Use of Markov Chain Monte Carlo Analysis with a Physiologically‐Based Pharmacokinetic Model of Methylmercury to Estimate Exposures in U.S. Women of Childbearing Age

Abstract

A Bayesian approach, implemented using Markov Chain Monte Carlo (MCMC) analysis, was applied with a physiologically-based pharmacokinetic (PBPK) model of methylmercury (MeHg) to evaluate the variability of MeHg exposure in women of childbearing age in the U.S. population. The analysis made use of the newly available National Health and Nutrition Survey (NHANES) blood and hair mercury concentration data for women of age 16-49 years (sample size, 1,582). Bayesian analysis was performed to estimate the population variability in MeHg exposure (daily ingestion rate) implied by the variation in blood and hair concentrations of mercury in the NHANES database. The measured variability in the NHANES blood and hair data represents the result of a process that includes interindividual variation in exposure to MeHg and interindividual variation in the pharmacokinetics (distribution, clearance) of MeHg. The PBPK model includes a number of pharmacokinetic parameters (e.g., tissue volumes, partition coefficients, rate constants for metabolism and elimination) that can vary from individual to individual within the subpopulation of interest. Using MCMC analysis, it was possible to combine prior distributions of the PBPK model parameters with the NHANES blood and hair data, as well as with kinetic data from controlled human exposures to MeHg, to derive posterior distributions that refine the estimates of both the population exposure distribution and the pharmacokinetic parameters. In general, based on the populations surveyed by NHANES, the results of the MCMC analysis indicate that a small fraction, less than 1%, of the U.S. population of women of childbearing age may have mercury exposures greater than the EPA RfD for MeHg of 0.1 microg/kg/day, and that there are few, if any, exposures greater than the ATSDR MRL of 0.3 microg/kg/day. The analysis also indicates that typical exposures may be greater than previously estimated from food consumption surveys, but that the variability in exposure within the population of U.S. women of childbearing age may be less than previously assumed.

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).…”

“…For example, through consumption of contaminated fish, the upper level of 95% CI of methylmercury (MeHg) and polychlorinated biphenyl (PCB) exposure of the overall population was of interest (Allen et al 2007;Gosselin et al 2006;Karmaus et al 2004). Using a series of MCMC runs to implement the posterior distributions of their PBTK model parameters in a stepwise fashion, Allen et al (2007) treated the posteriors obtained as priors to simulate population exposure distribution of MeHg intake from blood samples of the National Health and Nutrition Survey (NHANES) in the United States. Lyons et al (2008) similarly applied PBTK model with MCMC simulations to estimate population exposure of chloroform from NHANES data.…”

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.…”

Population Effects and Variability

“…As with any model, PBTK models can be run backwards (numerically at least) to reconstruct exposures from the TK measurements. That application, even if obvious, is a bit more recent (28).…”