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Ex Priori: Exposure-based Prioritization across Chemical Space
Citation:
Goldsmith, M., D. Vallero, P. Egeghy, D. Chang, Chris Grulke, C. Tan, AND J. Wambaugh. Ex Priori: Exposure-based Prioritization across Chemical Space. Presented at ISES 2014, Cincinnati, OH, October 12 - 16, 2014.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA mission to protect human health and the environment. HEASD research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.
Description:
EPA's Exposure Prioritization (Ex Priori) is a simplified, quantitative visual dashboard that makes use of data from various inputs to provide rank-ordered internalized dose metric. This complements other high throughput screening by viewing exposures within all chemical space simultaneously (not chemical by chemical). Within modern society, exposure to a wide range of chemicals through our daily habits and routines is ubiquitous and largely unavoidable. The initial focus to estimate exposure to chemicals in products used in microenvironments (uE) necessitates a “systems” model to delineate data needs arising from numerous knowledge bases to integrate product formulations, purchasing and use activities, and human activities. This will indicate products likely to be in the uE and how people come into contact with chemicals in these products. Ex Priori will quantitatively extrapolate single-point estimates of both exposure and internal dose for multiple exposure scenarios, factors, products, and pathways in rank order by biological dose. To rank-order internalized dose from everyday consumer product exposures for a given individual profile, the approach uses multiple integrated data streams including (a) everyday product ingredient data; (b) pharmacokinetic factors, (c) consumer product category-specific “exposure factor surrogates” and (d) time/activity estimates (human factors). These different data streams integrated within an interface such that different exposure scenarios for “individual,” “population,” or “occupational” time-use profiles can be interchanged and quantitatively explored to prioritize tailored chemical exposure, and ultimately dose, allows us to estimate multi-chemical signatures of exposure, internalized dose (uptake), remaining dose or body burden and elimination. This overview shares lessons learned that translate into how the future of data-driven informatics-based approaches in support of chemical risk assessment can evolve.Disclaimer: The views expressed in presentation are those of the author and do not reflect the views or policies of the United States Environmental Protection Agency.