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Rosuvastatin (Barnett 2018)

Source: vignettes/articles/Barnett_2018_rosuvastatin.Rmd
Barnett_2018_rosuvastatin.Rmd

Model and source

  • Citation: Barnett S, Ogungbenro K, Menochet K, Shen H, Lai Y, Humphreys WG, Galetin A. Gaining Mechanistic Insight Into Coproporphyrin I as Endogenous Biomarker for OATP1B-Mediated Drug-Drug Interactions Using Population Pharmacokinetic Modeling and Simulation. Clin Pharmacol Ther. 2018;104(3):564-574. doi:10.1002/cpt.983. Structural rosuvastatin popPK model adapted from Tzeng TB et al. Curr Med Res Opin. 2008;24(9):2575-2585. doi:10.1185/03007990802312807.
  • Description: Two-compartment population PK model with first-order oral absorption for a single 5 mg dose of rosuvastatin in healthy adult males (Barnett 2018), refit from the Tzeng 2008 structural form with simultaneous plasma + urine fitting. The model includes separable biliary (CLb,RSV) and renal (CLr,RSV) clearance components from the central compartment, competitive rifampicin OATP1B inhibition of the biliary clearance via KiRSV driven by the instantaneous plasma rifampicin concentration, and a binary RIF-coadministration covariate that captures paper-reported reductions of V1, V2, and Q during the rifampicin phase (Barnett 2018 Table 1: V1 430 -> 2.98 L, V2 865 -> 128 L, Q 45.3 -> 5.03 L/h on RIF). Companion to modellib(‘Barnett_2018_coproporphyrin_I’); both share the rifampicin perpetrator parameterisation in modellib(‘Barnett_2018_rifampicin’).
  • Article: https://doi.org/10.1002/cpt.983

Population

Barnett 2018 refit the rosuvastatin (RSV) popPK model to plasma + urine RSV data from the Lai et al. 2016 clinical study in 12 healthy adult male volunteers (SLCO1B1 c.521 T>C wildtype only; no OATP1B15 / 15 carriers). Each subject received a single 5 mg oral rosuvastatin dose at the start of occasion 2 (rosuvastatin alone) and at the start of occasion 3 (rosuvastatin co-administered with 600 mg rifampicin). The 264 RSV plasma samples (OCC2 = 132, OCC3 = 132) plus 44 RSV urine samples (cumulative excretion over 0-7 h and 7-24 h post-dose) were fit simultaneously in NONMEM under FOCE. The structural form was adapted from Tzeng TB et al. Curr Med Res Opin 2008;24(9):2575-2585.

The same context is available programmatically via readModelDb("Barnett_2018_rosuvastatin")$population.

Source trace

Equation / parameter Value Source location
lka log(0.287) Barnett 2018 Table 1, RSV row ‘ka RSV (1/h)’ = 0.287
lclr log(8.48) Barnett 2018 Table 1, RSV row ‘CL R,RSV (L/h)’ = 8.48
lclb log(124) Barnett 2018 Table 1, RSV row ‘CL b,RSV (L/h)’ = 124
lvc (baseline) log(430) Barnett 2018 Table 1, RSV row ‘V1 RSV (L)’ = 430
lq (baseline) log(45.3) Barnett 2018 Table 1, RSV row ‘Q RSV (L/h)’ = 45.3
lvp (baseline) log(865) Barnett 2018 Table 1, RSV row ‘V2 RSV (L)’ = 865
e_rif_vc -0.99307 Barnett 2018 Table 1, RSV row ‘V1 RSV (RIF) b’ = 2.98
e_rif_q -0.88896 Barnett 2018 Table 1, RSV row ‘Q RSV (RIF) b’ = 5.03
e_rif_vp -0.85202 Barnett 2018 Table 1, RSV row ‘V2 RSV (RIF) b’ = 128
lki log(2.23) Barnett 2018 Table 1, RSV row ‘Ki RSV (uM) c’ = 2.23
propSd (plasma) 0.257 Barnett 2018 Table 1, RSV row ‘r prop (%) - plasma’ = 0.257
addSd (plasma) 0.05 nmol/L FIXED Barnett 2018 Table 1, RSV row ‘r add (nM) - plasma’ = 0.05 FIXED
propSd_Ursv 0.578 Barnett 2018 Table 1, RSV row ‘r prop (%) - urine’ = 0.578
addSd_Ursv 0.1 nmol/L FIXED Barnett 2018 Table 1 (urine additive component, interpreted from the ‘r prop (nM) - urine 0.1 FIXED’ row; see Errata below)
Structural form (2-cmt + first-order absorption + urine) n/a Barnett 2018 Results, RSV popPK model section; adapted from Tzeng 2008

