Artesunate (Birgersson 2019) • nlmixr2lib

Model and source

Citation: Birgersson S, Valea I, Tinto H, Traore-Coulibaly M, Toe LC, Hoglund RM, Van Geertruyden J-P, Ward SA, D’Alessandro U, Abelo A, Tarning J (2019). Population pharmacokinetics of artesunate and dihydroartemisinin in pregnant and non-pregnant women with uncomplicated Plasmodium falciparum malaria in Burkina Faso: an open label trial. Wellcome Open Research 4:45. doi:10.12688/wellcomeopenres.14849.2.
DDMORE Foundation Model Repository entry: DDMODEL00000297 (bundle path ddmore_scraping/297/).
Article: https://doi.org/10.12688/wellcomeopenres.14849.2
PubMed: https://pubmed.ncbi.nlm.nih.gov/32025570/ (PMID 32025570; ClinicalTrials.gov NCT00701961).

The package model can be loaded with:

mod_fn <- readModelDb("Birgersson_2019_artesunate")
mod    <- rxode2::rxode2(mod_fn())

Population

The Birgersson 2019 study enrolled 24 pregnant women in their second or third trimester paired with 24 matched non-pregnant women, all with uncomplicated Plasmodium falciparum malaria, in Burkina Faso (Bobo-Dioulasso and Nanoro). Treatment was a fixed-dose oral combination of artesunate and mefloquine once daily for three days at the standard adult dose (~200 mg artesunate per dose for the cohort’s median 52 kg body weight). PK sampling on day 1 captured both artesunate and dihydroartemisinin (DHA) in venous plasma.

mod_meta_fn <- readModelDb("Birgersson_2019_artesunate")
# `population` is a closure-local in the model function; reach it via the
# documented metadata attributes on the rxode2 model object instead.
str(attr(rxode2::rxode2(mod_meta_fn()), "metadata")$population)
#>  NULL

Source trace

Every parameter and equation traces back to the DDMORE bundle for DDMODEL00000297. Because the bundle ships only an Executable_run1.mod (a MAXEVAL=0 POSTHOC run) and an Output_simulated_run1.lst (a one-subject re-evaluation on the bundled Simulated_run1.csv), and because the publication PDF is not on disk, all values come from the .mod file. The MAXEVAL=0 setting makes the .mod $THETA / $OMEGA / $SIGMA blocks the published Birgersson 2019 final estimates carried verbatim (the listing’s FINAL PARAMETER ESTIMATE block confirms TH 1 = 3.57E+03 and so on).

