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CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity
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 a...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Environmental Health Perspectives
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064318/ https://www.ncbi.nlm.nih.gov/pubmed/32074470 http://dx.doi.org/10.1289/EHP5580 |
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author | Mansouri, Kamel Kleinstreuer, Nicole Abdelaziz, Ahmed M. Alberga, Domenico Alves, Vinicius M. Andersson, Patrik L. Andrade, Carolina H. Bai, Fang Balabin, Ilya Ballabio, Davide Benfenati, Emilio Bhhatarai, Barun Boyer, Scott Chen, Jingwen Consonni, Viviana Farag, Sherif Fourches, Denis García-Sosa, Alfonso T. Gramatica, Paola Grisoni, Francesca Grulke, Chris M. Hong, Huixiao Horvath, Dragos Hu, Xin Huang, Ruili Jeliazkova, Nina Li, Jiazhong Li, Xuehua Liu, Huanxiang Manganelli, Serena Mangiatordi, Giuseppe F. Maran, Uko Marcou, Gilles Martin, Todd Muratov, Eugene Nguyen, Dac-Trung Nicolotti, Orazio Nikolov, Nikolai G. Norinder, Ulf Papa, Ester Petitjean, Michel Piir, Geven Pogodin, Pavel Poroikov, Vladimir Qiao, Xianliang Richard, Ann M. Roncaglioni, Alessandra Ruiz, Patricia Rupakheti, Chetan Sakkiah, Sugunadevi Sangion, Alessandro Schramm, Karl-Werner Selvaraj, Chandrabose Shah, Imran Sild, Sulev Sun, Lixia Taboureau, Olivier Tang, Yun Tetko, Igor V. Todeschini, Roberto Tong, Weida Trisciuzzi, Daniela Tropsha, Alexander Van Den Driessche, George Varnek, Alexandre Wang, Zhongyu Wedebye, Eva B. Williams, Antony J. Xie, Hongbin Zakharov, Alexey V. Zheng, Ziye Judson, Richard S. |
author_facet | Mansouri, Kamel Kleinstreuer, Nicole Abdelaziz, Ahmed M. Alberga, Domenico Alves, Vinicius M. Andersson, Patrik L. Andrade, Carolina H. Bai, Fang Balabin, Ilya Ballabio, Davide Benfenati, Emilio Bhhatarai, Barun Boyer, Scott Chen, Jingwen Consonni, Viviana Farag, Sherif Fourches, Denis García-Sosa, Alfonso T. Gramatica, Paola Grisoni, Francesca Grulke, Chris M. Hong, Huixiao Horvath, Dragos Hu, Xin Huang, Ruili Jeliazkova, Nina Li, Jiazhong Li, Xuehua Liu, Huanxiang Manganelli, Serena Mangiatordi, Giuseppe F. Maran, Uko Marcou, Gilles Martin, Todd Muratov, Eugene Nguyen, Dac-Trung Nicolotti, Orazio Nikolov, Nikolai G. Norinder, Ulf Papa, Ester Petitjean, Michel Piir, Geven Pogodin, Pavel Poroikov, Vladimir Qiao, Xianliang Richard, Ann M. Roncaglioni, Alessandra Ruiz, Patricia Rupakheti, Chetan Sakkiah, Sugunadevi Sangion, Alessandro Schramm, Karl-Werner Selvaraj, Chandrabose Shah, Imran Sild, Sulev Sun, Lixia Taboureau, Olivier Tang, Yun Tetko, Igor V. Todeschini, Roberto Tong, Weida Trisciuzzi, Daniela Tropsha, Alexander Van Den Driessche, George Varnek, Alexandre Wang, Zhongyu Wedebye, Eva B. Williams, Antony J. Xie, Hongbin Zakharov, Alexey V. Zheng, Ziye Judson, Richard S. |
author_sort | Mansouri, Kamel |
collection | PubMed |
description | 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 [Formula: see text] 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 |
format | Online Article Text |
id | pubmed-7064318 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Environmental Health Perspectives |
record_format | MEDLINE/PubMed |
