Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. EPA, Research Triangle Park, NC, USA.
Department of Environmental Sciences & Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
J Expo Sci Environ Epidemiol. 2022 Nov;32(6):783-793. doi: 10.1038/s41370-022-00492-z. Epub 2022 Nov 8.
Estimates of exposure are critical to prioritize and assess chemicals based on risk posed to public health and the environment. The U.S. Environmental Protection Agency (EPA) is responsible for regulating thousands of chemicals in commerce and the environment for which exposure data are limited. Since 2009 the EPA's ExpoCast ("Exposure Forecasting") project has sought to develop the data, tools, and evaluation approaches required to generate rapid and scientifically defensible exposure predictions for the full universe of existing and proposed commercial chemicals. This review article aims to summarize issues in exposure science that have been addressed through initiatives affiliated with ExpoCast. ExpoCast research has generally focused on chemical exposure as a statistical systems problem intended to inform thousands of chemicals. The project exists as a companion to EPA's ToxCast ("Toxicity Forecasting") project which has used in vitro high-throughput screening technologies to characterize potential hazard posed by thousands of chemicals for which there are limited toxicity data. Rapid prediction of chemical exposures and in vitro-in vivo extrapolation (IVIVE) of ToxCast data allow for prioritization based upon risk of adverse outcomes due to environmental chemical exposure. ExpoCast has developed (1) integrated modeling approaches to reliably predict exposure and IVIVE dose, (2) highly efficient screening tools for chemical prioritization, (3) efficient and affordable tools for generating new exposure and dose data, and (4) easily accessible exposure databases. The development of new exposure models and databases along with the application of technologies like non-targeted analysis and machine learning have transformed exposure science for data-poor chemicals. By developing high-throughput tools for chemical exposure analytics and translating those tools into public health decisions ExpoCast research has served as a crucible for identifying and addressing exposure science knowledge gaps.
估算暴露量对于根据对公共健康和环境构成的风险优先考虑和评估化学品至关重要。美国环境保护署（EPA）负责监管商业和环境中数以千计的化学品，这些化学品的暴露数据有限。自 2009 年以来，EPA 的 ExpoCast（“暴露预测”）项目一直致力于开发数据、工具和评估方法，以对现有和拟议的商业化学品的全部范围生成快速且具有科学依据的暴露预测。本文旨在总结 ExpoCast 附属计划中解决的暴露科学问题。ExpoCast 研究通常侧重于将化学暴露作为一个统计系统问题，旨在为数千种化学物质提供信息。该项目是 EPA 的 ToxCast（“毒性预测”）项目的补充，后者使用体外高通量筛选技术来描述因缺乏毒性数据而受到限制的数千种化学物质的潜在危害。快速预测化学暴露和体外-体内外推（IVIVE）ToxCast 数据可以根据环境化学暴露不良后果的风险进行优先级排序。ExpoCast 已经开发了 (1) 可靠预测暴露和 IVIVE 剂量的综合建模方法，(2) 用于化学物质优先级排序的高效筛选工具，(3) 用于生成新暴露和剂量数据的高效且经济实惠的工具，以及 (4) 易于访问的暴露数据库。新暴露模型和数据库的开发以及非靶向分析和机器学习等技术的应用，改变了数据匮乏化学品的暴露科学。通过开发用于化学暴露分析的高通量工具，并将这些工具转化为公共卫生决策，ExpoCast 研究为识别和解决暴露科学知识差距提供了帮助。
