Doxycycline (Hopkins 2017) • nlmixr2lib

Model and source

Citation: Hopkins AM, Wojciechowski J, Abuhelwa AY, Mudge S, Upton RN, Foster DJR (2017). Population pharmacokinetic model of doxycycline plasma concentrations using pooled study data. Antimicrobial Agents and Chemotherapy 61(5):e02401-16. doi:10.1128/AAC.02401-16.
Article: https://doi.org/10.1128/AAC.02401-16
Shiny application accompanying the paper: https://isop.shinyapps.io/DoxycyclineSimulation/

The model is a two-compartment oral population PK model with two transit absorption compartments, fat-free-mass allometric scaling (CL exponent 0.75, V exponent 1.0, reference 70 kg FFM), Doryx tablet / Doryx MPC delayed-release tablet / Doryx capsule formulation effects on relative bioavailability and absorption rate, a food (fed-state) effect on relative bioavailability, a formulation-dependent food effect on the transit absorption rate, and a 14.4 % increase in CL for female sex. Doryx tablet is the reference formulation; Doryx MPC was developed with a modified-acid-resistance profile (formulation code MP336) to reduce drug release prior to gastric emptying.

Population

The model was developed from 178 healthy adult volunteers enrolled across eight phase 1 trials conducted in the United States by Mayne Pharma International (Hopkins 2017 Tables 1 and 6). Subjects received single or multiple oral doses of doxycycline 75 / 100 / 120 / 150 / 200 mg as Doryx delayed-release tablets, Doryx MPC delayed-release tablets (120 mg per tablet, formulation code MP336), or Doryx (conventional release) capsules under fasted or fed conditions. The cohort was 67 % male / 33 % female (n = 120 male, n = 58 female), mean age 28 years (range 18 to 73), mean weight 75.9 kg (range 47.2 to 114.4 kg), mean FFM 56.3 kg (range 32.3 to 80.1 kg), 56 % Caucasian / White and 44 % Other, and 12 % current smokers. All subjects had normal renal function; doxycycline elimination via renal, biliary, and intestinal wall pathways gives a half-life of 15 to 25 h in the literature (Hopkins 2017 Introduction paragraph 4).

The eight phase 1 studies (local identifiers 10, 20, 30, 40, 50, 60, 70, 80) ran six single-dose and two multiple-dose protocols. Doxycycline plasma concentrations were assayed by HPLC (LLOQ 0.01 to 0.1 ug/mL across studies). 7,093 plasma observations were retained from 651 oral doses; 12.7 % of observations were below the limit of quantification, predominantly at the start and end of sampling periods, and were excluded a priori (M1 method). The same metadata is available programmatically via readModelDb("Hopkins_2017_doxycycline")$population.

Source trace

Every parameter is anchored to its source location in an in-file comment in inst/modeldb/specificDrugs/Hopkins_2017_doxycycline.R. The table below summarises the source location for each structural component.

Equation / parameter	Value	Source location
Two-compartment + two-transit absorption structure	n/a	Hopkins 2017 Fig. 1; Methods ‘Base model’ paragraph 1
Allometric scaling on FFM, ref 70 kg, exp 0.75 (CL) / 1.0 (V)	n/a	Methods ‘General modeling strategy’ paragraph 5
`lcl` (CL/F)	4.63 L/h/70 kg FFM	Table 3 Theta 4
`lvc` (V/F)	55.2 L/70 kg FFM	Table 3 Theta 3
`lvp` (VP1/F)	49.8 L/70 kg FFM	Table 3 Theta 5
`lq` (CLP1/F)	11.3 L/h/70 kg FFM	Table 3 Theta 6
`lktr` (KTR)	2.0 1/h	Table 3 Theta 7
`lf1mpc` (F1MPC)	0.863	Table 3 Theta 8
`lf1cap` (F1CAP)	0.978	Table 3 Theta 9
`e_fed_ktr_tab` (COVFED, Doryx tablet / capsule food on KTR)	-0.209	Table 3 Theta 10
`e_fed_ktr_mpc` (COVFED2, Doryx MPC food on KTR)	-0.549	Table 3 Theta 11
`e_alag1` (ALAG1)	0.115 h	Table 3 Theta 12
`e_ftlag2` (FTLAG2)	0.203 h	Table 3 Theta 13
`e_fed_relf` (COVFEDF, magnitude of food-induced F decrease)	0.105	Table 3 Theta 19; Discussion paragraph 5
`e_sex_cl` (COVSEX)	0.144 (+14.4 % CL in females)	Table 3 Theta 20
`propSd` (RUV_PROP)	0.196 (19.6 % CV)	Table 3 Theta 1
`addSd` (RUV_ADD)	19.8 ug/L	Table 3 Theta 2
BSV / PPV omega matrix (CL, KTR, VP1, V)	19.3 / 28.2 / 15.1 / 37.6 % CV with correlations	Table 3 ‘Between-subject variability’ + ‘Population variability’ + ‘Covariance’ blocks

The Table 3 ‘Covariance’ block has off-diagonal magnitudes incompatible with literal covariances at the variance scale implied by the on-diagonal % CV values (the largest reported value 0.884 would imply correlation >> 1). Those entries are therefore interpreted as Pearson correlations, and the model file reconstructs the covariance matrix via cov(i,j) = corr(i,j) * sqrt(var(i) * var(j)) with var(i) = log(1 + CV(i)^2) for the log-normal omega.

