIES_RABBIT for the family conventions.
• Units: (binary)
• Type: binary
• Scope: general – sheep are a standard food-animal and large-animal-surgery species and recur in antiparasitic, antimicrobial, and residue-depletion pharmacokinetics.
• Reference category: 0 (not a sheep; in a model whose reference species is human, all SPECIES_* indicators equal to 0 selects the human parameter set).
• Source aliases:
  • SPECIES (categorical column, level “sheep” / “ewe” / “lamb”) – decomposed into a binary indicator. Used in Cheng_2026_levamisole_2cm.R / Cheng_2026_levamisole_mpbpk.R.
• Example models: Cheng_2026_levamisole_2cm.R (selects the sheep weight-normalised clearance 0.99 L/h/kg from Cheng 2026 Table 1, joint 2CM column, at the paper’s 26 kg reference weight), Cheng_2026_levamisole_mpbpk.R (selects the sheep clearance 0.994 L/h/kg from Cheng 2026 Table 1, joint mPBPK column, together with the sheep blood-volume fraction 59 mL/kg from Supplemental Table S1A).
• Notes: Registered under the forward-looking instruction in the SPECIES_RABBIT Notes; no ratification sidecar required. Lambs are recorded under this indicator rather than a separate canonical, because body weight – already carried as WT – is the covariate that distinguishes them; a model that needs a maturation effect distinct from body size should add an age covariate rather than a second species indicator. See SPECIES_GOAT Notes for why goats and sheep are kept as separate indicators. Ratified canonically alongside the Cheng 2026 levamisole interspecies extraction.

SPECIES_PIG (canonical for domestic pig species indicator in cross-species / animal-to-human translation models)

• Description: 1 = the subject is a domestic pig (Sus scrofa domesticus), including minipigs; 0 = the subject belongs to another species in the same model. Time-fixed per subject. A member of the SPECIES_<NAME> one-hot family; see SPECIES_RABBIT for the family conventions.
• Units: (binary)
• Type: binary
• Scope: general – the pig and minipig are recurring non-rodent translational species for oral absorption and dermal work, and the pig is also a major food animal in residue-depletion pharmacokinetics.
• Reference category: 0 (not a pig; in a model whose reference species is human, all SPECIES_* indicators equal to 0 selects the human parameter set).
• Source aliases:
  • SPECIES (categorical column, level “pig” / “swine” / “minipig”) – decomposed into a binary indicator. Used in Cheng_2026_levamisole_2cm.R / Cheng_2026_levamisole_mpbpk.R.
• Example models: Cheng_2026_levamisole_2cm.R (selects the pig weight-normalised clearance 0.27 L/h/kg from Cheng 2026 Table 1, joint 2CM column, at the paper’s 39.2 kg reference weight), Cheng_2026_levamisole_mpbpk.R (selects the pig clearance 0.371 L/h/kg from Cheng 2026 Table 1, joint mPBPK column, the pig blood-volume fraction 60 mL/kg from Supplemental Table S1A, and the pig-specific tissue-to-plasma partition coefficient Kp,pig = 5.62 from Table 3).
• Notes: Registered under the forward-looking instruction in the SPECIES_RABBIT Notes; no ratification sidecar required. Like SPECIES_CHICKEN, this indicator selects both a species-specific clearance and a species-specific distribution parameter in the founding example, because pigs showed a tissue-to-plasma partition coefficient roughly four times the pooled cross-species value despite unremarkable plasma protein binding. Minipigs are recorded under this indicator rather than a separate canonical; Cheng 2026 notes that minipig distribution behaviour is not well characterised, so a future model that needs to separate minipig from domestic pig should register SPECIES_MINIPIG and document why the split is load-bearing. Ratified canonically alongside the Cheng 2026 levamisole interspecies extraction.

CNSREG_PFC (canonical for medial-prefrontal-cortex sampling-region indicator in regional-CNS PK-PD models)

• Description: 1 = the observation was sampled from the medial prefrontal cortex; 0 = it was sampled from the reference CNS region. Member of the CNSREG_<region> family, which identifies the anatomical central-nervous-system region (brain nucleus, cortical area, or spinal-cord segment) a per-observation measurement was taken from, in models that fit one joint structural model across several CNS sampling sites and let a region indicator select region-specific typical values. Per-observation rather than per-subject: a single animal commonly contributes one sample from each region.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (the model’s reference CNS region; the amygdala in the founding example).
• Source aliases:
  • BRAIN AREA (categorical column, level PFC) – decomposed to a binary indicator. Used in AlfoseaCuadrado_2024_reserpine_rat.R.
• Example models: AlfoseaCuadrado_2024_reserpine_rat.R (selects the prefrontal-cortex typical value of the monoamine precursor production rate, kpin = (exp(lkpin) * (1 - CNSREG_PFC - CNSREG_SC) + exp(lkpin_pfc) * CNSREG_PFC + exp(lkpin_sc) * CNSREG_SC) * exp(etalkpin), with kin = 6.97 (amygdala), 2.10 (prefrontal cortex) and 1.78 (spinal cord) mg/L/h sharing one 97 % inter-animal variability term).
• Notes: The CNSREG_ prefix was chosen over a BRAIN_ prefix so that spinal-cord levels are described correctly – the founding example’s three levels are two brain regions plus the lumbar spinal cord, and the spinal cord is not brain. Distinct from the brain_<region> compartment namespace in compartment-names.md (brain_cortex, brain_hippocampus, …), which names ODE states holding a drug concentration in a region; CNSREG_<region> is a data column marking which region an observation came from and carries no mass balance. Also distinct from REGION_<geography> (geographic enrollment region), TUMTP_<type> (tumour histology) and SAMPLE_<design> (blood-collection design flags on residual error). Data assemblers must preserve mutual exclusivity – no record may set more than one CNSREG_* indicator to 1 – because the reference-level arithmetic 1 - CNSREG_PFC - CNSREG_SC would otherwise go negative and silently produce nonsense rather than erroring. Register a sibling canonical (CNSREG_HIPPOCAMPUS, CNSREG_STRIATUM, CNSREG_NAC, …) rather than overloading this name when a new region appears; promote the family to general scope once a second model ratifies it. Ratified canonically alongside the Alfosea-Cuadrado 2024 reserpine extraction (operator sidecar oare_PMC11359992, 2026-08-05).

CNSREG_SC (canonical for spinal-cord sampling-region indicator in regional-CNS PK-PD models)

• Description: 1 = the observation was sampled from the spinal cord (the lumbar portion in the founding example); 0 = it was sampled from the reference CNS region. Sibling of CNSREG_PFC in the CNSREG_<region> family. Per-observation rather than per-subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (the model’s reference CNS region; the amygdala in the founding example).
• Source aliases:
  • BRAIN AREA (categorical column, level SC) – decomposed to a binary indicator. Used in AlfoseaCuadrado_2024_reserpine_rat.R. Note the source column is named for brain area even though this level is spinal cord.
• Example models: AlfoseaCuadrado_2024_reserpine_rat.R (selects the spinal-cord typical value of the monoamine precursor production rate kin = 1.78 mg/L/h).
• Notes: See CNSREG_PFC for the family’s naming rationale, the distinction from the brain_<region> compartment namespace, and the mutual-exclusivity requirement. Ratified canonically alongside the Alfosea-Cuadrado 2024 reserpine extraction (operator sidecar oare_PMC11359992, 2026-08-05).

Infectious-disease subtype indicators

HCV_GT1B (canonical for hepatitis C virus genotype-1 subtype indicator: GT1B vs GT1A)

• Description: Binary indicator of the HCV genotype-1 subtype assigned to the subject. 1 = patient infected with HCV genotype 1B (GT1B); 0 = patient infected with HCV genotype 1A (GT1A; the reference subtype in the source paper’s IC50 estimates). Time-fixed per subject because the HCV subtype is determined at the time of infection and does not change over the modelled treatment window. Distinct from the broader genotype number (HCV genotype 1, 2, 3, …) because the GT1A vs GT1B contrast within genotype 1 carries meaningful PK/PD differences (replicon susceptibility, resistance-associated substitutions, drug-resistance trajectory) for direct-acting antivirals; a future model that needs a genotype-2-vs-1 contrast or a 4-level genotype indicator should register a sibling canonical rather than overloading HCV_GT1B.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no human serum; the active fraction factive is forced to 1).
• Source aliases:
  • HS – Garonzik 2016 (paper Methods + Table 2, “% Human Serum”; the model column was renamed from the short HS to the spelled-out canonical HUMAN_SERUM_PCT on 2026-05-27 to align with the register’s naming standards and avoid an ambiguous two-letter abbreviation).
• Example models: Garonzik_2016_daptomycin.R, Wang_2018_daclatasvir_asunaprevir.R (multiplicative effect on IC50 of both drugs via the fixed scaling factors SCL_IC50_DCV = 0.18 and SCL_IC50_ASV = 0.30; the encoding is ic50_dcv_t0 = exp(lic50_dcv_gt1a + etalic50_dcv) * scl_ic50_dcv^HCV_GT1B and ic50_asv_t0 = exp(lic50_asv_gt1a + etalic50_asv) * scl_ic50_asv^HCV_GT1B. Also switches the DCV resistance coefficient Kr_DCV between 0.43 /day for GT1A and 0.13 /day for GT1B; Kr_ASV is the same for both subtypes), Canini_2018_setrobuvir.R (switches setrobuvir sigmoid-Emax EC50, Hill coefficient, and viral clearance rate c between the two per-genotype typical values and their independent IIVs).
• Notes: Specific scope because the discrete serum-percentage levels and the associated factive estimates are tied to the Garonzik 2016 daptomycin in-vitro design. An in-vitro experimental condition rather than a human pop-PK covariate; HUMAN_SERUM_PCT values outside the studied discrete set make factive = 0 inside the model (a deliberately conspicuous failure rather than silent interpolation). The spelled-out name follows the register’s anti-abbreviation principle (cf. the DIS_BUNIONECTOMY entry’s avoidance of DIS_BUN). Future in-vitro protein-binding-versus-serum experiments should extend the example list. Ratified canonically on 2026-05-27 alongside the Garonzik 2016 daptomycin extraction.

Envenomation / venom source

SNAKEFAMILY_ELAPID (canonical for snake-family categorical indicator of venom source)

• Description: Binary indicator of the snake-family origin of the venom bolus delivered by a single bite event. 1 = bite from a snake of the family Elapidae (front-fanged elapids – cobras, kraits, mambas, sea snakes, Australian terrestrial elapids such as taipans / brown snakes / death adders); 0 = bite from a snake of the family Viperidae (true vipers and pit vipers, including Bothrops, Crotalus, Daboia / Vipera russelli, Vipera aspis / berus / ammodytes, Bitis, Hypnale, Cerastes). The covariate is a per-bite-event property of the dose source (the snake), not a property of the patient; in a dataset of envenomed patients each subject’s row(s) carry one value across the entire follow-up.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (healthy opioid-naive).
• Source aliases: none.
• Example models: Mann_2022_respiratory_physiology.R, Sanhajariya_2018_snake_venom.R (Sanhajariya 2018 Table A1 covariate model: f(central) <- exp(lfdepot + e_snakefamily_elapid_fdepot * SNAKEFAMILY_ELAPID + etalfdepot) with lfdepot = log(1) fixed and e_snakefamily_elapid_fdepot = log(0.569)).
• Notes: Scope: specific because the two parameter sets are tied to the Mann 2022 chronic-vs-naive opioid pharmacology calibration. A future model that captures a graded tolerance (e.g., a continuous “tolerance index”) rather than a two-class binary should register a separate continuous canonical. Ratified canonically on 2026-05-29 alongside the Mann 2022 translational-model extraction.

CONMED_TARIQUIDAR (canonical for concomitant tariquidar (P-glycoprotein inhibitor) co-administration indicator)

• Description: 1 = subject co-administered tariquidar (a third-generation P-glycoprotein / breast-cancer-resistance-protein inhibitor used as an experimental probe to block efflux at the blood-brain barrier), 0 = vehicle-only / no concomitant tariquidar. Time-fixed per subject in the Syvanen 2011 source study (single 15 mg/kg IV bolus given 20-30 min before the PET tracer injection means the indicator is operationally constant across the 60-minute PET acquisition window); time-varying use in future per-occasion studies is permitted.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no tariquidar co-administration; vehicle is 5% glucose in saline).
• Source aliases:
  • Paper-specific treatment-arm indicator – used in Syvanen_2011_verapamil_rat.R (the paper distinguishes a tariquidar-treatment arm from a vehicle arm; the canonical column is 1 for the 21 tariquidar-treated rats and 0 for the 21 vehicle-treated rats).
• Example models: Syvanen_2011_verapamil_rat.R (multiplicative theta^COVARIATE form on three structural parameters: vp = exp(lvp + e_conmed_tariquidar_vp * CONMED_TARIQUIDAR) with e_conmed_tariquidar_vp = log(1.20) = 0.1823 (20% increase in plasma peripheral 1 volume); vbr1 = ... * exp(e_conmed_tariquidar_vbr1 * CONMED_TARIQUIDAR + ...) with e_conmed_tariquidar_vbr1 = log(2.41) = 0.8796 (2.41-fold increase in fast-exchange brain compartment volume); qin = exp(lqin + e_conmed_tariquidar_qin * CONMED_TARIQUIDAR) with e_conmed_tariquidar_qin = log(12.0) = 2.4849 (12.0-fold increase in BBB influx clearance). The paper screened tariquidar as a covariate on Qout as well but did not retain it.), Syvanen_2012_quinidine_rat.R (multiplicative exponential effects on CL, Q_out, Q_in, and f2: e_conmed_tariquidar_cl = log(0.835) = -0.180; e_conmed_tariquidar_qout = log(0.366) = -1.005; e_conmed_tariquidar_qin = log(2.65) = 0.975; e_conmed_tariquidar_f2 = log(5.66) = 1.733. Combined Q_in / Q_out shift raises the pseudo-equilibrium brain ECF : plasma ratio 7.2-fold and the combined f2 shift raises the total-brain : plasma ratio about 40-fold per paper Results p91), Westerhout_2013_quinidine.R (P-gp split into separate passive CL_X,p and P-gp-mediated CL_X,P-gp clearances per the paper Appendix mass-balance equations; the P-gp components are switched off when CONMED_TARIQUIDAR = 1 via the multiplier pgp_active = 1 - CONMED_TARIQUIDAR and contribute additively (to efflux clearances CL_X-PL) or subtractively (to influx clearances CL_PL-X) when CONMED_TARIQUIDAR = 0; 1.9-fold P-gp effect on systemic CL_E from Table 4 SBPK combined column, with full bidirectional P-gp components on plasma <-> brain_deep / brain_ecf / brain_csf_lv exchanges and P-gp absent at the CM in this final model).
• Notes: Scope: specific because the only on-disk source is the Syvanen 2011 rat PET BBB-transport paper and the canonical column meaning is intrinsically tied to the experimental P-gp blockade design rather than a general clinical-coadministration indicator. Per-model covariateData[[CONMED_TARIQUIDAR]]$notes must document the dose / regimen of tariquidar used (Syvanen 2011: 15 mg/kg IV bolus in 3 mL/kg of 5% glucose in saline, administered 20-30 min before the radiotracer) and any per-subject vs per-record time-varying convention. Analogous to [[CONMED_PROBENECID]] in the Xie 2000 rat BBB-transport paper – both are experimentally-administered transporter inhibitors used to dissect a tracer’s brain-vs-plasma distribution, encoded as a binary co-administration indicator regardless of the inhibitor’s clinical use case. Future preclinical-PET extractions that test tariquidar (or a related Pgp inhibitor like elacridar / zosuquidar with comparable mechanism) on a different probe-substrate should reuse this canonical when the inhibitor is tariquidar; structurally distinct P-gp inhibitors should register a separate canonical with the inhibitor’s INN in the name (e.g., CONMED_ELACRIDAR). Ratified canonically on 2026-06-03 alongside the Syvanen 2011 (R)-[11C]verapamil rat PET extraction.

