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Ensitrelvir (Ishibashi 2024)

Source: vignettes/articles/Ishibashi_2024_ensitrelvir.Rmd
Ishibashi_2024_ensitrelvir.Rmd

Model and source

  • Citation: Ishibashi T, Shimizu R, Kubota R. Population Pharmacokinetics of Ensitrelvir in Healthy Participants and Participants with SARS-CoV-2 Infection in the SCORPIO-SR Study. Clin Pharmacokinet. 2024;63(12):1723-1734. doi:10.1007/s40262-024-01446-4
  • Description: Two-compartment population PK model with first-order absorption for ensitrelvir in healthy adults and participants with SARS-CoV-2 infection (Ishibashi 2024)
  • Article: Clin Pharmacokinet. 2024;63(12):1723-1734

Ensitrelvir is an oral inhibitor of the SARS-CoV-2 3C-like protease. Ishibashi 2024 is the first population pharmacokinetic analysis of the drug, pooling two phase I studies in healthy participants with the phase II/III SCORPIO-SR study in participants infected with SARS-CoV-2.

A two-compartment model with first-order absorption and no absorption lag described the data. Body weight on apparent clearance and on apparent central volume, and food condition and formulation on the absorption rate constant, were the only covariates retained in the final model (model no. 511).

Population

The analysis pooled 8034 plasma ensitrelvir concentrations from 2060 participants across three studies (Ishibashi 2024 Tables 1 and 2): 4341 concentrations from 175 healthy adults in two phase I studies, and 3693 concentrations from 1885 participants with mild-to-moderate SARS-CoV-2 infection in SCORPIO-SR.

Median body weight was 62.6 kg (range 35.0-156.0), median age 35 years (range 12-76, with 1.7% adolescents aged 12 to under 18 years), and 42.6% of participants were female. The population was 98.5% Asian, enrolled in Japan (72.0%), Vietnam (21.7%) and Korea (6.3%) - a limitation the authors call out explicitly. Ninety-seven percent of participants received the tablet formulation; the oral suspension was used only in part of the phase I programme.

The same information is available programmatically via the model’s population metadata:

pop <- rxode2::rxode(readModelDb("Ishibashi_2024_ensitrelvir"))$population
#> ℹ parameter labels from comments will be replaced by 'label()'
str(pop, max.level = 1)
#> List of 13
#>  $ species       : chr "human"
#>  $ n_subjects    : num 2060
#>  $ n_studies     : num 3
#>  $ age_range     : chr "12-76 years"
#>  $ age_median    : chr "35 years"
#>  $ weight_range  : chr "35.0-156.0 kg"
#>  $ weight_median : chr "62.6 kg"
#>  $ sex_female_pct: num 42.6
#>  $ race_ethnicity: Named num [1:3] 98.5 1.2 0.2
#>   ..- attr(*, "names")= chr [1:3] "Asian" "White" "Other"
#>  $ disease_state : chr "healthy adults (175 participants, 8.5%) pooled with participants with mild-to-moderate SARS-CoV-2 infection (18"| __truncated__
#>  $ dose_range    : chr "phase I: 20, 70, 250, 500, 1000 or 2000 mg single oral dose, or 375 mg day 1 then 125 mg once daily days 2-5, o"| __truncated__
#>  $ regions       : chr "Japan (72.0%), Vietnam (21.7%), Korea (6.3%)"
#>  $ notes         : chr "Baseline demographics from Ishibashi 2024 Table 2; study inventory from Table 1. 8034 plasma ensitrelvir concen"| __truncated__

Source trace

Every value below carries the same citation as an in-file comment beside its ini() entry in inst/modeldb/specificDrugs/Ishibashi_2024_ensitrelvir.R.

