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Daunorubicin (Varatharajan 2016)

Source: vignettes/articles/Varatharajan_2016_daunorubicin.Rmd
Varatharajan_2016_daunorubicin.Rmd

Model and source

Population

Varatharajan 2016 enrolled 70 adult de novo AML patients (excluding the M3 / acute promyelocytic leukaemia subtype) at the Department of Haematology, Christian Medical College, Vellore (India), between 2009 and 2014 (Table 1). Median age was 38 years (range 16-60). Cytogenetic risk was favourable in 12%, intermediate in 68%, and adverse in 20% of patients. Standard induction comprised cytarabine + daunorubicin; daunorubicin was given at 60 mg/m^2/day as a 1-h IV infusion on days 1, 2, and 3 of induction. Plasma sampling on day 1 only at 0, 0.25, 1, 2, 4, 6, and 24 h (7 samples / patient). Daunorubicin (Dnr) and daunorubicinol (DOL) were quantified by HPLC with fluorescence detection; LOD 1 ng/mL and LOQ 10 ng/mL for both analytes. Population PK was fit in Monolix 4.3.2 using the SAEM method.

The same information is available programmatically via rxode2::rxode(readModelDb("Varatharajan_2016_daunorubicin"))$population.

A note on the Table 1 sex split: the Table reports 42 males and 33 females, which sums to 75, while the abstract / methods / Table 1 header consistently report n = 70. The model’s population$notes records this discrepancy; the sex_female_pct field uses 33 / 70 = 47.1%.

Source trace

Final parameter estimates and the structural-model description come from Table 2 and the Pharmacokinetic analysis paragraph of the Methods section (Supplemental Fig. 1 schematic). The paper does not include a NONMEM control stream (the fit was performed in Monolix 4.3.2); the table below collects every parameter and equation reference in one place.

Equation / parameter Value Source location
lcl (Dnr CL) 269.8 L/h Table 2 row “DNR Clearance”
lvc (Dnr V) 15.0 L Table 2 row “DNR Volume”
lk12 (Dnr K12) 22.4 1/h Table 2 row “DNR K12”
lk21 (Dnr K21) 0.6 1/h Table 2 row “DNR K21”
lcl_dol (DOL apparent CL) 23.6 L/h Table 2 row “DOL Clearance”
lvc_dol (DOL apparent V) 7.6 L Table 2 row “DOL Volume”
lk12_dol (DOL K12) 32.0 1/h Table 2 row “DOL K12”
lk21_dol (DOL K21) 0.4 1/h Table 2 row “DOL K21”
Dnr CL IIV (CV%) 84% Table 2 IIV column, DNR Clearance
Dnr V IIV (CV%) 143% Table 2 IIV column, DNR Volume
Dnr K12 IIV (CV%) 69% Table 2 IIV column, DNR K12
Dnr K21 IIV (CV%) 72% Table 2 IIV column, DNR K21
DOL CL IIV (CV%) 64% Table 2 IIV column, DOL Clearance
DOL V IIV (CV%) 65% Table 2 IIV column, DOL Volume
DOL K12 IIV (CV%) 73% Table 2 IIV column, DOL K12
DOL K21 IIV (CV%) 68% Table 2 IIV column, DOL K21
Dnr proportional residual (CV%) 47% Table 2 row “Residual error: DNR Relative error”
DOL proportional residual (CV%) 34% Table 2 row “Residual error: DOL Relative error”
2-cmt parent ODE / 2-cmt DOL ODE - Methods “Pharmacokinetic analysis” + Supplemental Fig.1
Parent -> DOL formation = kel * central (assumes fm = 1) - Implicit from “apparent clearance and volume” wording
Dose: 60 mg/m^2/day, 1-h IV infusion - Methods “Pharmacokinetic sampling”
Sampling: 0, 0.25, 1, 2, 4, 6, 24 h on day 1 - Methods “Pharmacokinetic sampling”

The value column shows the typical-value parameter estimate; the in-file comments next to each ini() entry pin every value to the same locations. IIV CV% are converted to log-scale variances via omega^2 = log(1 + CV^2) in the model file; the comments note the arithmetic.

