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), 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).
• 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.

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).

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).
• Source aliases: none; FFM is the universal abbreviation.
• 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).
• 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.

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]]$notes).
• Notes: Specific scope until a second adult-popPK model ratifies the name; at that point promote to general. Per-model covariateData[[IBW]]$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).
• 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.
• Source aliases: none.
• Example models: Yamada_2025_zolbetuximab.R (reference 1.70 m^2; exponents 1.06 on clearances and 0.968 on volumes).
• 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.”

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)).
• 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.
• 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.
• Example models: CarlssonPetri_2021_liraglutide.R, Schoemaker_2018_levetiracetam.R (DDMODEL00000239).
• Notes: Age-group indicator used alongside ADOLESCENT; paper’s age cutoffs must be captured in covariateData[[CHILD]]$notes.

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.

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).
• Example models: Voller_2017_phenobarbital.R (linear-deviation effect on CL: clbw = 1 + 0.369 * (WT_BIRTH - 2.59)).
• 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.

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).
• 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).
• 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; 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).

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.

Pregnancy / hormonal status

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.

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 (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:
  • 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.

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.

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.

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.

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.

CRCL (canonical for creatinine-based renal function, BSA-normalized)

• Description: Creatinine-based renal function expressed in mL/min/1.73 m^2. Accepts either an MDRD-/CKD-EPI-estimated glomerular filtration rate or a measured creatinine clearance that has been BSA-normalized as 1.73 x CrCl / BSA. The per-model covariateData[[CRCL]]$description and notes must state which method the source paper used.
• Units: mL/min/1.73 m^2
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (CRCL / ref)^exponent. Reference values observed: 80 mL/min/1.73 m^2 (Cirincione 2017, MDRD eGFR), 90 mL/min/1.73 m^2 (Li 2019, calculated GFR), 100 mL/min/1.73 m^2 (Xu 2019, measured-CrCl BSA-normalized).
• Reference category: n/a – used with power scaling (CRCL / ref)^exponent. Reference values observed: 80 mL/min/1.73 m^2 (Cirincione 2017, MDRD eGFR), 100 mL/min/1.73 m^2 (Xu 2019, measured-CrCl BSA-normalized), 90 mL/min/1.73 m^2 (Bajaj 2017, CKD-EPI eGFR).
• Source aliases:
  • eGFR – MDRD-estimated glomerular filtration rate; used in Cirincione_2017_exenatide.R and Kotani_2022_astegolimab.R. Bajaj_2017_nivolumab.R uses the CKD-EPI variant.
  • EGFR – all-caps variant.
  • CRCL_BSA – BSA-normalized creatinine clearance (measured CrCl / BSA x 1.73); used in Xu_2019_sarilumab.R.
  • 1.73*CrCl/BSA – the formula form appearing in Xu 2019 Eq. for Vm.
  • cGFR – calculated/estimated GFR, BSA-normalized; used in Li_2019_abatacept.R.
  • CLCR – source-paper column name; underlying assay form varies. Used in Delattre_2010_amikacin.R (raw Cockcroft-Gault, NOT BSA-normalized; median 55.5 mL/min in critically ill septic adults) and in MedellinGaribay_2015_gentamicin.R (Schwartz formula CLCR = K * length / SCr with K in {0.33, 0.45, 0.55}, BSA-normalized to mL/min/1.73 m^2). Document the assay form per model in covariateData[[CRCL]]$description.
• 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).
• Notes: The two estimation methods (MDRD/CKD-EPI vs measured CrCl) produce values in the same units and are operationally interchangeable as a covariate on clearance. Document the method explicitly in each model’s covariateData[[CRCL]]$description so future reviewers can trace the source assay.

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.

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.

HEMODIAL (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:
  • 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^HEMODIAL on CL and 0.5^HEMODIAL on Vc, so dialysis subjects have 30% lower CL and 50% lower central volume than non-dialysis subjects).
• 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 (PERIT_DIAL, CRRT_STATUS) if a future paper retains them as covariates. Goti 2018 treats HEMODIAL 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 (or a separate per-session covariate) and the per-model covariateData[[HEMODIAL]]$notes would document the time resolution. When pairing HEMODIAL 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.

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:
  • 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) * CRRT_STATUS + e_crcl_cl * CRCL_in_Lh * (1 - 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 HEMODIAL (intermittent hemodialysis IHD only) and from DIAL (per-time-point session gate in within-subject time-varying dialysis-clearance models such as Liesenfeld 2013 dabigatran). Anticipated as a future canonical in the HEMODIAL register entry alongside PERIT_DIAL 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 CRRT_STATUS with finer per-modality columns layered on top, or warrant its own modality-specific canonical (CVVH_STATUS, SLED_STATUS, etc.). When pairing 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 CRRT_STATUS = 1. Ratified canonically on 2026-05-18 alongside the Shekar 2014 meropenem extraction.

ALB (canonical for serum albumin)

• Description: Serum albumin concentration.
• Units: g/dL or g/L – document the unit used in each model via covariateData[[ALB]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (ALB / ref)^exponent.
• Source aliases:
  • BALB (baseline albumin) – used in Zhou_2021_belimumab.R. Maps directly to ALB; baseline-vs-time-varying status documented in per-model notes.
  • HSA (human serum albumin) – used in Fauchet_2015_lopinavir_unbound.R where the column name follows the paper’s protein-binding-equation notation distinguishing serum albumin from alpha-1 acid glycoprotein. Maps directly to canonical ALB; no value transformation. Reported in g/L; converted to umol/L inside model() via molecular weight 66500 g/mol for the K_HSA linear-binding term.
• Example models: Fasanmade_2009_infliximab.R (g/dL, reference 4.1), Thakre_2022_risankizumab.R (g/L, reference 45), Chua_2025_mirikizumab.R, Moein_2022_etrolizumab.R, Tiraboschi_2025_amlitelimab.R, Yamada_2025_zolbetuximab.R, Li_2019_abatacept.R (g/dL, reference 4.0; the Li 2019 Methods states ‘mg/dL’ which is a publication typo – see the model’s covariateData[[ALB]]$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).
• Notes: Ratified canonically on 2026-04-19 after cross-model review. Unit varies by paper (g/dL in US-convention papers, g/L in SI-convention papers); the per-model covariateData[[ALB]]$units field is load-bearing. Effect-coefficient magnitude is meaningless without the unit.

TPRO (canonical for total serum protein)

• Description: Total serum protein concentration (sum of albumin + globulins; baseline or time-varying).
• Units: g/L or g/dL – document the unit used in each model via covariateData[[TPRO]]$units (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.