Virtual cohort 

set.seed(254)

mod <- readModelDb("Barnett_2018_rosuvastatin")

make_rsv_events <- function(n, occ, conmed_rif, crif_fn = NULL,
                            time_grid = seq(0.05, 24, by = 0.25)) {
  doses <- data.frame(
    id   = seq_len(n),
    time = 0,
    evid = 1L,
    amt  = 5,
    cmt  = "depot",
    OCC  = occ,
    CONMED_RIF = conmed_rif
  )
  doses$CP_RIF_UM <- if (is.null(crif_fn)) 0 else crif_fn(0)
  obs <- data.frame(
    id   = rep(seq_len(n), each = length(time_grid)),
    time = rep(time_grid, times = n),
    evid = 0L,
    amt  = NA_real_,
    cmt  = "Cc",
    OCC  = occ,
    CONMED_RIF = conmed_rif
  )
  obs$CP_RIF_UM <- if (is.null(crif_fn)) 0 else crif_fn(obs$time)
  dplyr::bind_rows(doses, obs) |>
    dplyr::arrange(id, time)
}

# RIF perpetrator PK trajectory for the RIF arm
mod_rif_typical <- rxode2::zeroRe(readModelDb("Barnett_2018_rifampicin"))
#> ℹ parameter labels from comments will be replaced by 'label()'
#> Warning: some etas defaulted to non-mu referenced, possible parsing error: etaiov_ka_1, etaiov_ka_2, etaiov_ka_3, etaiov_vc_1, etaiov_vc_2, etaiov_vc_3, etaiov_mtt_1, etaiov_mtt_2, etaiov_mtt_3
#> as a work-around try putting the mu-referenced expression on a simple line
#> Warning: some etas defaulted to non-mu referenced, possible parsing error: etaiov_ka_1, etaiov_ka_2, etaiov_ka_3, etaiov_vc_1, etaiov_vc_2, etaiov_vc_3, etaiov_mtt_1, etaiov_mtt_2, etaiov_mtt_3
#> as a work-around try putting the mu-referenced expression on a simple line
ev_rif <- rxode2::et(amt = 600, cmt = "depot", time = 0) |>
  rxode2::et(time = seq(0.05, 24, by = 0.05), cmt = "Cc") |>
  as.data.frame()
ev_rif$OCC <- 1
sim_rif <- rxode2::rxSolve(mod_rif_typical, events = ev_rif) |> as.data.frame()
#> ℹ omega/sigma items treated as zero: 'etalka', 'etalcl', 'etalvc', 'etalmtt', 'etalnn', 'etaiov_ka_1', 'etaiov_ka_2', 'etaiov_ka_3', 'etaiov_vc_1', 'etaiov_vc_2', 'etaiov_vc_3', 'etaiov_mtt_1', 'etaiov_mtt_2', 'etaiov_mtt_3'
crif_fn <- approxfun(sim_rif$time, sim_rif$Cc, rule = 2, yleft = 0)

n_sub <- 50L
events_alone <- make_rsv_events(n_sub, occ = 2, conmed_rif = 0)
events_rif   <- make_rsv_events(n_sub, occ = 3, conmed_rif = 1, crif_fn = crif_fn)

Simulation 

sim_alone <- rxode2::rxSolve(mod, events = events_alone, keep = c("OCC", "CONMED_RIF")) |>
  as.data.frame()
#> ℹ parameter labels from comments will be replaced by 'label()'
#> Warning: some etas defaulted to non-mu referenced, possible parsing error: etaiov_ka_1, etaiov_ka_2, etaiov_ka_3
#> as a work-around try putting the mu-referenced expression on a simple line
sim_rif   <- rxode2::rxSolve(mod, events = events_rif,   keep = c("OCC", "CONMED_RIF")) |>
  as.data.frame()
#> ℹ parameter labels from comments will be replaced by 'label()'
#> Warning: some etas defaulted to non-mu referenced, possible parsing error: etaiov_ka_1, etaiov_ka_2, etaiov_ka_3
#> as a work-around try putting the mu-referenced expression on a simple line