DIS_POSTSE_KAINATE (canonical for the post-status-epilepticus state induced by kainic-acid pre-treatment in rats)

• Description: 1 = animal underwent kainic-acid-induced status epilepticus (SE) some number of days prior to the modelled observation window, 0 = animal received saline (or another non-epileptogenic control). Time-fixed per animal in the Syvanen 2011 source study (the SE-induction-to-PET interval is a per-arm design constant – 7 days post-induction at scanning); the canonical column models the chronic post-SE state at a defined post-induction interval, NOT the acute SE episode itself.

L_ANTAGONIST_pM (canonical for time-varying opioid-antagonist effect-site concentration input to the Mann 2022 binding layer)

• Description: Time-varying opioid-antagonist effect-site concentration in picomolar (pM), supplied as a data covariate to the multi-ligand competitive mu-receptor binding model. Antagonist analogue of L_OPIOID_pM. In a composed Mann 2022 + Laffont 2024 / 2025 chain, the upstream antagonist PK layer (Laffont_2024_naloxone or Laffont_2024_nalmefene) is post-processed in the vignette by (a) converting time to minutes, (b) convolving plasma concentration with the Mann 2022 ke0 = 0.001774 1/s effect-site equilibration (carried into Laffont 2024 Supp Table S3 unchanged for both nalmefene and naloxone), and (c) converting ng/mL to pM via the antagonist’s free-base molecular weight (naloxone 327.37 g/mol, nalmefene 339.43 g/mol); the resulting per-subject time series is supplied as this covariate.
• Units: pM (picomolar)
• Type: continuous
• Scope: specific
• Reference category: n/a.
• Source aliases: none.
• Example models: Mann_2022_mu_receptor_binding.R.
• Notes: Scope: specific. Same pM-unit / Table-S2-Kon-unit alignment requirement as L_OPIOID_pM. Ratified canonically on 2026-05-29 alongside the Mann 2022 translational-model extraction.

CAR_OPIOID (canonical for time-varying fraction of mu-opioid receptors bound by an agonist input to the Mann 2022 physiology layer)

• Description: Time-varying fraction (0..1) of mu-opioid receptors bound by an opioid agonist. The Mann 2022 respiratory-physiology layer consumes this as a data covariate to drive opioid-induced reductions in wakefulness drive (W - Wmax * CAR^P3) and in chemoreflex drives (factor 1 - CAR^P1). In the composed Mann 2022 chain, this is the RL_op output of Mann_2022_mu_receptor_binding.R; in standalone physiology-only use, the operator supplies CAR_OPIOID as a time-varying data column.
• Units: fraction (0..1)
• Type: continuous
• Scope: specific
• Reference category: n/a; 0 = no receptor occupancy = baseline ventilation.
• Source aliases: RL_op / CAR (binding-model output name).
• Example models: Mann_2022_respiratory_physiology.R.
• Notes: Scope: specific because the semantics are anchored to mu-opioid receptor occupancy in the Mann 2022 translational chain. Future opioid-pharmacology models that consume a different receptor-occupancy concept (e.g., kappa-opioid or delta-opioid) should register a separately named canonical with the receptor subtype in the name (e.g., CAR_KAPPA). Ratified canonically on 2026-05-29 alongside the Mann 2022 translational-model extraction.

Q_TOTAL_LPM (canonical for total cardiac output input to the Mann 2022 opioid-PK shock-state Q_Scale feedback)

• Description: Time-varying total cardiac output Qb + Qt (cerebral + peripheral-tissue blood flow), in L/min, supplied to the Mann 2022 IV-opioid PK models so they can evaluate the FDA delaymymod.c lines 358-368 shock-state Q_Scale feedback: Q_Scale = 1 + 1 / (1 + exp((1.6 - Q_TOTAL_LPM / 4.87) / 0.05)), clamped to [1, 2]. Q_Scale scales the effective central volume of distribution down (vc_eff = vc / Q_Scale) so that hyperperfusion-driven concentration of opioid in the central / biophase compartment is captured during overdose-induced chemoreflex hyperperfusion. Without this feedback the standalone PK model under-estimates effect-site concentration in shock conditions and produces PaO2 troughs too shallow to reach the cardiac-arrest threshold.
• Units: L/min
• Type: continuous
• Scope: specific
• Reference category: 4.87 L/min (FDA delaymymod.c baseline Q_0; gives Q_Scale = 1, no amplification). Standalone PK use should leave Q_TOTAL_LPM at this baseline.
• Source aliases: q_total / Q_total / Q (physiology layer state name).
• Example models: Mann_2022_fentanyl_iv.R, Mann_2022_carfentanil_iv.R.
• Notes: Scope: specific because the 4.87 / 1.6 / 0.05 numerics are anchored to the FDA delaymymod.c Q_0 baseline and the empirical sigmoid centred at Q/Q_0 = 1.6. In the composed Mann 2022 chain Q_TOTAL_LPM is the Q_total = qb + qt output of Mann_2022_respiratory_physiology.R; in standalone PK-only use the operator supplies Q_TOTAL_LPM = 4.87 (or whatever fixed baseline appropriate). Registered canonically on 2026-06-07 alongside the FDA shock-state PK amplification fix.

OPIOID_PATIENT_TYPE (canonical for opioid-naive vs chronic-opioid-user indicator in the Mann 2022 respiratory-depression PD layer)

• Description: Binary indicator selecting the pharmacodynamic-sensitivity parameter set in the Mann 2022 respiratory-physiology layer. 0 = healthy opioid-naive volunteer (P1 = 2.875, P3 = 0.9); 1 = chronic opioid user with established tolerance (P1 = 4.226, P3 = 1.323). P2 (metabolism exponent) is shared across both patient types at 0.06319. The naive vs chronic split is empirically calibrated against Algera 2021 (Clin Pharmacol Ther 2021;109(3):637-645) and Stoeckel 1982 (Br J Anaesth 1982;54(10):1087-1095); the numeric P1, P3 values are taken from FDA simulateToGetOD_IM.R lines 185-192 (Mann 2022 reference implementation).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (healthy opioid-naive).
• Source aliases: none.
• Example models: Mann_2022_respiratory_physiology.R.
• Notes: Scope: specific because the two parameter sets are tied to the Mann 2022 chronic-vs-naive opioid pharmacology calibration. A future model that captures a graded tolerance (e.g., a continuous “tolerance index”) rather than a two-class binary should register a separate continuous canonical. Ratified canonically on 2026-05-29 alongside the Mann 2022 translational-model extraction.

DIS_HF_OR_LF_SEV (canonical for severe heart failure OR severe liver failure pooled indicator)

• Description: 1 = subject has severe heart failure (low cardiac output or pulmonary oedema) OR severe liver failure (advanced cirrhosis), 0 = neither. Pooled indicator used by source papers that observe similar PK effects from end-stage heart and end-stage liver disease (mechanistically: both reduce hepatic perfusion and cytochrome-mediated drug metabolism) and combine them into a single covariate when the per-group counts are too small for separate estimation. Mild and moderate heart or liver dysfunction are excluded by definition; the indicator captures only the severe ends of each axis.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no severe heart failure and no severe liver failure; includes normal function and mild/moderate dysfunction in either axis).
• Source aliases:
  • severe HF or LF – narrative composite used in Fattinger_1991_quinidine.R (Table 1 row ‘CL_nonrenal for patients with severe HF or LF’; severe HF defined as low output or pulmonary oedema, n = 2; severe LF defined as serum bilirubin > 30 umol/L AND prothrombin time < 60% of normal, n = 3; pooled because the two effects on non-renal CL were of similar magnitude and per-group counts were small).
• Example models: Fattinger_1991_quinidine.R (multiplicative reduction of the non-renal CL arm of total apparent CL from 12.6 L/h to 6.8 L/h when set to 1; log-multiplicative effect e_dis_hf_or_lf_sev_cl_nonren = log(6.8/12.6) = log(0.5397); Table 1 reduction in objective function 10.8, P < 0.005).
• Notes: Scope kept specific because the pooled HF-or-LF semantic is paper-specific (Fattinger 1991 pools the two severe end-organ-failure axes because the cohort had n = 2 + 3 subjects in those groups and similar effect sizes). A future model that retains severe HF and severe LF as separate covariates (with enough subjects in each to estimate them independently) should use the existing HEPIMP_SEV canonical for the severe-hepatic axis and register a parallel CARDIMP_SEV canonical for the severe-cardiac axis rather than reuse this pooled indicator. The DIS_HF_OR_LF_SEV pooling preserves the load-bearing convention of the source paper without forcing later users to artificially separate the two axes when the published evidence base lumps them. Ratified canonically on 2026-06-04 alongside the Fattinger 1991 quinidine extraction.

FORM_QUIN_SR (canonical for slow-release quinidine bisulphate vs immediate-release quinidine sulphate formulation indicator)

• Description: 1 = subject received slow-release quinidine bisulphate (e.g., Kinidin duriles, Astra; oral slow-release tablets); 0 = subject received immediate-release quinidine sulphate (e.g., Chinidin sulfuricum, Siegfried; oral immediate-release). Per-dose-occasion indicator: in mixed-formulation cohorts a single subject may receive both formulations across dose records, with FORM_QUIN_SR set on each dose record to identify the formulation. The indicator drives the structural switch between formulation-specific zero-order absorption durations and relative bioavailability (slow-release QBS shows ~6 h duration of absorption vs ~1.4 h for immediate-release QS, and ~1.36-fold higher relative bioavailability vs the QS reference).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (immediate-release quinidine sulphate; the typical-value absorption-duration and bioavailability reference, with F = 1 fixed by convention per Fattinger 1991 abstract item 3).
• Source aliases: none – the source paper narratives the formulation as “quinidine sulphate (Chinidin sulfuricum)” vs “slow release quinidine bisulphate (Kinidin duriles)” without a single NMTRAN column name; the model column carries the canonical orientation directly.
• Example models: Fattinger_1991_quinidine.R (structural switch driving dur(central) between exp(ldur_qs + etaldur_qs) = 1.37 h * exp(eta) and exp(ldur_qbs) = 6.0 h, and f(central) between 1 (QS reference) and exp(lfdepot) = 1.36 (QBS); the IIV on QS absorption duration applies only when FORM_QUIN_SR = 0 per Methods page 282).
• Notes: Specific scope because the quinidine sulphate-vs-bisulphate contrast is tied to the specific drug. Drug-specific member of the FORM_* family alongside FORM_TAC_IR (tacrolimus immediate-release vs prolonged-release), FORM_LINAG_TAB1 (linagliptin tablet 1), FORM_VISMO_PHASEI (vismodegib Phase I dry-blend capsule), FORM_THEO_APNECUT (Apnecut vs theophylline-alcohol), FORM_ASV_LIQUID (asunaprevir liquid), and FORM_ABA_PHASE2 (abatacept SC Phase-2). Doses must be entered in mg of quinidine BASE (apply the Windholz 1983 stoichiometric factors of 0.829 mg base per mg quinidine sulphate and 0.663 mg base per mg quinidine bisulphate before passing to the model); the f(central) term then captures only the formulation-driven absorption difference, not the stoichiometric salt-vs-base conversion. Ratified canonically on 2026-06-04 alongside the Fattinger 1991 quinidine extraction.

FORM_VPA_SR (canonical for sustained-release valproic acid tablet formulation indicator)

• Description: 1 = the valproic acid dose was given as a sustained-release tablet; 0 = the per-paper reference oral formulation (oral syrup in Zhang_2023_*). Per-dose-record indicator. Selects the sustained-release absorption rate constant in models that FIX a formulation-specific Ka set rather than estimating absorption.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (oral syrup). Pairs with FORM_TABLET to encode the three-level syrup / conventional-tablet / sustained-release-tablet stratification the valproate literature uses, with syrup as the reference level (both indicators 0) – the same derived-reference pattern as FORM_TABLET + FORM_CAPSULE in Kleideiter_2017_cebranopadol.R.
• Source aliases:
  • SR tablet – used in Zhang_2023_valproic_acid_base.R and Zhang_2023_valproic_acid_exponent.R (Supplementary Table S3 footnote: “Ka was fixed to 2.64, 1.57, 0.46 for syrup, conventional tablet and SR tablet, respectively”).
  • HS – Jiang 2007 uses an inverted indicator (HS = 1 for SR tablet) inside an additive Ka expression, Ka = 0.251 + 2.24 * (1 - HS); tabulated in Zhang 2023 Table 1. A model extracting Jiang 2007 directly should populate FORM_VPA_SR and negate in the model body rather than registering an inverted sibling.
• Example models: Zhang_2023_valproic_acid_base.R, Zhang_2023_valproic_acid_exponent.R (both encode ka <- exp(lka + e_form_tablet_ka * FORM_TABLET + e_form_vpa_sr_ka * FORM_VPA_SR) with lka FIXED at log(2.64) for the syrup reference and both shifts FIXED at the log-ratio of the published formulation-specific Ka values), Zhang_2023_valproic_acid_onebindingsite.R, Zhang_2023_valproic_acid_langmuir.R, Zhang_2023_valproic_acid_ddemax.R, Zhang_2023_valproic_acid_nonsaturable.R (all six models of the Zhang 2023 comparison encode ka <- exp(lka + e_form_tablet_ka * FORM_TABLET + e_form_vpa_sr_ka * FORM_VPA_SR) with lka FIXED at log(2.64) for the syrup reference and both shifts FIXED at the log-ratio of the published formulation-specific Ka values), Zhang_2024_valproic_acid.R (a two-level cohort – oral solution or sustained-release tablet only – so FORM_TABLET is absent and the model encodes ka <- exp(lka + e_form_vpa_sr_ka * FORM_VPA_SR) with lka FIXED at log(2.64); Zhang 2024 cites the same Ding 2015 source for the fixed 2.64 / 0.46 1/h pair).
• Notes: Specific scope because the fixed Ka triple (2.64 / 1.57 / 0.46 1/h) is a valproate-specific literature convention propagated across several paediatric valproate popPK papers (Ding 2015, Gu 2021, Teixeira-da-Silva 2022 and thence Zhang 2023). Distinct from FORM_QUIN_SR (quinidine bisulphate slow-release, where the contrast additionally carries a salt-vs-base stoichiometric conversion) and from the modified-release FORM_FLV_BID_XR / FORM_LOV_BID_XR pair (fluvastatin / lovastatin extended-release, where the indicator also encodes a dosing-frequency change). Trough-only therapeutic-drug-monitoring datasets identify absorption poorly, which is exactly why the source papers fix rather than estimate these constants – do not re-estimate lka from a trough-only cohort.

