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 10^0.5. 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.

This database includes large output files of MCMC simulations in PBPK modeling in a manuscript titled 'Quantitative Characterization of Population-wide Tissue- and Metabolite-specific Variability in Perchloroethylene Toxicokinetics in Male Mice' submitted to Tox.Sci.

The hierarchical Bayesian population statistical model was applied for PBPK model calibration and estimation of model parameters and their uncertainty and variability as previously described (Bois 2000a, 2000b; Weihsueh A. Chiu et al. 2009; Hack 2006) and the updated conceptual representation described in  Dalaijamts et al. (2018, 2020) was used.

PBPK modeling in conjunction with statistical modeling, including the MC/MCMC simulations, was performed using GNU MCSim v.5.6.5 software (Bois 2009). The modeling processes are complex comprised with different steps and components.

Hierarchical Metropolis-Hastings algorithms within the Gibbs sampler (Gelfand and Adrian 1990; Geman and Geman 1984) was used in MCMC simulation. The MCMC simulation generates posterior parameter values at the population level, and parameter values for the strains used in the experiments.

PBPK model was applied to predict of population variability and uncertainty of internal dose metrics, including AUCs of perc, TCA, and GSH-conjugation metabolites and perc disposition dose metrics, at 36 h post oral exposures to single doses of 10, 100, and 1000 mg perc per kg b.w. using parameter posteriors of population-generated random strains

Stages of the PBPK modeling run on softwares as follow:

• MCMC simulations of preliminary and final models were run on MCSim v.5.6.5 software on Linux/Unix environment in Terra cluster in the High Performance Research Center, Texas A&M University.
• Global Sensitivity analysis was run on 'pksensi' version 1.2.0 R package developed by our lab.
• Evaluation of model fits were run on MCSim v.5.6.5 software on Windows.
• Dose metric predictions were run on MCSim under R on Windows.

Dataset of PBPK model outputs for perc in 48 mouse strains

The dataset contains MCMC simulation output files of posterior parameter values at the population level, and parameter values for the strains used in the experiments in each of 4 markov chains. MCMC output files include: