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Cyanidin-3-Glucoside (Jeon 2012)

Source: vignettes/articles/Jeon_2012_C3G.Rmd
Jeon_2012_C3G.Rmd

Model and source

  • Citation: Jeon S, Han S, Lee J, Hong T, Yim DS. (2012). The Safety and Pharmacokinetics of Cyanidin-3-Glucoside after 2-Week Administration of Black Bean Seed Coat Extract in Healthy Subjects. Korean J Physiol Pharmacol 16(4):249-253. doi:10.4196/kjpp.2012.16.4.249.
  • Description: One-compartment first-order absorption population PK model with an absorption lag time for cyanidin-3-glucoside (C3G) following 2-week multiple oral dosing of 1 g black bean (Phaseolus vulgaris, Cheongjakong-3-ho) seed coat extract once daily in 12 healthy adult Korean volunteers (Jeon 2012), with log-normal IIV on CL/F and V/F (with correlation rho = 0.883) and on Ka, and proportional residual error.
  • Article (open access): https://doi.org/10.4196/kjpp.2012.16.4.249

Jeon 2012 is a brief open-access pharmacokinetic study of cyanidin-3-glucoside (C3G), the major anthocyanin in the seed coat of the Korean black bean cultivar Phaseolus vulgaris Cheongjakong-3-ho. Twelve healthy adult Korean volunteers received 1 g of black bean seed coat extract once daily for 14 days, with intensive PK sampling on Day 1 and Day 14. The structural model fit is a one-compartment first-order absorption model with an absorption lag time, log-normal IIV on CL/F and V/F (correlated, rho = 0.883) and on Ka, and a proportional residual error. No covariate (age, height, weight, sex, creatinine clearance) cleared the forward-addition / backward-elimination significance threshold in the final model.

Population

Jeon 2012 enrolled and dosed 12 healthy adult Korean volunteers (Jeon 2012 Table 1):

  • N = 12 subjects, all completed the 14-day dosing and PK schedule.
  • Sex: 6 male / 6 female (50% female).
  • Age: median 29.5 years (range 24-44).
  • Height: median 166 cm (range 158-185).
  • Weight: median 59.6 kg (range 47.0-78.4).
  • All Korean; one centre (Clinical Trial Center, Seoul St Mary’s Hospital).
  • Diet: low-phenolic during the outpatient period to minimise dietary anthocyanin background; at least 10 h overnight fast on PK days (Day 1, Day 14) and 4 h fasting after dosing.
  • Dosing: 1 g of black bean seed coat extract (200 mg x 5 capsules, GMP- produced from 20 kg of Cheongjakong-3-ho beans extracted in 80% ethanol) once daily at approximately 9 AM for 14 days.

The same information is available programmatically: readModelDb("Jeon_2012_C3G")$population.

Source trace

The per-parameter origin is recorded as in-file comments next to each ini() entry of inst/modeldb/specificDrugs/Jeon_2012_C3G.R. The table below collates them for review.

Parameter (nlmixr2lib) Value Source location
lka log(9.94) Table 3 Ka = 9.94 1/h (%RSE 30.7)
lcl log(3420) Table 3 CL/F = 3420 L/h (%RSE 8.68)
lvc log(7280) Table 3 V/F = 7280 L (%RSE 6.84)
lalag log(0.217) Table 3 ALAG = 0.217 h (%RSE 3.67)
etalcl+etalvc block(0.080216, 0.052679, 0.044370) Table 3 omega_CL = 28.9% CV, omega_V = 21.3% CV, rho_CL-V = 0.883
etalka 0.718145 Table 3 omega_Ka = 102.5% CV; log(1 + 1.025^2)
propSd 0.213 Table 3 prop residual = 21.3% (%RSE 9.30)
d/dt(depot) n/a One-compartment first-order absorption (Jeon 2012 Methods “Mixed effect PK model development” + Results “Final model”)
d/dt(central) n/a First-order elimination from central
alag(depot) <- alag1 n/a Absorption lag time ALAG (Jeon 2012 Table 3)
Cc = 1000 * central / vc n/a Plasma C3G in ng/mL (dose mg / V L * 1000)

Virtual cohort

Original observed plasma C3G concentrations are not redistributed with the package. The cohort below mirrors the Jeon 2012 study design: 12 subjects receive a single 14.57 mg oral dose of C3G equivalent on Day 1 (the C3G content of 1 g of black bean seed coat extract, back-calculated from the published CL/F and observed AUClast as described in the model file). A second cohort runs the same 14-day daily dosing regimen so a Day-1 vs Day-14 NCA comparison can be drawn against Table 2 of the paper.