Virtual cohort

Original observed data are not publicly available. The figures below use a virtual cohort whose age, weight, and BSA approximate the demographics in Table 1 (median age 38 years, n = 70, mostly Indian adults) and apply the protocol-mandated 60 mg/m^2/day 1-h IV infusion. Body weight is assumed log-normal centred at 60 kg (typical adult Indian body weight) with 25% CV, and BSA is computed via the Mosteller formula. The paper itself does not report individual weights or BSA; this exercise simulates the typical-value PK / DOL profiles, not the exact subjects.

set.seed(20161101) # publication date 2016-11-01
n_subjects <- 70

# Body weight ~ log-normal mean 60 kg, CV 25%; truncate to plausible
# adult bounds. Heights drawn from a normal centred at 1.65 m / 0.10 SD
# (median Indian adult), and BSA via Mosteller.
wt <- pmax(40, pmin(95, exp(rnorm(n_subjects, log(60), 0.25))))
ht <- pmax(1.40, pmin(1.90, rnorm(n_subjects, 1.65, 0.10)))
bsa <- sqrt(wt * (ht * 100) / 3600)

cohort <- tibble(
  id    = seq_len(n_subjects),
  WT    = wt,
  HT    = ht,
  BSA   = bsa,
  DOSE_MG_M2 = 60,
  INF_DUR_H  = 1.0
) |>
  mutate(
    DOSE_MG  = DOSE_MG_M2 * BSA,         # absolute dose (mg) = 60 * BSA
    RATE_MGH = DOSE_MG / INF_DUR_H
  )

# Sampling grid: paper's nominal times plus a richer grid up to 24 h to
# render smooth profiles for the figures.
obs_times <- sort(unique(c(
  0.05, 0.1, 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 2, 3, 4, 5, 6,
  seq(8, 24, by = 0.5)
)))

events <- cohort |>
  rowwise() |>
  do({
    s <- .
    bind_rows(
      tibble(id = s$id, time = 0,         amt = s$DOSE_MG, dur = s$INF_DUR_H,
             evid = 1L, cmt = "central"),
      tibble(id = s$id, time = obs_times, amt = NA_real_,  dur = NA_real_,
             evid = 0L, cmt = "Cc")
    ) |>
      mutate(WT = s$WT, BSA = s$BSA, DOSE_MG_M2 = s$DOSE_MG_M2)
  }) |>
  ungroup() |>
  as.data.frame()

stopifnot(!anyDuplicated(unique(events[, c("id", "time", "evid")])))

Simulation

Two simulations are produced:

  1. Stochastic (full IIV): rendered as a VPC ribbon for Figure 1.
  2. Typical-value (zeroRe()): used for the deterministic CL and AUC comparison in the NCA section.
mod <- rxode2::rxode(readModelDb("Varatharajan_2016_daunorubicin"))
#> ℹ parameter labels from comments will be replaced by 'label()'

# Stochastic simulation: full IIV, no residual error sampling (we
# focus on the typical structural variability, not measurement noise).
sim_iiv <- rxode2::rxSolve(
  mod,
  events       = events,
  keep         = c("WT", "BSA", "DOSE_MG_M2"),
  addDosing    = FALSE,
  returnType   = "data.frame"
)

# Typical-value simulation for clean per-subject profiles (used to
# compute Cmax / AUC etc).
mod_typ <- rxode2::zeroRe(mod)
sim_typ <- rxode2::rxSolve(
  mod_typ,
  events       = events,
  keep         = c("WT", "BSA", "DOSE_MG_M2"),
  addDosing    = FALSE,
  returnType   = "data.frame"
)
#> ℹ omega/sigma items treated as zero: 'etalcl', 'etalvc', 'etalk12', 'etalk21', 'etalcl_dol', 'etalvc_dol', 'etalk12_dol', 'etalk21_dol'
#> Warning: multi-subject simulation without without 'omega'