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 (most common in popPK papers); occasionally mg/dL (1 g/L = 100 mg/dL). Document per-model via covariateData[[CSF_TPRO]]$units.
• 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 (typical in SI-convention papers); also reported as mg/dL in US-convention papers – document the unit used in each model via covariateData[[IGG]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (IGG / ref)^exponent. Reference values observed: 14.8 g/L (Zhou 2021), 9.65 g/L (Yang 2021).
• Source aliases:
  • BIGG (baseline IgG) – used in Zhou_2021_belimumab.R.
  • IGGBL (baseline IgG) – used in Yang_2021_cemiplimab.R.
• Example models: Zhou_2021_belimumab.R (g/L, reference 14.8; baseline-only; exponent 0.293 on CL), Yang_2021_cemiplimab.R (g/L, reference 9.65; small positive exponent 0.184 on shared CL/Q), Struemper_2017_belimumab.R (g/L, reference 13.7; baseline-only; exponent 0.347 on CL).
• Notes: Mechanistically meaningful for monoclonal-antibody PK because endogenous IgG competes with the therapeutic mAb for FcRn-mediated recycling. The per-model covariateData[[IGG]]$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 (typical SI-convention reporting); also reported as mg/dL in US-convention papers – document the unit used in each model via covariateData[[IGM]]$units.
• 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: mg/dL or umol/L – document the unit used in each model via covariateData[[TBILI]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (TBILI / ref)^exponent.
• Source aliases:
  • BIL (legacy NONMEM short label for total bilirubin) – used in NA_NA_lidocaine.R (DDMODEL00000281; binarised at threshold 0.53 mg/dL with BIL_HIGH = as.integer(BIL > 0.53)).
  • BILT (Urien 2005 capecitabine paper’s NONMEM short label for “total bilirubin”) – used in Urien_2005_capecitabine.R (umol/L, reference 8.8; power scaling on the capecitabine non-transformation CL10 and on the 5’-DFUR -> 5-FU rate constant K34).
• Example models: Yamada_2025_zolbetuximab.R (mg/dL, reference 0.38; small positive exponent 0.0347 on V1), NA_NA_lidocaine.R (mg/dL, source column BIL; binary effect at threshold 0.53 mg/dL on the GX elimination rate constant K30), Urien_2005_capecitabine.R (umol/L, reference 8.8; source column BILT; positive exponent +0.32 on capecitabine non-transformation CL10 and negative exponent -0.36 on the 5’-DFUR -> 5-FU rate constant K34).
• Notes: Hepatic-function marker. Unit varies by paper (US convention mg/dL, SI convention umol/L; 1 mg/dL ~= 17.1 umol/L). The per-model covariateData[[TBILI]]$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: mg/dL or umol/L – document the unit used in each model via covariateData[[DBIL]]$units.
• 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 (IU/L; the two labels are used interchangeably in the clinical-PK literature). 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 (IU/L; the two labels are used interchangeably in the clinical-PK literature). 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 (IU/L; the two labels are used interchangeably in the clinical-PK literature). 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).
• Notes: Liver-function / cholestasis marker; routine clinical-chemistry covariate. Commonly tested alongside ALT / AST / GGT / TBILI. Ratified canonically alongside the Gupta 2016 lenvatinib extraction.

GGT (canonical for gamma-glutamyltransferase)

• Description: Serum gamma-glutamyltransferase activity (baseline or time-varying); hepatic / cholestatic biliary-enzyme marker.
• Units: U/L (IU/L; the two labels are used interchangeably in the clinical-PK literature). 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 (IU/L; interchangeable). 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.

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.
• Example models: Lin_2024_casirivimab.R (multiplicative fractional change on CL), Lu_2022_patritumab.R (paired with HEPIMP_MOD_MISSING; multiplicative fractional effect 0.706 on CLDXd for mild impairment vs the normal-hepatic-function reference).
• 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_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:
  • 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), register a HEPIMP_MOD canonical instead. Ratified canonically on 2026-04-28.

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%”).
• 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_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_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.

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 (IU/L; interchangeable). 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.

DIAL (canonical for hemodialysis-active indicator (time-varying))

• 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_dialysis term should gate it by DIAL = 1 so that the interdialytic clearance reduces to the intrinsic body clearance.
• Source aliases: none known; the source paper for the ratification model uses DIAL as the data column name directly (Liesenfeld 2013 Methods).
• Example models: Liesenfeld_2013_dabigatran.R (Michaels-equation gate; cl_total <- cl + DIAL * Michaels(BFR, DFR, KoA)).
• Notes: Distinct from a renal-impairment indicator (subject-level baseline class) and from a renal-replacement-therapy modality indicator (categorical: IHD vs CRRT vs SLED) – DIAL is the per-time-point gate that turns the dialysis-clearance term on and off. 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. Ratified canonically on 2026-05-16 alongside the Liesenfeld 2013 dabigatran extraction.

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.

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.

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.

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]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (WBC / ref)^exponent. Reference values observed: 10 x 10^9/L (Mould 2007, typical CLL Vmax normalization).
• Source aliases: none known (WBC is the universal clinical-PK abbreviation).
• Example models: Mould_2007_alemtuzumab.R (reference 10 x 10^9/L; exponent 0.194 on Vmax).
• Notes: Time-varying in treatment studies where the drug depletes the leukaemic clone (e.g., alemtuzumab in CLL): WBC must be supplied at every observation time in the event dataset. In diseases where WBC is not therapeutically targeted the column can be treated as a baseline-only covariate; record the per-model convention in covariateData[[WBC]]$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]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (HCT / ref)^exponent. Reference values observed: 45 % (Nestorov 2014, study-population median for severe hemophilia A adults).
• Source aliases: none; HCT is the universal NONMEM / clinical-PK abbreviation.
• Example models: Nestorov_2014_factorviii.R (reference 45 %, exponent -0.419 on V1).
• Notes: Higher HCT (more red-cell volume) leaves a smaller plasma fraction within total body volume; for plasma-restricted distribution (e.g., factor VIII activity, which circulates in plasma) the central volume of distribution decreases as HCT rises, so the exponent is negative. Document baseline-vs-time-varying status in covariateData[[HCT]]$notes. Distinct from HGB (mass concentration of hemoglobin); the two correlate but enter different mechanistic relationships.

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.

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 $INPUT).
• Example models: NA_NA_tte_gompertz.R (BAST PTTE 2017 / DDMODEL00000243 Event 1 hazard model; centred at NEUT = 4133/mm^3; coefficient -1.56e-4 on the NONMEM rescaled scale, equivalent to exp(-1.56e-4 * (NEUT - 4133)) on the hazard), Ozawa_2007_docetaxel.R (Friberg-extension myelosuppression PD; per-subject baseline ANC supplied via the NEUT column, used as the initial condition for the proliferating, three transit, and circulating compartments per the Methods text ‘Circ (t = 0) was fixed at its observed value’).
• Notes: General scope because absolute neutrophil count is a routine clinical-laboratory measurement that recurs across cytotoxic-chemotherapy myelosuppression models (centred-deviation hazard models, Friberg-family per-subject baseline initial conditions, and time-varying ANC outputs). Promoted to general scope on 2026-05-10 to support Ozawa_2007_docetaxel.R. The NEUT canonical units are cells/mm^3, but the reporting unit 10^9 cells/L (numerically NEUT_per_mm3 / 1000) is also common in oncology papers; per-model covariateData[[NEUT]]$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).