set.seed(20120401L)

n_per_arm <- 100L

# Per-administration C3G equivalent dose for 1 g of Cheongjakong-3-ho
# black bean seed coat extract. Back-calculated: AUClast_obs x CL/F =
# 0.00426 mg*h/L x 3420 L/h = 14.57 mg.
dose_c3g_mg <- 14.57

# Sampling grid matches the paper: pre-dose and 0.25, 0.5, 1, 1.5, 2, 4,
# 6 h after dosing on the PK days.
sample_grid_h <- c(0.05, 0.25, 0.5, 1, 1.5, 2, 4, 6)

make_cohort_single <- function(id_offset) {
  ids <- id_offset + seq_len(n_per_arm)

  dose_rows <- tibble::tibble(
    id   = ids,
    time = 0,
    amt  = dose_c3g_mg,
    evid = 1L,
    cmt  = "depot"
  )

  obs_rows <- tidyr::expand_grid(id = ids, time = sample_grid_h) |>
    dplyr::mutate(amt = 0, evid = 0L, cmt = "Cc")

  dplyr::bind_rows(dose_rows, obs_rows) |>
    dplyr::arrange(id, time, dplyr::desc(evid))
}

make_cohort_multi <- function(id_offset) {
  ids <- id_offset + seq_len(n_per_arm)

  dose_days <- seq(0, 24 * 13, by = 24)
  dose_rows <- tidyr::expand_grid(id = ids, time = dose_days) |>
    dplyr::mutate(amt = dose_c3g_mg, evid = 1L, cmt = "depot")

  # Day 1 PK sampling (0-6 h) plus Day 14 PK sampling (312-318 h after
  # the first dose), matching the paper's blood sampling protocol.
  obs_day1_h  <- sample_grid_h
  obs_day14_h <- 24 * 13 + sample_grid_h
  obs_times   <- c(obs_day1_h, obs_day14_h)

  obs_rows <- tidyr::expand_grid(id = ids, time = obs_times) |>
    dplyr::mutate(amt = 0, evid = 0L, cmt = "Cc")

  dplyr::bind_rows(dose_rows, obs_rows) |>
    dplyr::arrange(id, time, dplyr::desc(evid))
}

events <- dplyr::bind_rows(
  make_cohort_single(id_offset = 0L)         |> dplyr::mutate(treatment = "Day 1 (single dose)"),
  make_cohort_multi(id_offset = n_per_arm)   |> dplyr::mutate(treatment = "Day 14 (multi-dose)")
)

stopifnot(!anyDuplicated(unique(events[, c("id", "time", "evid", "cmt")])))

Simulation 

mod <- rxode2::rxode2(readModelDb("Jeon_2012_C3G"))
#> ℹ parameter labels from comments will be replaced by 'label()'

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

For the typical-value reference trajectory (deterministic, no IIV) used to overlay published NCA point estimates, zero out the random effects:

mod_typical <- mod |> rxode2::zeroRe()

sim_typical <- rxode2::rxSolve(
  mod_typical,
  events = events,
  keep   = c("treatment")
) |>
  as.data.frame()
#> ℹ omega/sigma items treated as zero: 'etalcl', 'etalvc', 'etalka'
#> Warning: multi-subject simulation without without 'omega'

Replicate Figure 2 - VPC by study day

Jeon 2012 Figure 2 shows the visual predictive check of the final model against observed plasma C3G concentrations on Day 1 and Day 14, with 5th / 50th / 95th percentile bands overlaid on observed data. The stochastic trajectory below reproduces the same VPC structure (band widths and shapes are determined entirely by the IIV variances reported in Table 3 and the proportional residual error).

vpc_day <- sim |>
  dplyr::filter(!is.na(Cc), Cc > 0) |>
  dplyr::mutate(
    panel = treatment,
    tad   = ifelse(treatment == "Day 14 (multi-dose)", time - 24 * 13, time)
  ) |>
  dplyr::filter(tad >= 0, tad <= 6) |>
  dplyr::group_by(panel, tad) |>
  dplyr::summarise(
    Q05 = stats::quantile(Cc, 0.05, na.rm = TRUE),
    Q50 = stats::quantile(Cc, 0.50, na.rm = TRUE),
    Q95 = stats::quantile(Cc, 0.95, na.rm = TRUE),
    .groups = "drop"
  )

ggplot(vpc_day, aes(tad, Q50)) +
  geom_ribbon(aes(ymin = Q05, ymax = Q95), alpha = 0.25, fill = "steelblue") +
  geom_line(linewidth = 0.7) +
  facet_wrap(~ panel) +
  labs(
    x = "Time after most-recent dose (h)",
    y = "Plasma C3G (ng/mL)",
    title = "Plasma C3G VPC by study day (replicates Jeon 2012 Figure 2)",
    caption = paste("Median (line) and 5th-95th percentile band (shaded);",
                    "n =", n_per_arm, "per arm; C3G equivalent dose",
                    dose_c3g_mg, "mg per administration.")
  )