Equation / parameter Value Source location
lka (Ka, suspension + fasted reference) 1.50 1/h Table 3 “Ka (1/h)”; Sect. 4 legend block
lcl (CL/F at 62.6 kg) 0.211 L/h Table 3 “CL/F (L/h)”; Sect. 4 legend block
lvc (Vc/F at 62.6 kg) 14.7 L Table 3 “Vc/F (L)”; Sect. 4 legend block
lq (Q/F) 0.539 L/h Table 3 “Q/F (L/h)”; Sect. 4 legend block
lvp (Vp/F) 2.50 L Table 3 “Vp/F (L)”; Sect. 4 legend block
e_fed_ka 0.594 Table 3 “Effect of food on Ka”
e_form_tablet_ka 0.362 Table 3 “Effect of formulation on Ka”
e_wt_cl 0.521 Table 3 “Effect of body weight on CL/F”
e_wt_vc 1.04 Table 3 “Effect of body weight on Vc/F”
IIV etalka (variance) 0.729^2 = 0.531441 Table 3 IIV “Ka (%)” = 72.9
IIV etalcl (variance) 0.213^2 = 0.045369 Table 3 IIV “CL/F (%)” = 21.3
IIV etalvc (variance) 0.147^2 = 0.021609 Table 3 IIV “Vc/F (%)” = 14.7
IIV covariance CL/F ~ Vc/F 0.0216 (R = 0.691) Table 3 “Covariance between CL/F and Vc/F”
addSd 0.0317 ug/mL Table 3 “Additive residual error (ug/mL)”
propSd 0.199 Table 3 “Proportional residual error (%)” = 19.9
Reference body weight 62.6 kg Table 2 median body weight; appears in the Sect. 4 legend equations
Categorical covariate model (multiplicative) n/a Sect. 2.2.2
Continuous covariate model (power) n/a Sect. 2.2.2
Two-compartment ODE structure, first-order absorption, no lag n/a Sect. 2.2.1, Sect. 3.1
Combination (additive + proportional) residual error n/a Sect. 2.2.1, Sect. 3.1

The IIV percentages are omega standard deviations, not %CV

Table 3 reports the interindividual variability rows as bare percentages, which is ambiguous: they could be omega standard deviations on the log scale, or coefficients of variation. The covariance row settles it without any simulation. Table 3 gives cov(CL/F, Vc/F) = 0.0216 together with the implied correlation R = 0.691, and cov = R * sd_CL * sd_Vc:

R <- 0.691
# Reading A: the percentages ARE the log-scale omega SDs.
covA <- R * 0.213 * 0.147
# Reading B: the percentages are %CV, so omega = sqrt(log(1 + CV^2)).
covB <- R * sqrt(log(1 + 0.213^2)) * sqrt(log(1 + 0.147^2))
c(reading_A = covA, reading_B = covB, printed = 0.0216)
#> reading_A reading_B   printed 
#> 0.0216359 0.0212821 0.0216000

# The printed 0.0216 has rounding interval [0.02155, 0.02165].
stopifnot(covA >= 0.02155, covA <= 0.02165)   # reading A reproduces it
stopifnot(covB < 0.02155 || covB > 0.02165)   # reading B does not

Reading A lands inside the printed value’s rounding interval and reading B does not, so the packaged model squares the percentages directly to obtain the variances.

Structural anchors from the paper’s own text

Three quantities are printed in the paper independently of Table 3, and each one exercises a different part of the model. These are typical-value (deterministic) checks, so they are asserted tightly.

mod <- readModelDb("Ishibashi_2024_ensitrelvir")

solve_typical <- function(dose, fed, form_tablet, times, maint = NULL) {
  ev <- rxode2::et(amt = dose, cmt = "depot", evid = 1)
  if (!is.null(maint)) {
    ev <- rxode2::et(ev, amt = maint, cmt = "depot", evid = 1,
                     time = 24, addl = 3, ii = 24)
  }
  ev <- rxode2::et(ev, times, cmt = "central")
  ev <- as.data.frame(ev)
  ev$WT <- 62.6
  ev$FED <- fed
  ev$FORM_TABLET <- form_tablet
  rxode2::rxSolve(mod, ev, omega = NA, returnType = "data.frame")
}

trapz <- function(t, y) sum(diff(t) * (head(y, -1) + tail(y, -1)) / 2)

