Skip to contents

Rifampicin (Barnett 2018)

Source: vignettes/articles/Barnett_2018_rifampicin.Rmd
Barnett_2018_rifampicin.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 rifampicin popPK model adapted from Wilkins JJ et al. Antimicrob Agents Chemother. 2008;52(6):2138-2148; see modellib(‘Wilkins_2008_rifampicin’) for the original transit-chain form.
  • Description: One-compartment population PK model with Wilkins/Savic transit-compartment absorption for a single 600 mg oral dose of rifampicin in healthy adult males (Barnett 2018), refit from the Wilkins 2008 structural form. The rifampicin model is one of three popPK models developed jointly in Barnett 2018 to support OATP1B drug-drug-interaction modeling with coproporphyrin I and rosuvastatin; the rifampicin compartmental output is the time-varying CRIF input that drives the competitive OATP1B inhibition term in the sibling coproporphyrin I and rosuvastatin models.
  • Article: https://doi.org/10.1002/cpt.983

Population

Barnett 2018 refit the rifampicin popPK model to plasma rifampicin data from the Lai et al. 2016 clinical drug-drug-interaction (DDI) study in 12 healthy adult male volunteers (SLCO1B1 c.521 T>C wildtype only; no OATP1B15 / 15 carriers). Each subject received a single 600 mg oral rifampicin dose at the start of occasion 1 (rifampicin alone) and at the start of occasion 3 (rifampicin co-administered with 5 mg rosuvastatin), with a 7-day washout between occasions and rifampicin omitted on occasion 2. Per-subject demographic distributions (age range, body weight range, race/ethnicity, region) were not tabulated in Barnett 2018; the Methods section cites the source clinical study (Lai Y et al. Pharmacol Res Perspect 2016) for those baseline characteristics. The 276 rifampicin plasma samples used for modeling purposes were collected over the 24 h post-dose window on occasion 1 (n = 144) and occasion 3 (n = 132).

The same demographic context is available programmatically via readModelDb("Barnett_2018_rifampicin")$population.

Source trace

The per-parameter origin is recorded as an in-file comment next to each ini() entry in inst/modeldb/specificDrugs/Barnett_2018_rifampicin.R. The table below collects the most important entries in one place for review.

Equation / parameter Value Source location
lka log(2.09) Barnett 2018 Table 1, RIF row ‘ka (1/h)’
lcl log(3.97) Barnett 2018 Table 1, RIF row ‘CL (L/h)’
lvc log(24.7) Barnett 2018 Table 1, RIF row ‘V (L)’
lmtt log(0.74) Barnett 2018 Table 1, RIF row ‘MTT (h)’
lnn log(8.63) Barnett 2018 Table 1, RIF row ‘n’
IIV ka / CL / V / MTT / n 46% / 30.7% / 19.5% / 69.1% / 24.3% CV Barnett 2018 Table 1, IIV column
IOV ka / V / MTT 49.2% / 6.6% / 47.6% CV Barnett 2018 Table 1, IOV column
propSd 0.313 (= 31.3%) Barnett 2018 Table 1, RIF row ‘r prop (%)’
addSd 0.01 umol/L FIXED Barnett 2018 Table 1, RIF row ‘r add (uM)’
Structural form (1-cmt + transit absorption) n/a Barnett 2018 Results, Rifampicin popPK model section; structural model adapted from Wilkins JJ et al. AAC 2008;52(6):2138-2148.

The transit-chain input rate uses the Savic 2007 / Wilkins 2008 analytical form via rxode2’s transit(n, mtt) function; see the model file’s model() block for the exact implementation. Barnett 2018 Table 1 footnote ‘a’ notes that the NONMEM covariance step for this fit failed, so per-parameter standard errors are not reported by the source.

Virtual cohort

The packaged model is parameterised for a typical adult male healthy volunteer. Stochastic VPC-style simulations use the published IIV and IOV variances to reproduce between-subject and between-occasion spread; deterministic typical-value simulations zero out the random effects via rxode2::zeroRe().

set.seed(254)

mod <- readModelDb("Barnett_2018_rifampicin")

n_sub <- 50L

make_cohort_events <- function(n, time_grid = seq(0.1, 24, by = 0.1), occ = 1) {
  doses <- data.frame(
    id   = seq_len(n),
    time = 0,
    evid = 1L,
    amt  = 600,
    cmt  = "depot",
    OCC  = occ
  )
  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
  )
  dplyr::bind_rows(doses, obs) |>
    dplyr::arrange(id, time)
}

events <- make_cohort_events(n_sub, occ = 1)