Coagulation / hemostasis biomarkers

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).
• 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.

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.

Disease severity scores

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 EASI = 0. Effect enters as an additive term in models that pool AD patients with HV (e.g., Tiraboschi_2025_amlitelimab.R).
• Source aliases:
  • BEASI (baseline 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[[EASI]]$notes. Canonical name is 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.

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 MGADL = 0 by definition. Effect enters as a baseline covariate on MG-ADL response parameters in gMG cohorts.
• Source aliases: none known.
• 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[[MGADL]]$notes. Canonical name is MGADL without a BL prefix to match the EASI / AGE / WT / ALB pattern.

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 BCVA state or as a power-form effect on response parameters. Reference value observed: 55 letters (Mulyukov 2018 narrative: mean study-population baseline BCVA).
• Source aliases:
  • BVA (baseline visual acuity) – used in Mulyukov_2018_ranibizumab.R.
• Example models: Mulyukov_2018_ranibizumab.R (baseline BCVA used as the center for the initial-condition draw g0 = BCVA + eta_g0).
• Notes: Ophthalmology-specific. Baseline-only in Mulyukov 2018 (carried once per subject and used only as the starting BCVA for the indirect-response model). Canonical name drops the B prefix to match the EASI / AGE / WT / ALB pattern (baseline-vs-time-varying status recorded in covariateData[[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: specific
• 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).
• 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).
• Notes: Specific scope because the ordinal scale 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). 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 MGADL / EASI (continuous severity scores not modelled as Markov states).

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 CDR_SOB / 2 in the DOT power-form and CDR_SOB - 1 in the mixture-logit additive form).
• Source aliases:
  • 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 ((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 * (CDR_SOB - 1) with e_cdr_sob_slow = -1.27)).
• Notes: Canonical name is CDR_SOB without a _BL suffix to match the EASI / MGADL / BCVA pattern (baseline-vs-time-varying status recorded in covariateData[[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.

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[[ADAS_COG]]$units)
• Type: continuous
• Scope: general
• Reference category: n/a – covariate effects typically centred on a dataset median (e.g., Delor 2013 centres on ADAS_COG = 12.67).
• Source aliases:
  • ADAS_bsl – used in Delor_2013_alzheimer.R (baseline ADAS-cog total-11 at study entry).
  • ADAS, ADAS_COG_11, ADAS_COG_13 – alternative bare-name forms seen across ADNI / CAMD datasets.
• Example models: Delor_2013_alzheimer.R (time-fixed baseline covariate; ADAS-cog total-11 form; enters the per-subject DOT power form (ADAS_COG / 12.67)^e_adas_cog_dot with e_adas_cog_dot = -0.0439).
• Notes: Canonical name is ADAS_COG (no _BL suffix; baseline-vs-time-varying recorded in notes). The total-11 vs total-13 form must be documented per-model in covariateData[[ADAS_COG]]$units because the same numeric 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 ADAS_COG in its covariateData. Ratified canonically on 2026-05-16 alongside the Delor 2013 extraction.

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: (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 MMSE = 26).
• Source aliases:
  • MMSE_bsl – used in Delor_2013_alzheimer.R (baseline 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 (MMSE / 26)^e_mmse_alpha with e_mmse_alpha = -2.01).
• Notes: Canonical name is MMSE (no _BL suffix; baseline-vs-time-varying recorded in notes). MMSE is the inverse-direction counterpart of CDR_SOB / ADAS_COG (MMSE high = healthy; CDR_SOB / ADAS_COG high = impaired); covariate-effect coefficient signs are therefore typically opposite to those for CDR_SOB / ADAS_COG. Ratified canonically on 2026-05-16 alongside the Delor 2013 extraction.

FAQ (canonical for Functional Assessment Questionnaire score)

• Description: Functional Assessment Questionnaire (Pfeffer 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: (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 FAQ = 1).
• Source aliases:
  • FAQ_bsl – used in Delor_2013_alzheimer.R (baseline 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 * (FAQ - 1) with e_faq_slow = -0.341).
• Notes: Canonical name is FAQ (no _BL suffix; baseline-vs-time-varying recorded in notes). Distinct from the cognitive scores (CDR_SOB / ADAS_COG / MMSE): 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.

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 RHPNM = 1 (the healthy reference, e.g., Delor 2013).
• Source aliases:
  • 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 * (RHPNM - 1) with e_rhpnm_slow = 7.5, a strongly positive effect indicating that less atrophic hippocampi (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 (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.

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.

AMLOAD (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: none beyond the source-paper AMLOAD column.
• 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(AMLOAD>=2) + e_amload3_vp_sap * I(AMLOAD>=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 AMLIVER for the binary hepatic-involvement modifier. Ratified canonically on 2026-05-15 alongside the DDMODEL00000262 / Sahota 2015 extraction.

AMLIVER (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: none beyond the source-paper AMLIVER column.
• 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 * AMLIVER); 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 AMLOAD 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.

Critical-illness severity

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).
• 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. ### 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.

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 EASI / AGE / WT / ALB pattern; baseline-vs-time-varying status is documented in covariateData[[EOS]]$notes.

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).
• 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.

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).
  • AGP1 – used in Ozawa_2007_docetaxel.R (Appendix I $INPUT block); reported in mg/dL with conversion to canonical g/L via AAG_g_per_L = AGP_mg_per_dL / 100.
• Example models: Netterberg_2017_docetaxel.R (piecewise-linear effects on baseline ANC with separate low-AAG and high-AAG slopes around median 1.34 g/L; linear effect on the drug-effect slope SL via (1 + theta * (AAG - 1.34))), Ozawa_2007_docetaxel.R (multiplicative power-form effect on the linear drug-effect slope: SLOPE = theta_SLOPE * (AAG / 0.94)^e_aag_slope with e_aag_slope = -1.38; reference value 0.94 g/L from the published NONMEM control stream AGPm = 94 mg/dL).
• Notes: General scope because serum AAG is a routine clinical-laboratory measurement that recurs across cytotoxic-chemotherapy population-PK / PD analyses (Bruno 1996/1998 docetaxel popPK uses AAG as a CL covariate; Kloft 2006 and downstream Friberg-family myelosuppression models use it on baseline ANC and drug-effect slope; Ozawa 2007 uses a power-form effect on the drug-effect slope only). Time-fixed at baseline unless the source paper states otherwise. The Kloft 2006 piecewise-linear breakpoint at 1.34 g/L corresponds to the population median in their pooled cancer cohort; the Ozawa 2007 normalisation reference 0.94 g/L is the cohort median used inside that paper’s NONMEM control stream. Future papers may use different breakpoints / reference values, so document the per-model reference in covariateData[[AAG]]$notes. Distinct from CRP (a different acute-phase reactant with different binding properties).