FORM_LNP_SM102 (canonical for SM-102 ionizable-lipid RNA-lipid-nanoparticle formulation indicator)

• Description: 1 = the RNA-lipid-nanoparticle (LNP) dose was formulated with SM-102 as its ionizable lipid; 0 = the per-paper reference ionizable lipid (DLin-MC3-DMA / MC3 in Wang_2024_*). Subject- / arm-level indicator selecting an entire ionizable-lipid-specific parameter column. In an LNP the ionizable lipid is roughly half the particle by mole and governs plasma-protein adsorption, receptor-mediated uptake, particle disassembly and biodegradability, so swapping it changes the disposition parameters rather than only a bioavailability factor – which is why the indicator selects several parameters at once.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (DLin-MC3-DMA / MC3, the ionizable lipid of the first approved siRNA-LNP drug patisiran and the standard against which newer lipids are benchmarked). Pairs with FORM_LNP_LIPID5 to encode the three-level MC3 / SM-102 / Lipid 5 stratification, with MC3 as the reference level when both indicators are 0 – the same derived-reference pattern as FORM_TABLET + FORM_CAPSULE in Kleideiter_2017_cebranopadol.R.
• Source aliases:
  • SM-102 – the column heading of the Wang 2024 Figure 4A fitted-parameter table.
• Example models: Wang_2024_ionizableLipid_rat_pbpk.R (selects the spleen and “other organs” permeabilities, the “other organs” elimination rate, and the Eq. (8) scaling factors on the uptake, disassembly and metabolism rates; the liver permeability is deliberately not switched because Wang 2024 section 3.1.2 reports that “the permeability rate of MC3 applies to SM-102”).
• Notes: Follows the auto-approved FORM_<drug>_<formulation> canonical family, with LNP standing for the delivery system rather than for a single INN because the same ionizable lipid is used across many different RNA payloads. Distinct from the small-molecule FORM_* dosage-form indicators (FORM_TABLET, FORM_CAPSULE), which contrast presentations of one drug substance; here the indicator identifies a different chemical entity within the carrier. Register a sibling FORM_LNP_<lipid> canonical for each further ionizable lipid rather than overloading this entry.

FORM_LNP_LIPID5 (canonical for Lipid 5 ionizable-lipid RNA-lipid-nanoparticle formulation indicator)

• Description: 1 = the RNA-LNP dose was formulated with Lipid 5 as its ionizable lipid; 0 = the per-paper reference ionizable lipid (MC3 in Wang_2024_*).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (DLin-MC3-DMA / MC3). Paired with FORM_LNP_SM102; both 0 selects MC3, and the two are mutually exclusive.
• Source aliases:
  • Lipid 5 – the column heading of the Wang 2024 Figure 4A fitted-parameter table.
• Example models: Wang_2024_ionizableLipid_rat_pbpk.R (selects all three permeabilities including the liver one, which Wang 2024 section 3.1.2 reports “had to be down-regulated to get a satisfactory fitting” unlike SM-102, plus the “other organs” elimination rate and the Eq. (8) uptake / disassembly / metabolism scaling factors).
• Notes: Sibling of FORM_LNP_SM102 under the FORM_<drug>_<formulation> family. Lipid 5 and SM-102 differ only in the position of one ester linker, which is why the two share several parameters but not the liver permeability.

FORM_LNP_DMAPBLP78 (canonical for 78 nm DMAP-BLP RNA-lipid-nanoparticle formulation indicator)

• Description: 1 = the RNA-LNP dose was formulated with the ionizable lipid DMAP-BLP at a particle diameter of about 78 nm; 0 = the per-paper reference formulation (MC3 at about 80 nm in Wang_2024_*). This indicator carries a joint lipid-and-size contrast rather than size alone, because the source study changed both together.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (MC3 LNP at about 80 nm). Pairs with FORM_LNP_DMAPBLP45 to encode the three-level 80 nm MC3 / 78 nm DMAP-BLP / 45 nm DMAP-BLP stratification, with the MC3 arm as the reference level when both indicators are 0.
• Source aliases:
  • DMAP-BLP – the column heading of the Wang 2024 Figure 5B fitted-parameter table.
  • DMAP-DLP – the spelling used in Wang 2024 Results section 3.1.3 and Figure S6 for the same lipid; the Methods and figure-table spelling DMAP-BLP is canonical here.
• Example models: Wang_2024_ionizableLipid_mouse_pbpk.R (selects the spleen and “other organs” permeabilities and the plasma dissociation rate kf = 0.0414 1/h; the liver permeability, uptake rate and “other organs” elimination rate are inherited from the 80 nm arm under the Figure 5B “#” footnote).
• Notes: Follows the auto-approved FORM_<drug>_<formulation> family. Particle diameter is embedded in the indicator name rather than carried as a separate continuous covariate because the source paper fitted a discrete parameter set per particle-size arm rather than a size-parameterised relationship; a future model that fits a continuous size-permeability relationship should register a continuous SIZE_LNP_NM covariate instead of extending this family.

FORM_LNP_DMAPBLP45 (canonical for 45 nm DMAP-BLP RNA-lipid-nanoparticle formulation indicator)

• Description: 1 = the RNA-LNP dose was formulated with the ionizable lipid DMAP-BLP at a particle diameter of about 45 nm; 0 = the per-paper reference formulation (MC3 at about 80 nm in Wang_2024_*).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (MC3 LNP at about 80 nm). Paired with FORM_LNP_DMAPBLP78; both 0 selects the MC3 arm, and the two are mutually exclusive.
• Source aliases:
  • DMAP-BLP – the column heading of the Wang 2024 Figure 5C fitted-parameter table.
• Example models: Wang_2024_ionizableLipid_mouse_pbpk.R (selects an independently re-fitted parameter set: a much higher uptake rate of 125.06 vs 5.24 mL/mmol/h, a lower liver permeability, and the plasma dissociation rate kf = 0.1089 1/h from Figure 6A).
• Notes: Sibling of FORM_LNP_DMAPBLP78. The contrast between the two is the paper’s central size comparison: the smaller particle is taken up into the liver much faster yet knocks down its target gene less effectively.

FORM_LNP_MC3 (canonical for DLin-MC3-DMA ionizable-lipid RNA-lipid-nanoparticle formulation indicator)

• Description: 1 = the RNA-LNP was formulated with DLin-MC3-DMA (MC3) as its ionizable lipid; 0 = the per-paper reference ionizable lipid (C12-200 in Wang_2024_ionizableLipid_hela_qsp.R). Use this indicator when MC3 is a comparator rather than the reference; when MC3 is the reference, leave the sibling FORM_LNP_* indicators at 0 instead of adding this column.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (the per-paper reference ionizable lipid; C12-200 in Wang 2024’s cellular model, which is the formulation the shared trafficking rates were fitted to). Pairs with FORM_LNP_L319 to encode the three-level C12-200 / MC3 / L319 stratification.
• Source aliases:
  • MC3 – the column heading of the Wang 2024 Figure 9D parameter-comparison table.
• Example models: Wang_2024_ionizableLipid_hela_qsp.R (selects the MC3 endosomal-escape rate krel = 0.0058 1/h; MC3’s own cytoplasmic-delivery fraction frel was not measured and is assumed equal to the L319 value of 0.5).
• Notes: Sibling of FORM_LNP_SM102 / FORM_LNP_LIPID5 / FORM_LNP_L319 under the FORM_<drug>_<formulation> family. MC3 is the reference level in Wang 2024’s in-vivo models but a comparator in its cellular model, which is why it needs its own indicator; the reference level of an FORM_LNP_* set is always documented per model rather than assumed.

FORM_LNP_L319 (canonical for L319 ionizable-lipid RNA-lipid-nanoparticle formulation indicator)

• Description: 1 = the RNA-LNP was formulated with L319 as its ionizable lipid; 0 = the per-paper reference ionizable lipid (C12-200 in Wang_2024_ionizableLipid_hela_qsp.R).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (C12-200). Paired with FORM_LNP_MC3; both 0 selects C12-200, and the two are mutually exclusive.
• Source aliases:
  • L319 – the column heading of the Wang 2024 Figure 9D parameter-comparison table.
• Example models: Wang_2024_ionizableLipid_hela_qsp.R (selects the L319 endosomal-escape rate krel = 0.0157 1/h; L319 is the only formulation for which the cytoplasmic-delivery fraction frel was measured directly, at 0.5).
• Notes: Sibling of FORM_LNP_MC3 under the FORM_<drug>_<formulation> family.

FORM_WIXELA_INHUB (canonical for the Wixela Inhub fluticasone propionate / salmeterol dry-powder-inhaler test-product indicator)

• Description: 1 = the inhalation was taken from the Wixela Inhub dry powder inhaler (Viatris / Mylan), the generic test product in the Rosenborg 2025 bioequivalence analysis; 0 = Advair Diskus (GSK), the brand reference product. Per-dose-record indicator – in a two-way crossover the same subject carries 1 on one period’s dose row and 0 on the other’s.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (Advair Diskus reference inhaler), which is the relative-bioavailability anchor F = 1.
• Source aliases:
  • TREA – Rosenborg 2025 supplement Sect. 1: “Treatment alternative, test (TREA=1) and reference (TREA=2) formulation”. The supplement’s NONMEM code derives TREA1 / TREA2 indicators from it; FORM_WIXELA_INHUB is TREA1.
• Example models: Rosenborg_2025_fluticasone_300ug.R, Rosenborg_2025_fluticasone_750ug.R, Rosenborg_2025_fluticasone_1500ug.R, Rosenborg_2025_salmeterol.R (gates both the monophasic absorption rate constant, ka <- exp(lka_test * FORM_WIXELA_INHUB + lka_ref * (1 - FORM_WIXELA_INHUB) + etalka), and relative bioavailability, f(depot) <- exp((lfdepot + etalfdepot) * FORM_WIXELA_INHUB)).
• Notes: Both arms are dry powder inhalers delivering the same fixed-dose combination of fluticasone propionate and salmeterol, so the contrast is a product / device difference rather than a dosage-form difference; reusing FORM_POWDER would be uninformative when both arms are powders. The same indicator serves the fluticasone propionate and the salmeterol models because a single inhalation delivers both actives from one device. Unlike FORM_NOSCAPINE_TEST, which gates bioavailability alone, this indicator also switches the absorption rate constant: Rosenborg 2025 Table 2 reports a separate k41(test) and k41(ref) per model, sharing one inter-individual random effect. Specific scope because the contrast identifies two named commercial inhaler products. Ratified 2026-08-19 alongside the Rosenborg 2025 fluticasone propionate / salmeterol extraction.

FORM_NOSCAPINE_TEST (canonical for the reformulated noscapine oral-suspension test-product indicator)

• Description: 1 = the noscapine dose was given as the reformulated oral suspension (InfectoPharm, Heppenheim) that served as the test product in the Chen 2024 bioequivalence study; 0 = the Nipaxon 5 mg/mL oral suspension (McNeil, Solna) reference product. Per-dose-record indicator – in a crossover design the same subject carries 1 on one period’s dose row and 0 on the other’s.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (Nipaxon reference oral suspension), which is the relative-bioavailability anchor F = 1.
• Source aliases:
  • formulation (test vs reference) – Chen 2024 (Sect. 2.1 describes the two products; Table 4 reports the single formulation parameter F1 (%) = 82.8).
• Example models: Chen_2024_noscapine.R (gates relative bioavailability only: f(transit1) <- exp((lf1 + etalf1) * FORM_NOSCAPINE_TEST) with lf1 = log(0.828), so reference doses get F = 1 exactly while test doses get 82.8% carrying 34.1% CV inter-individual variability).
• Notes: Both products are oral suspensions of the same strength, so the contrast is a product difference, not a dosage-form difference – hence the drug-plus-product naming rather than reuse of FORM_SUSPENSION (which distinguishes a suspension from another dosage form and would be uninformative when both arms are suspensions). Bioavailability is the only parameter that differs by formulation: Chen 2024 Sect. 3.3.2 reports that “only apparent bioavailability differed between test and reference preparations”, and the Discussion attributes it to “a lower amount of drug released from the suspension” rather than to an absorption-rate difference. Contrast with FORM_QUIN_SR / FORM_VPA_SR, where the formulation indicator switches an absorption-rate or duration parameter as well. Specific scope because the contrast identifies two named commercial noscapine products. Ratified 2026-08-06 alongside the Chen 2024 noscapine extraction.

Receptor-binding ligand selection and pharmacological-chain inputs

OPIOID_ID (canonical for opioid-agonist ligand selector in the Mann 2022 mu-receptor binding panel)

• Description: Integer 1..13 selecting which ligand from the Mann 2022 Supplement 1 Table S2 binding-kinetic panel (1..12) or the Laffont 2024 Supplementary Table S3 scaling-derived extension (13 = nalmefene) occupies the opioid-agonist slot of a multi-ligand competitive mu-opioid-receptor binding model at simulation time. The same compiled binding model can simulate any agonist in the panel without re-instantiation by varying OPIOID_ID per subject (or per simulation arm). Mapping (preserved verbatim in Mann_2022_mu_receptor_binding.R::ini()): 1 = alfentanil; 2 = buprenorphine; 3 = butyryl fentanyl; 4 = carfentanil; 5 = fluorobutyryl fentanyl; 6 = fentanyl; 7 = fluoroisobutyryl fentanyl; 8 = furanyl fentanyl; 9 = isobutyryl fentanyl; 10 = naloxone; 11 = remifentanil; 12 = sufentanil; 13 = nalmefene (added by task 131 from Laffont 2024 Supp Table S3 scaling approach over Cassel 2005). Out-of-range values cause every dispatch indicator to evaluate to 0, which zeros the agonist slot rates rather than throwing an error.
• Units: (categorical)
• Type: categorical
• Scope: specific
• Reference category: n/a (every value selects a distinct ligand parameter set).
• Source aliases: none (new canonical introduced with Mann 2022 extraction).
• Example models: Mann_2022_mu_receptor_binding.R.
• Notes: Scope: specific because the integer-to-ligand mapping is anchored to Mann 2022 Table S2 row order, with the Laffont 2024 / Laffont 2025 13th-ligand extension preserving that anchoring (nalmefene appended at index 13, not inserted alphabetically). The 2026-05-29 sidecar 132/request-001 operator response authorises this 13th-ligand expansion; task 131 (2026-05-29) executed it after confirming the nalmefene Kon/Koff/n values are explicitly published as final values in Laffont 2024 Supplementary Table S3 (a primary source on disk). Ratified canonically on 2026-05-29 alongside the Mann 2022 translational-model extraction; 13th-ligand extension committed by task 131 on the same day.