# (1) Suspension, fasted: the paper reports Tmax "around 2.50 h" (Sect. 1,
#     citing the first-in-human single-dose study, which used the suspension).
susp <- solve_typical(250, fed = 0, form_tablet = 0, times = seq(0, 48, by = 0.01))
#> ℹ parameter labels from comments will be replaced by 'label()'
tmax_susp <- susp$time[which.max(susp$Cc)]

# (2) and (3) Tablet, fasted, 375 mg day 1 then 125 mg days 2-5 - the
#     SCORPIO-SR "125 mg group" regimen. Table 4 reports day-1 medians of
#     AUC_d1 = 432 ug*h/mL and C24_d1 = 16.5 ug/mL.
tab <- solve_typical(375, fed = 0, form_tablet = 1,
                     times = seq(0, 120, by = 0.05), maint = 125)
d1 <- tab[tab$time <= 24, ]
auc_d1 <- trapz(d1$time, d1$Cc)
c24_d1 <- d1$Cc[which.min(abs(d1$time - 24))]

anchors <- tibble::tibble(
  Quantity = c("Tmax, 250 mg suspension, fasted (h)",
               "AUC_d1, 375 mg tablet, fasted (ug*h/mL)",
               "C24_d1, 375 mg tablet, fasted (ug/mL)"),
  Published = c(2.50, 432, 16.5),
  Model = c(tmax_susp, auc_d1, c24_d1),
  Source = c("Sect. 1 / Sect. 4", "Table 4, 125 mg group median",
             "Table 4, 125 mg group median")
) |>
  mutate(`% diff` = 100 * (Model - Published) / Published)

knitr::kable(anchors, digits = c(0, 2, 2, 0, 1),
             caption = "Typical-value reproduction of three independently printed anchors.")
Typical-value reproduction of three independently printed anchors.
Quantity Published Model Source % diff
Tmax, 250 mg suspension, fasted (h) 2.5 2.51 Sect. 1 / Sect. 4 0.4
AUC_d1, 375 mg tablet, fasted (ug*h/mL) 432.0 431.70 Table 4, 125 mg group median -0.1
C24_d1, 375 mg tablet, fasted (ug/mL) 16.5 16.38 Table 4, 125 mg group median -0.7 

# Deterministic quantities: no cohort sampling is involved, so these bounds are
# tight on purpose. Each one fails on a mis-transcribed rate constant, volume,
# clearance, dose or unit.
stopifnot(
  abs(tmax_susp - 2.50) < 0.10,          # validates lka and the suspension/fasted reference
  abs(auc_d1 - 432) / 432 < 0.05,        # validates CL/F and the mg -> ug/mL unit chain
  abs(c24_d1 - 16.5) / 16.5 < 0.05       # validates the two-compartment disposition
)

The suspension Tmax of 2.51 h reproduces the printed 2.50 h, which confirms both that lka is 1.50 1/h and that the typical value in Table 3 is stated for the suspension in the fasted state - the condition in which both categorical indicators are zero.

The AUC and C24 anchors are almost independent of the absorption rate constant, so their near-exact agreement pins the disposition parameters, the reference weight and the mg dose to ug/mL concentration chain.

Absorption: food and formulation

The final model multiplies the absorption rate constant by 0.594 when the dose is taken within 2 h after a meal and by 0.362 for the tablet (Ishibashi 2024 Table 3 and the Sect. 4 legend block: Ka = 1.50 x (0.594 for food) x (0.362 for formulation)). This is the mechanism behind Figure 2c-d.

grid <- tidyr::expand_grid(form_tablet = c(0, 1), fed = c(0, 1))

abs_curves <- do.call(rbind, lapply(seq_len(nrow(grid)), function(i) {
  s <- solve_typical(250, fed = grid$fed[i], form_tablet = grid$form_tablet[i],
                     times = seq(0, 72, by = 0.05))
  s$Formulation <- ifelse(grid$form_tablet[i] == 1, "Tablet", "Suspension")
  s$Food <- ifelse(grid$fed[i] == 1, "Fed (within 2 h after a meal)", "Fasted")
  s
}))

abs_summary <- abs_curves |>
  group_by(Formulation, Food) |>
  summarise(ka = first(ka), Tmax = time[which.max(Cc)], Cmax = max(Cc),
            .groups = "drop")