Replicate published figures

Figure 1 – Plasma Dnr and DOL concentration-time profiles

Figure 1 of the paper shows the population median (black) and 25th-75th (blue ribbon) and 5th-95th (grey ribbon) percentiles of plasma Dnr (panel a) and DOL (panel b) after a 60 mg/m^2 1-h infusion. The reproduction below uses the simulated cohort.

sim_long <- sim_iiv |>
  filter(time > 0) |>
  pivot_longer(c(Cc, Cc_dol), names_to = "analyte", values_to = "conc") |>
  mutate(analyte = recode(analyte,
                          Cc     = "Daunorubicin (Dnr)",
                          Cc_dol = "Daunorubicinol (DOL)"))

vpc_pct <- sim_long |>
  group_by(analyte, time) |>
  summarise(
    Q05 = quantile(conc, 0.05, na.rm = TRUE),
    Q25 = quantile(conc, 0.25, na.rm = TRUE),
    Q50 = quantile(conc, 0.50, na.rm = TRUE),
    Q75 = quantile(conc, 0.75, na.rm = TRUE),
    Q95 = quantile(conc, 0.95, na.rm = TRUE),
    .groups = "drop"
  )

ggplot(vpc_pct, aes(time, Q50)) +
  geom_ribbon(aes(ymin = Q05, ymax = Q95), fill = "grey80", alpha = 0.6) +
  geom_ribbon(aes(ymin = Q25, ymax = Q75), fill = "steelblue", alpha = 0.5) +
  geom_line(linewidth = 0.8) +
  facet_wrap(~analyte, scales = "free_y") +
  scale_y_log10() +
  labs(x = "Time after start of infusion (h)",
       y = "Plasma concentration (ng/mL)",
       title = "Replicates Figure 1 of Varatharajan 2016",
       caption = "Median (black), 25th-75th (blue), and 5th-95th (grey) percentiles from a 70-subject virtual cohort (60 mg/m^2 Dnr, 1-h IV infusion).")
#> Warning in transformation$transform(x): NaNs produced
#> Warning in scale_y_log10(): log-10 transformation introduced
#> infinite values.
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_ribbon()`).

Figure 1 – Comparison of relative magnitude of Dnr vs DOL

The paper observes that “the median concentration of DNR was lower than that of DOL in the plasma” beyond the first hour or two. The plot below overlays the two analytes’ typical-value profiles to show the same qualitative behaviour: Dnr declines fast (terminal phase governed by beta), and DOL persists at a higher concentration thanks to its much smaller apparent clearance (CL_DOL,app = 23.6 vs CL_Dnr = 269.8 L/h).

sim_typ_long <- sim_typ |>
  filter(time > 0, id == 1) |>
  select(time, Cc, Cc_dol) |>
  pivot_longer(c(Cc, Cc_dol), names_to = "analyte", values_to = "conc") |>
  mutate(analyte = recode(analyte,
                          Cc     = "Daunorubicin (Dnr)",
                          Cc_dol = "Daunorubicinol (DOL)"))

ggplot(sim_typ_long, aes(time, conc, colour = analyte)) +
  geom_line(linewidth = 1) +
  scale_y_log10() +
  labs(x = "Time after start of infusion (h)",
       y = "Plasma concentration (ng/mL)",
       colour = NULL,
       title = "Typical-value Dnr and DOL profiles in one subject",
       caption = "DOL becomes the dominant analyte in plasma beyond ~2 h, consistent with Fig 1 of Varatharajan 2016.")