ANTAGONIST_ID (canonical for opioid-antagonist ligand selector in the Mann 2022 mu-receptor binding panel)

• Description: Integer 1..13 selecting which ligand from the Mann 2022 Supplement 1 Table S2 (1..12) or the Laffont 2024 Supplementary Table S3 scaling-derived extension (13 = nalmefene) occupies the antagonist slot of the multi-ligand competitive mu-opioid-receptor binding model at simulation time. Uses the same integer-to-ligand mapping as OPIOID_ID. Mann 2022 itself uses only naloxone (10) as the antagonist; Laffont 2024 / Laffont 2025 add nalmefene (13) as the second commercially-relevant intranasal antagonist option. The wider 1..13 range is retained so downstream tasks can flip slots without code changes (e.g., partial-agonist buprenorphine 2 in the antagonist slot to model receptor occupancy without full agonist effect).
• Units: (categorical)
• Type: categorical
• Scope: specific
• Reference category: n/a (every value selects a distinct ligand parameter set).
• Source aliases: none.
• Example models: Mann_2022_mu_receptor_binding.R.
• Notes: Scope: specific. Same row-order semantics as OPIOID_ID (Mann 2022 Table S2 rows 1..12 verbatim; Laffont 2024 Supp Table S3 nalmefene at row 13). Out-of-range values zero the antagonist slot rates safely. Ratified canonically on 2026-05-29 alongside the Mann 2022 translational-model extraction; 13th-ligand extension committed by task 131 on the same day.

L_OPIOID_pM (canonical for time-varying opioid-agonist effect-site concentration input to the Mann 2022 binding layer)

• Description: Time-varying opioid-agonist effect-site (biophase) concentration in picomolar (pM), supplied as a data covariate to the multi-ligand competitive mu-receptor binding model. The pM unit matches the per-second Kon units (pM^-n s^-1) tabulated in Mann 2022 Supplement 1 Table S2; the binding ODE inside Mann_2022_mu_receptor_binding.R consumes this column directly. In a composed Mann 2022 chain, the upstream IV-opioid PK layer (Mann_2022_fentanyl_iv.R or Mann_2022_carfentanil_iv.R) exposes its effect-site Ce_pM as this covariate; in standalone use, the operator supplies it as a time-varying data column on the subject’s records.
• Units: pM (picomolar)
• Type: continuous
• Scope: specific
• Reference category: n/a.
• Source aliases: Ce_pM (upstream PK output name when composed in-chain).
• Example models: Mann_2022_mu_receptor_binding.R.
• Notes: Scope: specific because the unit choice (pM) is tied to the Mann 2022 binding-rate parameterisation. A future binding model that uses nM or mg/L should register a separately named canonical (e.g., L_OPIOID_nM) rather than reusing this name with a different unit, because the binding model’s downstream math depends on the unit match. Ratified canonically on 2026-05-29 alongside the Mann 2022 translational-model extraction.

AGONIST_CODE (canonical for vasoactive-agonist ligand selector in the Grzesk 2016 vascular-reactivity sigmoidal Emax CRC model)

• Description: Integer 1..4 selecting which vasoactive agonist from the Grzesk 2016 Table I CRC panel occupies the agonist slot of the sigmoidal Emax concentration-response model at simulation time. The same compiled model can simulate any of the four agonists by varying AGONIST_CODE per record (or per simulation arm). Mapping (preserved verbatim in Grzesk_2016_m3M3FBS.R::ini() parameter names): 1 = phenylephrine (PHE; alpha1-adrenergic receptor agonist); 2 = arg-vasopressin (AVP; V1 vasopressin receptor agonist); 3 = mastoparan-7 (heterotrimeric G-protein direct activator); 4 = Bay K8644 (L-type voltage-gated calcium channel agonist). Out-of-range values cause every dispatch indicator to evaluate to 0, which zeros the sigmoidal Emax expression rather than throwing an error.
• Units: (categorical)
• Type: categorical
• Scope: specific
• Reference category: n/a (every value selects a distinct (EC50, Emax) parameter pair; companion M3M3FBS_PRESENT selects within each agonist between the control and +m-3M3FBS sub-pairs).
• Source aliases: none – Grzesk 2016 Tables I and II label the four agonists in prose (“PHE”, “AVP”, “mastoparan-7”, “Bay K8644”); the model column is the canonical AGONIST_CODE.
• Example models: Grzesk_2016_m3M3FBS.R (Grzesk 2016 Table I + Table II Phase 2: per-agonist EC50 (M/L) and maximal-perfusion-pressure Emax (mmHg) pairs; companion M3M3FBS_PRESENT flips between control and m-3M3FBS-pretreatment sub-pairs).
• Notes: Specific scope because the integer-to-agonist mapping is anchored to Grzesk 2016 Table I row order. The Grzesk-group sibling papers (Biomed Rep 2 / 2014 pertussis toxin; Mol Med Report 5 / 2012 calcium blockers; Exp Ther Med 4 / 2012; etc.) reuse the same isolated-perfused-tail-artery scaffold with overlapping agonist panels (PHE / AVP / mastoparan-7 are common across the series; the third “channel” probe varies); a future extraction of any of those papers can extend the integer mapping at row 5+ rather than re-introduce a parallel selector canonical. Distinct from OPIOID_ID / ANTAGONIST_ID (Mann 2022 opioid-binding panel) because the agonists here are not within a single receptor-binding family – PHE, AVP, mastoparan-7, and Bay K8644 hit four mechanistically distinct contractile pathways (alpha1, V1, G-protein direct, L-type Ca channel), and the model uses agonist-specific (EC50, Emax) pairs rather than a shared binding-kinetic parameter set. Ratified canonically alongside the Grzesk 2016 extraction.

STUDY_TOMORROW (canonical for TOMORROW (1199.30) IPF phase II study indicator)

• Description: 1 = subject enrolled in the TOMORROW phase II trial (NCT00514683; Richeldi 2011 NEJM), the BI 1199.30 protocol that evaluated nintedanib 50 mg QD, 50 mg BID, 100 mg BID, or 150 mg BID vs placebo over 52 weeks in idiopathic pulmonary fibrosis (IPF); 0 = subject from any other study in the Schmid 2017 nintedanib pooled-PK analysis (NSCLC phase II, LUME-Lung 1, or LUME-Lung 2). Used as a per-subject categorical indicator for the trial-effect covariate on nintedanib first-order absorption rate ka.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-TOMORROW; in Schmid 2017 the trial-effect reference for ka is the LUME-Lung 1 + LUME-Lung 2 phase III NSCLC pair).
• Source aliases: derived per subject from the trial identifier.
• Example models: Schmid_2017_nintedanib.R (combined with STUDY_NSCLC_NIN_PH2 to drive the ka phase-II trial-group effect = 2.20x; also captures the ka2 BIBF 1202 metabolite phase-II trial-group effect = 0.756x).
• Notes: Specific scope because the trial-effect grouping is paper-specific. Ratified canonically on 2026-06-27 alongside the Schmid 2017 nintedanib extraction.

STUDY_NSCLC_NIN_PH2 (canonical for nintedanib NSCLC phase II (Reck 2011) study indicator)

• Description: 1 = subject enrolled in the nintedanib phase II NSCLC trial reported by Reck et al. 2011 (Annals of Oncology 22(6):1374-1381; BI 1199.4 protocol), which compared nintedanib 150 or 250 mg BID monotherapy in relapsed advanced NSCLC; 0 = subject from any other study in the Schmid 2017 pooled-PK analysis (TOMORROW IPF phase II, LUME-Lung 1, or LUME-Lung 2). Used as a per-subject categorical indicator for two distinct trial-effect covariates on F1 (relative bioavailability) and ka (absorption rate).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-NSCLC-phase-II; in Schmid 2017 the trial-effect reference for F1 is the LUME-Lung 1 + TOMORROW pair, and the reference for ka / ka2 is the LUME-Lung 1 + LUME-Lung 2 pair).
• Source aliases: derived per subject from the trial identifier.
• Example models: Schmid_2017_nintedanib.R (combined with STUDY_LUMELUNG2 to drive the F1 trial-group effect = 1.30x; combined with STUDY_TOMORROW to drive the ka phase-II trial-group effect = 2.20x and the ka2 BIBF 1202 metabolite phase-II trial-group effect = 0.756x).
• Notes: Specific scope. The Schmid 2017 paper’s prose refers to this trial as “NSCLC phase II [23]”; the BI protocol code (1199.4) is not explicit in the paper but is the standard BIBF 1120 NSCLC phase II Reck 2011 study. Ratified canonically on 2026-06-27 alongside the Schmid 2017 nintedanib extraction.

STUDY_LUMELUNG1 (canonical for LUME-Lung 1 (nintedanib + docetaxel NSCLC phase III) study indicator)

• Description: 1 = subject enrolled in LUME-Lung 1 (NCT00805194; Reck 2014 Lancet Oncology 15(2):143-155; BI 1199.13 protocol), the phase III trial of nintedanib 200 mg BID + docetaxel 75 mg/m^2 vs placebo + docetaxel in second-line advanced NSCLC; 0 = subject from any other study in the Schmid 2017 pooled-PK analysis (TOMORROW IPF phase II, NSCLC phase II, or LUME-Lung 2). Used as a per-subject categorical indicator alongside the other three Schmid 2017 study indicators.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-LUME-Lung-1).
• Source aliases: derived per subject from the trial identifier.
• Example models: Schmid_2017_nintedanib.R (acts as the reference cohort for the F1 trial-effect and as part of the ka / ka2 reference cohort).
• Notes: Specific scope. Ratified canonically on 2026-06-27 alongside the Schmid 2017 nintedanib extraction.

STUDY_LUMELUNG2 (canonical for LUME-Lung 2 (nintedanib + pemetrexed NSCLC phase III) study indicator)

• Description: 1 = subject enrolled in LUME-Lung 2 (NCT00806819; Hanna 2016 Lung Cancer 102:65-73; BI 1199.14 protocol), the phase III trial of nintedanib 200 mg BID + pemetrexed 500 mg/m^2 vs placebo + pemetrexed in second-line non-squamous NSCLC; 0 = subject from any other study in the Schmid 2017 pooled-PK analysis (TOMORROW IPF phase II, NSCLC phase II, or LUME-Lung 1). Used as a per-subject categorical indicator paired with STUDY_NSCLC_NIN_PH2 for the F1 trial-group effect.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (non-LUME-Lung-2).
• Source aliases: derived per subject from the trial identifier.
• Example models: Schmid_2017_nintedanib.R (combined with STUDY_NSCLC_NIN_PH2 to drive the F1 trial-group effect = 1.30x; acts as the reference cohort for the ka / ka2 trial-effect).
• Notes: Specific scope. Ratified canonically on 2026-06-27 alongside the Schmid 2017 nintedanib extraction.

DOSE_AGT_UG (canonical for per-observation angiotensin challenge dose in ug of angiotensin II equivalents)

• Description: Per-observation intravenous-bolus dose of exogenous angiotensin administered as a pharmacologic probe during an angiotensin-challenge phase I protocol, expressed as ug of angiotensin II equivalents. For an angiotensin II bolus this is the literal injected mass; for an angiotensin I bolus, multiply by Q = 0.78 (molar-weight ratio between angiotensin I and angiotensin II) before populating this column. Each row in the event table carries the dose given immediately before the observed BP peak was sampled.
• Units: ug (Ang II equivalents)
• Type: continuous
• Scope: specific
• Reference category: none (continuous).
• Source aliases:
  • D x Q – used in BuchwalderCsajka_1999_angiotensin.R (Buchwalder-Csajka 1999 Table 1 row 5 column header, where D is the raw angiotensin dose in ug and Q is the molar-weight conversion factor).
• Example models: BuchwalderCsajka_1999_angiotensin.R (drives the algebraic Emax dose-response E = Emax * DOSE_AGT_UG / (DOSE_AGT_UG + ED50) separately for SBP and DBP).
• Notes: Per-observation challenge-dose column for the algebraic Emax angiotensin-challenge PD model. Differs from the standard rxode2 AMT / EVID = 1 dosing column because the model is purely algebraic (no PK, no ODEs); the dose enters the model() block as a covariate symbol rather than via a dosing event. Specific scope because the column’s semantics (Q-corrected angiotensin dose) are tied to angiotensin-challenge protocols; future angiotensin-challenge extractions may reuse this canonical. Ratified canonically on 2026-06-10 alongside the Buchwalder-Csajka 1999 extraction.

DOSE_ISOPROTERENOL_UG (canonical for per-observation isoproterenol challenge dose in ug)

• Description: Per-observation intravenous-bolus dose of isoproterenol (isoprenaline) administered as a beta-adrenergic agonist probe during an isoproterenol sensitivity test (IST), expressed in ug. Each observation row in the event table carries the isoproterenol dose given immediately before the heart-rate response was sampled; rows outside an IST challenge carry 0, which recovers the drug-free baseline heart rate.
• Units: ug
• Type: continuous
• Scope: specific
• Reference category: none (continuous). Set to 0 for observation rows with no isoproterenol challenge; the Emax term then vanishes and the readout reduces to the baseline E0.
• Source aliases:
  • D – used in Hwang_2023_carvedilol.R (Hwang 2023 equations (3) and (4) and Fig. 1, where D is the isoproterenol dose driving the direct-effect Emax heart-rate response).
• Example models: Hwang_2023_carvedilol.R (drives the competitive-antagonism direct-effect Emax heart-rate model HR = E0 + Emax * DOSE_ISOPROTERENOL_UG / (ED50 * (1 + Cc / IC50) + DOSE_ISOPROTERENOL_UG), in which the carvedilol plasma concentration inflates the apparent isoproterenol ED50).
• Notes: Per-observation challenge-dose column for algebraic Emax agonist-challenge PD models, following the DOSE_AGT_UG precedent (angiotensin challenge; Buchwalder-Csajka 1999). Differs from the standard rxode2 AMT / EVID = 1 dosing column because the challenge agonist has no PK compartment in the model: the dose enters model() as a covariate symbol rather than via a dosing event. Hwang 2023 Fig. 1 draws an isoproterenol compartment with an elimination rate constant k, but no value for k is reported anywhere in the paper and the final-model equation printed in the same figure is algebraic in the dose D, so the dose-as-covariate encoding is the one the published parameters support. Specific scope because the semantics are tied to isoproterenol sensitivity-test protocols; future beta-blocker IST extractions should reuse this canonical. Ratified canonically on 2026-07-28 alongside the Hwang 2023 carvedilol extraction.

