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CoMPARA:雄激素受体活性协作建模项目。

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity.

机构信息

National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA.

ScitoVation LLC, Research Triangle Park, North Carolina, USA.

出版信息

Environ Health Perspect. 2020 Feb;128(2):27002. doi: 10.1289/EHP5580. Epub 2020 Feb 7.

Abstract

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) 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 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 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.

摘要

背景

内分泌干扰化学物质(EDCs)是模拟天然激素相互作用并改变其合成、运输或代谢途径的外源性化学物质。EDCs 对人类和野生动物健康造成不良影响的可能性,促使人们开发了用于评估生物活性的科学和监管方法。这一需求正通过高通量筛选(HTS)方法和计算建模得到满足。

目的

为支持内分泌干扰物筛选计划,美国环境保护署(EPA)领导了两个全球性联盟,对化学物质进行虚拟筛选,以评估其潜在的雌激素和雄激素活性。在这里,我们描述了合作雄激素受体活性筛选项目(CoMPARA)的工作,该项目遵循合作雌激素受体活性预测项目(CERAPP)的步骤。

方法

CoMPARA 的筛选化学物质列表是在 CERAPP 的 32464 种化学物质列表的基础上构建的,其中包括其他感兴趣的化学物质以及模拟的 ToxCast™代谢物,共计 55450 种化学结构。来自 25 个国际团体的计算毒理学科学家为结合、激动剂和拮抗剂活性预测贡献了 91 个预测模型。这些模型基于从 11 个 ToxCast™/Tox21 HTS 测定中综合数据集中编译的 1746 种化学物质的共同训练集。

结果

使用从不同来源提取的经过审核的文献数据对所得模型进行了评估。为了克服单一模型方法的局限性,将 CoMPARA 预测结果合并为共识模型,该模型对评估集的预测准确性约为 80%。

讨论

讨论了共识预测的优势和局限性,并举例说明了化学物质;然后,将这些模型集成到免费和开源的 OPERA 应用程序中,以实现具有定义适用性域和准确性评估的新化学物质的筛选。该实施用于筛选 EPA DSSTox 数据库中的所有化学物质,并将其预测的 AR 活性可在 EPA CompTox Chemicals 仪表板和国家毒理学计划综合化学环境中获得。https://doi.org/10.1289/EHP5580.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8509/7064318/e52aabbe31cd/ehp-128-027002-g001.jpg

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