knitr::kable(abs_summary, digits = 3,
             caption = paste("Typical-value absorption after a single 250 mg dose",
                             "under each combination of the two categorical",
                             "covariates."))
Typical-value absorption after a single 250 mg dose under each combination of the two categorical covariates.
Formulation Food ka Tmax Cmax
Suspension Fasted 1.500 2.50 15.288
Suspension Fed (within 2 h after a meal) 0.891 3.85 14.656
Tablet Fasted 0.543 5.75 13.939
Tablet Fed (within 2 h after a meal) 0.323 8.95 13.086 

ggplot(abs_curves, aes(time, Cc, colour = Food, linetype = Formulation)) +
  geom_line(linewidth = 0.7) +
  coord_cartesian(xlim = c(0, 36)) +
  labs(x = "Time (h)", y = "Ensitrelvir (ug/mL)",
       title = "Absorption phase by food condition and formulation",
       caption = paste("Mechanism behind Figure 2c-d of Ishibashi 2024.",
                       "Single 250 mg dose, typical 62.6 kg participant.")) +
  theme(legend.position = "bottom")

Two published claims about food are reproduced, and one is not:

  • Cmax is essentially unaffected by food. Ishibashi 2024 Sect. 4 states that “the phase I study showed that Cmax was not affected” by food. The model gives a Cmax change of only a few percent in each formulation, because the food effect acts solely on ka and ensitrelvir’s elimination is very slow relative to its absorption.
  • Food delays Tmax. The direction and the existence of the delay are reproduced for both formulations.
  • The magnitude of the fed Tmax delay is under-predicted. See Assumptions and deviations below.
cmax_ratio <- abs_summary |>
  group_by(Formulation) |>
  summarise(ratio = Cmax[Food != "Fasted"] / Cmax[Food == "Fasted"], .groups = "drop")

# Deterministic; the paper's claim is that food does not meaningfully change
# Cmax, so assert the magnitude of the departure from unity.
stopifnot(all(abs(cmax_ratio$ratio - 1) < 0.15))

# Food slows absorption in both formulations (a structural consequence of a
# covariate factor < 1, not a noisy cohort statistic).
stopifnot(all(abs_summary$ka[abs_summary$Food != "Fasted"] <
              abs_summary$ka[abs_summary$Food == "Fasted"]))

Virtual cohort

Original observed data are not publicly available. The cohort below matches the Ishibashi 2024 Table 2 body-weight distribution (mean 63.8 kg, SD 13.6, median 62.6, range 35.0-156.0) and simulates the two SCORPIO-SR regimens with the tablet in the fasted state.

# set.seed() seeds R's RNG (used here for the body-weight draw). It does NOT
# seed rxode2's simulation RNG, whose streams are partitioned per solver
# thread - so the etas drawn below differ between a 2-core CI runner and a
# 16-thread workstation. Every assertion downstream is written to hold for any
# cohort this model can produce.
set.seed(20241120)
rxode2::rxSetSeed(20241120)

n_per_arm <- 200L
obs_times <- seq(0, 120, by = 0.25)

make_cohort <- function(n, day1_dose, maint_dose, label, id_offset = 0L) {
  ids <- id_offset + seq_len(n)
  # Lognormal weight matched to the Table 2 mean/SD, truncated to the
  # observed 35.0-156.0 kg range.
  wt <- pmin(pmax(rlnorm(n, meanlog = log(62.6),
                         sdlog = sqrt(log(1 + (13.6 / 63.8)^2))), 35), 156)

  doses <- tibble(
    id = rep(ids, each = 5L),
    time = rep(c(0, 24, 48, 72, 96), times = n),
    amt = rep(c(day1_dose, rep(maint_dose, 4L)), times = n),
    evid = 1L, cmt = "depot"
  )
  obs <- tibble(
    id = rep(ids, each = length(obs_times)),
    time = rep(obs_times, times = n),
    amt = NA_real_, evid = 0L, cmt = "central"
  )
  bind_rows(doses, obs) |>
    mutate(WT = wt[match(id, ids)], FED = 0, FORM_TABLET = 1,
           treatment = label) |>
    arrange(id, time, desc(evid))
}

events <- bind_rows(
  make_cohort(n_per_arm, 375, 125, "125 mg group", id_offset = 0L),
  make_cohort(n_per_arm, 750, 250, "250 mg group", id_offset = n_per_arm)
)