spelling | pubmed-70643182020-03-12 CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity Mansouri, Kamel Kleinstreuer, Nicole Abdelaziz, Ahmed M. Alberga, Domenico Alves, Vinicius M. Andersson, Patrik L. Andrade, Carolina H. Bai, Fang Balabin, Ilya Ballabio, Davide Benfenati, Emilio Bhhatarai, Barun Boyer, Scott Chen, Jingwen Consonni, Viviana Farag, Sherif Fourches, Denis García-Sosa, Alfonso T. Gramatica, Paola Grisoni, Francesca Grulke, Chris M. Hong, Huixiao Horvath, Dragos Hu, Xin Huang, Ruili Jeliazkova, Nina Li, Jiazhong Li, Xuehua Liu, Huanxiang Manganelli, Serena Mangiatordi, Giuseppe F. Maran, Uko Marcou, Gilles Martin, Todd Muratov, Eugene Nguyen, Dac-Trung Nicolotti, Orazio Nikolov, Nikolai G. Norinder, Ulf Papa, Ester Petitjean, Michel Piir, Geven Pogodin, Pavel Poroikov, Vladimir Qiao, Xianliang Richard, Ann M. Roncaglioni, Alessandra Ruiz, Patricia Rupakheti, Chetan Sakkiah, Sugunadevi Sangion, Alessandro Schramm, Karl-Werner Selvaraj, Chandrabose Shah, Imran Sild, Sulev Sun, Lixia Taboureau, Olivier Tang, Yun Tetko, Igor V. Todeschini, Roberto Tong, Weida Trisciuzzi, Daniela Tropsha, Alexander Van Den Driessche, George Varnek, Alexandre Wang, Zhongyu Wedebye, Eva B. Williams, Antony J. Xie, Hongbin Zakharov, Alexey V. Zheng, Ziye Judson, Richard S. Environ Health Perspect Research 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 [Formula: see text] 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 Environmental Health Perspectives 2020-02-07 /pmc/articles/PMC7064318/ /pubmed/32074470 http://dx.doi.org/10.1289/EHP5580 Text en EHP is an open-access journal published with support from the National Institute of Environmental Health Sciences, National Institutes of Health. All content is public domain unless otherwise noted. |
spellingShingle | Research Mansouri, Kamel Kleinstreuer, Nicole Abdelaziz, Ahmed M. Alberga, Domenico Alves, Vinicius M. Andersson, Patrik L. Andrade, Carolina H. Bai, Fang Balabin, Ilya Ballabio, Davide Benfenati, Emilio Bhhatarai, Barun Boyer, Scott Chen, Jingwen Consonni, Viviana Farag, Sherif Fourches, Denis García-Sosa, Alfonso T. Gramatica, Paola Grisoni, Francesca Grulke, Chris M. Hong, Huixiao Horvath, Dragos Hu, Xin Huang, Ruili Jeliazkova, Nina Li, Jiazhong Li, Xuehua Liu, Huanxiang Manganelli, Serena Mangiatordi, Giuseppe F. Maran, Uko Marcou, Gilles Martin, Todd Muratov, Eugene Nguyen, Dac-Trung Nicolotti, Orazio Nikolov, Nikolai G. Norinder, Ulf Papa, Ester Petitjean, Michel Piir, Geven Pogodin, Pavel Poroikov, Vladimir Qiao, Xianliang Richard, Ann M. Roncaglioni, Alessandra Ruiz, Patricia Rupakheti, Chetan Sakkiah, Sugunadevi Sangion, Alessandro Schramm, Karl-Werner Selvaraj, Chandrabose Shah, Imran Sild, Sulev Sun, Lixia Taboureau, Olivier Tang, Yun Tetko, Igor V. Todeschini, Roberto Tong, Weida Trisciuzzi, Daniela Tropsha, Alexander Van Den Driessche, George Varnek, Alexandre Wang, Zhongyu Wedebye, Eva B. Williams, Antony J. Xie, Hongbin Zakharov, Alexey V. Zheng, Ziye Judson, Richard S. CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity |
title | CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity |
title_full | CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity |
title_fullStr | CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity |
title_full_unstemmed | CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity |
title_short | CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity |
title_sort | compara: collaborative modeling project for androgen receptor activity |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064318/ https://www.ncbi.nlm.nih.gov/pubmed/32074470 http://dx.doi.org/10.1289/EHP5580 |
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