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High-Throughput Simulation of Environmental Chemical Fate for Exposure Prioritization
Citation:
Wambaugh, J., D. Reif, S. GANGWAL, J. MITCHELL-BLACKWOOD, J. ARNOT, O. JOLLIET, R. Judson, T. Knudsen, P. Egeghy, J. Rabinowitz, D. Vallero, Woodrow Setzer, AND E. Hubal. High-Throughput Simulation of Environmental Chemical Fate for Exposure Prioritization. Presented at SOCIETY OF TOXICOLOGY Contemporary Concepts in Toxicology (CCT) at USEPA, RESEARCH TRIANGLE PARK, NC, May 08 - 11, 2012.
Impact/Purpose:
As we anticipate the need for new models to predict exposure due to consumer use of chemicals, this research provides a framework for the systematic evaluation of the predictions those and other future models.
Description:
The U.S. EPA must consider lists of hundreds to thousands of chemicals when allocating resources to identify risk in human populations and the environment. High-throughput screening assays to characterize biological activity in vitro have allowed the ToxCastTM program to identify potential chemical hazard, but without similar evaluation or estimation of potential for chemical exposure, high-throughput risk assessment for chemicals cannot be sufficiently addressed. Using two environmental fate and transport models (USEtox and RAIDAR) identified by the EPA Exposure-Based Prioritization Challenge, more than 1600 chemicals (including most ToxCast chemicals) have been ranked with respect to far-field exposure potential (e.g. partitioning into environmental media such as water or air). For most of these chemicals the descriptors necessary for modeling (i.e. model parameters) were not available and had to be predicted based upon structure using EPI Suite and QikProp. Any modeling effort should be guided by data in order to asses the uncertainty in extrapolating across data gaps. For this reason, the predictions of the two models were evaluated with respect to exposures inferred from the Centers for Disease Control National Health and Nutrition Examination Survey (NHANES). The NHANES data set has allowed an empirical determination of uncertainty, via Bayesian inference, which quantifies confidence intervals about the model predictions. Two lists of priority chemicals have been predicted – one for chemicals with the highest exposure potential per unit emission and a second list of compounds where some coarse measure of environmental release (e.g. production volume) was available. These chemicals, for which the predicted exposure exceeds even the broad uncertainties of the bulk of the chemicals, are identified as targets for further data collection, ideally including better release characterization, in home exposure assessment and biomonitoring. As we anticipate the need for new models to predict exposure due to consumer use of chemicals, this research provides a framework for the systematic evaluation of the predictions those and other future models. [This abstract does not necessarily reflect U.S. EPA policy].