BACKGROUND 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. OBJECTIVES 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. METHODS 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 (HLBs) 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. RESULTS We compiled a human exposome database of >20,000 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 >95,000 metabolites. In addition, a user-interactive Java software (Oracle)-based search GUI was generated to enable open access to this new resource. DISCUSSION 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.

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基于风险的化学品排名和生成优先的人类暴露组数据库。

背景技术由于现代社会普遍使用化学制品，人类越来越多地暴露于数千种化学制品，这些化学制品构成了人类暴露体的主要部分。如果建立一个全面的、基于风险的人类暴露组数据库，将有利于人类暴露组学研究的快速进展。此外，一旦外源性物质在暴露后被生物转化，具有不同的半衰期，单独监测母体化合物可能无法反映实际的人类暴露情况。为了解决这些问题，需要一个全面的、风险优先的人类暴露组数据库。目标我们的目标是建立一个全面的风险优先人类暴露组数据库，包括物理化学特性和风险预测，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。方法我们通过文本挖掘和数据库融合构建了一个综合的风险优先的人类暴露组数据库。随后，通过将从模型的系统实证评估中获得的暴露水平与由毒性估计软件工具预测的毒性数据和从 ToxCast 数据库计算出的毒理学优先指数相结合，对化学品进行优先排序。使用迭代片段选择方法评估所有化学品的生物转化半衰期 (HLB)，并使用先前开发的 BioTransformer 机器学习方法预测生物转化产物。结果我们编译了一个包含 > 20,000 种化学物质的人体暴露组数据库，根据概率危险商对 13,441 种化学物质进行了优先排序，根据风险指数对 7,770 种化学物质进行了优先排序，并提供了一个包含 > 95,000 种代谢物的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式 Java 软件 (Oracle) 的搜索 GUI，以实现对这一新资源的开放访问。讨论 我们的数据库可用于指导化学品管理和增强科学理解，以快速有效地确定化学品的优先级，以便在流行病学调查中进行全面的生物监测。