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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.

In recent years, a great deal of research has been conducted to identify genetic polymorphisms. One focus has been to characterize variability in metabolic enzyme systems that could impact internal doses of pharmaceuticals or environmental pollutants. Methods are needed for using this metabolic information to estimate the resulting variability in tissue doses associated with chemical exposure. We demonstrate here the use of physiologically based pharmacokinetic (PBPK) modeling in combination with Monte Carlo analysis to incorporate information on polymorphisms into the analysis of toxicokinetic variability. Warfarin and parathion were used as case studies to demonstrate this approach. Our results suggest that polymorphisms in the PON1 gene, that give rise to allelic variants of paraoxonase, which is involved in the metabolism of paraoxon (a metabolite of parathion), make only a minor contribution to the overall variability in paraoxon tissue dose, while polymorphisms in the CYP2C9 gene, which gives rise to allelic variants of the major metabolic enzyme for warfarin, account for a significant portion of the overall variability in (S)-warfarin tissue dose. These analyses were used to estimate chemical-specific adjustment factors (CSAFs) for the human variability in toxicokinetics for both parathion and warfarin. Implications of alternatives in the calculation of CSAFs are explored. Key decision points for applying the PBPK-Monte Carlo approach to evaluate toxicokinetic variability for other chemicals are also discussed.

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A Bayesian network model was developed to integrate diverse types of data to conduct an exposure-dose-response assessment for benzene-induced acute myeloid leukemia (AML). The network approach was used to evaluate and compare individual biomarkers and quantitatively link the biomarkers along the exposure-disease continuum. The network was used to perform the biomarker-based dose-response analysis, and various other approaches to the dose-response analysis were conducted for comparison. The network-derived benchmark concentration was approximately an order of magnitude lower than that from the usual exposure concentration versus response approach, which suggests that the presence of more information in the low-dose region (where changes in biomarkers are detectable but effects on AML mortality are not) helps inform the description of the AML response at lower exposures. This work provides a quantitative approach for linking changes in biomarkers of effect both to exposure information and to changes in disease response. Such linkage can provide a scientifically valid point of departure that incorporates precursor dose-response information without being dependent on the difficult issue of a definition of adversity for precursors.