Bayesian population analysis of a harmonized physiologically based pharmacokinetic model of trichloroethylene and its metabolites

Self Cite

199

138

Background: In support of the Integrated Risk Information System (IRIS), the U.S. Environmental Protection Agency (EPA) completed a toxicological review of trichloroethylene (TCE) in September 2011, which was the result of an effort spanning > 20 years.Objectives: We summarized the key findings and scientific issues regarding the human health effects of TCE in the U.S. EPA’s toxicological review.Methods: In this assessment we synthesized and characterized thousands of epidemiologic, experimental animal, and mechanistic studies, and addressed several key scientific issues through modeling of TCE toxicokinetics, meta-analyses of epidemiologic studies, and analyses of mechanistic data.Discussion: Toxicokinetic modeling aided in characterizing the toxicological role of the complex metabolism and multiple metabolites of TCE. Meta-analyses of the epidemiologic data strongly supported the conclusions that TCE causes kidney cancer in humans and that TCE may also cause liver cancer and non-Hodgkin lymphoma. Mechanistic analyses support a key role for mutagenicity in TCE-induced kidney carcinogenicity. Recent evidence from studies in both humans and experimental animals point to the involvement of TCE exposure in autoimmune disease and hypersensitivity. Recent avian and in vitro mechanistic studies provided biological plausibility that TCE plays a role in developmental cardiac toxicity, the subject of substantial debate due to mixed results from epidemiologic and rodent studies.Conclusions: TCE is carcinogenic to humans by all routes of exposure and poses a potential human health hazard for noncancer toxicity to the central nervous system, kidney, liver, immune system, male reproductive system, and the developing embryo/fetus.

Section: Role Of Metabolism In Tce Toxicitymentioning

confidence: 99%

Human Health Effects of Trichloroethylene: Key Findings and Scientific Issues

Chiu

Jinot

Scott

et al. 2013

Self Cite

199

138

“…A comprehensive PBPK model by Hack et al (2006) was updated using the Bayesian framework for estimation and characterization of the PBPK model parameter uncertainties (Chiu et al 2009; Evans et al 2009). The updated model was used for the dose–response assessment in the U.S. EPA’s TCE toxicological review (U.S.…”

Section: Introductionmentioning

confidence: 99%

“…In the present study, we first showed that significant strain and time effects are observed in the metabolism of TCE. Next, we calibrated and further refined PBPK models of TCE (Evans et al 2009; Hack et al 2006). We added one-compartment models for S -(1,2-dichlorovinyl)glutathione (DCVG) and S -(1,2-dichlorovinyl)- l -cysteine (DCVC) and a two-compartment model for dichloroacetic acid (DCA).…”

Section: Introductionmentioning

confidence: 99%

Physiologically Based Pharmacokinetic (PBPK) Modeling of Interstrain Variability in Trichloroethylene Metabolism in the Mouse

Chiu

Campbell

Clewell

et al. 2014

Self Cite

Background: Quantitative estimation of toxicokinetic variability in the human population is a persistent challenge in risk assessment of environmental chemicals. Traditionally, interindividual differences in the population are accounted for by default assumptions or, in rare cases, are based on human toxicokinetic data.Objectives: We evaluated the utility of genetically diverse mouse strains for estimating toxicokinetic population variability for risk assessment, using trichloroethylene (TCE) metabolism as a case study.Methods: We used data on oxidative and glutathione conjugation metabolism of TCE in 16 inbred and 1 hybrid mouse strains to calibrate and extend existing physiologically based pharmacokinetic (PBPK) models. We added one-compartment models for glutathione metabolites and a two-compartment model for dichloroacetic acid (DCA). We used a Bayesian population analysis of interstrain variability to quantify variability in TCE metabolism.Results: Concentration–time profiles for TCE metabolism to oxidative and glutathione conjugation metabolites varied across strains. Median predictions for the metabolic flux through oxidation were less variable (5-fold range) than that through glutathione conjugation (10-fold range). For oxidative metabolites, median predictions of trichloroacetic acid production were less variable (2-fold range) than DCA production (5-fold range), although the uncertainty bounds for DCA exceeded the predicted variability.Conclusions: Population PBPK modeling of genetically diverse mouse strains can provide useful quantitative estimates of toxicokinetic population variability. When extrapolated to lower doses more relevant to environmental exposures, mouse population-derived variability estimates for TCE metabolism closely matched population variability estimates previously derived from human toxicokinetic studies with TCE, highlighting the utility of mouse interstrain metabolism studies for addressing toxicokinetic variability.Citation: Chiu WA, Campbell JL Jr, Clewell HJ III, Zhou YH, Wright FA, Guyton KZ, Rusyn I. 2014. Physiologically based pharmacokinetic (PBPK) modeling of interstrain variability in trichloroethylene metabolism in the mouse. Environ Health Perspect 122:456–463; http://dx.doi.org/10.1289/ehp.1307623

“…Both the method of Allen et al (2007) and the method presented here are based on the work of Gelman et al (1996), which was presented as a general method of parameter estimation in PBPK models. This method originally was applied to PBPK model calibration, and examples and reviews can be found in a number of articles (Bernillon and Bois, 2000; Covington et al 2007; Hack et al 2006; Jonsson 2001). …”

mentioning

confidence: 99%

Computational Toxicology of Chloroform: Reverse Dosimetry Using Bayesian Inference, Markov Chain Monte Carlo Simulation, and Human Biomonitoring Data

Lyons

Yang²,

Mayeno³

et al. 2008

BackgroundOne problem of interpreting population-based biomonitoring data is the reconstruction of corresponding external exposure in cases where no such data are available.ObjectivesWe demonstrate the use of a computational framework that integrates physiologically based pharmacokinetic (PBPK) modeling, Bayesian inference, and Markov chain Monte Carlo simulation to obtain a population estimate of environmental chloroform source concentrations consistent with human biomonitoring data. The biomonitoring data consist of chloroform blood concentrations measured as part of the Third National Health and Nutrition Examination Survey (NHANES III), and for which no corresponding exposure data were collected.MethodsWe used a combined PBPK and shower exposure model to consider several routes and sources of exposure: ingestion of tap water, inhalation of ambient household air, and inhalation and dermal absorption while showering. We determined posterior distributions for chloroform concentration in tap water and ambient household air using U.S. Environmental Protection Agency Total Exposure Assessment Methodology (TEAM) data as prior distributions for the Bayesian analysis.ResultsPosterior distributions for exposure indicate that 95% of the population represented by the NHANES III data had likely chloroform exposures ≤ 67 μg/L in tap water and ≤ 0.02 μg/L in ambient household air.ConclusionsOur results demonstrate the application of computer simulation to aid in the interpretation of human biomonitoring data in the context of the exposure–health evaluation–risk assessment continuum. These results should be considered as a demonstration of the method and can be improved with the addition of more detailed data.