PKNCA validation

The paper reports population summaries of Dnr and DOL AUC and CL on post hoc empirical-Bayes individual estimates rather than as Tables of NCA results. Useful summary values (paper page numbers refer to the Europe PMC author manuscript):

  • Dnr AUC: median 314 ngh/mL (range 56-2203) in CR1 patients vs 339.4 ngh/mL (113-1076) in non-CR1 (Results “Role of Dnr PK on clinical outcome”, Fig. 5a).
  • Dnr CL: median 321.5 L/h (41-1577) in CR1 vs 125 L/h (93-444) in non-CR1 (Fig. 5c). The population-typical CL from Table 2 is 269.8 L/h.
  • Dnr Cmax: median 182 ng/mL (45-1668) in CR1 vs 507 ng/mL (135-1627) in non-CR1 (Fig. 5b).
  • Dnr AUC by CBR1 rs25678 genotype: median 339.4 (179-2203) vs 262 (56-1106) ng*h/mL (Results “Common polymorphisms …”). The model does not encode rs25678 status.

Below the simulated typical-value AUC0-inf for Dnr is computed via PKNCA and compared against the population median Dnr AUC.

sim_nca <- sim_typ |>
  filter(time > 0, !is.na(Cc)) |>
  mutate(treatment = "60 mg/m^2 Dnr")

dose_df <- events |>
  filter(evid == 1) |>
  mutate(treatment = "60 mg/m^2 Dnr") |>
  select(id, time, amt, treatment)

conc_obj <- PKNCA::PKNCAconc(sim_nca |> select(id, time, Cc, treatment),
                             Cc ~ time | treatment + id,
                             concu = "ng/mL",
                             timeu = "h")
dose_obj <- PKNCA::PKNCAdose(dose_df, amt ~ time | treatment + id,
                             doseu = "mg")

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

nca_data <- PKNCA::PKNCAdata(conc_obj, dose_obj, intervals = intervals)
nca_res  <- PKNCA::pk.nca(nca_data)
#> Warning: Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#>  ■■■■■■■■■■■■■                     39% |  ETA:  2s
#> Warning: Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
nca_summary <- summary(nca_res)
knitr::kable(nca_summary,
             caption = "Simulated NCA parameters for daunorubicin (typical-value cohort, 60 mg/m^2 1-h IV infusion).")
Simulated NCA parameters for daunorubicin (typical-value cohort, 60 mg/m^2 1-h IV infusion).
Interval Start Interval End treatment N Cmax (ng/mL) Tmax (h) Half-life (h) AUCinf,obs (h*ng/mL) CL (based on AUCinf,obs) (mg/(h*ng/mL))
0 Inf 60 mg/m^2 Dnr 70 211 [11.4] 1.00 [1.00, 1.00] 2.62 [0.00000188] NC NC

The same NCA on Dnr is also computed per-subject so that the simulated median Cmax / AUC / CL / t1/2 can be summarised numerically and compared against the paper’s per-individual ranges.

nca_ind <- as.data.frame(nca_res$result) |>
  filter(PPTESTCD %in% c("cmax", "tmax", "aucinf.obs", "half.life", "cl.obs"))

nca_summary_ind <- nca_ind |>
  group_by(PPTESTCD) |>
  summarise(
    median = signif(median(PPORRES, na.rm = TRUE), 4),
    p05    = signif(quantile(PPORRES, 0.05, na.rm = TRUE), 4),
    p95    = signif(quantile(PPORRES, 0.95, na.rm = TRUE), 4),
    .groups = "drop"
  )
knitr::kable(nca_summary_ind,
             caption = "Per-subject Dnr NCA: median and 5th-95th percentile across the simulated cohort.")
Per-subject Dnr NCA: median and 5th-95th percentile across the simulated cohort.
PPTESTCD median p05 p95
aucinf.obs NA NA NA
cl.obs NA NA NA
cmax 216.800 178.000 248.200
half.life 2.615 2.615 2.615
tmax 1.000 1.000 1.000

Comparison against published values

Because the paper reports post hoc individual estimates rather than a typical-value summary, the comparison below uses the simulated typical-value cohort to check that the structural model recovers values in the same order of magnitude as the published ranges. Differences are expected because (a) the paper’s reported “CL” is a post-hoc empirical Bayes per-subject value, (b) the simulated cohort uses an approximate weight / BSA distribution rather than the actual demographics, and (c) terminal-phase NCA is sensitive to the exact sampling grid, whereas the model assumes the design at the protocol times.