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]]$notes whether the column is baseline-only or time-varying.
• Units: pg/mL (= ng/L; the two labels are numerically equivalent).
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (IL6 / ref)^exponent or with the log-transformed form (log(IL6 * 1000) / log(ref * 1000))^exponent that some legacy NONMEM analyses adopt. Reference values observed: 20 pg/mL (Frey 2013 baseline; the formula (log(IL-6 * 1000)/9.9)^exponent is the algebraic equivalent of (log(IL-6) / log(20))^exponent after the constant-factor rescaling that ties the reference log to 9.9 = log(20000)).
• Source aliases:
  • BLIL6, bIL6, IL6_BASE – baseline IL-6 (used in some NONMEM control streams; canonical drops the BL prefix per the EOS / EASI / AGE convention with baseline-vs-time-varying status documented in per-model notes).
  • IL-6, IL_6 – punctuation variants seen in publication tables and figures.
• Example models: Frey_2013_tocilizumab.R (baseline IL-6, reference 20 pg/mL; log-transformed power effects on EC50, BASE, and the DMARD background-effect parameter).
• Notes: Anti-IL-6 / anti-IL-6R drugs (tocilizumab, sarilumab, siltuximab) often see large transient increases in measured IL-6 after dosing – when the column is time-varying for these drugs, the assay typically detects total (free + drug-bound) IL-6 because the antibody complex slows clearance of IL-6. Document the assay type (free vs. total IL-6) and whether the value is pre-dose (baseline-only) or post-dose / time-varying in each model’s covariateData[[IL6]]$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.

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).
• 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.

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).
• 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_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.

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.

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_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).

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]]$units).
• Type: continuous
• Scope: specific
• Reference category: n/a – appears directly in derived-exposure expressions; not a covariate effect coefficient.
• Source aliases:
  • CL – used in the Hansson 2013 sunitinib biomarker / TGI / fatigue PD-model family (DDMODEL00000197 and siblings, including DDMODEL00000222) and in Schindler_2016_sunitinib.R (DDMODEL00000221) as the posthoc CL column from the paper’s upstream 2-compartment popPK fit.
• Example models: Hansson_2013a_sunitinib.R (DDMODEL00000197; typical-value reference 32.819 L/h, drawn from the bundle’s simulated dataset for subject 1 – broadly consistent with Houk et al. 2010 typical sunitinib CL), Hansson_2013b_sunitinib.R (DDMODEL00000198; tumor growth inhibition variant; same per-subject CL column as Hansson 2013a/c), Hansson_2013c_sunitinib.R (DDMODEL00000222; uses a per-record CL column with subject-specific values 30-43 L/h in the bundle’s three-subject simulated dataset), Schindler_2016_sunitinib.R (DDMODEL00000221; per-subject post-hoc CL fed in as the CL column, vignette uses 50 L/h literature-typical sunitinib CL/F per Houk 2010).
• Notes: Specific scope because the values are intrinsically tied to a specific upstream popPK fit (sunitinib in this case; another model adopting CLI would carry its own upstream-PK lineage). Renamed from the source’s CL column because cl is the canonical nlmixr2 PK parameter name (a parameter, not a data column). Each model’s covariateData[[CLI]]$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]]$units).
• Type: continuous
• Scope: specific
• Reference category: n/a – used directly inside model() as CL_INDIV in place of an estimated cl <- exp(lcl + etalcl).
• Source aliases: CLI – used in Friberg_2002_paclitaxel.R (NM-TRAN data column for per-subject paclitaxel CL).
• Example models: Friberg_2002_paclitaxel.R.
• Notes: Specific scope because the value is intrinsically tied to the modelled drug – there is no shared meaning across drugs. Each model’s covariateData[[CL_INDIV]]$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.
• 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.
• 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]]$units).
• Type: continuous
• Scope: specific
• Reference category: n/a – appears directly inside model() as the initial condition svegfr3(0) <- BAS_SVEGFR3 and inside the relative-change driver bm = (svegfr3 - BAS_SVEGFR3) / BAS_SVEGFR3.
• Source aliases:
  • BAS3 – used in Hansson_2013b_sunitinib.R (DDMODEL00000198) and Hansson_2013c_sunitinib.R (DDMODEL00000222) as the posthoc sVEGFR-3 baseline column from the paper’s upstream Hansson 2013a biomarker indirect-response fit (DDMODEL00000197).
• Example models: Hansson_2013b_sunitinib.R (DDMODEL00000198; tumor growth inhibition with sVEGFR-3 driven shrinkage), Hansson_2013c_sunitinib.R (DDMODEL00000222; bundle’s three-subject simulated dataset reports BAS_SVEGFR3 values 42554-57365 pg/mL).
• Notes: Specific scope because the value is intrinsically tied to a specific upstream biomarker model (sVEGFR-3 indirect response under sunitinib in this case). The downstream fatigue model only consumes individual posthoc baseline / MRT / EC50 of the upstream biomarker; it does not re-fit them. Each model’s covariateData[[BAS_SVEGFR3]]$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) and Hansson_2013c_sunitinib.R (DDMODEL00000222) 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).
• 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) and Hansson_2013c_sunitinib.R (DDMODEL00000222) 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).
• 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.

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).
• 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_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_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.

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).
• 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)).
• 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]]$units). The example model rescales via INS_BL / 6.945 to convert pmol/L to uU/mL.
• Type: continuous
• Scope: specific
• Reference category: n/a.
• Source aliases:
  • BASI (baseline insulin) – used in the DDMORE bundle’s Simulated_ddmoremockdata2.txt for DDMODEL00000228. Rename BASI -> INS_BL before passing to rxSolve.
• Example models: NA_NA_paracetamol.R (DDMODEL00000228 OGTT model: initialises the insulin-on-elimination effect compartment effect_ins(0) = INS_BL / 6.945 and feeds the steady-state baseline-glucose-production rate gpro = gss * (kg + kgi * INS_BL / 6.945) * vg * 180 / 1000).
• Notes: Distinct from INS (time-varying regressor); INS_BL is a per-subject baseline-state anchor used in initial conditions and steady-state derived quantities, not the dynamic regressor itself. Specific scope because the conversion factor (1/6.945) and the rescaled-units interpretation are paper-specific; future extractions that report baseline insulin in mIU/L or pmol/L directly without rescaling can ratify the same canonical and document the per-model units / conversion in covariateData[[INS_BL]]$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.

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.

