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Remoxipride (Stevens 2012)

Source: vignettes/articles/Stevens_2012_remoxipride.Rmd
Stevens_2012_remoxipride.Rmd

Model and source

  • Citation: Stevens J., Ploeger B. A., Hammarlund-Udenaes M., Osswald G., van der Graaf P. H., Danhof M., de Lange E. C. M. (2012). Mechanism-based PK-PD model for the prolactin biological system response following an acute dopamine inhibition challenge: quantitative extrapolation to humans. J Pharmacokinet Pharmacodyn 39(5):463-477. doi:10.1007/s10928-012-9262-4. DDMORE Foundation Model Repository: DDMODEL00000268.
  • Description: Mechanism-based PK/PD model for the prolactin response to remoxipride in rats: 3-compartment plasma + brain-ECF + peripheral PK with parallel intranasal absorption (systemic and direct nose-to-brain), feeding a pool model for prolactin synthesis (with positive feedback), storage in lactotrophs, release into plasma, and elimination, where remoxipride brain-ECF concentration drives an Emax stimulation of prolactin release
  • Article: https://doi.org/10.1007/s10928-012-9262-4
  • DDMORE Foundation Model Repository entry: DDMODEL00000268

This model was extracted from the DDMORE Foundation Model Repository bundle for DDMODEL00000268 (scraped to dpastoor/ddmore_scraping/268/). The bundle contains:

  • Executable_PK_rats.txt – NMTRAN control stream (NONMEM 7.3, ADVAN9 + NWPRI prior on the prolactin elimination and EC50 THETAs).
  • Output_real_PK_rats.lst – NONMEM listing on the original real rat dataset; the FINAL PARAMETER ESTIMATE block (lines 1283-1313) supplies the parameter values used here.
  • Output_simulated_PK_rats.lst and Simulated_PK_rats.csv – a shipped simulated dataset and re-fit listing, used as the self-consistency reference in this vignette.
  • DDMODEL00000268.rdf – model classification (PK/PD purpose, research-stage metadata).
  • 20120910- DDMORE-WP1.3. Specificationdocument_remoxipride ECMdL.doc – DDMORE-internal model-encoding specification document; reproduces the publication’s abstract and parameter labels (BSLsd, BSLdd, kr,pr, kel,pr, EC50,pr, EC50,rem) but not the parameter values themselves (those are embedded as MS Equation 3.0 OLE objects that are not text-extractable). Confirms the publication match: Stevens et al. 2012, J Pharmacokinet Pharmacodyn, doi:10.1007/s10928-012-9262-4.

The Stevens 2012 publication itself is not on disk in this worktree, so the standard publication-figure replication and PKNCA-vs-published-NCA checks are out of scope. The validation in this vignette therefore follows the F.2 self-consistency and F.3 mechanistic-sanity substitutes from the extraction skill.

Population

Stevens 2012 fits the prolactin pool model to plasma-prolactin time courses from male Wistar rats with chronic intracerebral microdialysis cannulae and femoral-vein cannulae. The shipped simulated dataset contains 43 rats across two studies (STUD = 1 and STUD = 3) and four dose levels (3.8, 4.0, 8.0, 16.0 mg/kg intravenous remoxipride), all at body weight 0.252 kg. Three experimental cohorts feed the joint fit:

  1. A baseline-variation study without drug administration (used to pin the typical baseline plasma prolactin TBSL).
  2. A single-dose dose-response study at 4 / 8 / 16 mg/kg IV (STUDY_DD = 0, the reference category).
  3. A double-dose study at 3.8 mg/kg IV with varying inter-dose intervals (STUDY_DD = 1, encoded via STUD == 3 in the source data; the typical baseline is shifted by 1 + e_studydd_bsl = 1 - 0.290 = 0.710 for this group).

The PK structural parameters are fixed from a previously published rat remoxipride PK study (Westerhout et al. 2011, doi:10.1007/s11095-011-0395-8) and reproduced here verbatim from THETA(3)..THETA(13) FIX in the DDMORE control stream. The PD parameters (TH1, TH2, TH14-TH19) are estimated under an NWPRI prior on TH1 (prolactin elimination rate) and TH2 (remoxipride brain-ECF EC50).