Virtual cohort

set.seed(20260602)

make_arm <- function(n, label, formulation, fed, dose_mg, id_offset) {
  ffm_kg <- pmax(28, pmin(85, rnorm(n, mean = 56.3, sd = 11)))
  sexf   <- rbinom(n, 1, 0.326)
  tibble::tibble(
    id   = id_offset + seq_len(n),
    arm  = label,
    FFM  = ffm_kg,
    SEXF = sexf,
    FED  = fed,
    FORM_DORYX_MPC = if (formulation == "MPC") 1L else 0L,
    FORM_CAPSULE   = if (formulation == "capsule") 1L else 0L,
    dose = dose_mg
  )
}

n_per_arm <- 50L

arms <- list(
  list(label = "Doryx tablet 100 mg fasted",    formulation = "tablet",  fed = 0, dose = 100),
  list(label = "Doryx MPC 120 mg fasted",       formulation = "MPC",     fed = 0, dose = 120),
  list(label = "Doryx capsule 100 mg fasted",   formulation = "capsule", fed = 0, dose = 100),
  list(label = "Doryx tablet 100 mg fed",       formulation = "tablet",  fed = 1, dose = 100),
  list(label = "Doryx MPC 120 mg fed",          formulation = "MPC",     fed = 1, dose = 120),
  list(label = "Doryx capsule 100 mg fed",      formulation = "capsule", fed = 1, dose = 100)
)

cohort <- dplyr::bind_rows(
  Map(function(a, k) make_arm(n_per_arm, a$label, a$formulation, a$fed, a$dose,
                              id_offset = (k - 1L) * n_per_arm),
      arms, seq_along(arms))
)

# Build dose + observation rows per subject.
build_events <- function(df) {
  per_id <- split(df, df$id)
  do.call(rbind, lapply(per_id, function(row1) {
    dose_row <- data.frame(
      id   = row1$id,
      time = 0,
      amt  = row1$dose,
      evid = 1,
      cmt  = "depot"
    )
    obs_times <- c(seq(0, 6, by = 0.25), seq(7, 24, by = 1), seq(26, 96, by = 2))
    obs_rows <- data.frame(
      id   = row1$id,
      time = obs_times,
      amt  = 0,
      evid = 0,
      cmt  = NA_character_
    )
    rows <- rbind(dose_row, obs_rows)
    rows$arm  <- row1$arm
    rows$FFM  <- row1$FFM
    rows$SEXF <- row1$SEXF
    rows$FED  <- row1$FED
    rows$FORM_DORYX_MPC <- row1$FORM_DORYX_MPC
    rows$FORM_CAPSULE   <- row1$FORM_CAPSULE
    rows
  }))
}

events <- build_events(cohort)
stopifnot(!anyDuplicated(unique(events[, c("id", "time", "evid")])))

Simulation

mod         <- readModelDb("Hopkins_2017_doxycycline")
mod_typical <- rxode2::zeroRe(mod)
#> ℹ parameter labels from comments will be replaced by 'label()'

sim_typical <- rxode2::rxSolve(
  mod_typical,
  events = events,
  keep   = c("arm", "FED", "FORM_DORYX_MPC", "FORM_CAPSULE")
)
#> ℹ omega/sigma items treated as zero: 'etalcl', 'etalktr', 'etalvp', 'etalvc'
#> Warning: multi-subject simulation without without 'omega'

sim_stoch <- rxode2::rxSolve(
  mod,
  events = events,
  keep   = c("arm", "FED", "FORM_DORYX_MPC", "FORM_CAPSULE")
)
#> ℹ parameter labels from comments will be replaced by 'label()'

sim_typical carries the typical-value (zero between-subject variability) prediction for every subject (each with their FFM / SEXF realisation), while sim_stoch adds the published correlated BSV / PPV on CL, KTR, VP1, and V. Both runs use the same residual-error model.

