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High Throughput Heuristics for Prioritizing Human Exposure to Environmental Chemicals

机译：高吞吐量启发式算法，优先考虑人类对环境化学品的接触

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摘要

The risk posed to human health by any of the thousands of untested anthropogenic chemicals in our environment is a function of both the hazard presented by the chemical and the extent of exposure. However, many chemicals lack estimates of exposure intake, limiting the understanding of health risks. We aim to develop a rapid heuristic method to determine potential human exposure to chemicals for application to the thousands of chemicals with little or no exposure data. We used Bayesian methodology to infer ranges of exposure consistent with biomarkers identified in urine samples from the U.S. population by the National Health and Nutrition Examination Survey (NHANES). We performed linear regression on inferred exposure for demographic subsets of NHANES demarked by age, gender, and weight using chemical descriptors and use information from multiple databases and structure-based calculators. Five descriptors are capable of explaining roughly 50% of the variability in geometric means across 106 NHANES chemicals for all the demographic groups, including children aged 6-11. We use these descriptors to estimate human exposure to 7968 chemicals, the majority of which have no other quantitative exposure prediction. For thousands of chemicals with no other information, this approach allows forecasting of average exposure intake of environmental chemicals.
机译：我们环境中数千种未经测试的人为化学物质中任何一种对人类健康构成的风险，既取决于该化学物质带来的危害，又取决于其暴露程度。但是，许多化学物质缺乏估计的摄入量，限制了对健康风险的理解。我们旨在开发一种快速的启发式方法来确定潜在的人类对化学品的暴露程度，以将其应用于几乎没有或没有暴露数据的数千种化学品中。我们使用贝叶斯方法来推断与国家卫生和营养检查调查（NHANES）从美国人群尿液样本中鉴定出的生物标志物一致的暴露范围。我们使用化学描述符对NHANES的人口统计学子集的推断暴露量进行了线性回归，该暴露量由年龄，性别和体重表示，并使用了来自多个数据库和基于结构的计算器的信息。五个描述符能够解释所有人口群体（包括6至11岁的儿童）在106种NHANES化学品中几何平均值的大约50％的变化。我们使用这些描述符来估算人类对7968种化学物质的暴露，其中大多数没有其他定量暴露预测。对于没有其他信息的数千种化学药品，这种方法可以预测环境化学药品的平均摄入量。

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  • 来源
    《Environmental Science & Technology》 |2014年第21期|12760-12767|共8页
  • 作者

    John F. Wambaugh; Anran Wang; Kathie L. Dionisio; Alicia Frame; Peter Egeghy; Richard Judson; R. Woodrow Setzer;

  • 作者单位

    National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States;

    National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States,North Carolina State University, Department of Statistics, Raleigh, North Carolina 27695-8203, United States,Oak Ridge Institute for Science and Education Grantee, P.O. Box 117, Oak Ridge, Tennessee 37831-0117, United States;

    National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States;

    National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States,Oak Ridge Institute for Science and Education Grantee, P.O. Box 117, Oak Ridge, Tennessee 37831-0117, United States;

    National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States;

    National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States;

    National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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