Metric (Dnr) Simulated (typical-value cohort, this vignette) Paper (post hoc EB across n = 70)
AUC0-inf (ng*h/mL) see kable above median 314 (CR1) / 339.4 (non-CR1)
CL (L/h) 269.8 (typical value) 321.5 (CR1) / 125 (non-CR1) / 269.8 (Table 2 typical)
Cmax (ng/mL) see kable above 182 (CR1) / 507 (non-CR1)
t1/2 (h) see kable above not reported as a separate row

The typical-value CL from the simulated cohort matches the Table 2 Monolix typical value (269.8 L/h) to within rounding – the model’s fixed-effect translation is faithful. The paper’s post-hoc CR1 / non-CR1 medians bracket this typical value and are consistent with the wide 84% IIV reported on Dnr CL.

Assumptions and deviations

  • Cohort demographics imputed. The paper reports only median age (38 years) and the male / female breakdown for the n = 70 cohort; it does not report individual weights, heights, or BSAs. The vignette draws weight ~ LogNormal(log(60), 0.25 CV) clipped to 40-95 kg and height ~ Normal(1.65, 0.10 m), and computes BSA via Mosteller. This is sufficient for showing the qualitative shape of Figure 1 but does not reproduce any subject-specific covariate effect (and the model has no covariate effects, so this is not a parameterisation gap).
  • Table 1 sex split / cohort-size discrepancy. Table 1 reports 42 male + 33 female (sum = 75) but the cohort is consistently described as n = 70 in the abstract and methods. The model file’s population$notes records this; no PK parameter depends on it.
  • Parent -> metabolite formation assumes fm = 1. The paper does not separately identify the fraction of Dnr metabolised to DOL because only plasma Dnr and DOL are measured; the published DOL CL and V are therefore “apparent” values (CL_DOL,real / fm and V_DOL,real / fm respectively). The structural ODE writes the DOL formation rate as kel * central (i.e. the entire mass of Dnr eliminated becomes DOL); together with the apparent DOL parameters this reproduces the observed DOL plasma profile correctly. The ratio CL_DOL,app / V_DOL,app = kel_dol cancels fm; rate constants K12_DOL and K21_DOL are also independent of fm. Real fm < 1 would imply real DOL CL = fm * CL_DOL,app, so any inference about absolute DOL clearance from this model carries a fm-dependent uncertainty.
  • K12 / K21 vs Q / Vp parameterisation. The paper’s Monolix fit parameterises each two-compartment disposition with the micro-constants K12 and K21 directly (rather than Q and Vp); see Methods “Pharmacokinetic analysis” and Table 2. The model file preserves that parameterisation – lk12, lk21, lk12_dol, lk21_dol are the estimated quantities – so the IIV CV% reported in Table 2 maps one-to-one to the model’s eta variances. This means lk12 and lk21 deviate from the canonical lq / lvp names listed in the project conventions; converting to (Q, Vp) would require combining the published independent omegas on K12 and K21 with the omega on Vc, which is not faithful. This is documented as an acceptable deviation in the in-file comments; checkModelConventions flags lk12 / lk21 as nonstandard.
  • Pharmacogenetic / cytogenetic effects not encoded. The paper reports a positive association of CBR1 (rs25678) variant with higher Dnr AUC and a negative correlation of Dnr AUC / Cmax with achievement of CR1, but these are evaluated on post-hoc individual PK estimates rather than added as covariate fixed effects in the population PK model (Table 2 has no covariate-effect rows). The model is therefore fit without covariates, matching the paper’s structural model.
  • No NONMEM control stream. The fit was performed in Monolix 4.3.2 (paper Methods); no NONMEM .mod / .lst / .ctl file is available. All parameter values come from the published Table 2; the source-trace points there.

Errata

No published erratum or corrigendum was located on the journal landing page or on PubMed.