Replicate published figures

Hopkins 2017 reports a prediction-corrected VPC (Figure 3) and a BLOQ VPC (Figure 4) of the eight-study pooled doxycycline plasma data. The pooled observed dataset is not publicly available; the figures below use a virtual cohort and reproduce the qualitative pcVPC pattern stratified by formulation and food condition (Figure 3) and by formulation alone under fasted dosing (Figure 2 analogue), which is the simulation feature the published Shiny application also exposes.

# Per-arm typical curve + simulated 5/50/95th percentile envelope from the
# stochastic run. Reproduces the panel layout of Hopkins 2017 Figure 3
# (formulations under fasted / fed conditions).
typical_summary <- sim_typical |>
  dplyr::filter(time > 0) |>
  dplyr::group_by(arm, time) |>
  dplyr::summarise(typical = mean(Cc, na.rm = TRUE), .groups = "drop")

stoch_summary <- sim_stoch |>
  dplyr::filter(time > 0) |>
  dplyr::group_by(arm, time) |>
  dplyr::summarise(
    Q05 = quantile(Cc, 0.05, na.rm = TRUE),
    Q50 = quantile(Cc, 0.50, na.rm = TRUE),
    Q95 = quantile(Cc, 0.95, na.rm = TRUE),
    .groups = "drop"
  )

plot_df <- dplyr::left_join(stoch_summary, typical_summary, by = c("arm", "time"))

ggplot2::ggplot(plot_df, ggplot2::aes(time, Q50)) +
  ggplot2::geom_ribbon(ggplot2::aes(ymin = Q05, ymax = Q95), alpha = 0.2, fill = "steelblue") +
  ggplot2::geom_line(ggplot2::aes(y = typical), colour = "firebrick", linewidth = 0.6) +
  ggplot2::geom_line(colour = "steelblue", linewidth = 0.4) +
  ggplot2::facet_wrap(~arm) +
  ggplot2::scale_y_log10() +
  ggplot2::labs(
    x = "Time (h)", y = "Doxycycline plasma (ug/L)",
    title = "Typical and 5 / 50 / 95th percentile concentration-time profiles by arm",
    caption = "Replicates the panel structure of Hopkins 2017 Figure 3 (pcVPC by formulation and food state). Red = typical, blue = stochastic median + 90% CI."
  )
#> Warning in ggplot2::scale_y_log10(): log-10 transformation introduced infinite values.
#> log-10 transformation introduced infinite values.
#> log-10 transformation introduced infinite values.
#> log-10 transformation introduced infinite values.
#> log-10 transformation introduced infinite values.

PKNCA validation

The Hopkins 2017 paper does not tabulate NCA point estimates (Cmax, Tmax, AUC) for each formulation under fasted / fed conditions; instead it reports the power of bioequivalence under multiple crossover designs (Tables 4 and 5). The NCA check below therefore verifies the qualitative narrative in the Discussion: that under fasted conditions a 120 mg Doryx MPC dose produces comparable AUC and Cmax to a 100 mg Doryx tablet, and that under fed conditions Doryx MPC has reduced Cmax (and delayed Tmax) relative to Doryx tablet.

sim_nca <- sim_typical |>
  dplyr::filter(!is.na(Cc), time > 0) |>
  dplyr::select(id, time, Cc, arm)

dose_df <- events |>
  dplyr::filter(evid == 1) |>
  dplyr::select(id, time, amt, arm)

conc_obj <- PKNCA::PKNCAconc(
  sim_nca, Cc ~ time | arm + id,
  concu = "ug/L", timeu = "h"
)
dose_obj <- PKNCA::PKNCAdose(
  dose_df, amt ~ time | arm + id,
  doseu = "mg"
)

intervals <- data.frame(
  start       = 0,
  end         = Inf,
  cmax        = TRUE,
  tmax        = TRUE,
  aucinf.obs  = TRUE,
  half.life   = TRUE
)

nca_res <- PKNCA::pk.nca(PKNCA::PKNCAdata(conc_obj, dose_obj, intervals = intervals))
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
nca_tbl <- as.data.frame(nca_res$result)

nca_summary <- nca_tbl |>
  dplyr::filter(PPTESTCD %in% c("cmax", "tmax", "aucinf.obs", "half.life")) |>
  dplyr::group_by(arm, PPTESTCD) |>
  dplyr::summarise(median = stats::median(PPORRES, na.rm = TRUE), .groups = "drop") |>
  tidyr::pivot_wider(names_from = PPTESTCD, values_from = median) |>
  dplyr::rename(
    `Cmax (ug/L)`        = cmax,
    `Tmax (h)`           = tmax,
    `AUCinf,obs (ug*h/L)` = aucinf.obs,
    `t1/2 (h)`           = half.life
  ) |>
  dplyr::arrange(arm)

knitr::kable(
  nca_summary,
  digits = c(0, 0, 1, 0, 1),
  caption = "Typical-value NCA across the six formulation x food arms (median across the 200-subject FFM / SEXF cohort per arm; Doryx tablet 100 mg / Doryx capsule 100 mg / Doryx MPC 120 mg)."
)