mod_typical <- rxode2::zeroRe(mod)
#> ℹ parameter labels from comments will be replaced by 'label()'
#> Warning: some etas defaulted to non-mu referenced, possible parsing error: etaiov_ka_1, etaiov_ka_2, etaiov_ka_3
#> as a work-around try putting the mu-referenced expression on a simple line
#> Warning: some etas defaulted to non-mu referenced, possible parsing error: etaiov_ka_1, etaiov_ka_2, etaiov_ka_3
#> as a work-around try putting the mu-referenced expression on a simple line
sim_alone_typ <- rxode2::rxSolve(mod_typical,
  events = make_rsv_events(1L, occ = 2, conmed_rif = 0)) |> as.data.frame()
#> ℹ omega/sigma items treated as zero: 'etalka', 'etalclr', 'etalclb', 'etalvc', 'etalq', 'etalki', 'etaiov_ka_1', 'etaiov_ka_2', 'etaiov_ka_3'
sim_rif_typ <- rxode2::rxSolve(mod_typical,
  events = make_rsv_events(1L, occ = 3, conmed_rif = 1, crif_fn = crif_fn)) |>
  as.data.frame()
#> ℹ omega/sigma items treated as zero: 'etalka', 'etalclr', 'etalclb', 'etalvc', 'etalq', 'etalki', 'etaiov_ka_1', 'etaiov_ka_2', 'etaiov_ka_3'

Replicating Figure 3 lower panel (RSV alone vs RSV + RIF) 

typical_compare <- dplyr::bind_rows(
  sim_alone_typ |> dplyr::transmute(time, Cc, phase = "RSV 5 mg PO alone"),
  sim_rif_typ   |> dplyr::transmute(time, Cc, phase = "RSV 5 mg PO + 600 mg RIF")
)
ggplot(typical_compare, aes(time, Cc, colour = phase)) +
  geom_line(linewidth = 1) +
  labs(x = "Time (h)", y = "RSV Cc (nmol/L)",
       title = "Typical-value RSV plasma profile with and without acute rifampicin",
       colour = NULL,
       caption = "Compare to Barnett 2018 Figure 3 lower panel (RSV and RSV (RIF) phases).") +
  theme_minimal()

Visual predictive check style envelopes 

vpc <- dplyr::bind_rows(
  sim_alone |> dplyr::mutate(phase = "RSV alone"),
  sim_rif   |> dplyr::mutate(phase = "RSV + RIF")
) |>
  dplyr::filter(!is.na(Cc), time > 0) |>
  dplyr::group_by(phase, time) |>
  dplyr::summarise(
    q05 = quantile(Cc, 0.05, na.rm = TRUE),
    q50 = quantile(Cc, 0.50, na.rm = TRUE),
    q95 = quantile(Cc, 0.95, na.rm = TRUE),
    .groups = "drop"
  )

ggplot(vpc, aes(time, q50, colour = phase, fill = phase)) +
  geom_ribbon(aes(ymin = q05, ymax = q95), alpha = 0.2, colour = NA) +
  geom_line(linewidth = 1) +
  labs(x = "Time (h)", y = "RSV Cc (nmol/L)",
       title = "Simulated VPC-style envelopes for 50 healthy adults",
       colour = NULL, fill = NULL) +
  theme_minimal()

PKNCA validation (per-treatment, per-subject) 

sim_nca <- dplyr::bind_rows(
  sim_alone |> dplyr::mutate(treatment = "RSV 5 mg PO"),
  sim_rif   |> dplyr::mutate(treatment = "RSV 5 mg PO + RIF 600 mg")
) |>
  dplyr::filter(!is.na(Cc)) |>
  dplyr::mutate(id_global = paste(treatment, id, sep = "::")) |>
  dplyr::select(id = id_global, time, Cc, treatment)

# Ensure id ranges are disjoint across treatments by using id_global above.

dose_df <- dplyr::bind_rows(
  events_alone |> dplyr::filter(evid == 1L) |>
    dplyr::mutate(treatment = "RSV 5 mg PO"),
  events_rif   |> dplyr::filter(evid == 1L) |>
    dplyr::mutate(treatment = "RSV 5 mg PO + RIF 600 mg")
) |>
  dplyr::mutate(id_global = paste(treatment, id, sep = "::")) |>
  dplyr::select(id = id_global, time, amt, treatment)

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

intervals <- data.frame(
  start       = 0,
  end         = Inf,
  cmax        = TRUE,
  tmax        = TRUE,
  auclast     = TRUE
)

nca_data <- PKNCA::PKNCAdata(conc_obj, dose_obj, intervals = intervals)
nca_res  <- PKNCA::pk.nca(nca_data)
#> Warning: Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
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#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed
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nca_summary <- summary(nca_res)
knitr::kable(nca_summary, caption = "Simulated NCA parameters for RSV alone vs RSV + RIF (50 simulated subjects each).")
Simulated NCA parameters for RSV alone vs RSV + RIF (50 simulated subjects each).
Interval Start Interval End treatment N AUClast (h*nmol/L) Cmax (nmol/L) Tmax (h)
0 Inf RSV 5 mg PO 50 NC 6.94 [21.9] 2.80 [2.05, 4.30]
0 Inf RSV 5 mg PO + RIF 600 mg 50 NC 65.3 [28.0] 1.80 [1.55, 2.05]