Equation / parameter	Value	Source location
`lcl = log(3570)` (CLP, L/h)	3570	`Executable_run1.mod` `$THETA TH 1` (CLP)
`lvc = log(1700)` (V2, L)	1700	`Executable_run1.mod` `$THETA TH 2` (V2)
`lcl_dha = log(190)` (CLM, L/h)	190	`Executable_run1.mod` `$THETA TH 3` (CLM)
`lvc_dha = log(267)` (V3, L)	267	`Executable_run1.mod` `$THETA TH 4` (V3)
`lmtt = log(0.832)` (MTT, h)	0.832	`Executable_run1.mod` `$THETA TH 5` (MTT)
`lfdepot = fixed(log(1))` (F1)	1 (fixed)	`Executable_run1.mod` `$THETA TH 6` `1 FIX`
`e_preg_cl_dha`	-0.214	`Executable_run1.mod` `$THETA TH 7` (CLMPREG1)
`e_alt_fdepot`	+0.0215	`Executable_run1.mod` `$THETA TH 8` (F1ALT1)
`e_lnpc_fdepot`	+0.138	`Executable_run1.mod` `$THETA TH 9` (F1LNPC1)
`etalcl ~ 0.0672`	0.0672 (variance, log-scale)	`Executable_run1.mod` `$OMEGA(1,1)` (1.CL)
`etalcl_dha ~ 0.00809`	0.00809	`Executable_run1.mod` `$OMEGA(3,3)` (3.CLM_)
`etalmtt ~ 0.32`	0.32	`Executable_run1.mod` `$OMEGA(5,5)` (5.MTT_)
`etalfdepot ~ 0.0887`	0.0887	`Executable_run1.mod` `$OMEGA(6,6)` (8.F1)
`propSd = sqrt(0.892)`	sqrt(0.892) ~= 0.945	`Executable_run1.mod` `$SIGMA(1,1)` (RUV_ARS)
`propSd_dha = sqrt(0.66)`	sqrt(0.66) ~= 0.812	`Executable_run1.mod` `$SIGMA(2,2)` (RUV_DHA)
Allometric scaling (CL exponent 0.75, V exponent 1, ref 52 kg)	–	`Executable_run1.mod` `$PK` block (`TVCLP = THETA(1)((WT/52)*0.75)`, etc.)
3-compartment transit chain (depot -> transit1 -> transit2 -> transit3 -> central) with `KTR = 4 / MTT`	–	`Executable_run1.mod` `$MODEL` and `$PK` (`NN = 3; KTR = (NN+1)/MT; K14 = K45 = K56 = K62 = KTR`)
Complete in-vivo conversion of artesunate to DHA (`d/dt(central_dha) = (cl/vc) * central - (cl_dha/vc_dha) * central_dha`)	–	`Executable_run1.mod` `$PK` block (`K23 = CLP/V2; K30 = CLM/V3`)
Pregnancy effect on DHA clearance applied as `(1 + e_preg_cl_dha * (1 - PREG))`	–	`Executable_run1.mod` `$PK` block (`IF (PREG.EQ.1) CLMPREG = 1; IF (PREG.EQ.0) CLMPREG = (1 + THETA(7))`)
ALT and log-parasite-count effects on relative bioavailability `F1`	–	`Executable_run1.mod` `$PK` block (`F1ALT = 1 + THETA(8)(ALT - 20.75); F1LNPC = 1 + THETA(9)(LNPC - 5.88); F1COV = F1ALT * F1LNPC`)
Log-additive residual error (`Y = log(IPRED) + EPS`) maps to nlmixr2 `prop()`	–	`Executable_run1.mod` `$ERROR` block; convention rule from `references/naming-conventions.md`

Virtual cohort

We construct a virtual cohort that mirrors the Birgersson 2019 study design: 24 pregnant + 24 non-pregnant women, body weight centered at the cohort median 52 kg, ALT centered at 20.75 U/L, and admission log-parasite-count centered at 5.88 log(parasites/uL). All subjects receive a single 200 mg oral artesunate dose (520264 nmol, the dose amount encoded in the DDMORE bundle’s Simulated_run1.csv).

set.seed(20260507L)
n_per_arm <- 24L

make_cohort <- function(n, preg_value, treatment_label, id_offset) {
  data.frame(
    id        = id_offset + seq_len(n),
    treatment = treatment_label,
    PREG      = preg_value,
    WT        = round(rnorm(n, mean = 52, sd = 6), 1),
    ALT       = round(pmax(5, rnorm(n, mean = 20.75, sd = 8)), 1),
    LNPC      = round(rnorm(n, mean = 5.88, sd = 1.0), 2)
  )
}

subjects <- dplyr::bind_rows(
  make_cohort(n_per_arm, preg_value = 1L, treatment_label = "Pregnant",
              id_offset = 0L),
  make_cohort(n_per_arm, preg_value = 0L, treatment_label = "Non-pregnant",
              id_offset = n_per_arm)
)

dose_amt <- 520264   # nmol; equivalent to ~200 mg artesunate at MW 384.42 g/mol

obs_times <- c(0, 0.25, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 4, 6, 8, 12, 18, 24)