AUC_EMPA (canonical for per-subject steady-state AUC of empagliflozin)

• Description: Per-subject (time-fixed) steady-state AUC of empagliflozin over the q24h dosing interval, supplied as a static drug-exposure covariate to PD models that consume the upstream popPK without instantiating a PK ODE. The source authors generate AUCss values from individual empirical Bayes estimates of an upstream empagliflozin popPK analysis (Mondick 2018 plus additional EASE-2 / EASE-3 data on file in the M-EASE-2 application) and pass them as a static covariate column to the M-EASE-2 PD model.
• Units: nmol*h/L (document per-model via covariateData[[AUC_EMPA]]$units if a different exposure unit is reported).
• Type: continuous
• Scope: specific
• Reference category: n/a – enters via direct Emax form emax_i * AUC_EMPA / (auc50 + AUC_EMPA) on the HbA1c drug-effect term. Set AUC_EMPA = 0 to recover the placebo arm (drug-effect term vanishes; HbA1c follows baseline + placebo drift only). Reference values observed: AUC50 = 498 nmol*h/L (Johnston 2019 Table 2 typical-value AUC at half-maximal effect; corresponds approximately to the simulated median AUCss for the 2.5 mg QD arm).
• Source aliases:
  • AUCss (or AUCSS,i) – the printed variable name in Johnston 2019 Equation 1 (the individual steady-state AUC entering the Emax expression).
• Example models: Johnston_2019_empagliflozin.R (M-EASE-2 exposure-response PD model for HbA1c in adults with type 1 diabetes; AUC_EMPA drives the direct Emax HbA1c-reduction term), Johnston_2021_empagliflozin_MEASE1.R (semi-mechanistic exposure-response PD model on TDID / MDG / HbA1c in adults with T1D; AUC_EMPA drives the direct Emax reductions on both TDID and MDG, which in turn drive HbA1c).
• Notes: Specific scope because the column meaning is tied to empagliflozin and to a q24h steady-state AUC convention. Sibling drug-specific AUC canonicals (AUC_CARBO, AUC_GEM, AUC_BAST_FW, AUC_PAZO, AUC_GCV, AUC_RTV) follow the same AUC_<DRUG> naming pattern. A future PK/PD model that uses a different empagliflozin exposure metric (trough concentration, q12h-interval AUC) should register a parallel canonical rather than overload AUC_EMPA. Ratified canonically alongside the Johnston 2019 M-EASE-2 extraction.

AUC_HTBZ (canonical for daily AUC of the valbenazine metabolite [+]-alpha-HTBZ at steady state)

• Description: Area under the plasma concentration-time curve of [+]-alpha-dihydrotetrabenazine ([+]-alpha-HTBZ, NBI-98782) over the once-daily 24 h dosing interval at steady state. [+]-alpha-HTBZ is the sole active metabolite of the prodrug valbenazine and carries essentially all of the VMAT2-inhibitory activity, so it – not the parent – is the exposure driver of valbenazine exposure-response models. Supplied per observation record as a data column; 0 for placebo-arm records, which makes any Emax term in it vanish exactly.
• Units: ng*h/mL. Must be in the same units as the model’s EC50 so the Emax term is dimensionless. Document per-model via covariateData[[AUC_HTBZ]]$units.
• Type: continuous
• Scope: specific
• Reference category: n/a – enters via the direct Emax form emax * AUC_HTBZ / (ec50 + AUC_HTBZ). Reference values observed: EC50 = exp(7.50) = 1808 ngh/mL (Nguyen 2025 Table 3 theta 6, printed back-transformed as 1820). Nguyen 2025 Table 2 gives the Huntington’s-disease cohort median (5th; 95th percentile) values as 176 (107; 328), 402 (244; 749), 657 (399; 1225) and 931 (566; 1736) ngh/mL at valbenazine 20, 40, 60 and 80 mg once daily; the E-R simulations explored 0-3000 ng*h/mL.
• Source aliases:
  • AUC[+]-alpha-HTBZ – the printed variable name in the Nguyen 2025 Emax equation (Methods, “Exposure-Efficacy Modeling”).
  • AUC 24,ss – the Nguyen 2025 Table 2 column heading for the same quantity.
• Example models: Nguyen_2025_valbenazine_tmc.R (drives the Emax drug effect on the change from baseline in the UHDRS Total Maximal Chorea score in Huntington’s-disease chorea; emax = -14.2 TMC points with lec50 = 7.50).
• Notes: Specific scope because the column meaning is tied to this metabolite and to the q24h steady-state AUC convention. Member of the AUC_<DRUG> family (AUC_CARBO, AUC_GEM, AUC_GCV, AUC_PAZO, AUC_RTV, AUC_VERUB, AUC_ADU, AUC_DON, AUC_GAN, AUC_LEC, AUC_IBRU, AUC_LCM, AUC_CBZ, AUC_AMPH, AUC_LEN, AUC_EMPA); AUC_LEN is the closest structural analogue, likewise a per-interval steady-state AUC carried out of an upstream population PK run into a downstream PD run as a data column. Named for the METABOLITE rather than for the administered drug (contrast AUC_LEN, AUC_EMPA) because the parent valbenazine exposure is explicitly NOT the driver – Nguyen 2025 selected the metabolite AUC, and a future valbenazine model driven by parent exposure would need a separate AUC_VBZ sibling. Companion to the htbz metabolite suffix registered in compartment-names.md; the upstream model that generates this column is modellib('Nguyen_2025_valbenazine'). A future model using a different [+]-alpha-HTBZ exposure metric (Cmax, Cmin, Cavg – all three are tabulated in Nguyen 2025 Table 2 and gave consistent E-R profiles) should register a parallel canonical rather than overload AUC_HTBZ. Registered alongside the Nguyen 2025 valbenazine extraction.

CONMED_CYP3A4_INH_STRONG (canonical for concomitant strong CYP3A4 inhibitor coadministration indicator)

• Description: 1 = subject / dose-record with concomitant coadministration of a strong CYP3A4 inhibitor (FDA/EMA classification of “strong”: AUC increase >= 5-fold for a sensitive CYP3A4 substrate; representative agents include itraconazole, ketoconazole, posaconazole, voriconazole, clarithromycin, and ritonavir-boosted regimens), 0 = no strong CYP3A4 inhibitor coadministration during the observation window (whether no CYP3A4 inhibitor at all, or a weak/moderate inhibitor only). Time-varying per record. Companion canonical to the broader CONMED_CYP3A4_INH that stratifies specifically by FDA/EMA inhibitor-strength tier – distinct from CONMED_CYP3A4_INH_HI (which stratifies by cumulative exposure duration rather than by inhibitor-strength category).
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no strong CYP3A4 inhibitor coadministration; subject may still be on a weak or moderate CYP3A4 inhibitor).
• Source aliases:
  • DDICYPSH – used in Mitra_2026_ziftomenib.R (Kura Oncology KOMET-001 + KO-MEN-003 NONMEM control-stream column; strong CYP3A4 inhibitor coadministration during a ziftomenib dose record; dominant driver in the R/R AML cohort is prophylactic antifungal azole use).
• Example models: Mitra_2026_ziftomenib.R (multiplicative effects: 0.459x on parent ziftomenib CL/F, 0.195x on KO-739 CL, 0.449x on KO-516 CL when CONMED_CYP3A4_INH_STRONG = 1; encoded as additive shifts on the log(CL) scale per the paper’s NONMEM PK block), Kemal_2026_nemtabrutinib.R (multiplicative effect on CL/F: (1 + -0.0119 * CONMED_CYP3A4_INH_STRONG), i.e. 1.2% lower CL/F under strong CYP3A4 inhibition; RSE 774%, 95% CI includes zero – retained in the full covariate model).
• Notes: Companion canonical to CONMED_CYP3A4_INH (which pools any inhibitor strength into a single 0/1 indicator) and to CONMED_CYP3A4_INH_HI / CONMED_CYP3A4_INH_LO (which stratify by cumulative exposure duration rather than by inhibitor-strength category). Registered per the CONMED_CYP3A4_INH Notes explicit guidance: “Future models that need stratified encoding (separate strong / moderate / weak indicators) should register companion canonicals (e.g. CONMED_CYP3A4_INH_STRONG, CONMED_CYP3A4_INH_MOD, CONMED_CYP3A4_INH_WEAK) rather than overloading CONMED_CYP3A4_INH.” Ratified canonically on 2026-07-24 alongside the Mitra 2026 ziftomenib extraction. Future extractions that need the sibling moderate- and weak-strength indicators should register CONMED_CYP3A4_INH_MOD and CONMED_CYP3A4_INH_WEAK following the same pattern.

DOSE_PTM_MG (canonical for administered pretomanid per-administration dose amount)

• Description: Administered oral dose of the nitroimidazooxazine antituberculosis agent pretomanid (formerly PA-824), in mg, for the current dose record. Per-dose-record covariate; must equal the amt of the corresponding dosing record. Not a PK covariate in the usual sense – the amount already appears on the dose record via amt – but it is required as an explicit regressor because pretomanid bioavailability is saturable in dose, so the dose amount has to be readable inside model() to compute f(depot).
• Units: mg
• Type: continuous
• Scope: specific
• Reference category: n/a – enters via the saturable bioavailability f(depot) = Fmax / (1 + DOSE_PTM_MG / ED50) with Fmax assumed 1 and ED50 = 554 mg in humans (Mehta 2023 Table 1). A 200 mg dose therefore gives F = 1 / (1 + 200/554) = 0.735.
• Source aliases:
  • dose – used in Mehta_2023_pretomanid_mpbpk.R (Mehta 2023 ESM S2, which writes doseIn = Fmax*dose/(1 + dose/ED50) followed by f(depot) = doseIn/dose; the packaged model uses the algebraically identical single-expression form, which avoids dividing by the dose amount).
• Example models: Mehta_2023_pretomanid_mpbpk.R (drives the dose-dependent bioavailability of the translational lung-lesion mPBPK model; simulated regimen 200 mg once daily, clinical validation data spanning 50-1200 mg).
• Notes: Follows the DOSE_<DRUG>_<UNITS> auto-approve family (siblings: DOSE_EMPA_MGD, DOSE_CIPARGAMIN_MG, DOSE_PHT_MGKGD). Units suffix is MG because pretomanid is dosed as a per-administration amount rather than a daily total. Ratified canonically alongside the Mehta 2023 pretomanid extraction.

INSDOSE_BL (canonical for baseline total daily insulin dose per body weight)

• Description: Per-subject (time-fixed) total daily exogenous insulin dose at study baseline, normalised to body weight. Captures the insulin requirement of patients on background insulin therapy (a typical input covariate in T1DM / T2DM popPK/PD models where the empagliflozin / SGLT-2 inhibitor / GLP-1 analogue effect is layered on top of pre-existing insulin treatment). Distinct from INS_BL (baseline fasting plasma insulin concentration, a measured biomarker in pmol/L) – INSDOSE_BL is the administered dose level, not the resulting plasma level.
• Units: U/kg/day (international units of insulin per kilogram body weight per day). Document per-model via covariateData[[INSDOSE_BL]]$units if a paper reports total daily units without body-weight normalisation.
• Type: continuous
• Scope: specific
• Reference category: n/a – enters via power form (INSDOSE_BL / ref)^e_insdose_bl_<param>. Reference values observed: 0.660 U/kg/day (Johnston 2019 reference-patient description).
• Source aliases:
  • IDB – “Insulin daily dose at Baseline” abbreviation used in Johnston 2019 Table 2.
• Example models: Johnston_2019_empagliflozin.R (M-EASE-2 covariate effect on the typical baseline HbA1c and on Emax in T1DM patients with background insulin therapy; effect exponents 0.0141 on baseline HbA1c and 0.0552 on Emax, both with CIs that cross zero – the covariate was retained as part of the full-random-effects covariate model rather than dropped), Johnston_2021_empagliflozin_popPK.R (Johnston 2021 empagliflozin popPK covariate on CL/F in adults with T1D; effect exponent 0.0469 with CI that spans zero – retained as part of the full-covariate model).
• Notes: Specific scope because the column meaning is tied to baseline total-daily-dose accounting in insulin-treated diabetes populations; future T1DM / T2DM popPK/PD extractions can reuse this canonical and document the per-model units / dose-counting convention in covariateData[[INSDOSE_BL]]$notes. A model that uses time-varying insulin dose (rather than baseline only) should register a parallel canonical (e.g., INSDOSE). Companion concept to INS_BL (plasma insulin concentration) and to HBA1C (glycemic control). Ratified canonically alongside the Johnston 2019 M-EASE-2 extraction.

INSDT_CSII (canonical for binary CSII vs MDI insulin delivery type indicator)

• Description: Binary indicator for the subject’s insulin delivery type at study entry. 1 = continuous subcutaneous insulin infusion (CSII; insulin pump therapy); 0 = multiple daily injections (MDI; subcutaneous injection regimen) reference. Per-subject and time-fixed within the analysis window (regimen switches during the study are not represented).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 = MDI (multiple daily injections).
• Source aliases:
  • INSDT – “Insulin Dose Type” abbreviation used in Johnston 2019 Table 2 (the two-level categorical column decomposed into the binary CSII indicator with MDI as the reference category).
• Example models: Johnston_2019_empagliflozin.R (M-EASE-2 covariate multiplier on baseline HbA1c, Emax, and placebo rate; CSII shifts placebo drift upward by a factor of 1.47 vs MDI – a moderate effect on the placebo-adjusted HbA1c change), Johnston_2021_empagliflozin_MEASE1.R (M-EASE-1 covariate multiplier on Emax_MDG in T1D; the reported CSII / MDI effect is very close to 1 (0.995) and is documented as retained-but-negligible in the full-covariate model).
• Notes: Specific scope because the CSII-vs-MDI contrast and reference category are study-specific. Follows the <COLUMN>_<LEVEL> decomposition pattern used for REGI_BID / TUMTP_GLIO. A future paper that retains the same INSDT covariate but with more than two levels (e.g., CSII vs MDI vs continuous glucose monitoring + open-loop algorithm) would register sibling canonicals (e.g., INSDT_CGM) rather than overload this entry. Ratified canonically alongside the Johnston 2019 M-EASE-2 extraction.

LSCI (canonical for amplitude of an inverse-Bateman lifestyle-change / placebo effect on energy intake in body-composition modelling)

• Description: Per-subject (or per-study-arm) amplitude of the inverse-Bateman lifestyle-change (LSC) effect on energy intake (EI) in body-composition models. Drives the magnitude of the placebo / dietary-restriction-induced fractional reduction in EI; combined with onset rate Kdiet and reduction rate Kred to form the LSC effect LSCeff(t) = 1 - LSCI * Kdiet * (exp(-Kdiet * t) - exp(-Kred * t)) / (Kred - Kdiet) applied multiplicatively to EI. Subjects in the same study arm typically share the same LSCI value; the column is time-fixed at study entry (a single LSCI per subject per arm).
• Units: fraction (0..1; can be negative for arms that report a paradoxical increase in EI)
• Type: continuous
• Scope: specific
• Reference category: none – LSCI = 0 corresponds to no lifestyle effect (the model collapses to pure body composition + drug effects).
• Source aliases:
  • LSCI – used directly in Bosch_2024_glp1ra_bodyweight.R (Bosch 2024 supplement S10 THETA7..THETA16 are per-study fitted LSCI values).
• Example models: Bosch_2024_glp1ra_bodyweight.R (introduces the canonical; per-study fitted values 0..0.548 across diet and GLP-1RA studies, with -0.0288 reported for the Blundell 2017 arm).
• Notes: Specific scope because the column’s semantics are tied to body-composition / energy-balance modelling. Future extractions that combine the Hall body composition framework with other drug classes or other lifestyle-intervention protocols may reuse this canonical; document the per-paper values in covariateData[[LSCI]]$notes so users can match the source-paper LSCI to the simulated arm. Ratified canonically on 2026-06-22 alongside the Bosch 2024 extraction.