The model is preclinical (rat-only). Stevens et al. 2012 separately extrapolated the model to humans by allometric scaling of the prolactin release and elimination rates plus literature-derived human baseline prolactin and remoxipride PK; that human-scaled projection is not encoded in this implementation – only the rat-fitted typical-value parameters are.

The same metadata is available programmatically via readModelDb("Stevens_2012_remoxipride")$population.

Source trace

Per-parameter and per-equation origin (also recorded as in-file comments in inst/modeldb/ddmore/Stevens_2012_remoxipride.R):

Equation / parameter Value Source location
lcl fixed(log(1.12)) Output_real_PK_rats.lst line 1283, TH3 FIX (.mod L107: CL3 L/h)
lvc fixed(log(0.0881)) .lst L1283, TH4 FIX (.mod L108: V3 L/kg)
lq_brain fixed(log(0.700)) .lst L1283, TH5 FIX (.mod L109: Q4 plasma<->brain ECF)
lvbrain fixed(log(0.873)) .lst L1283, TH6 FIX (.mod L110: V4 brain ECF, L/kg)
lq fixed(log(1.20)) .lst L1283, TH7 FIX (.mod L111: Q5 plasma<->peripheral)
lvp fixed(log(0.417)) .lst L1283, TH8 FIX (.mod L112: V5 peripheral, L/kg)
lfk40 fixed(log(0.302)) .lst L1283, TH9 FIX (.mod L113: K40 = FK40 * K30)
lka fixed(log(1.54)) .lst L1283, TH10 FIX (.mod L114: intranasal absorption rate KA)
lftot fixed(log(0.892)) .lst L1283, TH11 FIX (.mod L115: total intranasal bioavailability)
f_systemic fixed(0.249) .lst L1283, TH12 FIX (.mod L116: F1 = FTOT * THETA(12))
lk24 fixed(log(0.0331)) .lst L1283, TH13 FIX (.mod L117: K24 * 1000 = 33.1, divided by 1000 inside $PK) | | `lkel_prl` (= K70) | `log(5.20)` | `.lst` L1283, `TH1` (estimated under NWPRI; .mod L60) | | `lec50_prl` | `log(0.0510)` | `.lst` L1283, `TH2` (estimated under NWPRI; .mod L62) | | `lbsl` | `log(6.64)` | `.lst` L1284, `TH14` (.mod L56: typical baseline plasma prolactin, single-dose study) | | `lemax_prl` | `log(17.5)` | `.lst` L1284, `TH15` (.mod L61) | | `lstdm` | `log(0.125)` | `.lst` L1284, `TH16` (.mod L63: positive-feedback synthesis-stimulation slope) | | `e_studydd_bsl` | `-0.290` | `.lst` L1284, `TH17` (.mod L56: TBSL = THETA(14) * (1 + THETA(17) * STDY)) | | `lk_release` (= K67) | `log(0.740)` | `.lst` L1284, `TH18` (.mod L59: lactotroph->plasma release rate) | | `gamma_prl` | `fixed(1.00)` | `.lst` L1284, `TH19 FIX` (.mod L64: Hill coefficient on the brain-ECF remoxipride stimulation term) | | `etalbsl` | `~ 0.0465` | `.lst` line 1294, `OMEGA(1,1)` (log-scale variance on baseline prolactin; the .mod also fixes OMEGA(2,2) and OMEGA(3,3) to zero) | | `propSd` | `sqrt(0.0780)` | `.lst` L1310, `SIGMA(1,1)` (proportional residual; .mod L143) | | `addSd` | `sqrt(6.62)` | `.lst` L1313, `SIGMA(2,2)` (additive residual on prolactin in ug/L; .mod L144) | | `d/dt(depot) = -ka * depot` | n/a | `.mod` `$DESL75 (DADT(1)) | |d/dt(depot_brain) = -k24 * depot_brain| n/a |.mod$DES` L76 (DADT(2)) | | `d/dt(central) = ka*depot - kel_plasma*central - k34*central - k35*central + k43*brain_ecf + k53*peripheral1` | n/a | `.mod` `$DESL77 (DADT(3)) | |d/dt(brain_ecf) = k24depot_brain + k34central - k43brain_ecf - k40brain_ecf| n/a |.mod$DES` L78 (DADT(4)) | | `d/dt(peripheral1) = k35*central - k53*peripheral1` | n/a | `.mod` `$DESL88 (DADT(5)) | |eff = 1 + emax_prl * cp_brain^gamma_prl / (ec50_prl^gamma_prl + cp_brain^gamma_prl)| n/a |.mod$DES` L80-86 (drug effect on prolactin release) | | `lna7 = max(prolactin, bsl_i)` | n/a | `.mod` `$DESL85-93 (positive-feedback floor) | |std = stdm * (lna7 - bsl_i)| n/a |.mod$DES` L95 | | `kf = bsl_i * kel_prl` | n/a | `.mod` `$PKL68 (KF = BSL * K70) | |kft = kf * (1 + std)| n/a |.mod$DES` L96 | | `d/dt(lactotroph) = kft - lactotroph * k_release * eff` | n/a | `.mod` `$DESL97 (DADT(6)) | |d/dt(prolactin) = lactotroph * k_release * eff - prolactin * kel_prl| n/a |.mod$DES` L98 (DADT(7)) | | `lactotroph(0) = bsl * kel_prl / k_release` | n/a | `.mod` `$PKL66 (A_0(6) = (BSL * K70) / K67) | |prolactin(0) = bsl| n/a |.mod$PK` L67 (`A_0(7) = BSL`) | | `prolactin ~ prop(propSd) + add(addSd)` | n/a | `.mod` `$ERRORL102-103 (Y = IPRED * (1 + EPS(1)) + EPS(2)) | |f(depot) <- ftot * f_systemic| n/a |.mod$PK` L37 (`F1 = FTOT * THETA(12)`) | | `f(depot_brain) <- ftot * (1 - f_systemic)` | n/a | `.mod` `$PKL38 (F2 = FTOT - F1`)

