A Quantitative ADME-base Tool for Exploring Human Exposure to Consumer Product Ingredients
Citation:
Egeghy, P., M. Rock- Goldsmith, C. Henning, T. Hong, H. Hubbard, AND D. Vallero. A Quantitative ADME-base Tool for Exploring Human Exposure to Consumer Product Ingredients. A&WMA's 109th Annual Conf. & Exhibition, New Orleans, LA, June 20 - 23, 2016.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use both within and outside of EPA.
Description:
Exposure to a wide range of chemicals through our daily habits and routines is ubiquitous and largely unavoidable within modern society. The potential for human exposure, however, has not been quantified for the vast majority of chemicals with wide commercial use. Creative advances in exposure science are needed to support efficient and effective evaluation and management of chemical risks, particularly for chemicals in consumer products. The U.S. Environmental Protection Agency Office of Research and Development is developing, or collaborating in the development of, scientifically-defensible methods for making quantitative or semi-quantitative exposure predictions. The Exposure Prioritization (Ex Priori) model is a simplified, quantitative visual dashboard that provides a rank-ordered internalized dose metric to simultaneously explore exposures across chemical space (not chemical by chemical). Diverse data streams are integrated within the interface such that different exposure scenarios for “individual,” “population,” or “professional” time-use profiles can be interchanged to tailor exposure and quantitatively explore multi-chemical signatures of exposure, internalized dose (uptake), body burden, and elimination. Ex Priori has been designed as an adaptable systems framework that synthesizes knowledge from various domains and is amenable to new knowledge/information. As such, it algorithmically captures the totality of exposure across pathways. It incorporates information on metabolic biotransformation processes (ADME) to estimate internal dose for model evaluation (biomonitoring data) or potential risk comparison. Novel modeling approaches, such as Ex Priori, for evaluating chemicals based on their potential for biologically relevant human exposures will facilitate the broadening of human health risk assessment in a high throughput manner. Disclaimer - The views expressed here are those of the authors and do not necessarily reflect the views or policies of the U.S. Environmental Protection Agency.