WM_IBT (canonical for weight-management + intensive-behavioural-treatment intervention indicator)

• Description: Binary indicator that the subject is enrolled in a study arm with weight management and intensive behavioural treatment (e.g., the STEP-trial-family lifestyle-intervention protocols described in Wadden 2021 / Garvey 2022 / Rubino 2022). Used in QSP body-composition models to gate a body-weight-dependent activity effect on the exercise component of physical activity energy expenditure. Subjects in arms without an explicit weight-management + IBT protocol carry WM_IBT = 0.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (no weight management + intensive behavioural treatment).
• Source aliases:
  • IFLAG – used in Bosch_2024_glp1ra_bodyweight.R (Bosch 2024 supplement S10 IFLAG > 0 gates the activity effect).
• Example models: Bosch_2024_glp1ra_bodyweight.R (introduces the canonical; gates the body-weight-dependent activity effect on PAE for STEP-family arms in the Bosch 2024 dataset).
• Notes: Specific scope because the canonical name is tied to body-composition QSP modelling where the activity effect is empirically estimated for IBT-instrumented arms. Future extractions of similar lifestyle-intervention-aware body-composition or weight-loss-trajectory models may reuse this canonical; mark the per-paper IBT-intensity definition in covariateData[[WM_IBT]]$notes so users can distinguish “weekly clinic counselling + diary review” intensity (STEP 3) from “minimal-counselling -500 kcal/d” intensity (STEP 5 / STEP 8). Ratified canonically on 2026-06-22 alongside the Bosch 2024 extraction.

STRAIN_C57BI6 (canonical for C57BI/6 mouse-strain indicator)

• Description: Binary within-species mouse-strain indicator: 1 = subject is a C57BI/6 mouse, 0 = subject is a mouse of the reference strain (in the founding example, NMRI). Used to gate strain-specific fractional multipliers on structural PK parameters within a mouse popPK model when the source paper pools two strains and reports a fractional strain effect.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (NMRI mouse in Larsen 2018; other reference strains permitted for future extractions provided the per-paper reference is documented in covariateData[[STRAIN_C57BI6]]$notes).
• Source aliases: none.
• Example models: Larsen_2018_factorviia_mouse.R (Larsen 2018 pooled C57BI/6 + NMRI mice with fractional C57BI/6 effect on V and CL for rFVIIa: V_C57BI6 = V * 0.588, CL_C57BI6 = CL * 0.87 per Table 2).
• Notes: Analogous to the RACE_<GROUP> naming family for human race indicators: STRAIN_<GROUP> gives each within-species strain-of-interest its own binary indicator, with the reference strain implicitly encoded as 0. A future mouse popPK model that pools additional strains (e.g., BALB/c, C3H) should register a parallel canonical (STRAIN_BALBC, STRAIN_C3H, …) rather than overload this one. Ratified alongside the Larsen 2018 rFVIIa mouse extraction.

STRAIN_NUDE (canonical for athymic-nude mouse-strain indicator)

• Description: Binary within-species mouse-strain indicator: 1 = subject is an Hsd:Athymic Nude-Foxn1nu mouse, 0 = subject is a severe-combined-immunodeficient (SCID, C.B-17/IcrHan(R)Hsd-Prkdcscid) mouse. Used when a preclinical popPK analysis pools the two immunocompromised strains routinely used for xenograft work and estimates strain-specific absorption, bioavailability or study-scaling parameters.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (SCID mouse in DeJongh 2025; other reference strains permitted for future extractions provided the per-paper reference is documented in covariateData[[STRAIN_NUDE]]$notes).
• Source aliases:
  • STR – used in DeJongh_2025_azd7648_mouse.R (AZD7648 NONMEM dataset; STR = 0 SCID, STR = 1 nude).
  • STRN – used in DeJongh_2025_olaparib_mouse.R (olaparib NONMEM dataset; STRN = 1 SCID, STRN = 2 nude, so the indicator is STRN - 1).
• Example models: DeJongh_2025_azd7648_mouse.R (gates both the absorption rate constant – estimated at 2.77 1/h in SCID, fixed at 9.9 1/h in nude mice for want of absorption-phase samples – and a -52% relative bioavailability in nude mice), DeJongh_2025_olaparib_mouse.R (splits the study S1734 relative-bioavailability factor by strain).
• Notes: Member of the STRAIN_<GROUP> family established by STRAIN_C57BI6, which explicitly invites parallel canonicals for additional strains. Note the two DeJongh 2025 source datasets encode the same biological covariate on different numeric scales (0/1 vs 1/2); the canonical column is always the 0/1 indicator, and the per-model source_name records which raw column it came from. Ratified alongside the DeJongh 2025 AZD7648 + olaparib extraction.

DOSE_AZD7648_MGKGD (canonical for concomitant AZD7648 total daily dose per kg body weight)

• Description: Total daily dose of co-administered AZD7648 (a DNA-dependent protein kinase inhibitor) in mg per kg body weight per day. Carried on every record so a drug-drug-interaction equation can read the co-medication dose level without back-computing it from the AZD7648 event records. It is the total across the dosing day, so a 75 mg/kg twice-daily regimen carries the value 150. Zero for olaparib monotherapy and for vehicle controls.
• Units: mg/kg/day
• Type: continuous
• Scope: specific
• Reference category: n/a – enters as the driver of the Emax-shaped clearance-interaction term CmS = 1 - DOSE_AZD7648_MGKGD / (DOSE_AZD7648_MGKGD + CmD50) of DeJongh 2025 Equations 7-8, with CmD50 = 82.5 mg/kg/day. DOSE_AZD7648_MGKGD = 0 gives CmS = 1 (no interaction, olaparib clearance unchanged); DOSE_AZD7648_MGKGD = 82.5 halves olaparib clearance.
• Source aliases:
  • CMDDOS – used in DeJongh_2025_olaparib_mouse.R (olaparib population-PK NONMEM dataset, co-medication dose column).
  • AZD7648 / DOSE_AZD (mg/kg) – used in DeJongh_2025_azd7648_olaparib_xenograft_mouse.R (PK-PD dataset, supplementary file 7 of DeJongh 2025).
• Example models: DeJongh_2025_olaparib_mouse.R, DeJongh_2025_azd7648_olaparib_xenograft_mouse.R.
• Notes: Sibling canonical in the DOSE_<DRUG>_MGKGD family for daily co-medication doses, as anticipated by the DOSE_RTV_MGKG notes (“Drug-self-dose covariates for other drugs should register sibling canonicals (e.g., DOSE_<DRUG>_MGKG for per-administration or DOSE_<DRUG>_MGKGD for daily)”). Specific scope because the CmD50 value is tied to the DeJongh 2025 mouse analysis, and because only one source study (S1734) contained olaparib arms with and without AZD7648 co-treatment. Ratified alongside the DeJongh 2025 AZD7648 + olaparib extraction.

STUDY_S1143 (canonical for DeJongh 2025 olaparib mouse study S1143 cohort indicator)

• Description: 1 = animal enrolled in mouse study S1143 (2, 5, 10, 50 or 100 mg/kg olaparib as a single oral dose in nude mice); 0 = the reference study S11448. Selects the study-specific relative oral bioavailability of olaparib.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (study S11448, the 20 mg/kg intravenous / 50 mg/kg oral study in nude mice that anchors absolute bioavailability for the whole pooled analysis).
• Source aliases: derived from the STUDY column of the olaparib NONMEM dataset (STUDY == 1143 -> 1).
• Example models: DeJongh_2025_olaparib_mouse.R (selects lfdepot_s1143, F1 = 0.174).
• Notes: DeJongh 2025 Results: “Relative oral bioavailability was found to vary substantially between some studies, even within the same mice strain, and was accounted for in the final PK model by fitting a descriptive inter-study scaling factor on this parameter.” One binary per non-reference study, following the decomposed-binary-indicator convention rather than a single integer study code. Ratified alongside the DeJongh 2025 AZD7648 + olaparib extraction.

STUDY_S1721 (canonical for DeJongh 2025 olaparib mouse study S1721 cohort indicator)

• Description: 1 = animal enrolled in mouse study S1721 (50, 75 or 100 mg/kg olaparib as single and multiple oral doses in nude mice); 0 = the reference study S11448. Selects the study-specific relative oral bioavailability of olaparib.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (study S11448).
• Source aliases: derived from the STUDY column of the olaparib NONMEM dataset (STUDY == 1721 -> 1).
• Example models: DeJongh_2025_olaparib_mouse.R (selects lfdepot_s1721, F1 = 0.489).
• Notes: See STUDY_S1143. Ratified alongside the DeJongh 2025 AZD7648 + olaparib extraction.

STUDY_S1734 (canonical for DeJongh 2025 olaparib mouse study S1734 cohort indicator)

• Description: 1 = animal enrolled in mouse study S1734 (100 mg/kg olaparib as single and multiple oral doses in SCID and nude mice, with and without AZD7648 co-treatment); 0 = the reference study S11448. Selects the study-specific relative oral bioavailability of olaparib, which S1734 estimates separately per strain – so this indicator is combined with STRAIN_NUDE.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (study S11448).
• Source aliases: derived from the STUDY column of the olaparib NONMEM dataset (STUDY == 1734 -> 1).
• Example models: DeJongh_2025_olaparib_mouse.R (selects lfdepot_s1734scid, F1 = 0.598, or lfdepot_s1734nude, F1 = 0.238).
• Notes: S1734 is the only source study containing olaparib arms both with and without AZD7648 co-treatment, so it is the sole study informing the CmD50 drug-drug-interaction parameter; the source control stream applies the interaction term conditionally on this study. The library model applies the interaction unconditionally, which is equivalent because DOSE_AZD7648_MGKGD is 0 outside the co-treated arms. Ratified alongside the DeJongh 2025 AZD7648 + olaparib extraction.

STUDY_S1770 (canonical for DeJongh 2025 olaparib mouse study S1770 cohort indicator)

• Description: 1 = animal enrolled in mouse study S1770 (multiple oral olaparib doses with and without AZD7648 co-treatment in SCID mice); 0 = the reference study S11448. Selects the study-specific relative oral bioavailability of olaparib.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (study S11448).
• Source aliases: derived from the STUDY column of the olaparib NONMEM dataset (STUDY == 1770 -> 1).
• Example models: DeJongh_2025_olaparib_mouse.R (selects lfdepot_s1770, F1 = 1.48).
• Notes: See STUDY_S1143. Values above 1 are possible because the source parameterises olaparib clearance in absolute L/h while the volumes are weight-normalised L/kg; the per-study bioavailability factor absorbs that scale mismatch as well as genuine formulation / exposure differences. Ratified alongside the DeJongh 2025 AZD7648 + olaparib extraction.

STUDY_S1816 (canonical for DeJongh 2025 olaparib mouse study S1816 cohort indicator)

• Description: 1 = animal enrolled in mouse study S1816 (multiple oral olaparib doses with AZD7648 co-treatment in SCID mice); 0 = the reference study S11448. Selects the study-specific relative oral bioavailability of olaparib.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (study S11448).
• Source aliases: derived from the STUDY column of the olaparib NONMEM dataset (STUDY == 1816 -> 1).
• Example models: DeJongh_2025_olaparib_mouse.R (selects lfdepot_s1816, F1 = 4.18).
• Notes: This is the factor the authors carried into the DeJongh 2025 tumour PK-PD model (DeJongh_2025_azd7648_olaparib_xenograft_mouse.R fixes lfdepot_olaparib at 4.1817), because the xenograft studies had no olaparib PK of their own. See STUDY_S1770 on why the value exceeds 1. Ratified alongside the DeJongh 2025 AZD7648 + olaparib extraction.

IP_FA (canonical for tablet-transit inflection-point time from fundus to antrum)

• Description: Individual inflection-point time (h) at which the sigmoid step function governing tablet movement from the fundus to the antrum equals 0.5 (paper Equation 1 form: STEP(t) = 1 / (1 + exp(-SIG * (t - IP)))). Used in the Gastro-Intestinal Transit Time (GITT) absorption model of Henin 2012 to drive per-subject tablet residence time in the fundus. The paper samples IP per subject from a fixed log-normal distribution IP = MRT * exp(eta) with eta ~ N(0, VRT) and MRT / VRT taken from the upstream Bergstrand 2009 Markov-chain fit (Table II of Henin 2012): MRT_fundus = 0.4 h (fasted) / 1.04 h (fed), VRT_fundus = 0.46 / 1.09 h^2 (CV 100%).
• Units: h
• Type: continuous
• Scope: specific
• Reference category: NULL – IP_FA is a continuous per-subject residence time; the sigmoid step is centred at IP_FA regardless of whether the subject is fed or fasted, but the sampling distribution is fed / fasted stratified.
• Source aliases:
  • IP_F_A – used directly in the Henin 2012 model file annotations for the fundus-to-antrum inflection point.
• Example models: Henin_2012_felodipine.R (founding example; used in the felodipine GITT extraction for the extended-release tablet transit).
• Notes: Specific scope because the canonical name is tied to the GITT / MMM-derived semi-mechanistic absorption modelling framework where the tablet position is modelled by sigmoid STEP functions with per-subject inflection points. Only the “no return to fundus” subpopulation is encoded in the Henin 2012 extraction (7/12 felodipine subjects per the paper); the paper’s 3-component mixture with 0 / 1 / 2 antrum-to-fundus returns is documented as a deviation in the vignette. Future GITT-family extractions (Bergstrand 2009 upstream, or 2020+ mechanistic-absorption papers extending the same STEP-function idiom) should reuse this canonical. Ratified canonically alongside the Henin 2012 extraction.

IP_APSI (canonical for tablet-transit inflection-point time from antrum to proximal small intestine)

• Description: Individual inflection-point time (h) at which the sigmoid step function governing tablet movement from the antrum (or the enteric-coated-tablet whole-stomach exit) to the proximal small intestine equals 0.5. Central variable in the Gastro-Intestinal Transit Time (GITT) model (Henin 2012 Equation 1). For the extended-release felodipine model the population distribution is fed / fasted stratified: MRT_antrum = 0.32 h (fasted) / 1.58 h (fed), VRT_antrum = 0.15 / 2.50 h^2 (CV 100%). For enteric-coated diclofenac the same covariate represents the combined stomach transit (mean ~ 2 h ranging 1.5-3 h per paper Results).
• Units: h
• Type: continuous
• Scope: specific
• Reference category: NULL – continuous per-subject residence time.
• Source aliases:
  • IP_A_PSI – used in the Henin 2012 model file annotations for the antrum-to-proximal-small-intestine inflection point.
• Example models: Henin_2012_felodipine.R (extended-release with drug release in fundus + antrum + PSI + DSI + colon), Henin_2012_diclofenac.R (enteric-coated with stomach transit only, no drug release in stomach; IP_APSI represents the gastric emptying event).
• Notes: Specific scope. IP_APSI’s exact operational meaning differs slightly between the two Henin 2012 models: felodipine’s IP_APSI is the antrum -> PSI transition (fundus and antrum are separate compartments), while diclofenac’s IP_APSI is a lumped stomach -> PSI transition (fundus and antrum are not distinguished because the enteric coating prevents any drug release in the stomach). Both use the same STEP-function machinery and the same “no return to fundus” subpopulation simplification. Ratified canonically alongside the Henin 2012 extraction.