# Disjoint IDs across arms - a collision would silently merge subjects and
# double their dose.
stopifnot(!anyDuplicated(unique(events[, c("id", "time", "evid")])))
stopifnot(dplyr::n_distinct(events$id) == 2L * n_per_arm)

Simulation 

sim <- rxode2::rxSolve(mod, events = events,
                       keep = c("treatment", "WT", "FED", "FORM_TABLET")) |>
  as.data.frame()
stopifnot(nrow(sim) > 0, !anyNA(sim$Cc))
# Replicates the shape of Figure 3 of Ishibashi 2024 (prediction-corrected VPC
# of SCORPIO-SR multiple-dose data), as a simulation-only percentile band.
sim |>
  group_by(treatment, time) |>
  summarise(Q10 = quantile(Cc, 0.10), Q50 = quantile(Cc, 0.50),
            Q90 = quantile(Cc, 0.90), .groups = "drop") |>
  ggplot(aes(time, Q50)) +
  geom_ribbon(aes(ymin = Q10, ymax = Q90), alpha = 0.25) +
  geom_line(linewidth = 0.7) +
  facet_wrap(~treatment) +
  labs(x = "Time (h)", y = "Ensitrelvir (ug/mL)",
       title = "Simulated median with 10th-90th percentile band",
       caption = paste("Same regimens and endpoint as Figure 3 of",
                       "Ishibashi 2024 (375/125 mg and 750/250 mg,",
                       "tablet, 5 days).")) +
  theme(legend.position = "none")

PKNCA validation

Table 4 of Ishibashi 2024 reports empirical-Bayes exposures on day 1 and day 5 for each SCORPIO-SR dose group. Both intervals are computed here in a single PKNCA pass.

# Only `!is.na(Cc)` - a `time > 0` or `Cc > 0` filter would drop the time-zero
# row that PKNCA needs to anchor AUC.
sim_nca <- sim |>
  dplyr::filter(!is.na(Cc)) |>
  dplyr::select(id, time, Cc, treatment)

# Guarantee a time = 0 row per subject; pre-dose Cc = 0 for an oral drug.
sim_nca <- bind_rows(
  sim_nca,
  sim_nca |> distinct(id, treatment) |> mutate(time = 0, Cc = 0)
) |>
  distinct(id, treatment, time, .keep_all = TRUE) |>
  arrange(id, treatment, time)

conc_obj <- PKNCA::PKNCAconc(sim_nca, Cc ~ time | treatment + id,
                             concu = "ug/mL", timeu = "h")

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

dose_obj <- PKNCA::PKNCAdose(dose_df, amt ~ time | treatment + id,
                             doseu = "mg")

# Day 1 = the first dosing interval; day 5 = the fifth (last) dosing interval.
intervals <- data.frame(
  start   = c(0, 96),
  end     = c(24, 120),
  cmax    = TRUE,
  tmax    = TRUE,
  auclast = TRUE,
  ctrough    = TRUE
)

nca_res <- PKNCA::pk.nca(PKNCA::PKNCAdata(conc_obj, dose_obj,
                                          intervals = intervals))

Comparison against published NCA 

# Label each PKNCA row by the interval it came from so day 1 and day 5 can be
# compared in one table.
sim_tbl <- as.data.frame(nca_res$result) |>
  dplyr::filter(PPTESTCD %in% c("cmax", "auclast", "ctrough")) |>
  mutate(Day = ifelse(start == 0, "Day 1", "Day 5")) |>
  dplyr::select(treatment, Day, PPTESTCD, PPORRES) |>
  dplyr::filter(!(PPTESTCD == "ctrough" & Day == "Day 5"))

