1. Innovative Research for a Sustainable Future www.epa.gov/research mg kg -1 d -1 * For more information please send e-mail to: vallero.daneil@epa.gov AbstractOngoing Research for High Throughput Screening Works Cited Mapping ExpoCast onto ToxCast Vallero, D. 1*, Egeghy, P. 1, Buckley, T. 1, Wambaugh, J. 2, Isaacs, K. 1, Goldsmith, R. 1, Özkaynak, H. 1, Mitchell. J. 3 1 National Exposure Research Laboratory, U.S. EPA, Office of Research & Development, Research Triangle Park, NC; 2 National Center for Computational Toxicology, U.S. EPA, Office of Research & Development, Research Triangle Park, NC; 3 Michigan State University, Biosystems and Agricultural Engineering Department, East Lansing, MI The U.S. Environmental Protection Agency (EPA) established the program known as ToxCast to develop ways to predict potential toxicity of chemicals and to develop a cost- effective approach for prioritizing the thousands of chemicals that require toxicity testing. ToxCast uses advanced science tools to help to understand how normal human body processes are impacted by exposures to chemicals and to help determine which exposures are most likely to lead to adverse health effects. Such toxicity information must be integrated with exposure information to prioritize chemicals awaiting risk assessment (Egeghy et al., 2011). Thus, EPA is integrating its exposure-based chemical prioritization program, ExpoCast, with ToxCast to prioritize chemicals. EPA is advancing the state of the science with statistical and graphical extrapolations from data-rich to data-poor chemicals (Mitchell et al., 2013; Wambaugh et al., 2013; Mitchell-Blackwood et al., 2013), and is currently investigating the use of dust and other media as indicators of near-field exposure, application of high tier models (e.g., SHEDS-Lite and SHEDS-Fugacity models) to high throughput screening, dose ranking approaches, and “big data” techniques (including informatics and data mining) combined with product use and activity data (e.g. material safety data sheets, consumer self-reported data and the publicly available consumer product databases). Goal To develop innovative approaches, tools, and enhanced scientific understanding to rapidly and effectively prioritize chemicals for further safety testing based on exposure Approach Develop a framework for chemical exposures Align current databases, data collection protocols, and prioritization tools within the framework Evaluate and compare prioritization tools Identify crucial gaps in our knowledge, data, approaches, and tools Conduct scientific research to fill these gaps Collaborate with ToxCast developers Conduct scientific research to fill these gaps in collaboration with ToxCast researchers Exposure-Based Prioritization ToxCast + ExpoCast For each bioactivity identified by ToxCast, plot points (●) indicate the human doses predicted to cause similar serum concentrations for 231 chemicals by Wetmore et al. (2012) blue vertical barsExpoCast 95% credible intervals for chemicals with only far field sources of exposure (e.g., industrial pollution, shown with blue vertical bars) do not overlap with doses needed for ToxCast bioactivity., red vertical barsFor a handful of chemicals the ExpoCast credible intervals for chemicals with some near field sources (e.g. consumer use, red vertical bars) do overlap – these are the priority chemicals for further research Bioactivity Ex Priori is a simplified, quantitative visual dashboard that prioritizes chemicals based on internalized dose Makes use of data from various inputs to provide rank-ordered internalized dose metric Under development and showing promise for near field exposure scenarios, both direct and indirect Looks at all chemical space simultaneously (not chemical by chemical) Adaptation of High-Tier Exposure Models to High- Throughput Screening of Chemicals in Consumer Products SHEDS-Lite is being developed by EPA; the model integrates source-to-concentration-to-exposure-to-dose components using a probabilistic methodology Based on higher-tier approaches (e.g., SHEDS), it provides low-to- mid tier prediction of exposures for CSS chemicals Model reduction was via sensitivity and other analyses; model still includes key determinants of exposure while reducing input burden, increasing speed, and widening the chemical domain of applicability Model is parameterized using consumer product ingredient, chemical property, and consumer product usage information from a variety of EPA efforts Contains source-to-concentration (fugacity) and direct exposure modules for modeling both indirect and direct near-field exposures to chemicals in consumer products Predicts exposures and doses for various cohort groups by pathway in mg kg -1 day -1 Use of “Big Data” for Defining Consumer Product Use Profiles by State (Based on 2004-2012 Google Trends Infoveillance) Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy. 1.Mitchell, J., Collier, Z.A., Pabon, N., Egeghy, P., Cohen Hubal, E.A., Linkov, I. and Vallero, D.A. A Decision Analytic Approach to Exposure-Based Chemical Prioritization. PLOS One. Volume 8 | Issue 8 | e70911. 2.Wambaugh, J.F., Setzer, R.W., Reif, D.M., Gangwal, S., Mitchell-Blackwood, J. Arnot, J.A., Joliet, O., Frame, A., Rabinowitz, J.R., Knudsen, T.B., Judson, R.S., Egeghy, P., Vallero, D.A., and Cohen Hubal, E.A. (2013). High Throughput Models for Exposure-Based Chemical Prioritization in the ExpoCast Project. Environmental Science & Technology. (June 2013) doi:10.1021/es400482g. 3.Mitchell-Blackwood, J., Arnot, J. Jolliet, O., Egeghy, P., Georgopolous, P., Isukapalli, S., Wambaugh, J., Cohen-Hubal and Vallero, D. (2013). Comparison of models to prioritize chemicals based on exposure potential. Science of the Total Environment. Elsevier BV, Amsterdam, Netherlands, 458:555-567, 2013. 4.Wetmore, B. A., J. F. Wambaugh, S. S. Ferguson, M. A. Sochaski, D. M. Rotroff, K. Freeman, H. J. Clewell Iii, D. J. Dix, M. E. Andersen, K. A. Houck, B. Allen, R. S. Judson, R. Singh, R. J. Kavlock, A. M. Richard, And R. S. Thomas. Integration of Dosimetry, Exposure and High-Throughput Screening Data in Chemical Toxicity Assessment. Toxicological Sciences. Oxford University Press, Cary, NC, 125(1):157-174, (2012). 5.Gangwal, S., Reif, D., Mosher, S., Egeghy, P.P., Wambaugh, J.F., Judson, R. and Cohen-Hubal, E.A (2012). Incorporating exposure information into the toxicological prioritization index decision support framework. Science of the Total Environment:316-325. 6.Egeghy, P. Vallero, D and Cohen-Hubal, E. (2011). Exposure-based prioritization of chemicals for risk assessment. Environmental Science & Policy. 14(8):950-964. A variety of approaches are being considered ranging from a qualitative approach to semi- quantitative ToxPi scoring approach (Gangwal et al.,2012) to more sophisticated quantitative approaches, whereby overlapping distributions of hazard and exposure distributions are evaluated. Where, E =personal exposure during time period from t 1 to t 2 C(t) =concentration at interface, at t. Physico- chemical properties Pathways In vitro assaysExposure Mapping Exposure onto ToxPi Current adaptation of a higher tier model, i.e. SHEDS-Lite, for high throughput. Courtesy: R. Judson Complementing hazard information to account for activities, “function QSARs,” product use and other exposure attributes Predicted Rank Observed State Rank Aggregated Normalized Rank by State of Google Trends Search Volumes Associated with Consumer Products Model Evaluation: Comparing Field Study Consumer Use Data with Internet Search Data CATEGORYCORRELATION Personal Care0.59 Automotive0.48 Home Maintenance0.42 Landscape Yard0.27 Model Evaluation: Comparing State-Level Search Data with Time Spent in Related Activities in American Time Use Survey (ATUS) Rank