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_IPM_MGL (canonical for time-varying in-vitro imipenem concentration driving an antibacterial PD model)

• Description: Time-varying unbound imipenem concentration in the hollow-fiber infection model (HFIM) growth medium, supplied externally 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 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).
• Notes: Specific scope because the value is bound to imipenem 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_TOB_MGL). 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.

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.

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.

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_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).

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_NEAS (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:
  • 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.

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_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.
• Example models: Robbie_2012_palivizumab.R (fractional effect on CL; additional effect on Vc).
• 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.

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.

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_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.

Pediatric comorbidities

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 – 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.).

Comorbidities

DIAB (canonical for diabetes-mellitus comorbidity indicator)

• Description: 1 = patient has diabetes mellitus comorbidity (Type 1 or Type 2 not distinguished), 0 = no diabetes comorbidity. Time-fixed at study entry per subject.
• Units: (binary)
• Type: binary
• Scope: general
• Reference category: 0 (no diabetes comorbidity).
• Source aliases:
  • DIAB – used in Chen_2022_guselkumab.R.
  • Diabetes – used in Sherer_2012_AAA.R (Sherer 2012 Methods page 2 symbol “Diabetes”).
• Example models: Chen_2022_guselkumab.R (multiplicative effect on CL/F: 1.15^DIAB, +15% in patients with diabetes).
  • Chen_2022_guselkumab.R (multiplicative effect on CL/F: 1.15^DIAB, +15% in patients with diabetes).
  • 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%).
• Notes: Captures pre-existing diabetes mellitus as a comorbidity in non-diabetes-primary indications (e.g., psoriatic arthritis, psoriasis, vascular disease). Distinct from a primary disease-state indicator like DIS_UC. Type 1 vs Type 2 mellitus is not separated unless the source paper distinguishes them; in pooled-population PK analyses, the covariate is typically a single binary flag derived from medical history. 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).

T2DM (canonical for type-2-diabetes-mellitus-specific indicator)

• Description: 1 = patient has Type-2 diabetes mellitus specifically; 0 = normal-glucose-tolerance control (or other reference cohort pooled in the source analysis). Time-fixed at study entry per subject.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (normal-glucose-tolerance control).
• Source aliases:
  • T2DM – used in NA_NA_paracetamol.R (DDMODEL00000228).
  • 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 T2DM = 1 vs ZDF/Gmi fa/? non-diabetic littermate control coded T2DM = 0).
• Example models: NA_NA_paracetamol.R (DDMODEL00000228).
  • 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).
  • Lu_2014_sglt_qsp.R (multiplicative +17.6% shift on the typical-value Vmax2 of SGLT2 in the renal-glucose-reabsorption QSP model: Vmax2_T2DM = 110 mmol/h vs Vmax2_healthy = 93.5 mmol/h per Lu 2014 Table 2 calibration; coded for the Lu 2014 evaluation cohort, where the pre-modern-classification ‘diabetics’ of Mogensen 1971 are also coded T2DM = 1).
  • 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; all other rate constants and Vc common across cohorts per Han 2018 Results paragraph 1).
• Notes: Distinct from the existing DIAB canonical (which deliberately does not distinguish Type 1 vs Type 2). Specific scope because the reference cohort is study-specific and the mechanism in the example models is a Type-2-versus-healthy stratification of OGTT or SGLT response; a future T2DM-specific study (e.g., a popPK/PD analysis stratifying by HbA1c level) can ratify the same canonical and document the reference cohort in covariateData[[T2DM]]$notes.

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:
  • 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 * HYPERT; +0.539 logit units in patients with prior hypertension).
• Notes: Companion to 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 (HYPERT_TREATED).

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).
• 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).
• Notes: Time-varying within subject; the per-row value is the integer day count from the date of surgery to the observation date. 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).
• 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. 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 $INPUT form (used in Andrews_2017_tacrolimus.R; Andrews 2017 codes the column as a binary living-vs-deceased indicator).
• Example models: Andrews_2017_tacrolimus.R (multiplicative effect on apparent oral clearance: living-donor recipients have CL/F 26% lower than deceased-donor recipients of the same body weight, CYP3A5 genotype, eGFR, and hematocrit; Andrews 2017 Table 2 final-model coefficient: theta_donor_living = 0.74 for the living-donor cohort relative to the deceased-donor reference, equivalent to deceased-donor recipients having ~35% higher CL/F than living-donor recipients as reported in Section 3.4).
• Notes: General scope because donor source is a universally applicable solid-organ-transplant cohort attribute, captured in transplant-registry data for kidney, liver, heart, lung, and pancreas recipients. The reference category is 0 = living donor (not 0 = deceased donor) to match the “1 = the perturbed / index condition” convention used elsewhere in the register (e.g., HEMODIAL, POSTTX_DAY1, TX_LIVER); the index condition is “received a deceased-donor graft” because it carries the more variable graft quality and is the condition that perturbs clearance upward in published kidney-tacrolimus models. Source papers that encode the column as “1 = living donor” (e.g., Andrews 2017 itself parameterises the equation with a multiplier on living-donor recipients) should still record the column under the canonical orientation: set DONOR_DECEASED = 1 - source_living_indicator and document the value inversion in covariateData[[DONOR_DECEASED]]$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.

Disease state (cross-population indicators)

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).
• 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_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).
• 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 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). 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).
• 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 DIAB canonical (binary type-1-or-type-2 diabetes-mellitus comorbidity indicator) and the T2DM canonical (type-2-specific) 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).
• 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).
• 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.

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.

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).
• Source aliases: none known; source NONMEM control streams typically use ad-hoc names (e.g., PNH, DPNH).
• 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).
• 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.

MDSAML (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: none; MDSAML is the combined indicator used directly in source analyses.
• Example models: Ogasawara_2020_durvalumab.R (multiplicative factor 1.26 on CL; reference group is the union of MM and NHL subjects).
• Notes: Use MDSAML 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.

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")).
• 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).
• Notes: Use DIS_AML (rather than the combined MDSAML) when the source paper separates AML from MDS as distinct indicators. Scope: specific because the disease-pooling reference category is paper-defined.

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 MDSAML) 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_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).
• Notes: Used when a population PK/PD model pools healthy volunteers with COPD patients and the COPD-vs-HV contrast is retained as a covariate on PK parameters. 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).
• 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. 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).
• 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_INFECT_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.

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 (ADAS_COG, MMSE, CDR_SOB, 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.

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), and 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).
• 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.
• 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).

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).
• 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.

