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 GrisoniChris 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 Georgiev 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 Bay Wedebye, Antony J. Williams, Hongbin Xie, Alexey V. Zakharov, Ziye Zheng, Richard S. Judson

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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) 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
Original languageEnglish
Article number027002
JournalEnvironmental Health Perspectives
Volume128
Issue number2
Number of pages17
ISSN0091-6765
DOIs
Publication statusPublished - 2020

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