MCMC output files for: Quantitative characterization of population-wide tissue- and metabolite-specific variability in perchloroethylene toxicokinetics in male mice

Quantification of inter-individual variability is a continuing challenge
in risk assessment, particularly for compounds with complex metabolism and
multi-organ toxicity. Toxicokinetic variability for perchloroethylene
(perc) was previously characterized across three mouse strains and in one
mouse strain with various degrees of liver steatosis. To further
characterize the role of genetic variability in toxicokinetics of perc, we
applied Bayesian population physiologically-based pharmacokinetic (PBPK)
modeling to the data on perc and metabolites in blood/plasma and tissues
of male mice from 45 inbred strains from the Collaborative Cross (CC)
mouse population. After identifying the most influential PBPK parameters
based on global sensitivity analysis, we fit the model with a hierarchical
Bayesian population analysis using Markov chain Monte Carlo simulation. We
found that the data from three commonly used strains were not
representative of the full range of variability in perc and metabolite
blood/plasma and tissue concentrations across the CC population. Using
inter-strain variability as a surrogate for human inter-individual
variability, we calculated dose-dependent, chemical-, and tissue-specific
toxicokinetic variability factors (TKVFs) as candidate science-based
replacements for the default uncertainty factor for human toxicokinetic
variability of 100.5. We found that TKVFs for glutathione conjugation
metabolites of perc showed the greatest variability, often exceeding the
default, whereas those for oxidative metabolites and perc itself were
generally less than the default. Overall, we demonstrate how a combination
of a population-based mouse model such as the CC with Bayesian population
PBPK modeling can reduce uncertainty associated with toxicokinetic human
variability by deriving the chemical-specific adjustment factors needed to
increase accuracy and precision in quantitative risk assessment.