Risk-Based Chemical Ranking and Generating a Prioritized Human Exposome Database.
Risk-Based Chemical Ranking and Generating a Prioritized Human Exposome Database.
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DOI：
10.1289/ehp7722
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发表时间：
2021-04
影响因子：
10.4
通讯作者：
Fang M
中科院分区：
文献类型：
--
作者：
Zhao F;Li L;Chen Y;Huang Y;Keerthisinghe TP;Chow A;Dong T;Jia S;Xing S;Warth B;Huan T;Fang M
由于现代社会中化学品的普遍使用，人类越来越多地接触到数千种化学品，这些化学品构成了人类暴露的主要部分。建立一个全面的、基于风险的人体暴露数据库，将有助于人体暴露学研究的快速发展。此外，一旦一种外源性物质在暴露后发生生物转化，具有明显的半衰期，单独监测母体化合物可能无法反映实际的人体暴露情况。为了解决这些问题，需要一个全面的、风险优先的人类暴露数据库。我们的目标是建立一个全面的风险优先的人类暴露数据库，包括物理化学性质以及风险预测，并开发一个图形用户界面（GUI），能够在我们的数据库中搜索与化学品相关的内容。采用文本挖掘和数据库融合的方法，构建了一个综合的风险优先排序的人体暴露数据库。随后，将系统经验评估模型获得的暴露水平与毒性估计软件工具预测的毒性数据以及从ToxCast数据库计算的毒理学优先指数相结合，确定化学物质的优先级。使用迭代片段选择方法评估所有化学物质的生物转化半衰期，使用先前开发的BioTransformer机器学习方法预测生物转化产物。构建了人体化学物质暴露数据库，基于概率危害商对13441种化学物质进行了优先排序，基于风险指数对7770种化学物质进行了优先排序，并提供了代谢物预测生物转化代谢物数据库。此外，还生成了一个基于用户交互的Java软件（Oracle）的搜索GUI，以支持对这个新资源的开放访问。该数据库可用于指导化学品管理和提高科学认识，以便在流行病学调查中快速有效地优先选择综合生物监测的化学品。https://doi.org/10.1289/EHP7722
Due to the ubiquitous use of chemicals in modern society, humans are increasingly exposed to thousands of chemicals that contribute to a major portion of the human exposome. Should a comprehensive and risk-based human exposome database be created, it would be conducive to the rapid progress of human exposomics research. In addition, once a xenobiotic is biotransformed with distinct half-lives upon exposure, monitoring the parent compounds alone may not reflect the actual human exposure. To address these questions, a comprehensive and risk-prioritized human exposome database is needed. Our objective was to set up a comprehensive risk-prioritized human exposome database including physicochemical properties as well as risk prediction and develop a graphical user interface (GUI) that has the ability to conduct searches for content associated with chemicals in our database. We built a comprehensive risk-prioritized human exposome database by text mining and database fusion. Subsequently, chemicals were prioritized by integrating exposure level obtained from the Systematic Empirical Evaluation of Models with toxicity data predicted by the Toxicity Estimation Software Tool and the Toxicological Priority Index calculated from the ToxCast database. The biotransformation half-lives () of all the chemicals were assessed using the Iterative Fragment Selection approach and biotransformation products were predicted using the previously developed BioTransformer machine-learning method. We compiled a human exposome database of chemicals, prioritized 13,441 chemicals based on probabilistic hazard quotient and 7,770 chemicals based on risk index, and provided a predicted biotransformation metabolite database of metabolites. In addition, a user-interactive Java software (Oracle)-based search GUI was generated to enable open access to this new resource. Our database can be used to guide chemical management and enhance scientific understanding to rapidly and effectively prioritize chemicals for comprehensive biomonitoring in epidemiological investigations. https://doi.org/10.1289/EHP7722