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High-Throughput Simulation of Environmental Chemical Fate for Exposure Prioritization (Annual Meeting of ISES)
Citation:
WAMBAUGH, J. F., D. REIF, S. GANGWAL, J. MITCHELLBLACKWOOD, J. ARNOT, O. JOLLIET, R. S. JUDSON, T. B. KNUDSEN, P. P. EGEGHY, J. R. RABINOWITZ, D. A. VALLERO, R. W. SETZER, AND E. A. COHEN-HUBAL. High-Throughput Simulation of Environmental Chemical Fate for Exposure Prioritization (Annual Meeting of ISES). Presented at International Society of Exposure Science 22nd Annual Meeting, Seattle, WA, October 28 - November 01, 2012.
Impact/Purpose:
Chemicals with high exposure potential were identified as those for which the lower confidence limit of predicted exposure exceeded the upper limit of the bulk. These chemicals are candidates for more in depth exposure assessment and data collection, ideally to include better release characterization, proximal exposure assessment, and possible future? biomonitoring. As we anticipate new approaches and models for predicting exposure resulting from consumer use of chemicals, this research provides a framework for the systematic evaluation of high-throughput exposure predictions.
Description:
The U.S. EPA must consider thousands of chemicals when allocating resources to assess risk in human populations and the environment. High-throughput screening assays to characterize biological activity in vitro are being implemented in the ToxCastTM program to rapidly characterize potential hazard for hundreds of chemicals. However, without similar evaluation of potential exposure, high-throughput assessment of chemicals cannot be sufficiently addressed. Using two physico-chemical properties-based environmental fate and transport models (USEtox and RAIDAR) more than 1600 chemicals have been ranked with respect to exposure from far-field sources. For most of these chemicals, model parameters were not available and had to be estimated from their structures using EPI Suite (v 4.10, EPA, Washington, DC) and Epik (v 2.2, Schrödinger, LLC, New York, NY). Resulting exposure predictions from the two fugacity models were evaluated with respect to exposures inferred from the Centers for Disease Control National Health and Nutrition Examination Survey (NHANES) biomonitoring data. Linear regression on both models together provided a calibrated consensus prediction, the variance of which served as an empirical determination of uncertainty; Bayesian analysis quantified confidence intervals about the model predictions. Chemicals with high exposure potential were identified as those for which the lower confidence limit of predicted exposure exceeded the upper limit of the bulk. These chemicals are candidates for more in depth exposure assessment and data collection, ideally to include better release characterization, proximal exposure assessment, and biomonitoring. As we anticipate new approaches and models for predicting exposure resulting from consumer use of chemicals, this research provides a framework for the systematic evaluation of high-throughput exposure predictions. [This abstract does not necessarily reflect U.S. EPA policy].