HCV_GT1B (canonical for HCV genotype-1B vs 1A subtype indicator)

• Description: HCV genotype-1 subtype indicator. 1 = patient infected with HCV genotype 1B; 0 = patient infected with HCV genotype 1A (the source-paper reference subtype for the IC50 estimates). Time-fixed per subject (HCV subtype is determined at the time of infection and does not change over the modelled treatment window).
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (HCV GT1A; 77 percent of the Wang 2018 viral-dynamics cohort, 55 of 72 patients).
• Source aliases: none – the source encodes the complement (GT1B = 1 - “Genotype 1A (%)”, Wang 2018 Table 2) so that the canonical reference category matches the source-paper IC50,GT1A estimates; the model column is the canonical HCV_GT1B.
• Example models: Wang_2018_daclatasvir_asunaprevir.R (switches the daclatasvir and asunaprevir antiviral IC50 and the daclatasvir resistance coefficient between GT1A and GT1B values via fixed scaling factors: IC50,DCV 0.041 -> 0.0074 ug/L (SCL = 0.18), IC50,ASV 2.45 -> 0.74 ug/L (SCL = 0.30), Kr,DCV 0.43 -> 0.13 per day; Kr,ASV is the same for both subtypes).
• Notes: Specific scope because the GT1A-vs-GT1B contrast and the IC50 / resistance scaling factors are paper-specific to the Wang 2018 daclatasvir + asunaprevir viral-dynamics MBMA. Distinct from HCV_POS (the HCV coinfection / comorbidity flag in non-HCV-primary indications) – HCV_GT1B is a within-HCV-population subtype indicator for an HCV-primary antiviral analysis. Future HCV antiviral models that distinguish additional genotypes (GT2/3/4) should register sibling subtype canonicals rather than overload this binary 1B-vs-1A indicator. Ratified canonically alongside the Wang 2018 daclatasvir/asunaprevir 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. 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.

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).
• 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. 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.

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.

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).
• Example models: Budha_2023_tislelizumab.R (reference 63 mm), 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 caliper-measured baseline tumor volume in xenograft / preclinical studies)

• Description: 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). Per-subject (per-animal) baseline measurement at randomisation into a dosing group. Used both as a covariate stratifier (size at randomisation) 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.
• 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).
• 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).
• Notes: General scope because xenograft tumor-volume baselines have a shared meaning across preclinical TGI papers regardless of the drug or cell line. Use TUM_VOL whenever a preclinical paper supplies per-animal caliper tumor volumes as the per-subject initial state of a tumor-volume ODE; 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 in clinical models. Ratified canonically on 2026-05-12 alongside the Ouerdani 2015 pazopanib mouse extraction.

TUMTP_CHL (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 (categorical column with levels like cHL, GC, …) – decompose into TUMTP_CHL = as.integer(TUMTP == "cHL").
  • DIS (Zhou 2025; integer code with DIS == 1 flagging HL) – decompose into TUMTP_CHL = 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_CHL) as the on-effect indicator with reference category 1 = HL).
• Notes: Paired with TUMTP_GC 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_CHL) 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_GC (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_CHL).
• Source aliases:
  • TUMTP (categorical column) – decompose into TUMTP_GC = as.integer(TUMTP == "GC").
  • TTYPE (Quartino 2019; categorical column with levels MBC, EBC, HV, AGC, Others) – decompose into TUMTP_GC = as.integer(TTYPE == "AGC").
  • TTYPE4 (Wang 2024; level 4 of a five-level tumor-type factor labelled “GCGEJ” in the source) – decompose into TUMTP_GC = 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_GC indicator; document the per-paper stage-of-disease and GEJ-inclusion detail in covariateData[[TUMTP_GC]]$notes. ESCC (squamous histology) is captured by the separate TUMTP_ESCC indicator and is not pooled here.

TUMTP_OTH (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:
  • TTYPE (Quartino 2019) – decompose into TUMTP_OTH = as.integer(TTYPE == "Others").
  • PAT2 (Sathe 2024) – 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_OTH = 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_OTH = 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), 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_OTH) on CL and exp(e_oth_vc * TUMTP_OTH) on Vc, with the “Other” pool = the residual tumor-type bucket complementing TUMTP_LYMPH / TUMTP_GC / TUMTP_ESCC in the same model).
• Notes: Scope: specific because the set of histologies collapsed into “Others” is defined by the analysis plan of the source paper; two papers’ TUMTP_OTH 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”; “advanced mixed malignancies enrolled in the FIH Study XL184-001, n = 40 in Lacy 2018”) in covariateData[[TUMTP_OTH]]$notes. A given subject can have at most one of the TUMTP_<GROUP> indicators (including TUMTP_OTH) 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_OTH).
• 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_OTH = 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_OTH), 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).
• Notes: Scope: specific because the set of histologies collapsed into “Others” is defined by the analysis plan of the source paper; two papers’ TUMTP_OTH 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_OTH]]$notes. A given subject can have at most one of the TUMTP_<GROUP> indicators (including TUMTP_OTH) 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).
• 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.

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).

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_CHL / TUMTP_GC 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_CHL / TUMTP_GC / 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_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_CHL / TUMTP_GC / 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_CHL / TUMTP_GC / 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_CHL / TUMTP_GC / 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_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_CHL / TUMTP_GC / 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_CHL (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_BC (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 (categorical column with levels including BC, NSCLC, CRC) – decompose into TUMTP_BC = 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_CHL / TUMTP_GC / 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_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_CHL / TUMTP_GC / TUMTP_SCLC decomposition pattern. Distinct from gastroesophageal-junction adenocarcinoma (which is captured by the broader TUMTP_GC 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_CHL / TUMTP_GC / TUMTP_SCLC decomposition pattern. Distinct from TUMTP_LYMPH (heterogeneous lymphoma pool) and TUMTP_CHL (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_CHL / TUMTP_GC / 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).
• Notes: Follows the TUMTP_CHL / TUMTP_GC / 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) is paper-defined. Ratified canonically on 2026-05-09.

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, MDSAML 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_CHL / TUMTP_GC / 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).
• Notes: Follows the TUMTP_CHL / TUMTP_GC / 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_CHL / TUMTP_GC / 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.

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.

NWLS (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 NWLS 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 (NONMEM $INPUT column in DDMODEL00000218); the bundle’s Simulated_OS.csv re-labels the same column NWLSCOV. Downstream consumers should map NWLSCOV -> NWLS.
• Example models: Zecchin_2016_survival.R (Zecchin 2016 OS model, DDMODEL00000218; multiplicative effect on the Weibull hazard via exp(e_nwls_haz * NWLS) 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 NWLS 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 NWLS 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.

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.

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

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[[CALPRO]]$units.
• Type: continuous
• Scope: general
• Reference category: n/a – used with power scaling (CALPRO / ref)^exponent. Reference 700 mg/kg used in Rosario 2015 (overall population median).
• Source aliases: none.
• Example models: Rosario_2015_vedolizumab.R (reference 700 mg/kg; exponent +0.0310 on linear clearance CLL).
• Notes: Common IBD severity biomarker (inflammation of the gut epithelium). Assays typically report in ug/g stool; 1 ug/g = 1 mg/kg. Document per-model whether baseline-only or time-varying in covariateData[[CALPRO]]$notes.