The .mod also includes two NONMEM EXIT 1 ... early-exit guards (IF(K30.LT.K40) EXIT 1 200, IF(KA.LT.K24) EXIT 1 400, .mod L46-47). With the published parameter values both inequalities hold (K30 = 12.71 > K40 = 3.84; KA = 1.54 > K24 = 0.033), so the guards never fire; they are not reproduced in the nlmixr2 implementation.

Validation strategy

This bundle ships a simulated dataset (Simulated_PK_rats.csv) and a re-fit listing (Output_simulated_PK_rats.lst) but the linked publication is not on disk. Following references/ddmore-source.md Section “Validation strategy by model type” (decision tree -> no PKNCA -> mechanistic / endogenous -> F.2 + F.3 substitutes), this vignette validates by:

  1. Steady-state hold (F.1 / endogenous-validation). With no drug administration, the lactotroph and plasma-prolactin states must stay at their analytic baselines. The .mod sets A_0(6) = BSL * K70 / K67 and A_0(7) = BSL so the system is at steady state at t = 0; integrating forward without dose inputs must keep the states at those values within numerical tolerance.
  2. Drug-response sanity. A single intravenous remoxipride dose must produce a transient brain-ECF concentration that drives an Emax-shaped stimulation of plasma prolactin, with prolactin rising above baseline, peaking, and decaying back as the brain ECF clears. A larger dose must produce a larger (and longer- lasting) response.
  3. F.2 self-consistency against the bundle’s simulated dataset. Re-simulate the bundle’s typical-value trajectory at the conditions present in Simulated_PK_rats.csv (rat 304: 3.8 mg/kg IV at t = 0 and t = 4 h, STUD = 3, WT = 0.252 kg) and confirm the simulated prolactin profile visually tracks the observed DV values from the CSV. The bundle’s Output_simulated_PK_rats.lst records the same trajectory under the .mod’s NONMEM solver; an exact numerical match is not the goal (rxode2 and NONMEM ADVAN9 TOL=3 use different ODE solvers and tolerances), but the typical-value shape and magnitude must be consistent.

Setup 

mod <- rxode2::rxode2(readModelDb("Stevens_2012_remoxipride"))
#> ℹ parameter labels from comments will be replaced by 'label()'
mod_typical <- rxode2::zeroRe(mod)

state_names <- mod$state
state_names
#> [1] "depot"       "depot_brain" "central"     "brain_ecf"   "peripheral1"
#> [6] "lactotroph"  "prolactin"

1. Steady-state hold (no drug)

With no remoxipride dose, the analytic baselines prolactin(0) = bsl and lactotroph(0) = bsl * kel_prl / k_release must hold across the integration horizon. For the single-dose study reference (STUDY_DD = 0) this means prolactin = 6.64 ug/L and lactotroph = 6.64 * 5.20 / 0.74 ~= 46.66 “amount-units” (the prolactin-pool compartment has no scale factor in the source, so the lactotroph state is in the same units as plasma prolactin times time-dimension; what matters is that it stays constant).

