BACKGROUND Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays. RESULTS The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

中文翻译：

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CoMPARA：雄激素受体活性的协同建模项目。

背景技术破坏内分泌的化学物质（EDC）是异种生物，其模仿天然激素的相互作用并改变合成，运输或代谢途径。EDC对人类和野生生物造成不利健康影响的前景已导致评估生物活性的科学和监管方法的发展。使用高通量筛选（HTS）体外方法和计算模型解决了这一需求。目的为了支持内分泌干扰物筛查计划，美国环境保护局（EPA）领导了两个全球性财团对它们潜在的雌激素和雄激素活性进行化学筛选。在这里，我们描述了雄激素受体活性（CoMPARA）工作的协作建模项目，它遵循协作雌激素受体活性预测项目（CERAPP）的步骤。方法在CERAPP的32,464种化学物质清单的基础上，筛选出的化学物质的CoMPARA清单包括感兴趣的其他化学物质以及模拟的ToxCast™代谢物，共有55,450种化学结构。来自25个国际小组的计算毒理学科学家为结合，激动剂和拮抗剂活性预测提供了91种预测模型。通过11种ToxCast™/ Tox21 HTS体外试验的组合数据集汇编的1,746种化学药品的通用训练集来支持模型。结果使用从不同来源提取的精选文献数据评估了所得模型。为了克服单一模型方法的局限性，CoMPARA预测被合并到共识模型中，该模型为评估集提供了约80％的平均预测准确性。讨论使用示例性化学品讨论了共识预测的优缺点。然后，将模型实施到免费和开放源代码的OPERA应用程序中，从而能够通过定义的适用范围和准确性评估来筛选新化学品。该实现用于筛选约875,000种化学物质的整个EPA DSSTox数据库，并且其预测的AR活动已在EPA CompTox化学品仪表板和国家毒理学计划的“综合化学环境”中提供。https://doi.org/10.1289/EHP5580。讨论使用示例性化学品讨论了共识预测的优缺点。然后，将模型实施到免费和开放源代码的OPERA应用程序中，从而能够通过定义的适用范围和准确性评估来筛选新化学品。该实施方案用于筛选整个EPA DSSTox数据库，该数据库包含约875,000种化学物质，其预测的AR活动已在EPA CompTox化学品仪表板和国家毒理学计划的“综合化学环境”中提供。https://doi.org/10.1289/EHP5580。讨论使用示例性化学品讨论了共识预测的优缺点。然后，将模型实施到免费和开放源代码的OPERA应用程序中，从而能够通过定义的适用范围和准确性评估来筛选新化学品。该实现用于筛选约875,000种化学物质的整个EPA DSSTox数据库，并且其预测的AR活动已在EPA CompTox化学品仪表板和国家毒理学计划的“综合化学环境”中提供。https://doi.org/10.1289/EHP5580。该实现用于筛选约875,000种化学物质的整个EPA DSSTox数据库，并且其预测的AR活动已在EPA CompTox化学品仪表板和国家毒理学计划的“综合化学环境”中提供。https://doi.org/10.1289/EHP5580。该实现用于筛选约875,000种化学物质的整个EPA DSSTox数据库，并且其预测的AR活动已在EPA CompTox化学品仪表板和国家毒理学计划的“综合化学环境”中提供。https://doi.org/10.1289/EHP5580。