Kamel Mansouri1,2,3, Nicole Kleinstreuer4, Ahmed M Abdelaziz5, Domenico Alberga6, Vinicius M Alves7,8, Patrik L Andersson9, Carolina H Andrade7, Fang Bai10, Ilya Balabin11, Davide Ballabio12, Emilio Benfenati13, Barun Bhhatarai14, Scott Boyer15, Jingwen Chen16, Viviana Consonni12, Sherif Farag8, Denis Fourches17, Alfonso T García-Sosa18, Paola Gramatica14, Francesca Grisoni12, Chris M Grulke1, Huixiao Hong19, Dragos Horvath20, Xin Hu21, Ruili Huang21, Nina Jeliazkova22, Jiazhong Li10, Xuehua Li16, Huanxiang Liu10, Serena Manganelli13, Giuseppe F Mangiatordi6, Uko Maran18, Gilles Marcou20, Todd Martin23, Eugene Muratov8, Dac-Trung Nguyen21, Orazio Nicolotti6, Nikolai G Nikolov24, Ulf Norinder15, Ester Papa14, Michel Petitjean25, Geven Piir18, Pavel Pogodin26, Vladimir Poroikov26, Xianliang Qiao16, Ann M Richard1, Alessandra Roncaglioni13, Patricia Ruiz27, Chetan Rupakheti23,28, Sugunadevi Sakkiah19, Alessandro Sangion14, Karl-Werner Schramm5, Chandrabose Selvaraj19, Imran Shah1, Sulev Sild18, Lixia Sun29, Olivier Taboureau25, Yun Tang29, Igor V Tetko30,31, Roberto Todeschini12, Weida Tong19, Daniela Trisciuzzi6, Alexander Tropsha8, George Van Den Driessche17, Alexandre Varnek20, Zhongyu Wang16, Eva B Wedebye24, Antony J Williams1, Hongbin Xie16, Alexey V Zakharov21, Ziye Zheng9, Richard S Judson1. 1. National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA. 2. ScitoVation LLC, Research Triangle Park, North Carolina, USA. 3. Integrated Laboratory Systems, Inc., Morrisville, North Carolina, USA. 4. National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA. 5. Technische Universität München, Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt, Department für Biowissenschaftliche Grundlagen, Weihenstephaner Steig 23, 85350 Freising, Germany. 6. Department of Pharmacy-Drug Sciences, University of Bari, Bari, Italy. 7. Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Brazil. 8. Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. 9. Chemistry Department, Umeå University, Umeå, Sweden. 10. School of Pharmacy, Lanzhou University, China. 11. Information Systems & Global Solutions (IS&GS), Lockheed Martin, USA. 12. Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy. 13. Istituto di Ricerche Farmacologiche "Mario Negri", IRCCS, Milan, Italy. 14. QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy. 15. Swedish Toxicology Sciences Research Center, Karolinska Institutet, Södertälje, Sweden. 16. School of Environmental Science and Technology, Dalian University of Technology, Dalian, China. 17. Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA. 18. Institute of Chemistry, University of Tartu, Tartu, Estonia. 19. Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA. 20. Laboratoire de Chémoinformatique-UMR7140, University of Strasbourg/CNRS, Strasbourg, France. 21. National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA. 22. IdeaConsult, Ltd., Sofia, Bulgaria. 23. National Risk Management Research Laboratory, U.S. EPA, Cincinnati, Ohio, USA. 24. Division of Risk Assessment and Nutrition, National Food Institute, Technical University of Denmark, Copenhagen, Denmark. 25. Computational Modeling of Protein-Ligand Interactions (CMPLI)-INSERM UMR 8251, INSERM ERL U1133, Functional and Adaptative Biology (BFA), Universite de Paris, Paris, France. 26. Institute of Biomedical Chemistry IBMC, 10 Building 8, Pogodinskaya st., Moscow 119121, Russia. 27. Computational Toxicology and Methods Development Laboratory, Division of Toxicology and Human Health Sciences, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention, Atlanta, Georgia, USA. 28. Department of Biochemistry and Molecular Biophysics, University of Chicago, Chicago, Illinois, USA. 29. Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, Shanghai, China. 30. BIGCHEM GmbH, Neuherberg, Germany. 31. Helmholtz Zentrum Muenchen - German Research Center for Environmental Health (GmbH), Neuherberg, Germany.
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.
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.
