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Environ Health Perspect; DOI:10.1289/ehp.1510267

CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

Kamel Mansouri,1* Ahmed Abdelaziz,2 Aleksandra Rybacka,3 Alessandra Roncaglioni,Alexander Tropsha,5 Alexandre Varnek,6 Alexey Zakharov,7 Andrew Worth,8 Ann M. Richard,Christopher M. Grulke,1 Daniela Trisciuzzi,9 Denis Fourches,5 Dragos Horvath,6 Emilio Benfenati,4 Eugene Muratov,5 Eva Bay Wedebye,10 Francesca Grisoni,11 Giuseppe F. Mangiatordi,9 Giuseppina M. Incisivo,4 Huixiao Hong,12 Hui W. Ng,12 Igor V. Tetko,2 Ilya Balabin,13 Jayaram Kancherla,1 Jie Shen,14 Julien Burton,8 Marc Nicklaus,7 Matteo Cassotti,11 Nikolai G. Nikolov,10 Orazio Nicolotti,9 Patrik L. Andersson,3 Qingda Zang,15 Regina Politi,Richard D. Beger,16 Roberto Todeschini,11 Ruili Huang,17 Sherif Farag,5 Sine A. Rosenberg,10 Svetoslav Slavov,16 Xin Hu,17 and Richard S. Judson1
Author Affiliations open
1National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA; 2Institute of Structural Biology, Helmholtz Zentrum Muenchen – German Research Center for Environmental Health (GmbH), Munich, Germany; 3Chemistry Department, Umeå University, Umeå, Sweden; 4Environmental Chemistry and Toxicology Laboratory, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy; 5Laboratory for Molecular Modeling, University of North Carolina, Chapel Hill, North Carolina, USA; 6Laboratoire de Chemoinformatique, University of Strasbourg, Strasbourg, France; 7National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA; 8Institute for Health and Consumer Protection (IHCP), Joint Research Centre of the European Commission in Ispra, Ispra, Italy; 9Department of Pharmacy-Drug Sciences, University of Bari, Bari, Italy; 10National Food Institute, Division of Toxicology and Risk Assessment, Technical University of Denmark, Copenhagen, Denmark; 11Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy; 12National Center for Toxicological Research, Division of Bioinformatics and Biostatistics, U.S. Food and Drug Administration, Jefferson, Arizona, USA; 13High Performance Computing, Lockheed Martin, Research Triangle Park, North Carolina, USA; 14Research Institute for Fragrance Materials, Inc., Woodcliff Lake, New Jersey, USA; 15Integrated Laboratory Systems, Inc., Research Triangle Park, North Carolina, USA; 16National Center for Toxicological Research, Division of Systems Biology, U.S. Food and Drug Administration, Jefferson, Arizona, USA; 17National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland, USA.

*Additional affiliation: Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee.

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  • Background: Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the EPA Endocrine Disruptor Screening Program (EDSP).

    Objectives: Here, we describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) and demonstrate the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing.

    Methods: CERAPP combined multiple models developed in collaboration among 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure-activity relationship models and docking approaches were employed, mostly using a common training set of 1677 chemical structures provided by US EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were evaluated on a set of 7,522 chemicals curated from the literature. To overcome the limitations of single models, a consensus was built by weighting models on scores based on their evaluated accuracies.

    Results: Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing.

    Conclusion: This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches. This concept will be applied in future projects related to other endpoints.

  • This EHP Advance Publication article has been peer-reviewed, revised, and accepted for publication. EHP Advance Publication articles are completely citable using the DOI number assigned to the article. This document will be replaced with the copyedited and formatted version as soon as it is available. Through the DOI number used in the citation, you will be able to access this document at each stage of the publication process.

    Citation: Mansouri K, Abdelaziz A, Rybacka A, Roncaglioni A, Tropsha A, Varnek A, Zakharov A, Worth A, Richard AM, Grulke CM, Trisciuzzi D, Fourches D, Horvath D, Benfenati E, Muratov E, Wedebye EB, Grisoni F, Mangiatordi GF, Incisivo GM, Hong H, Ng HW, Tetko IV, Balabin I, Kancherla J, Shen J, Burton J, Nicklaus M, Cassotti M, Nikolov NG, Nicolotti O, Andersson PL, Zang Q, Politi R, Beger RD, Todeschini R, Huang R, Farag S, Rosenberg SA, Slavov S, Hu X, Judson RS. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. Environ Health Perspect; http://dx.doi.org/10.1289/ehp.1510267

    Received: 27 May 2015
    Accepted: 8 February 2016
    Advance Publication: 23 February 2016

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