Abstract

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 (${\text{HL}}_{\mathrm{B}}\mathrm{s}$) 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 $>\mathrm{20,000}$ 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 $>\mathrm{95,000}$ 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

中文翻译：

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基于风险的化学排名并生成优先的人类暴露数据库

摘要

背景：

由于现代社会普遍使用化学物质，人类越来越多地接触成千上万的化学物质，这些化学物质构成了人类暴露的主要部分。如果建立一个全面的，基于风险的人体暴露数据库，将有利于人体暴露研究的快速发展。此外，一旦异源生物被暴露后具有明显的半衰期生物转化，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，需要一个全面且风险优先的人类暴露数据库。

目标：

我们的目标是建立一个全面的，以风险为优先的，包括物理化学特性以及风险预测的人体暴露数据库，并开发一个图形用户界面（GUI），该图形用户界面能够对我们数据库中与化学物质相关的内容进行搜索。

方法：

通过文本挖掘和数据库融合，我们建立了一个以风险为优先的综合人类暴露数据库。随后，通过对从模型的系统性经验评估中获得的暴露水平与由毒性估算软件工具预测的毒性数据和由ToxCast数据库计算出的毒理学优先级指数进行集成，对化学品进行优先级排序。生物转化半衰期（${\text{HL}}_{\mathrm{乙}}\mathrm{s}$使用迭代片段选择方法对所有化学药品进行评估，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：

我们编译了一个人类暴露数据库 $>\mathrm{20,000}$ 化学品，基于概率危险商的优先级13,441化学品和基于风险指数的优先级7,770化学品，并提供了预测的生物转化代谢物数据库 $>\mathrm{95,000}$代谢产物。此外，还生成了基于用户交互Java软件（Oracle）的搜索GUI，以允许对此新资源进行开放访问。

讨论：

我们的数据库可用于指导化学物质管理并增强科学认识，以便迅速有效地确定化学物质的优先级，以进行流行病学调查中的综合生物监测。https://doi.org/10.1289/EHP7722