CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity
CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity
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DOI：
10.1289/ehp5580
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发表时间：
2020-02-01
影响因子：
10.4
通讯作者：
Judson, Richard S.
中科院分区：
文献类型：
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作者：
Mansouri, Kamel;Kleinstreuer, Nicole;Judson, Richard S.
背景：内分泌干扰物（EDCs）是一种模拟天然激素相互作用并改变合成、运输或代谢途径的外源性物质。由于EDCs可能对人类和野生动物的健康造成不利影响，因此发展了评价其生物活性的科学和监管方法。这一需求正在通过体外高通量筛选（HTS）方法和计算建模来解决。目的：为了支持内分泌干扰物筛选项目，美国环境保护署（EPA）领导了两个全球联盟，对化学物质潜在的雌激素和雄激素活性进行虚拟筛选。在这里，我们描述了雄激素受体活性协作建模项目（CoMPARA）的努力，它遵循了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建立的CoMPARA筛选化学物质清单，包括其他感兴趣的化学物质，以及模拟的ToxCast （TM）代谢物，共计55450种化学结构。来自25个国际组织的计算毒理学科学家为结合剂、激动剂和拮抗剂活性预测贡献了91个预测模型。模型的基础是由11个ToxCast (TM)/Tox21 HTS体外测定的综合数据集编译的1746种化学物质的共同训练集。结果：使用从不同来源提取的整理文献数据对所得模型进行评估。为了克服单模型方法的局限性，将CoMPARA预测合并为共识模型，为评估集提供约80%的平均预测精度。讨论：讨论了共识预测的优势和局限性的例子化学品；然后，将这些模型实现到免费和开源的OPERA应用程序中，以实现具有定义的适用范围和准确性评估的新化学品筛选。该方法被用于筛选整个EPA DSSTox数据库中875,000种化学物质，其预测的AR活性已在EPA CompTox化学仪表板和国家毒理学计划的综合化学环境中提供。
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 (TM) 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 (TM)/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 similar to 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.