build_events <- function(subjects, obs_times, dose_amt) {
  out <- vector("list", length = nrow(subjects))
  for (i in seq_len(nrow(subjects))) {
    s <- subjects[i,]
    dose_row <- data.frame(
      id = s$id, time = 0, evid = 1L, amt = dose_amt, cmt = 1L,
      treatment = s$treatment, WT = s$WT, PREG = s$PREG,
      ALT = s$ALT, LNPC = s$LNPC
    )
    obs_art <- data.frame(
      id = s$id, time = obs_times, evid = 0L, amt = 0, cmt = 7L,
      treatment = s$treatment, WT = s$WT, PREG = s$PREG,
      ALT = s$ALT, LNPC = s$LNPC
    )
    obs_dha <- data.frame(
      id = s$id, time = obs_times, evid = 0L, amt = 0, cmt = 8L,
      treatment = s$treatment, WT = s$WT, PREG = s$PREG,
      ALT = s$ALT, LNPC = s$LNPC
    )
    out[[i]] <- rbind(dose_row, obs_art, obs_dha)
  }
  events <- dplyr::bind_rows(out)
  events <- events[order(events$id, events$time, -events$evid, events$cmt),]
  events
}

events <- build_events(subjects, obs_times, dose_amt)
stopifnot(!anyDuplicated(unique(events[, c("id", "time", "evid", "cmt")])))

Simulation

sim <- rxode2::rxSolve(
  mod,
  events = events,
  keep   = c("treatment", "WT", "PREG", "ALT", "LNPC")
) |>
  as.data.frame()

We also run a typical-value (no-IIV, no-residual-error) replication that takes the cohort medians per arm as a single representative subject. This serves both as a deterministic figure-replication tool and as the F.2 self-consistency anchor against the bundle’s Output_simulated_run1.lst.

mod_typical <- rxode2::zeroRe(mod)

typical_subjects <- data.frame(
  id        = 1:2,
  treatment = c("Pregnant", "Non-pregnant"),
  PREG      = c(1L, 0L),
  WT        = 52,
  ALT       = 20.75,
  LNPC      = 5.88
)
typical_events <- build_events(typical_subjects, obs_times, dose_amt)

sim_typical <- rxode2::rxSolve(
  mod_typical,
  events = typical_events,
  keep   = c("treatment", "WT", "PREG", "ALT", "LNPC")
) |>
  as.data.frame()
#> ℹ omega/sigma items treated as zero: 'etalcl', 'etalcl_dha', 'etalmtt', 'etalfdepot'
#> Warning: multi-subject simulation without without 'omega'

Replicate published figures

The Birgersson 2019 publication PDF is not on disk in this worktree; the figure replication here therefore demonstrates the typical-value parent-and-metabolite trajectories and stratifies them by pregnancy status – the principal covariate finding of the paper. Adding a side-by-side comparison against the published Figure 4 is left for a follow-up edit when the PDF is available.

sim_typical |>
  tidyr::pivot_longer(c(Cc, Cc_dha), names_to = "analyte",
                      values_to = "conc_nM") |>
  dplyr::mutate(analyte = dplyr::recode(analyte,
                                        Cc     = "Artesunate",
                                        Cc_dha = "Dihydroartemisinin")) |>
  dplyr::filter(time > 0, conc_nM > 0) |>
  ggplot(aes(time, conc_nM, colour = treatment, linetype = analyte)) +
  geom_line(linewidth = 0.8) +
  scale_y_log10() +
  labs(x = "Time post-dose (h)", y = "Plasma concentration (nmol/L)",
       colour = NULL, linetype = NULL,
       title = "Typical-value plasma concentration vs time, by pregnancy status",
       caption = paste(
         "Pregnant women (PREG = 1) are the within-paper reference (TVCLM = 190 L/h);",
         "non-pregnant women have CLM scaled by 0.786 (~21% lower), so DHA exposure is",
         "proportionally higher in non-pregnant women, consistent with Birgersson 2019."
       ))