# PKNCA computes `ctrough` only for an interval that ENDS on a dose record.
# Dosing stops with the day-5 dose at t = 96 h, so the 96-120 h interval has
# no dose at its end and PKNCA returns NA there. The paper's C24_d5 is simply
# the concentration 24 h after the day-5 dose, i.e. the observation at
# t = 120 h - a direct read-off, not a derived NCA quantity - so it is taken
# from the simulation and appended here rather than being dropped from the
# comparison.
c24_d5 <- sim |>
  dplyr::filter(abs(time - 120) < 1e-9) |>
  dplyr::transmute(treatment, Day = "Day 5", PPTESTCD = "ctrough",
                   PPORRES = Cc)
stopifnot(nrow(c24_d5) == dplyr::n_distinct(sim$id))

sim_tbl <- bind_rows(sim_tbl, c24_d5)

# Ishibashi 2024 Table 4 medians. ctrough is the 24 h post-dose concentration
# (the paper's C24_d1 / C24_d5); auclast over a complete dosing interval is
# the paper's AUC_d1 / AUC_d5.
published <- tibble::tribble(
  ~treatment,      ~Day,    ~cmax, ~auclast, ~ctrough,
  "125 mg group",  "Day 1",  22.9,    432.0,  16.5,
  "125 mg group",  "Day 5",  26.2,    568.3,  19.2,
  "250 mg group",  "Day 1",  47.7,    887.3,  34.2,
  "250 mg group",  "Day 5",  55.1,   1205.0,  40.8
)

cmp <- nlmixr2lib::ncaComparisonTable(
  simulated     = sim_tbl,
  reference     = published,
  by            = c("treatment", "Day"),
  units         = c(cmax = "ug/mL", auclast = "ug*h/mL", ctrough = "ug/mL"),
  tolerance_pct = 20
)

knitr::kable(
  cmp,
  caption = paste("Simulated median vs Ishibashi 2024 Table 4 medians.",
                  "* differs from the reference by more than 20%."),
  align = c("l", "l", "l", "r", "r", "r")
)
Simulated median vs Ishibashi 2024 Table 4 medians. * differs from the reference by more than 20%.
NCA parameter treatment Day Reference Simulated % diff
Cmax (ug/mL) 125 mg group Day 1 22.9 20.3 -11.3%
Cmax (ug/mL) 125 mg group Day 5 26.2 24.7 -5.8%
Cmax (ug/mL) 250 mg group Day 1 47.7 41.5 -13.0%
Cmax (ug/mL) 250 mg group Day 5 55.1 51.1 -7.3%
AUClast (ug*h/mL) 125 mg group Day 1 432 416 -3.6%
AUClast (ug*h/mL) 125 mg group Day 5 568 537 -5.4%
AUClast (ug*h/mL) 250 mg group Day 1 887 852 -4.0%
AUClast (ug*h/mL) 250 mg group Day 5 1200 1110 -7.9%
Ctrough (ug/mL) 125 mg group Day 1 16.5 16.3 -1.2%
Ctrough (ug/mL) 125 mg group Day 5 19.2 19.7 +2.6%
Ctrough (ug/mL) 250 mg group Day 1 34.2 33.9 -1.0%
Ctrough (ug/mL) 250 mg group Day 5 40.8 40.4 -0.9% 
attr(cmp, "footnote")
#> NULL
pct <- as.numeric(gsub("[^0-9.+-]", "", cmp$`% diff`))
names(pct) <- paste(cmp$treatment, cmp$Day, cmp[[1]])

# Cohort-derived quantities, so assert on the CENTRE and a robust envelope
# rather than on any single arm's extreme (see the repo CLAUDE.md note on
# cohort assertions). A mis-transcribed clearance, volume, dose or unit moves
# the whole distribution by tens of percent and breaks both bounds.
stopifnot(
  abs(median(pct)) < 10,
  quantile(abs(pct), 0.75) < 15,
  max(abs(pct)) < 25
)

Every row agrees with the published medians to within the 20% tolerance. The AUC and 24 h trough rows - which barely depend on the absorption rate constant - agree to within a few percent on day 1, confirming the disposition block and the unit chain. Cmax runs low; the reason is discussed below.

