invitro_mc: Monte Carlo for in vitro toxicokinetic parameters including...

In httk: High-Throughput Toxicokinetics

Monte Carlo for in vitro toxicokinetic parameters including uncertainty and variability.

Description

Given a CAS in the HTTK data set, a virtual population from HTTK-Pop, some user specifications on the assumed distributions of Funbound.plasma and Clint, draw "individual" values of Funbound.plasma and Clint from those distributions. The methodology for this function was developed and described by Wambaugh et al. (2019) (doi: 10.1093/toxsci/kfz205).

Usage

invitro_mc(
  parameters.dt = NULL,
  samples,
  fup.meas.mc = TRUE,
  fup.pop.mc = TRUE,
  clint.meas.mc = TRUE,
  clint.pop.mc = TRUE,
  fup.meas.cv = 0.4,
  clint.meas.cv = 0.3,
  fup.pop.cv = 0.3,
  clint.pop.cv = 0.3,
  poormetab = TRUE,
  fup.lod = 0.01,
  fup.censored.dist = FALSE,
  adjusted.Funbound.plasma = TRUE,
  adjusted.Clint = TRUE,
  clint.pvalue.threshold = 0.05,
  minimum.Funbound.plasma = 1e-04
)

Arguments

parameters.dt	A data table of physiological and chemical-specific parameters
samples	The number of samples to draw.
fup.meas.mc	Logical – should we perform measurment (uncertainty) Monte Carlo for Funbound.plasma values (Default TRUE). If FALSE, the user may choose to provide columns for "unadjusted.Funbound.plasma" or "fup.mean" from their own methods.
fup.pop.mc	Logical – should we perform population (variability) Monte Carlo for Funbound.plasma values (Default TRUE)
clint.meas.mc	Logical – should we perform measurment (uncertainty) Monte Carlo for Clint values (Default TRUE)
clint.pop.mc	Logical – should we perform population (variability) Monte Carlo for Clint values (Default TRUE)
fup.meas.cv	Coefficient of variation of distribution of measured Funbound.plasma values.
clint.meas.cv	Coefficient of variation of distribution of measured Clint values.
fup.pop.cv	Coefficient of variation of distribution of population Funbound.plasma values.
clint.pop.cv	Coefficient of variation of distribution of population Clint values.
poormetab	Logical. Whether to include poor metabolizers in the Clint distribution or not.
fup.lod	The average limit of detection for Funbound.plasma, below which distribution will be censored if fup.censored.dist is TRUE. Default 0.01.
fup.censored.dist	Logical. Whether to draw Funbound.plasma from a censored distribution or not.
adjusted.Funbound.plasma	Uses Pearce et al. (2017) lipid binding adjustment for Funbound.plasma when set to TRUE (Default).
adjusted.Clint	Uses Kilford et al. (2008) hepatocyte incubation binding adjustment for Clint when set to TRUE (Default).
clint.pvalue.threshold	Hepatic clearance for chemicals where the in vitro clearance assay result has a p-values greater than the threshold are set to zero.
minimum.Funbound.plasma	Monte Carlo draws less than this value are set equal to this value (default is 0.0001 – half the lowest measured Fup in our dataset).
parameters	A list of chemical-specific model parameters containing at least Funbound.plasma, Clint, and Fhep.assay.correction.

Details

The Monte Carlo methods used here were recently updated and described by Breen et al. (submitted).

Value

A data.table with three columns: Funbound.plasma and Clint, containing the sampled values, and Fhep.assay.correction, containing the value for fraction unbound in hepatocyte assay.

Author(s)

Caroline Ring and John Wambaugh

References

Wambaugh, John F., et al. "Assessing Toxicokinetic Uncertainty and Variability in Risk Prioritization." Toxicological Sciences (2019).

Kilford, Peter J., et al. "Hepatocellular binding of drugs: correction for unbound fraction in hepatocyte incubations using microsomal binding or drug lipophilicity data." Drug Metabolism and Disposition 36.7 (2008): 1194-1197.

Pearce, Robert G., et al. "Evaluation and calibration of high-throughput predictions of chemical distribution to tissues." Journal of pharmacokinetics and pharmacodynamics 44.6 (2017): 549-565.

Examples

#Simply generate a virtual population of 100 individuals,
#using the direct-resampling method
set.seed(42)
# Pull mean vchemical=specific values:
chem.props <- parameterize_pbtk(chem.name="bisphenolaf")
# Convert to data.table with one row per sample:
parameters.dt <- monte_carlo(chem.props,samples=100)
# Use httk-pop to generate a population:
pop <- httkpop_generate(method='direct resampling', nsamp=100)
# Overwrite parameters specified by httk-pop:
parameters.dt[,names(pop):=pop]
# Vary in vitro parameters:
parameters.dt <- invitro_mc(parameters.dt,samples=100)

httk documentation built on March 7, 2023, 7:26 p.m.