IP_PSI_DSI (canonical for tablet-transit inflection-point time from proximal to distal small intestine)

• Description: Individual inflection-point time (h) at which the sigmoid step function governing tablet movement from the proximal small intestine to the distal small intestine equals 0.5. Not fed / fasted stratified in the Henin 2012 GITT framework. Sampled per subject as IP_PSI_DSI = MRT_psi * exp(eta) with MRT_psi = 1.17 h, VRT_psi = 1.37 h^2 (CV 50%) per Table II.
• Units: h
• Type: continuous
• Scope: specific
• Reference category: NULL – continuous per-subject residence time.
• Source aliases: none.
• Example models: Henin_2012_felodipine.R, Henin_2012_diclofenac.R.
• Notes: Specific scope. Time-zero-referenced: IP_PSI_DSI is the absolute clock time (relative to dose administration) at which the sigmoid switch crosses 0.5. The effective PSI residence time is IP_PSI_DSI - IP_APSI. Ratified canonically alongside the Henin 2012 extraction.

IP_DSI_C (canonical for tablet-transit inflection-point time from distal small intestine to colon)

• Description: Individual inflection-point time (h) at which the sigmoid step function governing tablet movement from the distal small intestine to the colon equals 0.5. Not fed / fasted stratified in the Henin 2012 GITT framework. Sampled per subject as IP_DSI_C = MRT_dsi * exp(eta) with MRT_dsi = 1.22 h, VRT_dsi = 1.48 h^2 (CV 58%) per Table II.
• Units: h
• Type: continuous
• Scope: specific
• Reference category: NULL – continuous per-subject residence time.
• Source aliases: none.
• Example models: Henin_2012_felodipine.R, Henin_2012_diclofenac.R.
• Notes: Specific scope. Time-zero-referenced; effective DSI residence time is IP_DSI_C - IP_PSI_DSI. Ratified canonically alongside the Henin 2012 extraction.

CONMED_LAMOTRIGINE (canonical for concomitant lamotrigine coadministration indicator)

• Description: 1 = subject is coadministered lamotrigine (phenyltriazine antiepileptic and mood stabiliser, cleared predominantly by UGT1A4 glucuronidation) at the pharmacokinetic observation, 0 = no concomitant lamotrigine. Time-varying when comedication start / stop events are captured; time-fixed at the analysis baseline in sources that record comedication only as a per-record flag.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant lamotrigine).
• Source aliases: none.
• Example models: Zhang_2024_sertraline.R (screened on sertraline CL/F and V/F via the paper’s categorical-covariate form and not retained; declared in covariatesDataExcluded to preserve the screen – founding example).
• Notes: Named-drug member of the CONMED_<INN> family. Distinct from the class-level CONMED_AED (any antiepileptic) and CONMED_EIAED (enzyme-INDUCING antiepileptic): lamotrigine is not a clinically meaningful CYP inducer, so a model that pools it into CONMED_EIAED would be mis-specified. Also distinct from CONMED_UGT_INH, which is the pooled UGT-inhibitor indicator – lamotrigine is a UGT substrate rather than a UGT inhibitor. Use the named indicator when the source paper screens lamotrigine specifically, and the class indicator when the source pools antiepileptics.

CONMED_QUETIAPINE (canonical for concomitant quetiapine coadministration indicator)

• Description: 1 = subject is coadministered quetiapine (dibenzothiazepine second-generation antipsychotic, cleared predominantly by CYP3A4) at the pharmacokinetic observation, 0 = no concomitant quetiapine. Time-varying when comedication start / stop events are captured; time-fixed at the analysis baseline in sources that record comedication only as a per-record flag.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant quetiapine).
• Source aliases: none.
• Example models: Zhang_2024_sertraline.R (screened on sertraline CL/F and V/F via the paper’s categorical-covariate form and not retained; declared in covariatesDataExcluded to preserve the screen – founding example).
• Notes: Named-drug member of the CONMED_<INN> family. Quetiapine is a CYP3A4 substrate and a weak CYP2D6 inhibitor; it is neither a strong CYP3A4 inhibitor nor an inducer, so it should not be folded into CONMED_CYP3A4_INH / CONMED_CYP3A4_IND. Register sibling named canonicals (CONMED_OLANZAPINE, CONMED_RISPERIDONE, …) rather than reusing this one when a source screens a different antipsychotic, and reserve a future class-level CONMED_ANTIPSYCHOTIC for sources that pool the class.

Molecule-level physicochemical / developability attributes

Covariates whose value is a property of the administered molecule rather than of the subject. They arise in inter-molecule-variability analyses (mAb developability panels, MBMA-style multi-compound PBPK fits) where many compounds are dosed under one protocol and a measured in-vitro attribute of each compound explains the between-compound spread in PK. Operationally they behave like any other covariate column: every record for a given subject carries the attribute of the compound that subject received, and the value is constant within subject.

CONMED_VENLAFAXINE (canonical for concomitant venlafaxine coadministration indicator)

• Description: 1 = subject is coadministered venlafaxine (serotonin-norepinephrine reuptake inhibitor, cleared predominantly by CYP2D6 to O-desmethylvenlafaxine with a CYP3A4 minor route) at the pharmacokinetic observation, 0 = no concomitant venlafaxine. Time-varying when comedication start / stop events are captured; time-fixed at the analysis baseline in sources that record comedication only as a per-record flag.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant venlafaxine).
• Source aliases: none.
• Example models: Zhang_2024_sertraline.R (screened on sertraline CL/F and V/F via the paper’s categorical-covariate form and not retained; declared in covariatesDataExcluded to preserve the screen – founding example).
• Notes: Named-drug member of the CONMED_<INN> family. Relevant to psychotropic popPK models both as a coadministered antidepressant and as a CYP2D6 substrate that can compete with other CYP2D6-cleared comedications. Register sibling named canonicals for other antidepressants screened by name rather than reusing this one; reserve a future class-level CONMED_ANTIDEPRESSANT for sources that pool the class.

STUDY_VANCO_CENTER2 (canonical for Shen 2024 vancomycin second-study-center indicator)

• Description: 1 = subject enrolled at the second of the two study centers pooled in the Shen 2024 Southern Chinese pediatric vancomycin analysis (Table 1 column N2, n = 176 patients / 249 concentrations); 0 = subject enrolled at the first center (Table 1 column N1, n = 210 patients / 272 concentrations). Selects between the two additive residual-error magnitudes the paper estimated per center.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (center N1).
• Source aliases: derived per subject from the recruiting-hospital identifier. Shen 2024 does not state which of Baoan Women’s and Children’s Hospital / Shenzhen Children’s Hospital is N1 and which is N2, so the indicator is anchored to the Table 1 column label rather than to a hospital name.
• Example models: Shen_2024_vancomycin.R (switches the additive residual SD between addSdCenter1 = 4.64 mg/L and addSdCenter2 = 4.53 mg/L, per Shen 2024 Table 3 rows RV1 / RV2 and Results “RV was best characterized by an additive error model for each of the two study centers”).
• Notes: Specific scope; tied to the two-hospital Shen 2024 vancomycin cohort. Member of the auto-approved STUDY_<id> family, and the first member keyed on a recruiting center rather than on a trial phase or protocol number – the STUDY_<DRUG>_PHASE<N> naming of STUDY_POSA_PHASE3 / STUDY_NIPOCALIMAB_PHASE1 / STUDY_SULDUR_PHASE2 does not apply because both Shen 2024 centers contributed the same kind of routine-TDM observations under one protocol. Subject-level (time-fixed). Distinct from the model’s age strata: center N1 has median age 0.95 y and N2 median 4.32 y, but both centers contribute patients on both sides of the 2-year clearance cutoff, so this indicator must not be conflated with the AGE <= 2 stratum switch. A future multi-center analysis of a different drug should register its own STUDY_<drug>_CENTER<N> sibling rather than reuse this entry, because the center numbering is source-table-specific.

CONMED_MEROPENEM (canonical for concomitant meropenem coadministration indicator)

• Description: 1 = subject is coadministered meropenem (carbapenem beta-lactam, eliminated predominantly by renal filtration and tubular secretion with partial hydrolysis by dehydropeptidase-I) at the pharmacokinetic observation, 0 = no concomitant meropenem. Time-varying when comedication start / stop events are captured; time-fixed at the analysis baseline in sources that record comedication only as a per-record flag.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant meropenem).
• Source aliases: none.
• Example models: Shen_2024_vancomycin.R (screened on vancomycin CL because it was coadministered in more than 10% of patients – 43.76% of records – and not retained; declared in covariatesDataExcluded to preserve the screen – founding example).
• Notes: Named-drug member of the CONMED_<INN> family. Relevant to renally cleared antibacterial popPK models as a potential competitor for tubular secretion. Register sibling named canonicals for other antibacterials screened by name rather than reusing this one; use the class-level [[CONMED_CARBAPENEM]] for sources that pool the carbapenems, or CONMED_ABX for sources that pool all other antibacterials.

CONMED_CARBAPENEM (canonical for concomitant carbapenem-class coadministration indicator)

• Description: 1 = subject is coadministered any carbapenem beta-lactam (meropenem, ertapenem, imipenem/cilastatin, doripenem, biapenem) at the pharmacokinetic observation, 0 = no concomitant carbapenem. A class-level pooled indicator for sources that record carbapenem exposure as a single flag and estimate one shared effect for the class rather than a per-agent effect. Time-varying when comedication start / stop events are captured; time-fixed at the analysis baseline in sources that record comedication only as a per-record flag.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant carbapenem).
• Source aliases:
  • CBP – used in Zhang_2024_valproic_acid.R (Zhang 2024 Table 1 and the Abstract clearance equation; the paper defines CBP as meropenem or ertapenem pooled).
• Example models: Zhang_2024_valproic_acid.R (exponential effect on valproate CL/F: cl *= exp(1.50 * CONMED_CARBAPENEM), i.e. exp(1.50) = 4.48-fold higher apparent clearance – a 448.2% increase – in the 22 of 443 patients (4.9%) coadministered a carbapenem, of whom 18 received meropenem and 3 ertapenem; founding example).
• Notes: Class-level counterpart to the named-agent canonicals CONMED_MEROPENEM and CONMED_MER; the name was reserved in CONMED_MEROPENEM’s notes for exactly this “source pools the class” case. Use CONMED_CARBAPENEM only when the source paper itself estimates a single pooled class effect and does not resolve the individual agents; use the per-INN indicators when the paper names and estimates effects for specific agents. Distinct from CONMED_ABX (which pools all non-modelled antibacterials, not one mechanistically coherent class). The carbapenem-valproate interaction that motivates this canonical is mechanistically specific rather than a generic antibacterial confounder: carbapenems inhibit acylpeptide hydrolase, blocking the deglucuronidation of valproate glucuronide back to valproate, and the resulting fall in valproate exposure is large enough that pooling meropenem with ertapenem is pharmacologically defensible (Zhang 2024 Discussion; Suzuki 2016; Li 2021). A future source that reports agent-resolved carbapenem effects should populate the per-INN canonicals instead of, or alongside, this one. Ratified canonically on 2026-08-10 alongside the Zhang 2024 valproic acid extraction.

CONMED_CEFOPERAZONE (canonical for concomitant cefoperazone coadministration indicator)

• Description: 1 = subject is coadministered cefoperazone (third-generation cephalosporin, eliminated predominantly by biliary excretion) at the pharmacokinetic observation, 0 = no concomitant cefoperazone. Time-varying when comedication start / stop events are captured; time-fixed at the analysis baseline in sources that record comedication only as a per-record flag.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant cefoperazone).
• Source aliases: none.
• Example models: Shen_2024_vancomycin.R (screened on vancomycin CL because it was coadministered in more than 10% of patients – 25.91% of records – and not retained; declared in covariatesDataExcluded to preserve the screen – founding example).
• Notes: Named-drug member of the CONMED_<INN> family. Cefoperazone is unusual among cephalosporins in being cleared mainly by the biliary rather than the renal route, so it should not be folded into a renal-competition class indicator. Register sibling named canonicals for other cephalosporins screened by name rather than reusing this one.

CONMED_CEFTRIAXONE (canonical for concomitant ceftriaxone coadministration indicator)

• Description: 1 = subject is coadministered ceftriaxone (third-generation cephalosporin, highly protein-bound, eliminated by both renal and biliary routes) at the pharmacokinetic observation, 0 = no concomitant ceftriaxone. Time-varying when comedication start / stop events are captured; time-fixed at the analysis baseline in sources that record comedication only as a per-record flag.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant ceftriaxone).
• Source aliases: none.
• Example models: Shen_2024_vancomycin.R (screened on vancomycin CL because it was coadministered in more than 10% of patients – 25.72% of records – and not retained; declared in covariatesDataExcluded to preserve the screen – founding example).
• Notes: Named-drug member of the CONMED_<INN> family. Ceftriaxone’s high albumin binding makes it a plausible displacement interactant as well as a coeliminated antibacterial, which is why sources screen it by name rather than pooling it into a class indicator.

CONMED_MANNITOL (canonical for concomitant mannitol coadministration indicator)

• Description: 1 = subject is coadministered mannitol (osmotic diuretic, freely filtered at the glomerulus and not reabsorbed) at the pharmacokinetic observation, 0 = no concomitant mannitol. Time-varying when comedication start / stop events are captured; time-fixed at the analysis baseline in sources that record comedication only as a per-record flag.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no concomitant mannitol).
• Source aliases: none.
• Example models: Shen_2024_vancomycin.R (screened on vancomycin CL because it was coadministered in more than 10% of patients – 12.67% of records – and not retained; declared in covariatesDataExcluded to preserve the screen – founding example).
• Notes: Named-drug member of the CONMED_<INN> family. Mechanistically distinct from the antibacterial CONMED_<INN> entries: mannitol raises urine flow and can transiently increase the renal clearance of filtered drugs, so it is screened in renally cleared popPK models as a renal-haemodynamic perturbation rather than as a metabolic interactant. Register CONMED_FUROSEMIDE and other named diuretics separately rather than reusing this entry; reserve a future class-level CONMED_DIURETIC for sources that pool the class.

