Wambaugh John F, Bare Jane C, Carignan Courtney C, Dionisio Kathie L, Dodson Robin E, Jolliet Olivier, Liu Xiaoyu, Meyer David E, Newton Seth R, Phillips Katherine A, Price Paul S, Ring Caroline L, Shin Hyeong-Moo, Sobus Jon R, Tal Tamara, Ulrich Elin M, Vallero Daniel A, Wetmore Barbara A, Isaacs Kristin K
National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, NC 27711, USA.
National Risk Management Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, Cincinnati, OH 45268, USA.
Curr Opin Toxicol. 2019 Aug 29;15:76-92. doi: 10.1016/j.cotox.2019.07.001.
Chemical risk assessment relies on knowledge of hazard, the dose-response relationship, and exposure to characterize potential risks to public health and the environment. A chemical with minimal toxicity might pose a risk if exposures are extensive, repeated, and/or occurring during critical windows across the human life span. Exposure assessment involves understanding human activity, and this activity is confounded by interindividual variability that is both biological and behavioral. Exposures further vary between the general population and susceptible or occupationally exposed populations. Recent computational exposure efforts have tackled these problems through the creation of new tools and predictive models. These tools include machine learning to draw inferences from existing data and computer-enhanced screening analyses to generate new data. Mathematical models provide frameworks describing chemical exposure processes. These models can be statistically evaluated to establish rigorous confidence in their predictions. The computational exposure tools reviewed here are oriented toward 'high-throughput' application, that is, they are suitable for dealing with the thousands of chemicals in commerce with limited sources of chemical exposure information. These new tools and models are moving chemical exposure and risk assessment forward in the 21st century.
化学风险评估依赖于危害知识、剂量反应关系以及暴露情况，以表征对公众健康和环境的潜在风险。一种毒性极小的化学物质，如果暴露范围广泛、反复发生和/或在人类生命关键阶段出现，可能会构成风险。暴露评估涉及了解人类活动，而这种活动会受到个体间生物和行为差异的干扰。一般人群与易感人群或职业暴露人群之间的暴露情况也存在差异。最近的计算暴露研究通过创建新工具和预测模型来解决这些问题。这些工具包括利用机器学习从现有数据中进行推断，以及通过计算机增强筛选分析来生成新数据。数学模型提供了描述化学暴露过程的框架。这些模型可以进行统计评估，以建立对其预测的严格信心。这里所综述的计算暴露工具旨在实现“高通量”应用，也就是说，它们适用于处理商业上存在的数千种化学物质，而化学暴露信息来源有限。这些新工具和模型正在推动21世纪的化学暴露和风险评估向前发展。