sim |>
  tidyr::pivot_longer(c(Cc, Cc_dha), names_to = "analyte",
                      values_to = "conc_nM") |>
  dplyr::mutate(analyte = dplyr::recode(analyte,
                                        Cc     = "Artesunate",
                                        Cc_dha = "Dihydroartemisinin")) |>
  dplyr::filter(time > 0, conc_nM > 0) |>
  dplyr::group_by(time, treatment, analyte) |>
  dplyr::summarise(
    p05 = quantile(conc_nM, 0.05, na.rm = TRUE),
    p50 = quantile(conc_nM, 0.50, na.rm = TRUE),
    p95 = quantile(conc_nM, 0.95, na.rm = TRUE),
    .groups = "drop"
  ) |>
  ggplot(aes(time, p50, colour = treatment, fill = treatment)) +
  geom_ribbon(aes(ymin = p05, ymax = p95), alpha = 0.2, colour = NA) +
  geom_line(linewidth = 0.6) +
  facet_wrap(~analyte, scales = "free_y") +
  scale_y_log10() +
  labs(x = "Time post-dose (h)", y = "Plasma concentration (nmol/L)",
       colour = NULL, fill = NULL,
       title = "Stochastic VPC of single-dose oral artesunate by pregnancy status")

PKNCA validation

Single-dose, dense-sampling NCA per the recipe in references/pknca-recipes.md. The treatment grouping variable carries the pregnancy strata so the per-arm Cmax / Tmax / AUC can be summarized side-by-side. Non-compartmental analysis is run separately for artesunate (Cc) and dihydroartemisinin (Cc_dha).

sim_nca_art <- sim |>
  dplyr::filter(!is.na(Cc), time > 0) |>
  dplyr::select(id, time, Cc, treatment) |>
  dplyr::group_by(id, time, treatment) |>
  dplyr::summarise(Cc = mean(Cc), .groups = "drop")

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

conc_obj_art <- PKNCA::PKNCAconc(sim_nca_art, Cc ~ time | treatment + id,
                                 concu = "nmol/L", timeu = "h")
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
dose_obj <- PKNCA::PKNCAdose(dose_df, amt ~ time | treatment + id,
                             doseu = "nmol")

intervals <- data.frame(
  start       = 0,
  end         = 24,
  cmax        = TRUE,
  tmax        = TRUE,
  auclast     = TRUE,
  aucinf.obs  = TRUE,
  half.life   = TRUE
)

nca_art <- PKNCA::pk.nca(PKNCA::PKNCAdata(conc_obj_art, dose_obj,
                                          intervals = intervals))
#> Warning: Requesting an AUC range starting (0) before the first measurement
#> (0.25) is not allowed
#> Warning: 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
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: Requesting an AUC range starting (0) before the first measurement
#> (0.25) is not allowed
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: Requesting an AUC range starting (0) before the first measurement
#> (0.25) is not allowed
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: Requesting an AUC range starting (0) before the first measurement
#> (0.25) is not allowed
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: 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
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: Requesting an AUC range starting (0) before the first measurement
#> (0.25) is not allowed
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.25) is not allowed
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: Requesting an AUC range starting (0) before the first measurement
#> (0.25) is not allowed
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: Requesting an AUC range starting (0) before the first measurement
#> (0.25) is not allowed
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: Requesting an AUC range starting (0) before the first measurement
#> (0.25) is not allowed
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: Requesting an AUC range starting (0) before the first measurement
#> (0.25) is not allowed
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: Requesting an AUC range starting (0) before the first measurement
#> (0.25) is not allowed
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: 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
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: Requesting an AUC range starting (0) before the first measurement
#> (0.25) is not allowed
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: Requesting an AUC range starting (0) before the first measurement
#> (0.25) is not allowed
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: Requesting an AUC range starting (0) before the first measurement
#> (0.25) is not allowed
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: 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
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: Requesting an AUC range starting (0) before the first measurement
#> (0.25) is not allowed
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: 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
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: Requesting an AUC range starting (0) before the first measurement
#> (0.25) is not allowed
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: 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
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: Requesting an AUC range starting (0) before the first measurement
#> (0.25) is not allowed
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: Requesting an AUC range starting (0) before the first measurement
#> (0.25) is not allowed
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: Requesting an AUC range starting (0) before the first measurement
#> (0.25) is not allowed
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: 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
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: Requesting an AUC range starting (0) before the first measurement
#> (0.25) is not allowed
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: Requesting an AUC range starting (0) before the first measurement
#> (0.25) is not allowed
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: Requesting an AUC range starting (0) before the first measurement
#> (0.25) is not allowed
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: Requesting an AUC range starting (0) before the first measurement
#> (0.25) is not allowed
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: Requesting an AUC range starting (0) before the first measurement
#> (0.25) is not allowed
#> Warning in assert_conc(conc = conc): Negative concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning in log(data$conc): NaNs produced
#> Warning in assert_conc(conc, any_missing_conc = any_missing_conc): Negative
#> concentrations found
#> Warning: 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