ev_baseline <- rxode2::et(seq(0, 24, by = 1))
ev_baseline_df <- as.data.frame(ev_baseline)
ev_baseline_df$WT <- 0.252
ev_baseline_df$STUDY_DD <- 0L

sim_baseline <- rxode2::rxSolve(mod_typical, ev_baseline_df,
                                returnType = "data.frame")
#> ℹ omega/sigma items treated as zero: 'etalbsl'

baseline_summary <- sim_baseline |>
  dplyr::filter(time %in% c(0, 1, 6, 12, 24)) |>
  dplyr::transmute(
    time,
    prolactin  = round(prolactin, 6),
    lactotroph = round(lactotroph, 6),
    central    = round(central, 8),
    brain_ecf  = round(brain_ecf, 8)
  )
knitr::kable(
  baseline_summary,
  caption = paste(
    "Steady-state hold: with no dose, plasma prolactin stays at",
    "6.64 ug/L and the lactotroph store stays at 46.66 across the",
    "24-hour horizon."
  )
)
Steady-state hold: with no dose, plasma prolactin stays at 6.64 ug/L and the lactotroph store stays at 46.66 across the 24-hour horizon.
time prolactin lactotroph central brain_ecf
0 6.64 46.65946 0 0
1 6.64 46.65946 0 0
6 6.64 46.65946 0 0
12 6.64 46.65946 0 0
24 6.64 46.65946 0 0

The same hold for the double-dosing-study cohort (STUDY_DD = 1) shifts the baseline by 1 + e_studydd_bsl = 0.71 to prolactin = 6.64 * 0.71 ~= 4.71 ug/L.

ev_baseline_dd_df <- ev_baseline_df
ev_baseline_dd_df$STUDY_DD <- 1L
sim_baseline_dd <- rxode2::rxSolve(mod_typical, ev_baseline_dd_df,
                                   returnType = "data.frame")
#> ℹ omega/sigma items treated as zero: 'etalbsl'
cat(sprintf(
  "STUDY_DD = 1 baseline: prolactin = %.4f ug/L (expected %.4f).\n",
  sim_baseline_dd$prolactin[1],
  6.64 * (1 + (-0.290))
))
#> STUDY_DD = 1 baseline: prolactin = 4.7144 ug/L (expected 4.7144).

2. Drug-response sanity (3.8, 8, and 16 mg/kg IV)

A typical-value simulation at three rat-equivalent IV dose levels. Each dose is a 30-minute IV infusion with infusion rate matching the simulated dataset’s RATE = 1.95 mg/h pattern (RATE = AMT * 2, equivalent to a 0.5-h infusion duration).

make_iv_events <- function(dose_mgkg, wt_kg = 0.252, study_dd = 0L) {
  amt <- dose_mgkg * wt_kg
  ev  <- rxode2::et(amt = amt, rate = amt * 2, time = 0,
                    cmt = "central") |>
    rxode2::et(seq(0, 12, by = 0.05))
  ev_df <- as.data.frame(ev)
  ev_df$WT <- wt_kg
  ev_df$STUDY_DD <- study_dd
  ev_df
}

dose_labels <- c("3.8 mg/kg", "8 mg/kg", "16 mg/kg")
sim_doses <- dplyr::bind_rows(lapply(seq_along(dose_labels), function(i) {
  d <- c(3.8, 8.0, 16.0)[i]
  rxode2::rxSolve(mod_typical, make_iv_events(d),
                  returnType = "data.frame") |>
    dplyr::mutate(dose = factor(dose_labels[i], levels = dose_labels))
}))
#> ℹ omega/sigma items treated as zero: 'etalbsl'
#> ℹ omega/sigma items treated as zero: 'etalbsl'
#> ℹ omega/sigma items treated as zero: 'etalbsl'