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 (CDAI / ref)^exponent. Reference 300 used in Rosario 2015 (typical moderate-CD score).
• Source aliases: none.
• 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 PMAYO in pooled UC+CD populations: each patient has exactly one disease-activity score (CDAI for CD, partial Mayo for UC). Gate via the IBD_CD indicator when pooling.

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 (PMAYO / ref)^exponent. Reference 6 used in Rosario 2015 (typical moderate-UC score).
• Source aliases: none.
• 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 CDAI in pooled UC+CD populations.

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 (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[[MAYO_E]]$reference_category.
• Source aliases:
  • 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 (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 PMAYO in datasets that report both.

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 CDAI / PMAYO and the biomarker-driven 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 PMAYO and 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 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).
• 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. Follows the CONMED_* family pattern (CONMED_AZA, CONMED_NSAID, etc.). Ratified canonically on 2026-05-10 alongside the Yukawa 1990 phenytoin extraction.

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_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_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) and Hashimoto_1994_zonisamide.R (paper covariate CBZ for carbamazepine coadministration on zonisamide Vmax).
• 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).
• 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), use the drug-specific canonicals [[CONMED_PB]], CONMED_CBZ, [[CONMED_VPA]] rather than collapsing into the class-level indicator. 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_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_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_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_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).
• 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).
• 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.

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).
• 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_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_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_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_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).
• 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_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: specific
• 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).
• Notes: Specific scope until a second model ratifies the name; 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_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).
• 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).
• 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). 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_VPA]] rather than collapsing into the class-level indicator. Ratified canonically on 2026-05-20 alongside the Schoemaker 2017 brivaracetam paediatric 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).
• Example models: Goel_2016_Sonidegib.R (multiplicative effect on F: 0.696^CONMED_PPI, ~30% lower bioavailability under CONMED_PPI coadministration).
• 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.

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).
• 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).
• 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).
• 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_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).
• 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).
• Notes: Scope: specific because Kuchimanchi 2018 narrowly defines the conmed_statin covariate as monotherapy only (“patients on a conmed_statin only and no other comedication”). 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. 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_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_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_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 T2DM (the diabetes covariate in the same model). 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_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.

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).
• 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 / EASI convention (no BL prefix); baseline-vs-time-varying status is per-model.

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).
• Source aliases:
  • SWOL – used in Li_2019_abatacept.R (Li 2019 Methods abbreviation).
• Example models: Li_2019_abatacept.R (power effect on CL with exponent 0.0965; not clinically relevant per Li 2019).
• Notes: The _28JOINT suffix distinguishes this from the 66/68-joint swollen count used in some earlier RA scales – register a separate canonical (SWOL_66JOINT or similar) if a future paper uses a different joint-count scale. Canonical name drops the BL prefix to match the EASI / AGE / WT / ALB convention where baseline-vs-time-varying status is documented in covariateData notes rather than the column name.

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.
• Example models: TerHeine_2014_tamoxifen.R (power-law effect on the tamoxifen -> endoxifen formation clearance: (CYP2D6 / 1560)^e_CYP2D6_cl_endx).
• Notes: TODO – consolidate the various CYP2D6 phenotype encodings (continuous probe-derived activity, copy-number-corrected score, categorical PM/IM/EM/UM phenotype, activity-score sum) into a single canonical CYP2D6 column with an enumerated notes-documented encoding field, OR introduce companion canonicals (CYP2D6_PHENO_GROUP for the categorical PM/IM/EM/UM grouping, CYP2D6_ACTSCORE for the AS sum) when a future model needs the categorical form. The current general-scope continuous canonical is sufficient for the Ter Heine 2014 use case but will likely need refinement as more CYP2D6-aware models are added. 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_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).
• 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).
• 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.

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.

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: 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: none standardized; source datasets typically encode the column as CYP3AI, CYP3A4I, CYP3AINH, or a free-text concomitant-medication indicator. Document the source-column name per-model in covariateData[[CYP3A4_INH]]$source_name.
• Example models: Yassen_2025_asundexian.R (proportional-shift effect on CL/F: (1 + e_cyp3a4_inh_cl * CYP3A4_INH) with e_cyp3a4_inh_cl = -0.0531; the asundexian dataset pools weak + moderate CYP3A4 inhibitors into the CYP3A4_INH = 1 category because strong inhibitors were a Phase II exclusion criterion).
• Notes: Per-model covariateData[[CYP3A4_INH]]$notes must document which inhibitor strengths (strong / moderate / weak) and which specific drug examples are pooled into the 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. CYP3A4_INH_STRONG, CYP3A4_INH_MOD, CYP3A4_INH_WEAK) rather than overloading CYP3A4_INH. The complementary CYP3A4-inducer indicator should follow the same pattern as a separate canonical (CYP3A4_IND) when first needed. Ratified canonically on 2026-05-08 alongside the Yassen 2025 asundexian extraction.

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 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: none standardized; source datasets typically encode the column as CYP3AIND, CYP3A4IND, INDU, INDUCER, or a free-text concomitant-medication indicator. Document the source-column name per-model in covariateData[[CYP3A4_IND]]$source_name.
• Example models: Gupta_2016_lenvatinib.R (multiplicative power-form effect on CL/F: 1.30^CYP3A4_IND with e_cyp3a4_ind_cl = log(1.30) ~ 0.262; the Gupta dataset pools any concomitant CYP3A4 inducer reported in the per-subject medication log into the CYP3A4_IND = 1 category, with n = 19 (2.4%) of the 779-subject pooled cohort flagged positive).
• Notes: Per-model covariateData[[CYP3A4_IND]]$notes must document which inducer strengths (strong / moderate / weak) and which specific drug examples are pooled into the 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. CYP3A4_IND_STRONG, CYP3A4_IND_MOD, CYP3A4_IND_WEAK) rather than overloading CYP3A4_IND. Sibling canonical to CYP3A4_INH (anticipated by that entry’s notes). Ratified canonically alongside the Gupta 2016 lenvatinib extraction.

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.

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).

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. 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. Imputation rules (LOCF / NOCB / baseline-as-negative) should be documented per-model.

Disease / treatment history

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.

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).

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: specific
• Reference category: 0 (homozygous wild-type 421C/C or heterozygous 421C/A; i.e., NOT homozygous variant).
• 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).
• 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).
• 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 here vs dominant any-A-allele in Roberts 2016 rs4148157). When a future paper uses the same recessive-model encoding for rs2231142 the scope may be promoted from specific to general; when a future paper uses a dominant-model encoding (any-A-allele = 1) for rs2231142, register a paired companion canonical SNP_ABCG2_RS2231142_CARRIER following the SLCO1B1_HAP15_HET / SLCO1B1_HAP15_HOM precedent 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.