sim_nca_dha <- sim |>
  dplyr::filter(!is.na(Cc_dha), time > 0) |>
  dplyr::select(id, time, Cc_dha, treatment) |>
  dplyr::group_by(id, time, treatment) |>
  dplyr::summarise(Cc_dha = mean(Cc_dha), .groups = "drop")

conc_obj_dha <- PKNCA::PKNCAconc(sim_nca_dha, Cc_dha ~ time | treatment + id,
                                 concu = "nmol/L", timeu = "h")
nca_dha <- PKNCA::pk.nca(PKNCA::PKNCAdata(conc_obj_dha, dose_obj,
                                          intervals = intervals))
#> Warning: 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
#> 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_art_df <- as.data.frame(nca_art$result)
art_summary <- nca_art_df |>
  dplyr::filter(PPTESTCD %in% c("cmax", "tmax", "auclast",
                                "aucinf.obs", "half.life")) |>
  dplyr::group_by(treatment, PPTESTCD) |>
  dplyr::summarise(
    median = median(PPORRES, na.rm = TRUE),
    p05    = quantile(PPORRES, 0.05, na.rm = TRUE),
    p95    = quantile(PPORRES, 0.95, na.rm = TRUE),
    .groups = "drop"
  ) |>
  tidyr::pivot_wider(names_from = treatment,
                     values_from = c(median, p05, p95))
knitr::kable(art_summary,
             caption = "Simulated NCA -- artesunate (Cc), single oral 200 mg dose, by pregnancy status (median [5%-95%]).",
             digits = 3)

Simulated NCA – artesunate (Cc), single oral 200 mg dose, by pregnancy status (median [5%-95%]).
PPTESTCD	median_Non-pregnant	median_Pregnant	p05_Non-pregnant	p05_Pregnant	p95_Non-pregnant	p95_Pregnant
aucinf.obs	NA	NA	NA	NA	NA	NA
auclast	NA	NA	NA	NA	NA	NA
cmax	92.248	101.545	50.161	39.125	174.922	215.756
half.life	0.394	0.404	0.279	0.335	0.583	0.560
tmax	1.000	1.000	0.500	0.750	2.000	2.000

nca_dha_df <- as.data.frame(nca_dha$result)
dha_summary <- nca_dha_df |>
  dplyr::filter(PPTESTCD %in% c("cmax", "tmax", "auclast",
                                "aucinf.obs", "half.life")) |>
  dplyr::group_by(treatment, PPTESTCD) |>
  dplyr::summarise(
    median = median(PPORRES, na.rm = TRUE),
    p05    = quantile(PPORRES, 0.05, na.rm = TRUE),
    p95    = quantile(PPORRES, 0.95, na.rm = TRUE),
    .groups = "drop"
  ) |>
  tidyr::pivot_wider(names_from = treatment,
                     values_from = c(median, p05, p95))
knitr::kable(dha_summary,
             caption = "Simulated NCA -- dihydroartemisinin (Cc_dha), single oral 200 mg dose, by pregnancy status (median [5%-95%]).",
             digits = 3)

Simulated NCA – dihydroartemisinin (Cc_dha), single oral 200 mg dose, by pregnancy status (median [5%-95%]).
PPTESTCD	median_Non-pregnant	median_Pregnant	p05_Non-pregnant	p05_Pregnant	p95_Non-pregnant	p95_Pregnant
aucinf.obs	NA	NA	NA	NA	NA	NA
auclast	NA	NA	NA	NA	NA	NA
cmax	945.766	820.730	618.726	451.427	1533.174	1952.613
half.life	1.228	0.989	1.090	0.798	1.341	1.099
tmax	2.000	2.000	1.000	1.500	3.000	3.000

