DOI: 10.5061/dryad.brv15dv94
title: MCMC output files for: Quantitative characterization of population-wide tissue- and metabolite-specific variability in perchloroethylene toxicokinetics in male mice

description: 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.
description: 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.
description: <strong>Dataset of PBPK model outputs for perc in 48 mouse
 strains</strong> 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:  
 Group No. Title
 File
 name 
 Descriptions 
 Usage 
 1 MCMC simulations of 43 parameters for
 48 strains 
 perc.mouse.48strains.43p.mcmc.1.50L.r.out
 perc.mouse.48strains.43p.mcmc.2.50L.r.out
 perc.mouse.48strains.43p.mcmc.3.50L.r.out
 perc.mouse.48strains.43p.mcmc.4.50L.r.out 
 Each file of 4 markov chains is a text
 fi<br clear="all" style="page-break-before:always;"> le with one row
 for each run, and columns display iteration labels, and a column
 for each parameter for population mean, population variability, and each
 of 48 strains, and error variances of data likelihood, the sum of the
 logarithms of each parameter density given its parents' values, the
 logarithm of the data likelihood, and the sum of the previous two
 values, and rows of last half of 100k iterations. All
 values are log transformed posterior scaling values. 
 used for convergence parameter
 analysis 
 2 
 perc.mouse.48strains.43p.mcmc.random5000.out
 randomly selected 5k iterations from a
 combination of Group 1 files used for further model analysis and
 model predictions of toxicokinetics 
 3
 MCMC simulation output from previous
 model for 3 strains 
 perc.mouse.mcmc.3strains.random5000.out 
 randomly selected 5k iterations from
 files of 4 chains from the previous model by Dalaijamts et al.
 2018 used for comparison of parameter
 distributions 
 4 MCMC simulation outputs in 58
 parameters for 3 strains 
 perc.mouse.3strains.58p.mcmc.1.out
 perc.mouse.3strains.58p.mcmc.2.out
 perc.mouse.3strains.58p.mcmc.3.out
 perc.mouse.3strains.58p.mcmc.4.out Same as Group 1, but for 58 parameters
 and for 3 strains with rows of 100k iterations. 
 Parameter uncertainty distributions in
 eFAST test of global sensitivity analysis 
 5
 
 perc.mouse.3strains.58p.mcmc.random5000.out 
 randomly selected 5k iterations from a
 combination of Group 4 files 
 6
 Variability and uncertainty of AUCs of
 perc, TCA, and GSH-conjugation metabolites using PBPK model parameter
 posteriors of population-generated random strains 
 “perc.mouse.48strains.43p.random.10mg.1.AUC.set.out” × 100 iterations × 3 doses Each file contains rows of 500 MC simulation outputs and columns for parameter posteriors of population mean, population variability and random strains, and AUCs Used to derive a chemical-specific adjustment factor, so called a “toxicokinetic variability factor (TKVF)”, for each chemical in target organs at 3 different exposure doses. 7 Variability and uncertainty of perc disposition, including absorbed and excreted dose of perc, amount metabolized to produce TCA and GSH-conjugation metabolites using PBPK model parameter posteriors of population-generated random strains “perc.mouse.48strains.43p.random.10mg.1.met.set.out” × 100 iterations × 3 doses Each file contains rows of 500 MC simulation outputs and columns for parameter posteriors of population mean, population variability and random strains, and perc disposition dose metrics Used to derive a chemical-specific adjustment factor, so called a “toxicokinetic variability factor (TKVF)”, for each chemical in target organs at 3 different exposure doses.