Simulation 

sim <- rxode2::rxSolve(mod, events = events, keep = c("OCC"))
#> 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
sim_df <- as.data.frame(sim)

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, 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
sim_typical <- rxode2::rxSolve(mod_typical, events = make_cohort_events(1L, occ = 1)) |>
  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'

Typical-value rifampicin plasma profile 

ggplot(sim_typical, aes(time, Cc)) +
  geom_line(linewidth = 1) +
  labs(x = "Time (h)", y = "Rifampicin Cc (umol/L)",
       title = "Single 600 mg oral rifampicin in a typical adult male",
       caption = "Typical-value (no IIV / no residual error) prediction; Barnett 2018 popPK parameters.") +
  theme_minimal()

Visual predictive check (50 simulated subjects) 

vpc <- sim_df |>
  dplyr::filter(!is.na(Cc), time > 0) |>
  dplyr::group_by(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)) +
  geom_ribbon(aes(ymin = q05, ymax = q95), alpha = 0.25) +
  geom_line(linewidth = 1) +
  labs(x = "Time (h)", y = "Rifampicin Cc (umol/L)",
       title = "Simulated VPC-style envelope for 50 healthy adults",
       caption = "Median + 90% PI from a stochastic simulation. Compare visually to Barnett 2018 Supplementary Figure S7.") +
  theme_minimal()

PKNCA validation 

sim_nca <- sim_df |>
  dplyr::filter(!is.na(Cc)) |>
  dplyr::select(id, time, Cc, OCC)
sim_nca$treatment <- "RIF 600 mg PO"

dose_df <- events |>
  dplyr::filter(evid == 1) |>
  dplyr::select(id, time, amt, OCC)
dose_df$treatment <- "RIF 600 mg PO"

conc_obj <- PKNCA::PKNCAconc(sim_nca, Cc ~ time | treatment + id,
                             concu = "umol/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,
  aucinf.obs  = TRUE,
  half.life   = 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 (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed
#> Requesting an AUC range starting (0) before the first measurement (0.1) is not allowed

nca_summary <- summary(nca_res)
knitr::kable(nca_summary, caption = "Simulated NCA parameters for the 50-subject virtual cohort (single 600 mg PO RIF).")
Simulated NCA parameters for the 50-subject virtual cohort (single 600 mg PO RIF).
Interval Start Interval End treatment N Cmax (umol/L) Tmax (h) Half-life (h) AUCinf,obs (h*umol/L)
0 Inf RIF 600 mg PO 50 22.8 [21.8] 2.50 [0.900, 5.80] 4.57 [1.88] NC

Comparison against the source paper

Barnett 2018 Table 1 does not tabulate observed NCA values for rifampicin; only the structural popPK estimates are reported, and the visual-predictive-check fit is shown in Supplementary Figure S7. A rough sanity comparison against externally published rifampicin PK in healthy adults (e.g., Acocella 1978, Burman 2001) places the expected Cmax after a single 600 mg oral dose in the 10-20 mg/L (12-24 umol/L) range and the half-life in the 2-5 h range; the simulated typical-value Cmax of ~24 umol/L and half-life of ~5 h fall within those expected envelopes.

Assumptions and deviations

  • No SE on per-parameter estimates. Barnett 2018 Table 1 footnote ‘a’ notes that the NONMEM covariance step for the rifampicin model failed; the point estimates were reported but per-parameter standard errors were not. The model file encodes IIV / IOV variances as if estimated and does not propagate parameter-estimation uncertainty.
  • IOV occasion structure. Barnett 2018 reports a single shared IOV variance per parameter (ka, V, MTT) across the rifampicin-dosing occasions. The model file encodes three OCC slots (OCC1, OCC2, OCC3) with the variance shared via NONMEM’s OMEGA BLOCK(1) SAME idiom; OCC2 (rosuvastatin-only period) is included for completeness even though no rifampicin data was collected on that occasion in the source study.
  • Demographics. Barnett 2018 does not tabulate per-subject body-weight / age / region data for the n = 12 cohort. The model file’s population$weight_range, population$age_range, etc. mark these as not extracted.
  • Structural model carried from Wilkins 2008. The 1-compartment + transit absorption form was selected by Barnett 2018 based on prior literature (Wilkins 2008); see modellib('Wilkins_2008_rifampicin') for the original DDMORE-curated implementation of that structural form. Barnett 2018 reports refit parameter point estimates, not the prior literature values.