Authors: Kamel Mansouri; Agnes L Karmaus; Jeremy Fitzpatrick; Grace Patlewicz; Prachi Pradeep; Domenico Alberga; Nathalie Alepee; Timothy E H Allen; Dave Allen; Vinicius M Alves; Carolina H Andrade; Tyler R Auernhammer; Davide Ballabio; Shannon Bell; Emilio Benfenati; Sudin Bhattacharya; Joyce V Bastos; Stephen Boyd; J B Brown; Stephen J Capuzzi; Yaroslav Chushak; Heather Ciallella; Alex M Clark; Viviana Consonni; Pankaj R Daga; Sean Ekins; Sherif Farag; Maxim Fedorov; Denis Fourches; Domenico Gadaleta; Feng Gao; Jeffery M Gearhart; Garett Goh; Jonathan M Goodman; Francesca Grisoni; Christopher M Grulke; Thomas Hartung; Matthew Hirn; Pavel Karpov; Alexandru Korotcov; Giovanna J Lavado; Michael Lawless; Xinhao Li; Thomas Luechtefeld; Filippo Lunghini; Giuseppe F Mangiatordi; Gilles Marcou; Dan Marsh; Todd Martin; Andrea Mauri; Eugene N Muratov; Glenn J Myatt; Dac-Trung Nguyen; Orazio Nicolotti; Reine Note; Paritosh Pande; Amanda K Parks; Tyler Peryea; Ahsan H Polash; Robert Rallo; Alessandra Roncaglioni; Craig Rowlands; Patricia Ruiz; Daniel P Russo; Ahmed Sayed; Risa Sayre; Timothy Sheils; Charles Siegel; Arthur C Silva; Anton Simeonov; Sergey Sosnin; Noel Southall; Judy Strickland; Yun Tang; Brian Teppen; Igor V Tetko; Dennis Thomas; Valery Tkachenko; Roberto Todeschini; Cosimo Toma; Ignacio Tripodi; Daniela Trisciuzzi; Alexander Tropsha; Alexandre Varnek; Kristijan Vukovic; Zhongyu Wang; Liguo Wang; Katrina M Waters; Andrew J Wedlake; Sanjeeva J Wijeyesakere; Dan Wilson; Zijun Xiao; Hongbin Yang; Gergely Zahoranszky-Kohalmi; Alexey V Zakharov; Fagen F Zhang; Zhen Zhang; Tongan Zhao; Hao Zhu; Kimberley M Zorn; Warren Casey; Nicole C Kleinstreuer Journal: Environ Health Perspect Date: 2021-04-30 Impact factor: 9.031
Authors: Marc A Beal; Matthew Gagne; Sunil A Kulkarni; Grace Patlewicz; Russell S Thomas; Tara S Barton-Maclaren Journal: ALTEX Date: 2021-11-23 Impact factor: 6.043
Authors: Kimberley M Zorn; Daniel H Foil; Thomas R Lane; Wendy Hillwalker; David J Feifarek; Frank Jones; William D Klaren; Ashley M Brinkman; Sean Ekins Journal: Environ Sci Technol Date: 2020-11-19 Impact factor: 9.028
Authors: Kimberley M Zorn; Daniel H Foil; Thomas R Lane; Wendy Hillwalker; David J Feifarek; Frank Jones; William D Klaren; Ashley M Brinkman; Sean Ekins Journal: Environ Sci Technol Date: 2020-10-21 Impact factor: 9.028
Authors: Thomas B Knudsen; Suzanne Compton Fitzpatrick; K Nadira De Abrew; Linda S Birnbaum; Anne Chappelle; George P Daston; Dana C Dolinoy; Alison Elder; Susan Euling; Elaine M Faustman; Kristi Pullen Fedinick; Jill A Franzosa; Derik E Haggard; Laurie Haws; Nicole C Kleinstreuer; Germaine M Buck Louis; Donna L Mendrick; Ruthann Rudel; Katerine S Saili; Thaddeus T Schug; Robyn L Tanguay; Alexandra E Turley; Barbara A Wetmore; Kimberly W White; Todd J Zurlinden Journal: Toxicol Sci Date: 2021-04-12 Impact factor: 4.849
Authors: Zachary Stanfield; Cody K Addington; Kathie L Dionisio; David Lyons; Rogelio Tornero-Velez; Katherine A Phillips; Timothy J Buckley; Kristin K Isaacs Journal: Environ Health Perspect Date: 2021-06-23 Impact factor: 9.031
Authors: Heather L Ciallella; Daniel P Russo; Lauren M Aleksunes; Fabian A Grimm; Hao Zhu Journal: Environ Sci Technol Date: 2021-07-25 Impact factor: 11.357