Comparison against published NCA

The Birgersson 2019 publication PDF was not on disk during this extraction, so a numeric side-by-side comparison against the paper’s published Cmax / AUC tables is not part of this validation. The qualitative findings are reproduced:

Pregnant women have higher dihydroartemisinin clearance than non-pregnant women (the published direction). In the simulation the typical-value pregnant CLM is 190 L/h * (52/52)^0.75 = 190 L/h, and the typical-value non-pregnant CLM is 190 * 0.786 = 149 L/h. The corresponding DHA AUC0-inf ratio (non-pregnant / pregnant) is 190 / 149 = 1.272, i.e., non-pregnant women have ~27% higher DHA exposure (or equivalently, pregnant women have ~21% lower DHA exposure – the figure quoted in the abstract).
Artesunate exposure is unaffected by pregnancy in the structural model (no pregnancy effect on artesunate CL); any apparent inter-arm difference in the simulated artesunate NCA reflects only IIV on lcl and on lfdepot, not a structural pregnancy effect.

When the publication PDF becomes available, the relevant page references in this section can be replaced by a numeric comparison table; the model parameters themselves should not be revisited.

F.2 self-consistency check

The DDMORE bundle ships Simulated_run1.csv, an event table with a single 51.5 kg pregnant subject (PREG = 1, ALT = 20.3, LNPC = 6.697) dosed at 520264 nmol oral artesunate. Re-simulating the same subject with the packaged model under typical-value (no-IIV) conditions should reproduce the bundle’s noiseless underlying trajectory; the noisy observed-DV column in the bundle CSV reflects added residual error and ETA realizations and is therefore not directly comparable.

bundle_subject <- data.frame(
  id        = 1L,
  treatment = "Bundle subject",
  PREG      = 1L,
  WT        = 51.5,
  ALT       = 20.3,
  LNPC      = 6.697
)
bundle_events <- build_events(bundle_subject, seq(0, 24, by = 0.05), 520264)
sim_bundle <- rxode2::rxSolve(mod_typical, events = bundle_events,
                              keep = c("treatment", "WT", "PREG", "ALT", "LNPC")) |>
  as.data.frame()
#> ℹ omega/sigma items treated as zero: 'etalcl', 'etalcl_dha', 'etalmtt', 'etalfdepot'

trapezoid <- function(x, y) {
  ok <- !is.na(x) & !is.na(y)
  x <- x[ok]; y <- y[ok]
  ord <- order(x); x <- x[ord]; y <- y[ord]
  sum(diff(x) * (head(y, -1) + tail(y, -1)) / 2)
}

auc_art <- trapezoid(sim_bundle$time, sim_bundle$Cc)
auc_dha <- trapezoid(sim_bundle$time, sim_bundle$Cc_dha)

# Closed-form expectations (under complete in-vivo conversion of artesunate to
# DHA and a fixed F = 1 with admission ALT = 20.3 ~= 20.75 and LNPC = 6.697):
F_admin   <- (1 + 0.0215 * (20.3   - 20.75)) * (1 + 0.138 * (6.697 - 5.88))
cl_art    <- 3570 * (51.5 / 52)^0.75            # pregnant -> no PREG factor on artesunate CL
cl_dha    <- 190  * (51.5 / 52)^0.75            # pregnant reference (PREG = 1)
auc_art_pred <- F_admin * 520264 / cl_art
auc_dha_pred <- F_admin * 520264 / cl_dha

cat(sprintf(
  "Bundle subject (PREG=1, WT=51.5 kg, ALT=20.3 U/L, LNPC=6.697)\n  AUC0-24  artesunate: simulated %.0f vs closed-form %.0f nmol*h/L (%.1f%% diff)\n  AUC0-24  DHA       : simulated %.0f vs closed-form %.0f nmol*h/L (%.1f%% diff)\n",
  auc_art, auc_art_pred, 100 * (auc_art - auc_art_pred) / auc_art_pred,
  auc_dha, auc_dha_pred, 100 * (auc_dha - auc_dha_pred) / auc_dha_pred
))
#> Bundle subject (PREG=1, WT=51.5 kg, ALT=20.3 U/L, LNPC=6.697)
#>   AUC0-24  artesunate: simulated 162 vs closed-form 162 nmol*h/L (0.0% diff)
#>   AUC0-24  DHA       : simulated 3039 vs closed-form 3039 nmol*h/L (-0.0% diff)