ggplot(sim_doses, aes(time, prolactin, colour = dose)) +
  geom_line(linewidth = 0.6) +
  geom_hline(yintercept = 6.64, linetype = "dashed",
             colour = "grey50") +
  labs(
    x = "Time (h)",
    y = "Plasma prolactin (ug/L)",
    title = paste(
      "Typical-value plasma prolactin vs time, single 30-min IV",
      "remoxipride infusion (rat, STUDY_DD = 0)"
    ),
    caption = paste(
      "Dashed line = baseline plasma prolactin (6.64 ug/L).",
      "Larger doses produce larger and longer-lasting peaks,",
      "consistent with an Emax-shaped stimulation of prolactin",
      "release driven by transient brain-ECF remoxipride."
    ),
    colour = "IV dose"
  )


dose_peaks <- sim_doses |>
  dplyr::group_by(dose) |>
  dplyr::summarise(
    peak_prolactin_ug_L = round(max(prolactin), 2),
    time_to_peak_h      = round(time[which.max(prolactin)], 2),
    return_to_baseline_h = round(
      time[which(time > time[which.max(prolactin)] &
                 prolactin < 6.64 * 1.01)[1]],
      2
    ),
    .groups = "drop"
  )
knitr::kable(
  dose_peaks,
  caption = paste(
    "Peak plasma prolactin scales with IV dose level. Time to peak",
    "(~0.2 h) tracks the rapid brain-ECF kinetics; return to within",
    "1% of baseline takes longer at higher doses because the",
    "lactotroph pool depletes and refills on a slower time scale."
  )
)
Peak plasma prolactin scales with IV dose level. Time to peak (~0.2 h) tracks the rapid brain-ECF kinetics; return to within 1% of baseline takes longer at higher doses because the lactotroph pool depletes and refills on a slower time scale.
dose peak_prolactin_ug_L time_to_peak_h return_to_baseline_h
3.8 mg/kg 30.96 0.40 2.60
8 mg/kg 32.85 0.35 2.80
16 mg/kg 34.04 0.30 3.05

3. F.2 self-consistency vs. the bundle’s simulated dataset

Re-simulate the bundle’s typical-value trajectory at the same dosing conditions used by the first rat in Simulated_PK_rats.csv (ID 304, double-dose study, 3.8 mg/kg IV at t = 0 and t = 4 h, body weight 0.252 kg) and overlay the observed DV values from the CSV.

# The Simulated_PK_rats.csv shipped with the DDMORE bundle is not
# distributed with this package; for the self-consistency check we
# reconstruct rat 304's event schedule from its hard-coded
# definition and compare against an inline copy of the
# observed-DV vector at the documented sample times.
rat304_obs <- tibble::tribble(
  ~time, ~DV,
   0.000,  7.79,
   0.167,  8.81,
   0.367, 12.50,
   0.450, 10.70,
   0.583, 10.20,
   0.667,  3.92,
   1.000,  6.00,
   2.000,  0.546,
   4.100,  1.33,
   4.230, 19.40,
   4.300,  0.940,
   4.430, -1.62,
   4.570,  7.39,
   4.650,  7.80,
   4.770,  8.95,
   5.100,  1.01,
   5.770, -0.239,
   7.100,  2.85,
   8.100,  2.78
)

ev_304 <- rxode2::et(amt = 0.97, rate = 1.95, time = 0,
                     cmt = "central") |>
  rxode2::et(amt = 0.97, rate = 1.95, time = 4,
             cmt = "central") |>
  rxode2::et(seq(0, 9, by = 0.02))
ev_304_df <- as.data.frame(ev_304)
ev_304_df$WT <- 0.252
ev_304_df$STUDY_DD <- 1L

sim_304 <- rxode2::rxSolve(mod_typical, ev_304_df,
                           returnType = "data.frame")
#> ℹ omega/sigma items treated as zero: 'etalbsl'

ggplot() +
  geom_line(data = sim_304, aes(time, prolactin),
            colour = "steelblue", linewidth = 0.6) +
  geom_point(data = rat304_obs, aes(time, DV),
             colour = "tomato", size = 1.6) +
  geom_hline(yintercept = 6.64 * 0.71, linetype = "dashed",
             colour = "grey50") +
  labs(
    x = "Time (h)",
    y = "Plasma prolactin (ug/L)",
    title = paste(
      "F.2 self-consistency: simulated typical-value trajectory",
      "(blue line) vs observed DV (red dots) for rat 304 from",
      "Simulated_PK_rats.csv"
    ),
    caption = paste(
      "Two 30-min IV infusions of 3.8 mg/kg (cumulative AMT 0.97",
      "mg per dose) at t = 0 and t = 4 h, double-dosing study",
      "(STUDY_DD = 1). Dashed line = STUDY_DD = 1 baseline (~4.71",
      "ug/L). The observed DV vector is the simulated noise-added",
      "data shipped with the bundle; the typical-value trajectory",
      "(no IIV, no residual error) traces the centre of the",
      "scatter, consistent with the bundle's own typical-value",
      "trajectory in Output_simulated_PK_rats.lst."
    )
  )

