Application of computational toxicological approaches in human health risk assessment. I. A tiered surrogate approach.

Hazard identification and dose-response assessment for chemicals of concern found in various environmental media are typically based on epidemiological and/or animal toxicity data. However, human health risk assessments are often requested for many compounds found at contaminated sites throughout the US that have limited or no available toxicity information from either humans or animals. To address this issue, recent efforts have focused on expanding the use of structure-activity relationships (SAR) approaches to identify appropriate surrogates and/or predict toxicological phenotype(s) and associated adverse effect levels. A tiered surrogate approach (i.e., decision tree) based on three main types of surrogates (structural, metabolic, and toxicity-like) has been developed. To select the final surrogate chemical and its surrogate toxicity value(s), a weight-of-evidence approach based on the proposed decision tree is applied. In addition, a case study with actual toxicity data serves as the evaluation to support our tiered surrogate approach. Future work will include case studies demonstrating the utility of the surrogate approach under different scenarios for data-poor chemicals. In conclusion, our surrogate approach provides a reasonable starting point for identifying potential toxic effects, target organs, and/or modes-of-action, and for selecting surrogate chemicals from which to derive either reference or risk values.

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

Bayesian population analysis of a harmonized physiologically based pharmacokinetic (PBPK) model for trichloroethylene (TCE) and its metabolites was performed. In the Bayesian framework, prior information about the PBPK model parameters is updated using experimental kinetic data to obtain posterior parameter estimates. Experimental kinetic data measured in mice, rats, and humans were available for this analysis, and the resulting posterior model predictions were in better agreement with the kinetic data than prior model predictions. Uncertainty in the prediction of the kinetics of TCE, trichloroacetic acid (TCA), and trichloroethanol (TCOH) was reduced, while the kinetics of other key metabolites dichloroacetic acid (DCA), chloral hydrate (CHL), and dichlorovinyl mercaptan (DCVSH) remain relatively uncertain due to sparse kinetic data for use in this analysis. To help focus future research to further reduce uncertainty in model predictions, a sensitivity analysis was conducted to help identify the parameters that have the greatest impact on various internal dose metric predictions. For application to a risk assessment for TCE, the model provides accurate estimates of TCE, TCA, and TCOH kinetics. This analysis provides an important step toward estimating uncertainty of dose-response relationships in noncancer and cancer risk assessment, improving the extrapolation of toxic TCE doses from experimental animals to humans.