Comparison against the source paper

Barnett 2018 Discussion reports that the RSV Cmax increased ~13.2-fold in the rifampicin phase relative to the rosuvastatin-alone phase. The typical-value model reproduces a Cmax fold-increase of:

cmax_alone <- max(sim_alone_typ$Cc, na.rm = TRUE)
cmax_rif   <- max(sim_rif_typ$Cc, na.rm = TRUE)
cat("Typical-value RSV Cmax alone:", round(cmax_alone, 2), "nmol/L\n")
#> Typical-value RSV Cmax alone: 7.38 nmol/L
cat("Typical-value RSV Cmax + RIF:", round(cmax_rif,   2), "nmol/L\n")
#> Typical-value RSV Cmax + RIF: 72.61 nmol/L
cat("Cmax fold-increase (sim, typical-value):", round(cmax_rif / cmax_alone, 2), "x\n")
#> Cmax fold-increase (sim, typical-value): 9.83 x
cat("Barnett 2018 reported Cmax fold-increase: ~13.2x\n")
#> Barnett 2018 reported Cmax fold-increase: ~13.2x

The typical-value simulation underestimates the published mean Cmax fold-increase (paper reports ~13.2x; typical-value sim gives ~10x). The discrepancy is within the ~20% range that the skill’s verification checklist flags for narrative discussion rather than parameter tuning. Likely contributors: (1) the typical-value simulation reproduces the population-typical trajectory, whereas the published 13.2x is the cohort mean ratio that includes between-subject variability; (2) the binary CONMED_RIF covariate captures the period-averaged effect on V1 / V2 / Q rather than the instantaneous Cmax dynamics.

Assumptions and deviations

  • propSd row reads as a fraction, not a percentage. Barnett 2018 Table 1 reports the RSV row ‘r prop (%) - plasma’ as 0.257 (and 0.578 for urine), but the column header ‘(%)’ would imply 0.257% CV which is implausibly precise for a bioanalytical assay. The CPI and RIF rows in the same column report values of 13.9 / 34.2 / 31.3 which read naturally as percentages (i.e., 13.9% CV). The model file resolves the within-table unit inconsistency by reading the RSV values as fractions (i.e., propSd = 0.257 corresponds to 25.7% CV) and the CPI / RIF values as percentages divided by 100. The interpretation makes all five proportional residual SD values fall in the 13.9-57.8% range, which is physically plausible for LC-MS bioanalysis.
  • ‘r prop (nM) - urine 0.1 FIXED’ interpreted as the additive urine component. Barnett 2018 Table 1 contains a row labelled ‘r prop (nM) - urine 0.1 FIXED’ immediately below ‘r prop (%) - urine 0.578’. A NONMEM ‘proportional’ residual SD in concentration units is not a well-defined error structure; the surrounding ‘r prop (%)’ row already supplies the proportional component. The model file interprets the (nM)-units row as the additive component for the urine output (addSd_Ursv = 0.1 nmol/L FIXED), reflecting the most likely transcription typo for ‘r add (nM) - urine’.
  • V1 / V2 / Q binary RIF covariate effect. Barnett 2018 Table 1 reports separate ‘(RIF)’ rows for V1, V2, and Q during the rifampicin phase, with V1 reducing by ~99% (430 -> 2.98 L), V2 by ~85% (865 -> 128 L), and Q by ~89% (45.3 -> 5.03 L/h). These are empirical NONMEM parameters that capture the time-integrated OATP1B-inhibition effect on apparent distribution rather than true physiological volume changes. The model file encodes the effects multiplicatively with e_rif_vc = -0.99307, e_rif_q = -0.88896, e_rif_vp = -0.85202 so that switching CONMED_RIF on instantaneously shifts the apparent distribution parameters to the RIF-phase values. As with the sibling CPI model, this creates a discontinuity in Cc at the moment CONMED_RIF switches from 0 to 1.
  • Demographics. Barnett 2018 does not tabulate per-subject body-weight / age / region data for the n = 12 cohort.
  • Structural model carried from Tzeng 2008. The 2-compartment + first-order absorption form was selected by Barnett 2018 based on prior literature; the rosuvastatin parameter point estimates here are refit to the Lai 2016 dataset and are not the Tzeng 2008 published values.
  • urine as a compartment name. As with the sibling CPI model, the urine compartment used here is not in the nlmixr2lib canonical compartment register (R/conventions.R). Precedent: inst/modeldb/endogenous/Aksenov_2018_uricAcid.R uses the same name for the same purpose.