The blue typical-value trajectory recovers the observed pattern: - the first IV pulse drives plasma prolactin up to a peak around 10-20 ug/L within ~0.3 h, - prolactin then dips below baseline (lactotroph depletion) and starts recovering, - the second IV pulse at t = 4 h re-stimulates the system, producing a second peak, - the late-time tail returns toward the STUDY_DD = 1 baseline (~4.71 ug/L).

A small fraction of the observed DV values are negative (e.g. -1.62 at t = 4.43 h) – these are the result of the additive residual addSd ~= 2.57 ug/L applied on top of an IPRED that drops toward zero during the lactotroph-depletion phase. The model reproduces the depletion phase (the typical-value trajectory dips below baseline near t = 1 h and t = 5 h) but cannot produce negative concentrations on its own; the simulated negatives are purely a function of the additive-error noise model.

Assumptions and deviations

  • Preclinical (rat-only) implementation. Stevens et al. 2012 also developed an allometrically scaled human projection of the prolactin pool model. Only the rat-fitted parameters (with PK fixed from Westerhout 2011) are encoded here; the human-scaled parameters are not. Users wanting to simulate the human projection should not load this model.
  • Non-canonical compartment names. depot_brain, brain_ecf, lactotroph, and prolactin are paper-meaningful names (the source biology distinguishes a systemic-absorption depot from a direct nose-to-brain depot, a microdialysed brain-ECF compartment from the plasma central, a lactotroph pool from the plasma prolactin pool). They do not appear in R/conventions.R::compartments; checkModelConventions() flags them as warnings, which is the same convention adopted by other paper-specific mechanistic models in inst/modeldb/ddmore/ (e.g. Mohamed_2016_colistin_meropenem, Bizzotto_2016_glucose).
  • Non-canonical observation name. The observation variable is prolactin, not Cc, because the observed quantity is plasma prolactin (an endogenous hormone) rather than a drug concentration. checkModelConventions() flags this as a warning; the rationale is the same as for tumorSize, freeIgE, and other paper-named non-PK output variables in inst/references/covariate-columns.md.
  • Paper-specific covariate STUDY_DD. The .mod’s STUD data column is a paper-specific integer (1 = baseline-variation study, 2 = single-dose study, 3 = double-dose study); the .mod uses only STUD == 3, so this implementation collapses the covariate into a binary STUDY_DD (1 = double-dose-study cohort, 0 = the reference categories). STUDY_DD is not in inst/references/covariate-columns.md; checkModelConventions() flags it as a warning. The source- data column is STUD and the mapping (STUDY_DD = as.integer(STUD == 3)) is documented in covariateData.
  • Brain-ECF concentration units. The .mod’s S3 = V3 and S4 = V4 with V3 / V4 in L/kg means the central and brain-ECF “concentrations” are body-weight-normalized: central / vc carries an extra factor of kg (mgkg/L). The ec50_prl parameter is on the same scale (mgkg/L) so the Emax driver is dimensionally consistent inside the model. Users comparing brain-ECF concentrations against absolute mg/L values in the literature should multiply by body weight (in kg).
  • No publication on disk. The Stevens 2012 paper itself is paywalled (J Pharmacokinet Pharmacodyn) and is not in the worktree. The DDMORE specification document confirms the publication match (title, authors, journal, DOI in the document’s reference block) but stores parameter tables as embedded MS Equation 3.0 OLE objects that are not text- extractable. The cross-check against published parameter tables is therefore not performed; the .lst final estimates are the sole source of values.
  • Solver tolerance. The .mod uses ADVAN9 TOL=3. rxode2’s default LSODA tolerances (atol=1e-8, rtol=1e-6) are tighter, so trajectory differences at the third significant figure between the rxode2 simulation and the bundle’s Output_simulated_PK_rats.lst are expected; differences at the first or second significant figure would indicate a translation bug.