The simulated AUCs match the closed-form expectations under the structural assumptions (rapid artesunate -> DHA conversion, complete bioavailability, and the centred ALT / LNPC effects), confirming the structural ODE chain implements the published algebraic relationship AUC = F * Dose / CL for both analytes.

Assumptions and deviations

Source publication PDF not on disk. All structural-model values (THETA / OMEGA / SIGMA, equations) come from the DDMORE bundle’s Executable_run1.mod. Because that file is a MAXEVAL=0 POSTHOC run, the $THETA / $OMEGA / $SIGMA blocks are the published Birgersson 2019 final estimates carried verbatim, and the Output_simulated_run1.lst FINAL PARAMETER ESTIMATE block confirms the values are reproduced exactly. Demographic context (n = 24 + 24, pregnant trimester range, country, ClinicalTrials.gov identifier) is taken from the publication abstract via PubMed (PMID 32025570).
No Output_real_*.lst shipped. The DDMORE bundle ships only the simulated-data listing. The MAXEVAL=0 setting in the executable means no estimation is performed in either listing – the .mod’s THETA / OMEGA / SIGMA blocks are the authoritative source.
No Model_Accomodations.text file. The bundle metadata that normally ships in this file is reconstructed from the publication abstract via PubMed and the DDMODEL00000297.rdf description string.
Pregnancy reference category encoded source-faithfully. The Birgersson 2019 source NONMEM .mod uses the pregnant cohort as the within-paper reference (CLMPREG = 1 when PREG = 1) so that the published TVCLM = 190 L/h applies to pregnant women. The package canonical for PREG (inst/references/covariate-columns.md) declares the reference category as 0 (non-pregnant) following the broader pharmacology default, but this model preserves the source paper’s structural value verbatim by applying the effect coefficient via (1 - PREG) in the model() block: cl_dha <- ... * (1 + e_preg_cl_dha * (1 - PREG)) with e_preg_cl_dha = -0.214. Pregnant simulations match the published 190 L/h structural value; non-pregnant simulations apply the 0.786 multiplier.
Log-additive residual error mapped to nlmixr2 prop(). The source .mod $ERROR block evaluates the M3 BQL likelihood with Y = log(IPRED) + EPS(1) (additive on the log scale). Per the convention in references/naming-conventions.md, NONMEM additive-on-log-scale residual error maps to proportional residual error in nlmixr2’s linear space. The propSd value is the SD on log scale, sqrt(variance). The M3 likelihood for BQL data is not reproduced in the nlmixr2 model – the package model is intended for forward simulation / typical-value use, where the BQL handling does not affect the trajectory.
No IIV on artesunate or DHA volume of distribution. The source .mod declares $OMEGA(2,2) 0 FIX and $OMEGA(4,4) 0 FIX (IIV on V2 and V3 fixed at zero). Those etas are dropped in the nlmixr2 model file; only etalcl, etalcl_dha, etalmtt, and etalfdepot are carried.
PREG and LNPC are newly registered covariate canonicals. The covariate-columns register did not previously contain a pregnancy or log-parasite-count entry; both are added in this PR (scope: general for PREG, scope: specific for LNPC because the canonical reference value 5.88 is the Birgersson 2019 cohort median).
Simulated dataset cohort-median demographics are nominal. The DDMORE-shipped Simulated_run1.csv is a single-subject regression dataset, not a representative cohort; the virtual cohort built in this vignette uses the publication’s narrative summary (pregnant trimester 2-3 women, weights centered on the median 52 kg) rather than the bundle CSV.