CP_ABEMACICLIB_UM (canonical for instantaneous abemaciclib plasma concentration as a time-varying CNS-PBPK input function)

• Description: Instantaneous total abemaciclib concentration in systemic plasma, supplied directly as a time-varying covariate column rather than computed from a coupled PK model. Serves as the input function (“forcing function”) of a permeability-limited CNS PBPK model: it drives delivery into the brain-blood compartment through the cerebral-blood-flow term Qbrain * (CP_ABEMACICLIB_UM - Cbb) and is never depleted by CNS uptake, so systemic and CNS pharmacokinetics are one-way coupled. Values must be interpolated linearly between the supplied times.
• Units: umol/L (= uM; abemaciclib MW = 506.6 g/mol so 1 umol/L = 0.507 mg/L = 507 ng/mL).
• Type: continuous
• Scope: specific
• Reference category: n/a – enters linearly as the donor concentration of the cerebral blood flow term. Reference peak: the population-mean profile of Wickramasinghe 2025 Table S2, determined in glioblastoma patients on twice-daily oral abemaciclib, reaches 0.4191 umol/L (about 212 ng/mL) at 124.61 h with a median inter-peak interval of 11.4 h.
• Source aliases:
  • Plasma1 (Wickramasinghe 2025 Table S2 input-file template; the paired sampling times are the Time1 column).
  • Cart (Data S1 of the same paper, the arterial concentration symbol in the brain-blood differential equation).
• Example models: Wickramasinghe_2025_abemaciclib_cns_pbpk.R (founding example; drives all nine states of the 9-CNS spatial brain / brain-tumour PBPK model).
• Notes: Specific scope; abemaciclib-specific. Follows the established CP_<drug>_<units> precedent (CP_RIF_UM, CP_GDC_UM, CP_GLASDEGIB_NGML, CP_RITUXIMAB_UGML), using the full INN rather than an abbreviation. Distinct in role from the perpetrator-drug members of the family (CP_RIF_UM, CP_GDC_UM), which enter a competitive-inhibition denominator: this one is a physiological input function, so a downstream user who substitutes their own profile changes the model’s driving exposure rather than the strength of an interaction. The 9-CNS ODE system is linear and homogeneous in concentration, so any self-consistent concentration unit may be used provided the supplied plasma profile and the interpreted compartment outputs share it; umol/L is the unit implied by the abemaciclib values in the Wickramasinghe 2025 template, whose own figure axes are labelled only “Concentrations”.

HEPARIN_RT (canonical for heparin-chromatography retention time of the administered antibody)

• Description: Retention time (minutes) at which the administered monoclonal antibody elutes from a heparin affinity column under a defined salt gradient, measured at the centre of the elution peak. A high-throughput surrogate for charge-mediated nonspecific binding, and the strongest in-vitro predictor of fast mAb clearance in the Liu 2023 developability panel. Enters the model as the driver of a sigmoidal relationship for the antibody-specific pinocytosis-uptake coefficient F1 of the Shah & Betts platform PBPK model.
• Units: min
• Type: continuous
• Scope: specific
• Reference category: NULL – continuous.
• Source aliases:
  • Heparin_RT – Liu 2023 Supplementary Table S1 column name.
• Example models: Liu_2023_mAb_mouse_pbpk.R.
• Notes: Assay-condition dependent, so retention times are only comparable within a single column chemistry and gradient. Liu 2023 used a HiTrap Heparin High Performance 1 mL column (Cytiva 17040601), 0.4 mg antibody loaded in 50 mM Tris pH 7.6 / 5 mM NaCl, washed 5 column volumes, then eluted on a linear 5-400 mM NaCl gradient over 20 column volumes; the reported value is the elution-peak centre. Observed range in that panel: 2.92-31.5 min (56-antibody training set) and 15.1-32.6 min (14-antibody validation set); the paper’s flag threshold for a fast-clearing antibody is 16.5 min. A related but distinct readout, Heparin_pB_buffer (percent B buffer at elution), was measured in the same runs and is highly correlated with HEPARIN_RT; it is not registered here because no extracted model uses it.

SNP_SLC22A1_RS2282143 (canonical for SLC22A1 (OCT1) 1022C>T (rs2282143) variant carrier indicator)

• Description: Binary genotype indicator for the SLC22A1 (OCT1) c.1022C>T single-nucleotide polymorphism, rs2282143 (protein change P341L / p.Pro341Leu), a decreased-function allele of the hepatic basolateral organic cation transporter 1. 1 = at least one variant (T) allele present; 0 = homozygous wild-type 1022CC. Time-fixed per subject (germline genotype).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (homozygous wild-type 1022CC).
• Source aliases:
  • GENO – Khwarg 2024 (paper Results ‘Effect of SLC22A1 genetic polymorhism on OCT1 activity’: “The SLC22A1 genotype (CC genotype, GENO = 0; CT genotype, GENO = 1) was applied as an exponent of FUP”, and Figure 2 legend).
  • SLC22A1 1022C>T / 1022C>T – Khwarg 2024 (paper Table 1, which keys each assayed SNP by rsID and gives 9 CC + 4 CT + 0 TT among the 13 genotyped subjects).
• Example models: Khwarg_2024_proguanil.R (exponent on the OCT1-uptake multiplier inside the well-stirred hepatic extraction ratio: eh = fub * clint * e_snp_oct1_clint^SNP_SLC22A1_RS2282143 / (qbh + fub * clint * e_snp_oct1_clint^SNP_SLC22A1_RS2282143) with e_snp_oct1_clint = 0.416; CT heterozygotes have 0.42-fold the CC hepatocyte uptake, raising proguanil and lowering cycloguanil exposure; Khwarg 2024 Table 3 FUP with RSE 54.1%).
• Notes: rsID-based canonical name because the source paper reports every assayed SNP by rsID (Table 1) alongside the nucleotide-position form. The sibling SLC22-family entries SNP_SLC22A2_808GT (OCT2) and SNP_SLC22A4_917CT (OCTN1) use the position-based form only because their source (Yoon 2013) reported no rsID; see the SNP_SLC22A2_808GT Notes, which anticipate exactly this rsID-form registration. Because the Khwarg 2024 cohort contained no 1022TT homozygotes (Table 1), the indicator resolves heterozygote-vs-wild-type; a future source observing TT homozygotes should either record the variant-allele count under a paired SNP_SLC22A1_RS2282143_T_COUNT canonical (following SNP_CYP2B6_RS3745274_T_COUNT) or document the pooling in covariateData[[SNP_SLC22A1_RS2282143]]$notes. Khwarg 2024 also assayed rs202220802 (1258-1260delATG), rs12208357 (181C>T), rs34059508 (1393G>C), rs55918055 (262T>C) and rs4646277 (848C>T); all 13 subjects were homozygous wild type for these, so no canonicals are registered for them. Allele frequency of the 1022C>T variant was 16.8% by Hardy-Weinberg in this cohort, against a reported 16.7% in Koreans, 11.0% in Chinese, 5.5% in Vietnamese and 2.0% in German populations (Khwarg 2024 Discussion), so this variant is materially more relevant in East Asian cohorts. Ratified canonically on 2026-08-11 alongside the Khwarg 2024 proguanil extraction.

CONC_PMB_MGL (canonical for static in-vitro polymyxin B concentration driving an antibacterial PD model)

• Description: Static (time-invariant) TOTAL polymyxin B concentration applied to the medium of an in-vitro bacterial experiment, supplied as an exogenous covariate that drives bacterial kill. Distinct from a state-derived plasma concentration (Cc) and from the CP_<DRUG> plasma-PD-driver family: this is an applied experimental concentration in the in-vitro matrix. Where the medium binds the drug, this covariate carries the TOTAL concentration and the model derives the unbound concentration internally.
• Units: mg/L
• Type: continuous
• Scope: specific
• Reference category: n/a – 0 mg/L is the drug-free growth control. Lacroix 2025 studied total PMB 0.5 to 128 mg/L for A. baumannii AB121-D0 and 8 to 512 mg/L for AB122-D12.
• Source aliases: none standardized (Lacroix 2025 writes C in Eq 1 and C_T in Fig. S2).
• Example models: Lacroix_2025_polymyxinB_AB121D0.R, Lacroix_2025_polymyxinB_AB122D12.R (founding examples; static total PMB per time-kill tube, converted to the unbound concentration Cu = C_T x fu that drives the Emax / sigmoidal-Emax kill rate).
• Notes: Specific scope because the value is bound to polymyxin B and to the in-vitro time-kill assay design. Member of the in-vitro applied-drug-concentration CONC_<DRUG>_MGL family (siblings CONC_DOR_MGL, CONC_MEM_MGL, CONC_RIF_MGL, CONC_INH_MGL, CONC_IPM_MGL, CONC_TOB_MGL). PMB was shown stable over the 30-h time-kill experiment, so the concentration is genuinely time-invariant and needs no PK ODE state. Pairs with MUCIN_PRESENT, which selects whether the medium binds PMB at all.

MUCIN_PRESENT (canonical for mucin supplementation of the in-vitro growth medium)

• Description: 1 = the in-vitro growth medium was supplemented with 1% (w/v) porcine-stomach mucin, the gel-forming glycoprotein that is the key solid component of airway mucus; 0 = unsupplemented medium (control arm). Time-fixed per experiment (each time-kill tube belongs to exactly one arm). Mucin is a binding matrix as well as a growth-condition modifier, so models typically use this indicator twice: to switch on a medium-binding unbound-fraction submodel, and to select mucin-specific values of the pharmacodynamic parameters it perturbs.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (medium without mucin).
• Source aliases: none – Lacroix 2025 writes Mucin in main-text Eq 4 and supplemental Fig. S2.
• Example models: Lacroix_2025_polymyxinB_AB121D0.R (founding example; gates the Eq 1 unbound-fraction model and selects emax_r_camhb vs emax_r_mucin), Lacroix_2025_polymyxinB_AB122D12.R (gates the same binding model and selects knet_camhb / knet_mucin and ec50_camhb / ec50_mucin).
• Notes: General scope because mucin supplementation is a standard, drug-agnostic in-vitro manipulation used to mimic the airway or gastrointestinal mucus barrier; polymyxins, aminoglycosides and fluoroquinolones are all reported to bind it. Register a distinct covariate rather than reusing this one if a source varies the mucin PERCENTAGE as a continuous exposure (Lacroix 2025 tested a single 1% level and modelled it as a binary categorical, matching the physiological composition of mucus in mild cystic fibrosis). Structurally analogous to M3M3FBS_PRESENT, the binary ex-vivo pretreatment indicator, whose notes anticipate sibling experimental-condition indicators being registered under their own names rather than reusing that entry. Distinct from CONMED_<INN> (clinically administered comedications) because mucin is a medium constituent, not a drug given to a subject.

CONMED_ZOLBETUXIMAB (canonical for concomitant zolbetuximab coadministration indicator)

• Description: 1 = the index chemotherapy dose was administered in the presence of zolbetuximab (a recombinant chimeric IgG1 monoclonal antibody targeting claudin 18.2, CLDN18.2), 0 = the same chemotherapy administered alone. Time-varying by design in the studies that use it: the ILUSTRO Cohort 2 schedule deliberately delayed the Cycle 1 zolbetuximab infusion to Day 3 so that Cycle 1 chemotherapy PK could be sampled without zolbetuximab, with Cycle 2 chemotherapy then given in its presence. The column therefore belongs on the dosing / observation records rather than being a subject-level baseline characteristic.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (chemotherapy alone, without zolbetuximab).
• Source aliases:
  • X_zolbetuximab – the symbol used in the Yamada 2025 covariate equation P = theta_p * (1 + theta_zolbetuximab * X_zolbetuximab) (Methods, “Evaluation of zolbetuximab effect as a covariate”).
• Example models: Yamada_2025_oxaliplatin.R (fractional effect -0.123 shared across V1, V2 and V3, i.e. a 12.3% reduction in each distribution volume of free platinum; retained by forward selection at alpha = 0.01 and backward elimination at alpha = 0.001 with dOFV -13.834, P = 0.0002 – founding example), Yamada_2025_fluorouracil.R (screened on 5-FU CL and V, neither significant, declared in covariatesDataExcluded to preserve the screen).
• Notes: Named-drug member of the CONMED_<INN> family, used as the perpetrator indicator in monoclonal-antibody / cytotoxic-chemotherapy drug-drug-interaction popPK analyses. Note the direction of the interaction question: the covariate flags the presence of the mAb on the PK parameters of the small-molecule victim drug (5-FU, oxaliplatin), not the reverse. When a source reports the effect on the mAb itself, the appropriate covariate is a chemotherapy-backbone indicator (e.g. CONMED_CHEMO) on the mAb model instead. Ratified canonically on 2026-08-18 alongside the Yamada 2025 fluorouracil / oxaliplatin extraction.

CONMED_TISACEL (canonical for scheduled tisagenlecleucel CAR T-cell product indicator)

• Description: 1 = the subject’s scheduled CD19-directed CAR T-cell product is tisagenlecleucel (tisa-cel), 0 = axicabtagene ciloleucel (axi-cel). Time-fixed per subject. Used in models of lymphodepleting chemotherapy given before CAR T-cell infusion, where the product a patient is scheduled to receive both selects the lymphodepletion dosing regimen and marks the treatment cohort.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (axicabtagene ciloleucel), which is the larger cohort in the founding example and carries the structural typical value.
• Source aliases:
  • CAR_T / CART – two-level categorical coded 1 = axi-cel, 2 = tisa-cel in VarelaGonzalezAller_2025_fludarabine.R (Table 2 footnote). Re-express onto the canonical binary orientation as CONMED_TISACEL = CART - 1; a source that codes the levels in the opposite order needs the complementary transformation, so verify the coding statement before mapping.
• Example models: VarelaGonzalezAller_2025_fludarabine.R (founding example; axi-cel-referenced multiplicative factor 3.9/4.4 = 0.8864 on the non-renal clearance arm of fludarabine, re-expressed from the paper’s two-level categorical clearance intercept; 38 axi-cel / 18 tisa-cel).
• Notes: Named-product member of the CONMED_<INN> family; both axicabtagene ciloleucel and tisagenlecleucel are INNs. Timing caveat, important for interpretation: in lymphodepletion popPK models the cell product is infused on day 0 while the conditioning chemotherapy is given on the preceding days, so the product has not been administered during any modelled observation. An effect on a conditioning drug’s PK is therefore a planned-treatment / cohort marker – a proxy for unmeasured differences between the populations selected for each product (disease biology, prior therapies, tumour burden) – and not a pharmacokinetic drug-drug interaction; the founding paper says so explicitly in its Discussion. Record that reading in the per-model covariateData[[CONMED_TISACEL]]$notes rather than implying a mechanistic interaction. Keep the per-product dose difference (30 vs 25 mg/m^2 fludarabine phosphate in the founding example) in the event table, not in this covariate. Register sibling named canonicals (CONMED_LISOCEL, CONMED_BREXUCEL, CONMED_IDECEL, CONMED_CILTACEL, …) for other cell products rather than overloading this entry, and reserve a future CONMED_CART_ANY for sources that pool CAR T-cell therapy as a class.