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Wambaugh JF, Setzer RW, Reif DM, et al. High-throughput models for exposure-based chemical prioritization in the ExpoCast project. Environ Sci Technol. 2013;47(15):8479-88doi: 10.1021/es400482g.
Wambaugh, J. F., Setzer, R. W., Reif, D. M., Gangwal, S., Mitchell-Blackwood, J., Arnot, J. A., Joliet, O., Frame, A., Rabinowitz, J., Knudsen, T. B., Judson, R. S., Egeghy, P., Vallero, D., & Cohen Hubal, E. A. (2013). High-throughput models for exposure-based chemical prioritization in the ExpoCast project. Environmental science & technology, 47(15), 8479-88. https://doi.org/10.1021/es400482g
Wambaugh, John F, et al. "High-throughput models for exposure-based chemical prioritization in the ExpoCast project." Environmental science & technology vol. 47,15 (2013): 8479-88. doi: https://doi.org/10.1021/es400482g
Wambaugh JF, Setzer RW, Reif DM, Gangwal S, Mitchell-Blackwood J, Arnot JA, Joliet O, Frame A, Rabinowitz J, Knudsen TB, Judson RS, Egeghy P, Vallero D, Cohen Hubal EA. High-throughput models for exposure-based chemical prioritization in the ExpoCast project. Environ Sci Technol. 2013 Aug 06;47(15):8479-88. doi: 10.1021/es400482g. Epub 2013 Jul 11. PMID: 23758710.
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Environ Sci Technol. 2013 Aug 06;47(15):8479-88. doi: 10.1021/es400482g. Epub 2013 Jul 11.

High-throughput models for exposure-based chemical prioritization in the ExpoCast project.

Environmental science & technology

John F Wambaugh, R Woodrow Setzer, David M Reif, Sumit Gangwal, Jade Mitchell-Blackwood, Jon A Arnot, Olivier Joliet, Alicia Frame, James Rabinowitz, Thomas B Knudsen, Richard S Judson, Peter Egeghy, Daniel Vallero, Elaine A Cohen Hubal

Affiliations

  1. National Center for Computational Toxicology, United States Environmental Protection Agency , Research Triangle Park, North Carolina 27711, United States. [email protected]

PMID: 23758710 DOI: 10.1021/es400482g

Abstract

The United States Environmental Protection Agency (U.S. EPA) must characterize potential risks to human health and the environment associated with manufacture and use of thousands of chemicals. High-throughput screening (HTS) for biological activity allows the ToxCast research program to prioritize chemical inventories for potential hazard. Similar capabilities for estimating exposure potential would support rapid risk-based prioritization for chemicals with limited information; here, we propose a framework for high-throughput exposure assessment. To demonstrate application, an analysis was conducted that predicts human exposure potential for chemicals and estimates uncertainty in these predictions by comparison to biomonitoring data. We evaluated 1936 chemicals using far-field mass balance human exposure models (USEtox and RAIDAR) and an indicator for indoor and/or consumer use. These predictions were compared to exposures inferred by Bayesian analysis from urine concentrations for 82 chemicals reported in the National Health and Nutrition Examination Survey (NHANES). Joint regression on all factors provided a calibrated consensus prediction, the variance of which serves as an empirical determination of uncertainty for prioritization on absolute exposure potential. Information on use was found to be most predictive; generally, chemicals above the limit of detection in NHANES had consumer/indoor use. Coupled with hazard HTS, exposure HTS can place risk earlier in decision processes. High-priority chemicals become targets for further data collection.

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