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Compara: Collaborative modeling project for androgen receptor activity

  • Kamel Mansouri
  • , Nicole Kleinstreuer
  • , Ahmed M. Abdelaziz
  • , Domenico Alberga
  • , Vinicius M. Alves
  • , Patrik L. Andersson
  • , Carolina H. Andrade
  • , Fang Bai
  • , Ilya Balabin
  • , Davide Ballabio
  • , Emilio Benfenati
  • , Barun Bhhatarai
  • , Scott Boyer
  • , Jingwen Chen
  • , Viviana Consonni
  • , Sherif Farag
  • , Denis Fourches
  • , Alfonso T. García-Sosa
  • , Paola Gramatica
  • , Francesca Grisoni
  • Chris M. Grulke, Huixiao Hong, Dragos Horvath, Xin Hu, Ruili Huang, Nina Jeliazkova, Jiazhong Li, Xuehua Li, Huanxiang Liu, Serena Manganelli, Giuseppe F. Mangiatordi, Uko Maran, Gilles Marcou, Todd Martin, Eugene Muratov, Dac Trung Nguyen, Orazio Nicolotti, Nikolai G. Nikolov, Ulf Norinder, Ester Papa, Michel Petitjean, Geven Piir, Pavel Pogodin, Vladimir Poroikov, Xianliang Qiao, Ann M. Richard, Alessandra Roncaglioni, Patricia Ruiz, Chetan Rupakheti, Sugunadevi Sakkiah, Alessandro Sangion, Karl Werner Schramm, Chandrabose Selvaraj, Imran Shah, Sulev Sild, Lixia Sun, Olivier Taboureau, Yun Tang, Igor V. Tetko, Roberto Todeschini, Weida Tong, Daniela Trisciuzzi, Alexander Tropsha, George Van Den Driessche, Alexandre Varnek, Zhongyu Wang, Eva B. Wedebye, Antony J. Williams, Hongbin Xie, Alexey V. Zakharov, Ziye Zheng, Richard S. Judson
  • United States Environmental Protection Agency
  • ScitoVation LLC
  • Integrated Laboratory Systems
  • National Institutes of Health
  • Technical University of Munich
  • University of Bari
  • Universidade Federal de Goiás
  • University of North Carolina at Chapel Hill
  • Umeå University
  • Lanzhou University
  • Lockheed Martin
  • University of Milan - Bicocca
  • IRCCS Istituto di ricerche farmacologiche Mario Negri - Milano, Bergamo, Ranica
  • University of Insubria
  • Karolinska Institutet
  • Dalian University of Technology
  • North Carolina State University
  • University of Tartu
  • United States Food and Drug Administration
  • Université de Strasbourg
  • IdeaConsult Ltd
  • Nestle
  • National Research Council of Italy
  • Technical University of Denmark
  • Universite de Paris
  • Russian Academy of Medical Sciences - Institute of Biomedical Chemistry
  • Agency for Toxic Substances and Disease Registry
  • The University of Chicago
  • East China University of Science and Technology
  • BIGCHEM GmbH
  • Helmholtz Zentrum München - German Research Center for Environmental Health

研究成果: Article同行評審

180 引文 斯高帕斯(Scopus)

摘要

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.

原文English
文章編號027002
期刊Environmental Health Perspectives
128
發行號2
DOIs
出版狀態Published - 2月 2020
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UN SDG

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  1. Good health and well being
    Good health and well being

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