Suchergebnisse

The development and application of appropriate physiologically based pharmacokinetic (PBPK) models of chemical contaminants will provide a rational basis for risk assessment extrapolation. Trichloroethylene (TCE) is a widespread contaminant found in soil, groundwater, and the atmosphere. Exposures to TCE and its metabolites have been found to be carcinogenic in rodents. In this study, a PBPK model for TCE and its major metabolite, trichloroacetic acid (TCA), is developed for humans. The model parameters, estimated from the relevant published literature on human exposures to TCE and its metabolites, are described. Key parameters describing the metabolism of TCE and the kinetics of TCA were estimated by optimization. The optimization was accomplished by simultaneously matching model predictions to observations of TCE concentrations in blood and exhaled breath, TCA plasma concentrations, and urinary TCA excretion from five published studies. The optimized human PBPK model provides an excellent description of TCE and TCA kinetics. The predictions were especially good for TCA plasma concentrations following repeated TCE inhalation, an exposure scenario similar to that occurring in the workplace. The human PBPK model can be used to estimate dose metrics resulting from TCE exposures and is therefore useful when considering the estimation of human health risks associated with such exposures.

Trichloroethylene (TCE) is a widespread environmental pollutant. TCE is classified as a rodent carcinogen by the U.S. Environmental Protection Agency (EPA). Using the rodent cancer bioassay findings and estimates of metabolized dose, the EPA has estimated lifetime exposure cancer risks for humans that ingest TCE in drinking water or inhale TCE. In this study, a physiologically based pharmacokinetic (PB‐PK) model for mice was used to simulate selected gavage and inhalation bioassays with TCE. Plausible dose‐metrics thought to be linked with the mechanism of action for TCE carcinogenesis were selected. These dose‐metrics, adjusted to reflect an average amount per day for a lifetime, were metabolism of TCE (AMET, mg/kg/day) and systemic concentration of TCA (AUCTCA, mg/L/day). These dose‐metrics were then used in a linearized multistage model to estimate AMET and AUCTCA values that correspond to liver cancer risks of 1 in 1 million in mice. A human PB‐PK model for TCE was then used to predict TCE concentrations in drinking water and air that would provide AMET and AUCTCA values equal to the predicted mice AMET and AUCTCA values that correspond to liver cancer risks of 1 in 1 million. For the dose‐metrics, AMET and AUCTCA, the TCE concentrations in air were 10.0 and 0.1 ppb TCE (continuous exposure), respectively, and in water, 7 and 4 μg TCE/L, respectively.

AbstractHuman variability is a very important factor considered in human health risk assessment for protecting sensitive populations from chemical exposure. Traditionally, to account for this variability, an interhuman uncertainty factor is applied to lower the exposure limit. However, using a fixed uncertainty factor rather than probabilistically accounting for human variability can hardly support probabilistic risk assessment advocated by a number of researchers; new methods are needed to probabilistically quantify human population variability. We propose a Bayesian hierarchical model to quantify variability among different populations. This approach jointly characterizes the distribution of risk at background exposure and the sensitivity of response to exposure, which are commonly represented by model parameters. We demonstrate, through both an application to real data and a simulation study, that using the proposed hierarchical structure adequately characterizes variability across different populations.

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 μg/kgg/day, and that there are few, if any, exposures greater than the ATSDR MRL of 0.3 μgg/kgg/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.

Twenty‐three chemicals were selected for comparison of the carcinogenic potencies estimated from epidemiological data to those estimated from animal carcinogenesis bioassays. The chemicals were all those for which reasonably strong evidence of carcinogenicity could be found in humans or animals and for which suitable data could be obtained for quantifying carcinogenic potencies in both humans and animals. Many alternative methods of analyzing the bioassay data were investigated. Almost all of the methods yielded potency estimates that were highly correlated with potencies estimated from epidemiological data; correlations were highly statistically significant (p < 0.001), with the corresponding correlation coefficients ranging as high as 0.9. These findings provide support for the general use of animal data to evaluate carcinogenic potential in humans and also for the use of animal data to quantify human risk.