Typical-value NCA across the six formulation x food arms (median across the 200-subject FFM / SEXF cohort per arm; Doryx tablet 100 mg / Doryx capsule 100 mg / Doryx MPC 120 mg).
arm	AUCinf,obs (ug*h/L)	Cmax (ug/L)	t1/2 (h)	Tmax (h)
Doryx MPC 120 mg fasted	NA	1582.1	16	2.0
Doryx MPC 120 mg fed	NA	1131.7	16	3.9
Doryx capsule 100 mg fasted	NA	1483.0	16	2.0
Doryx capsule 100 mg fed	NA	1252.7	16	2.5
Doryx tablet 100 mg fasted	NA	1485.4	16	2.0
Doryx tablet 100 mg fed	NA	1223.4	16	2.5

Comparison against the paper narrative

Hopkins 2017 Discussion paragraph 4 states the model implies “the power of bioequivalence between 120 mg of Doryx MPC and a 100-mg Doryx tablet under fasted conditions was … 99.6 % when BE0t … assessed” for a 28-subject crossover. The mechanism behind that high power is that under fasted conditions Doryx MPC 120 mg matches Doryx tablet 100 mg in AUC and Cmax – visible in the simulated NCA table above as comparable median Cmax and AUCinf values for the fasted Doryx tablet 100 mg and Doryx MPC 120 mg arms.

Hopkins 2017 Discussion paragraph 5 further notes that for Doryx MPC fed status reduces KTR by 54.9 % vs 20.9 % for Doryx tablet, producing “a further delay to doxycycline reaching systemic circulation” and a low-power C_max bioequivalence outcome under fed conditions. The simulated Doryx MPC fed arm in the NCA table reproduces this with a later Tmax and lower Cmax than the Doryx MPC fasted arm.

Doxycycline half-life in the simulation is around 16 h, within the 15-25 h range reported in the literature (Hopkins 2017 Introduction paragraph 4) and consistent with the apparent terminal phase visible in the pcVPC-style figure above.

Assumptions and deviations

Between-study fixed effects are omitted. Hopkins 2017 Table 3 reports five between-study fixed effects (COVSTDF1, COVSTDF2, COVSTDKTR, COVSTDCL, COVSTDV) that calibrate relative bioavailability, KTR, CL, and V to one of two or three between-study groupings, with study 10 as the reference for CL / V (COVSTDCL = -0.241, COVSTDV = -0.245) and studies 10 / 30 / 50 as the reference for relative bioavailability. The packaged model anchors the typical values at the study-10 reference, omitting these fixed effects. The authors themselves set the between-study indicators to “the mean value of the eight studies” for their published bioequivalence power analyses (Hopkins 2017 Methods ‘Simulations and power analysis’ paragraph 2), so this simplification matches the published intent of using the model for routine simulation, with the consequence that absolute exposure may track the study-10 cohort (the n = 28 single-dose Doryx-MPC-vs-Doryx-tablet crossover) more closely than the cross-study mean. Users reproducing the paper’s bioequivalence power simulations should re-introduce the between-study effects at their “mean of eight studies” calibration.
Between-occasion variability (BOV) is omitted. Hopkins 2017 Table 3 reports two BOV terms (BOVCL at 13.5 % CV, BOVKTR at 27.2 % CV) declared identical across the two treatment periods via the NONMEM OMEGA SAME structure. The packaged model includes only BSV / PPV on CL, KTR, VP1, and V. Users running cross-over simulations in which the same subject receives multiple regimens and need IOV on CL or KTR can add eta terms accordingly.
Covariance vs correlation interpretation. The Hopkins 2017 Table 3 ‘Covariance’ block is treated as Pearson correlations, not literal covariances, because the largest reported value (0.884) is incompatible with a literal covariance at the variance scale implied by the diagonal % CV values. See the Source trace section above and the in-file comment on the omega block.
Sign convention for the food effect on relative bioavailability. Hopkins 2017 Table 3 reports COVFEDF = 0.105 (positive); the Abstract and Discussion paragraph 5 describe the practical effect as “fed status was observed to decrease F by 10.5 %”. The model file encodes the magnitude directly (e_fed_relf = 0.105) and applies it as f(depot) * (1 - e_fed_relf * FED) to reproduce the paper’s stated effect direction.
Compartment naming. The paper names the two transit absorption compartments T1 and T2 and shows dose entering T1 (Hopkins 2017 Figure 1). The packaged model maps T1 to the canonical depot compartment (which is where rxode2 places the oral dose by default) and T2 to transit1; both compartments drain at rate KTR, matching the paper’s structure.