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

Abstract

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.

Exposure estimation using repeated blood concentration measurements

“…By using a series of mass-balance differential equations to describe the processes of absorption, distribution, metabolism, and elimination after exposure to a chemical at certain concentrations, a physiologically based toxicokinetic (PBTK) model can be applied to relate exposure to target tissue dose for the relationship between biomarker concentrations and exposures. It is thus possible using a PBTK model to relate to environmental concentrations from biomarker measurements for exposure reconstruction, as has been demonstrated in several studies (Allen et al 2007;Clewell et al 2008;Georgopoulos et al 2009;Lyons et al 2008;Tan et al 2006Tan et al , 2007. PBTK (or PBPK) modeling has developed rapidly and been applied in quantitative risk assessment over the past two decades (Andersen et al 1987(Andersen et al , 1993Andersen 2003;Bailer and Dankovic 1997); moreover, it is being increasingly adopted by regulatory agencies in Europe and North America (Loizou et al 2008).…”

A Physiologically Based Pharmacokinetic Model for Capreomycin

“…Although traditionally used for environmental toxicants, PBPK models are increasingly used for the prediction of absorption, distribution, metabolism, and excretion (ADME) of various drugs (45)(46)(47), including antibiotics (9,10,16,32). A relatively recent advance in PBPK modeling has been the incorporation of approaches for accounting for interindividual variabilities in anatomy, physiology, biochemistry, and chemical exposure (1,6,8,12,36,39). Among other things, these approaches allow a rigorous incorporation of uncertainties, as well as predictions of chemical ADME in susceptible subpopulations.…”

The role of human biological monitoring in health risk assessment

“…For chemicals that have sufficiently known sources and exposure pathways, more elaborate dose-reconstruction scenarios can be formulated using probabilistic models (Lyons et al, 2008;Georgopoulos et al, 2009;Tan et al, 2012).…”

“…The detailed methodology involved in the probabilistic approaches are beyond the scope of this review article and the readers are directed to related publications in this area (Clewell et al, 2008;Tan and Clewell, 2010;Tan et al, 2012;Grulke et al, 2013;Côté et al, 2014). This approach has been used to derive exposure distributions to several VOCs such as chloroform bromodichloromethane, dibromochloromethane, and bromoform using NHANES III dataset (Tan et al, 2007;Lyons et al, 2008;McNally et al, 2012).…”