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; future extractions that estimate separate het / hom effects should register paired SNP_ABCB1_RS1045642_HET and SNP_ABCB1_RS1045642_HOM indicators 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.

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).
• Notes: See CYP2C9_S1_COUNT for the broader rationale. Ratified canonically on 2026-05-16 alongside the Xia 2024 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).
• 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. Ratified canonically on 2026-05-16 alongside the Xia 2024 warfarin 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).
• 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).
• 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.

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).
• 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).
• 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.

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.

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).

Formulation / assay / study

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).
• 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 additionally carries a route-specific central volume of distribution).

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).
• 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).
• 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. Ratified canonically alongside the Denti 2018 levofloxacin extraction.

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).
• Notes: Specific scope because the arm-vs-abdomen contrast is paper-specific. Sister canonical to INJSITE_THIGH (thigh-vs-abdomen indicator anticipated for future SC-route models with thigh-specific absorption). Per-administration rather than per-subject – a subject in a multi-dose simulation can switch SC injection sites between doses; supply the indicator on each dose record. 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).

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.

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.

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().

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).

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).
• 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).
• 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.

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).
• 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 solution formulation indicator)

• Description: 1 = tablet formulation, 0 = solution.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (solution).
• 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).
• Notes: Scoped specific because the “tablet vs solution” contrast is tied to formulation-comparison study designs (Kyhl 2016 nalmefene tablet-vs-oral-solution; Tikiso 2021 abacavir + lamivudine FDC tablet vs abacavir liquid; Kleideiter 2017 cebranopadol tablet vs oral solution vs liquid-filled capsule). 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 liquid / solution rather than a separate tablet product. Future formulation-comparison models should either extend this entry’s example list when the comparator is a liquid / solution, or register a sibling canonical when contrasting two tablet products.

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_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_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_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).
• 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_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).
• 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_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_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_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_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).
• 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_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:
  • 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_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_DP2 sarilumab, FORM_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_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.

FORM_NS0 (canonical for NS0 cell-line formulation indicator)

• Description: 1 = NS0 cell-line formulation, 0 = other.
• Units: (binary)
• Type: binary
• Scope: specific
• Example models: Zhu_2017_lebrikizumab.R.

FORM_CHO_PHASE2 (canonical for CHO Phase 2 formulation indicator)

• Description: 1 = CHO Phase 2 formulation, 0 = other.
• Units: (binary)
• Type: binary
• Scope: specific
• Example models: Zhu_2017_lebrikizumab.R.

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).
• 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).
• 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. Ratified 2026-05-28 per the naming audit.

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).

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).
• Notes: Specific scope because the on-/off- HD/CRRT splitting is paper-specific. Future renal-replacement popPK extractions should reuse the QBL/QEFF pair. 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.

FORM_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:
  • 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_DP2 (sarilumab) and FORM_P2F2 (isatuximab) entries under the FORM_* family. Set to 0 for routine marketed-formulation simulation. Ratified canonically alongside the Retlich 2015 linagliptin extraction.

FORM_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:
  • 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_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_DP2 (sarilumab), FORM_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_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:
  • 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_DP2 (sarilumab), FORM_P2F2 (isatuximab), FORM_LINAG_TAB1 (linagliptin), FORM_VISMO_PHASEI (vismodegib), FORM_TAC_IR (tacrolimus IR vs PR), and FORM_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_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:
  • 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_SUBA) and 21.3% less F variability (ETASCALE = 1 + (-0.213) x FORM_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_DP2 (sarilumab), FORM_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_DP2 (sarilumab), FORM_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.

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: specific
• Reference category: n/a – used with power scaling (COHDOSE / ref)^exponent. Reference value observed: 1 mg/kg in Narwal 2013.
• 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).
• Example models: Narwal_2013_sifalimumab.R (reference 1 mg/kg, exponent 0.0542 on CL).
• Notes: Scope: specific because the interpretation depends on a study design where each subject stays on a single dose cohort. For fixed-dose simulations, set COHDOSE = nominal_dose_mg / WT per subject. When the subject receives a weight-based dose, COHDOSE is the mg/kg label (0.3, 1, 3, or 10 mg/kg for the MI-CP152 cohorts).

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).
• 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: Treatment-cohort indicator used in the Novakovic 2017 cladribine IRT model. 0 = placebo, 1 = cladribine 3.5 mg/kg cumulative-dose cohort, 2 = cladribine 5.25 mg/kg cumulative-dose cohort.
• Units: (categorical)
• Type: categorical
• Scope: specific
• Reference category: 0 (placebo).
• Source aliases: TRT – column name used in the DDMODEL00000223 input dataset (Simulated_Novakovic_2016_multiplesclerosis_cladribine_irt.csv).
• Example models: Novakovic_2017_cladribine.R.
• Notes: 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. Scope: specific because the cohort labelling (3.5 vs 5.25 mg/kg cumulative dose over 2 years) is tied to the CLARITY-program cladribine dosing schedule. Future models that need a generic on-treatment indicator 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_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_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.

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).

PHASE2 (canonical for Phase II study cohort indicator)

• Description: 1 = subject enrolled in the Phase II study (MORAb-003-002) of the Farrell 2012 pooled 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: 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 PHASE2 indicator selects between them.

PHASE1 (canonical for Phase I study cohort indicator)

• Description: 1 = subject enrolled in a Phase 1 study of the Valenzuela 2025 pooled 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: 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 PHASE2 – the reference category is inverted (Valenzuela 2025 picks Phase 1 as the 1-level).

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_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 / PHASE2 / ELISA / PHASE1 (per-study switches) but specific to the belimumab program. Subject-level (time-fixed); set from the trial identifier on each subject record.

STDY_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: 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 (STDY_VORI == 1) style indicators inline.

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).

ELISA (canonical for ELISA-vs-ECLIA bioanalytical assay indicator)

• Description: 1 = serum nipocalimab concentration measured by ELISA assay (LLOQ 0.150 ug/mL; studies MOM-M281-001, MOM-M281-007, MOM-M281-010); 0 = measured by ECLIA assay (LLOQ 0.010 ug/mL; studies EDI1001, EDI1002, MOM-M281-004). Used to switch the additive PK residual-error magnitude per assay.
• Units: (binary)
• Type: binary
• Scope: specific
• Reference category: 0 (ECLIA).
• Source aliases: derived per study from the bioanalytical method (Text S2 of Valenzuela 2025).
• Example models: Valenzuela_2025_nipocalimab.R.
• Notes: Valenzuela 2025 Table 3 reports additive PK residual 0.445 nmol/L (ELISA) vs 0.0342 nmol/L (ECLIA). Assay choice is study-fixed; include as a per-observation indicator so the correct assay residual is applied even when observations from different studies are pooled.

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_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.

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.

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).
• 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.

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_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.
• 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).
• 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. ## 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_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).
• 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.

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.

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).
• 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.
• 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_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.