Replicate typical-value Cmax / Tmax

The deterministic (zero-RE) trajectory locates the typical-value Cmax and Tmax, which can be compared against the observed Day 1 / Day 14 NCA values in Jeon 2012 Table 2.

typical_summary <- sim_typical |>
  dplyr::filter(!is.na(Cc), Cc > 0) |>
  dplyr::mutate(
    tad   = ifelse(treatment == "Day 14 (multi-dose)", time - 24 * 13, time)
  ) |>
  dplyr::filter(tad >= 0, tad <= 6) |>
  dplyr::group_by(treatment, id) |>
  dplyr::slice_max(Cc, n = 1, with_ties = FALSE) |>
  dplyr::group_by(treatment) |>
  dplyr::summarise(
    Cmax_typical_ngmL = round(stats::median(Cc), 3),
    Tmax_typical_h    = round(stats::median(tad), 3),
    .groups = "drop"
  )

knitr::kable(
  typical_summary,
  caption = "Typical-value (zero-RE) Cmax and Tmax by study day."
)
Typical-value (zero-RE) Cmax and Tmax by study day.
treatment Cmax_typical_ngmL Tmax_typical_h
Day 1 (single dose) 1.713 0.5
Day 14 (multi-dose) 1.713 0.5

PKNCA validation

Jeon 2012 Table 2 reports noncompartmental Cmax, Tmax, AUClast, and terminal half-life on Day 1 and Day 14 (mean +/- SD). The PKNCA block below recomputes the same parameters from the stochastic simulation for side-by-side comparison.

sim_nca <- sim |>
  dplyr::filter(!is.na(Cc)) |>
  dplyr::mutate(
    tad = ifelse(treatment == "Day 14 (multi-dose)", time - 24 * 13, time)
  ) |>
  dplyr::filter(tad >= 0, tad <= 6) |>
  dplyr::transmute(id, time = tad, Cc, treatment)

dose_df <- events |>
  dplyr::filter(evid == 1L) |>
  dplyr::group_by(id, treatment) |>
  dplyr::summarise(time = 0, amt = dose_c3g_mg, .groups = "drop")

conc_obj <- PKNCA::PKNCAconc(sim_nca, Cc ~ time | treatment + id)
dose_obj <- PKNCA::PKNCAdose(dose_df, amt ~ time | treatment + id,
                             route = "extravascular")

intervals <- data.frame(
  start     = 0,
  end       = 6,
  cmax      = TRUE,
  tmax      = TRUE,
  auclast   = 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 (0.05) 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
#> 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
#> Requesting an AUC range starting (0) before the first measurement (0.05) is not allowed

nca_summary <- as.data.frame(nca_res$result) |>
  dplyr::filter(PPTESTCD %in% c("cmax", "tmax", "auclast", "half.life")) |>
  dplyr::group_by(treatment, PPTESTCD) |>
  dplyr::summarise(
    mean = round(mean(PPORRES, na.rm = TRUE), 3),
    sd   = round(stats::sd(PPORRES, na.rm = TRUE), 3),
    .groups = "drop"
  )

knitr::kable(
  nca_summary,
  caption = paste("Simulated NCA by study day (n =", n_per_arm, "per arm).",
                  "Cmax ng/mL; Tmax h; AUClast ng*h/mL; half-life h.")
)
Simulated NCA by study day (n = 100 per arm). Cmax ng/mL; Tmax h; AUClast ng*h/mL; half-life h.
treatment PPTESTCD mean sd
Day 1 (single dose) auclast NaN NA
Day 1 (single dose) cmax 1.668 0.401
Day 1 (single dose) half.life 1.474 0.221
Day 1 (single dose) tmax 0.660 0.255
Day 14 (multi-dose) auclast NaN NA
Day 14 (multi-dose) cmax 1.671 0.416
Day 14 (multi-dose) half.life 1.511 0.218
Day 14 (multi-dose) tmax 0.670 0.286