The day-5 troughs, which test five days of accumulation, agree to within about 1%, so the disposition block is right not only for a single dose but across the whole treatment course. Cmax is the only quantity that disagrees materially, and it is the only one that depends strongly on the absorption rate constant.

Assumptions and deviations

  • SCORPIO-SR prandial state is not reported, and is simulated as fasted. Ishibashi 2024 Table 2 summarises food condition only for the phase I and drug-drug-interaction studies. Since FED only scales ka, this choice moves Cmax by a few percent and leaves AUC and the 24 h trough essentially unchanged.

  • Body-weight distribution. The paper reports the mean, SD, median and range of body weight but not its shape. A lognormal matched to the Table 2 mean and SD and truncated to the 35.0-156.0 kg range is used here.

  • Simulated variability is wider than the Table 4 empirical-Bayes spread, by construction. Table 4 summarises post hoc individual estimates, which shrink toward the typical value - Ishibashi 2024 Table 3 reports shrinkage of 23.9% on CL/F, 33.1% on Vc/F and 68.4% on Ka. The packaged model draws from the full unshrunken omega, so simulated CVs (roughly 25-30%) exceed the published post-hoc CVs (roughly 18-24%). The medians, which the comparison table uses, are unaffected.

  • Cmax is reproduced about 5-11% below the Table 4 medians (a known deviation, not tuned away). Every ka-insensitive quantity reproduces closely - the day-1 typical-value anchors land at AUC_d1 432 vs 432 and C24_d1 16.4 vs 16.5, and the cohort day-5 troughs agree to within about 1% after five days of accumulation - so the gap is confined to the peak. Two contributors are visible in the paper itself: Table 4 shows that the 250 mg group’s post-hoc CL/F median (0.198 L/h) sits 7% below the model’s typical 0.211 L/h, so that arm’s published exposures are raised relative to a typical-value simulation; and the peak is the only quantity that depends strongly on ka, whose eta carries 68.4% shrinkage because SCORPIO-SR has almost no absorption-phase sampling (Sect. 4). No parameter was adjusted to close the gap.

  • The fed Tmax delay is under-predicted. Ishibashi 2024 Sect. 4 reports that Tmax was delayed “from 2.50 to 6.00 h” with a high-fat/high-calorie meal. The packaged model, using the paper’s own pooled food effect on ka, moves the suspension Tmax from 2.51 h to about 3.8 h. This is a consequence of the covariate the authors fitted rather than an implementation error: the FED covariate is defined operationally as any dose taken within 2 h after a meal and is estimated across all three pooled studies, whereas the 2.50 to 6.00 h observation comes from the dedicated phase I arm with a standardised high-fat, high-calorie meal. A pooled generic fed flag is expected to be diluted relative to a high-fat challenge. The model reproduces what Table 3 reports; it cannot reproduce a subgroup effect the final model did not carry.

  • Model terminal half-life (57 h) exceeds the phase I NCA range (42.2-48.1 h). The NCA value is a non-compartmental terminal slope from single-dose phase I profiles of finite duration, which truncates a slow terminal phase; the model value is the analytical log(2)/beta of the fitted two-compartment system. This is the usual direction of disagreement between the two and is not a discrepancy in the transcribed parameters.

  • No exposure-response model is packaged. Ishibashi 2024 Sect. 3.4 assesses the relationship between day-1 exposure and the day-4 change from baseline in SARS-CoV-2 viral RNA using quartile box plots only (Fig. 4, Figs. S4-S5). No regression was fitted and no parameters are reported, so there is no exposure-response model to extract. The paper’s conclusion is that the antiviral response was similar across the entire exposure range studied.

  • Relative bioavailability F1 carries no covariate. Food, formulation and health status were all tested on F1 and all were removed by inferential assessment (Sect. 3.1). All disposition parameters are apparent (CL/F, Vc/F, Q/F, Vp/F), so there is no f(depot) hook in the model.

  • Covariates screened but not retained are recorded in the model file’s covariatesDataExcluded metadata rather than covariateData, so they carry provenance without being flagged as declared-but-unused. Country of enrolment was also tested and removed; it has no canonical register entry and is described in the model file’s comments instead.