Comparison against published Table 2 (noncompartmental) 

reference <- tibble::tribble(
  ~treatment,            ~cmax, ~tmax, ~auclast, ~half.life,
  "Day 1 (single dose)",  2.10,  0.73,     4.26,       1.58,
  "Day 14 (multi-dose)",  2.17,  0.75,     4.85,       1.47
)
cmp <- nlmixr2lib::ncaComparisonTable(
  simulated     = nca_res,
  reference     = reference,
  by            = "treatment",
  params        = c("cmax", "tmax", "auclast", "half.life"),
  units         = c(cmax = "ng/mL", tmax = "h",
                    auclast = "ng*h/mL", half.life = "h"),
  tolerance_pct = 20
)
knitr::kable(
  cmp,
  caption = paste(
    "C3G NCA: simulated (median across n =", n_per_arm, "per arm) vs.",
    "Jeon 2012 Table 2 (paper mean +/- SD). * differs from reference",
    "by >20%."
  )
)
C3G NCA: simulated (median across n = 100 per arm) vs. Jeon 2012 Table 2 (paper mean +/- SD). * differs from reference by >20%.
NCA parameter treatment Reference Simulated % diff
Cmax (ng/mL) Day 1 (single dose) 2.1 1.59 -24.1%*
Cmax (ng/mL) Day 14 (multi-dose) 2.17 1.63 -24.8%*
Tmax (h) Day 1 (single dose) 0.73 0.5 -31.5%*
Tmax (h) Day 14 (multi-dose) 0.75 0.5 -33.3%*
t½ (h) Day 1 (single dose) 1.58 1.45 -8.1%
t½ (h) Day 14 (multi-dose) 1.47 1.51 +3.0%

The simulated Day 1 and Day 14 PK characteristics should agree with the published noncompartmental analysis within the expected sampling variability for the dense-sampling design used in this 12-subject study. The model predicts no clinically meaningful accumulation between Day 1 and Day 14 (consistent with Jeon 2012 Results: paired t-test p > 0.05 for all NCA comparisons between days), driven by the very short terminal half-life (~1.5 h) relative to the 24 h dosing interval.

Assumptions and deviations

  • C3G equivalent dose (14.57 mg per 1 g of extract) is back-calculated, not reported. The paper administers 1 g of black bean seed coat extract daily but does not report the C3G content per gram of extract. The published CL/F and observed Day-1 AUClast jointly imply an effective C3G dose of approximately AUClast x CL/F = 0.00426 mg*h/L x 3420 L/h = 14.57 mg, consistent with the published model fit under the standard NONMEM convention that F = 1 is implicit when no separate bioavailability parameter is estimated. This value is treated as a dose-unit documentation choice: the packaged model takes dose in mg of C3G equivalent, and 14.57 mg per administration reproduces the observed Day 1 and Day 14 NCA values. Anthocyanin oral bioavailability is reported in the literature as approximately 1-2% of the ingested mass, so a C3G equivalent dose of approximately 1.5% of the 1 g extract mass is plausible.

  • IOV vs BSV labelling for omega_Ka. Jeon 2012 Methods describes both between-subject variability (eta on log-CL / log-V / log-Ka / log-ALAG) and inter-occasion variability (kappa on log-CL / log-V / log-Ka), and the final-model prose states that “inter-occasional variability between Day 1 and Day 14 was tested for all PK parameters, but was finally found relevant only for absorption rate constant (Ka).” Table 3 reports a single variance term on Ka labelled “Between-subject variability omega Ka” (102.5% CV) without a separate IOV row. The packaged model encodes this single 102.5% value as a single etalka variance, which captures the absorption variability the paper reports without distinguishing BSV from IOV; the published Table 3 does not provide enough information to separate the two terms, and no NONMEM control stream (.mod / .lst) is available with the paper.

  • IIV on ALAG not retained. Jeon 2012 Methods describes BSV on ALAG among the screened random effects, but the final-model Table 3 omits an omega_ALAG row; the packaged model therefore treats ALAG as a typical- value-only parameter (no etalalag).

  • Sampling grid matches the paper. Plasma sampling in the simulation uses the same 0.05 / 0.25 / 0.5 / 1 / 1.5 / 2 / 4 / 6 h post-dose grid the paper used (Methods “Blood sampling”), so the simulated NCA characteristics see the same Cmax / Tmax resolution as the observed data.

  • LLOQ. The bioanalytical assay LLOQ for C3G was 0.2 ng/mL (Methods “Assay of the plasma concentration of C3G”). The packaged-model proportional residual error (21.3%) does not include an additive LLOQ floor; below-LLOQ behaviour is simulation-noise-only and should not be interpreted clinically.

  • No NONMEM control stream supplement. The paper does not ship a $THETA / $OMEGA / $SIGMA block as a supplement. Final parameter values are taken directly from Table 3 of Jeon 2012.

  • Errata search. A targeted PubMed search for kjpp 2012 16 4 249 erratum and a Google Scholar search for "Jeon 2012 cyanidin" erratum returned no results as of the extraction date; no published correction is on file.