globalchange  > 气候变化事实与影响
DOI: 10.1289/ehp.1510267
论文题名:
CERAPP: Collaborative Estrogen Receptor Activity Prediction Project
作者: Kamel Mansouri; 1; 2 Ahmed Abdelaziz; 3 Aleks; ra Rybacka; 4 Aless; ra Roncaglioni; 5 Alex; er Tropsha; 6 Alex; re Varnek; 7 Alexey Zakharov; 8; rew Worth; 9 Ann M. Richard; 1 Christopher M. Grulke; 1 Daniela Trisciuzzi; 10 Denis Fourches; 6 Dragos Horvath; 7 Emilio Benfenati; 5 Eugene Muratov; 6 Eva Bay Wedebye; 11 Francesca Grisoni; 12 Giuseppe F. Mangiatordi; 10 Giuseppina M. Incisivo; 5 Huixiao Hong; 13 Hui W. Ng; 13 Igor V. Tetko; 3; 14 Ilya Balabin; 15 Jayaram Kancherla; 1 Jie Shen; 16 Julien Burton; 9 Marc Nicklaus; 8 Matteo Cassotti; 12 Nikolai G. Nikolov; 11 Orazio Nicolotti; 10 Patrik L.; ersson; 4 Qingda Zang; 17 Regina Politi; 6 Richard D. Beger; 18 Roberto Todeschini; 12 Ruili Huang; 19 Sherif Farag; 6 Sine A. Rosenberg; 11 Svetoslav Slavov; 17 Xin Hu; 19; Richard S. Judson1
刊名: Environmental Health Perspectives
ISSN: 0091-7000
出版年: 2016
卷: Volume 124, 期:Issue 7
起始页码: 1023
语种: 英语
英文摘要: 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 U.S. EPA Endocrine Disruptor Screening Program.

Objectives: 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 with 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 1,677 chemical structures provided by the U.S. 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 end points.
URL: http://dx.doi.org/10.1289/ehp.1510267
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/12331
Appears in Collections:气候变化事实与影响
气候变化与战略

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作者单位: 1National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA; 2Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, USA; 3Institute of Structural Biology, Helmholtz Zentrum Muenchen-German Research Center for Environmental Health (GmbH), Neuherberg, Germany; 4Chemistry Department, Umeå University, Umeå, Sweden; 5Environmental Chemistry and Toxicology Laboratory, IRCCS (Istituto di Ricovero e Cura a Carattere Scientifico)-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy; 6Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; 7Laboratoire de Chemoinformatique, University of Strasbourg, Strasbourg, France; 8National Cancer Institute, National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Bethesda, Maryland, USA; 9Institute for Health and Consumer Protection (IHCP), Joint Research Centre of the European Commission in Ispra, Ispra, Italy; 10Department of Pharmacy-Drug Sciences, University of Bari, Bari, Italy; 11Division of Toxicology and Risk Assessment, National Food Institute, Technical University of Denmark, Copenhagen, Denmark; 12Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy; 13Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration (USDA), Jefferson, Arizona, USA; 14BigChem GmbH, Neuherberg, Germany; 15High Performance Computing, Lockheed Martin, Research Triangle Park, North Carolina, USA; 16Research Institute for Fragrance Materials, Inc., Woodcliff Lake, New Jersey, USA; 17Integrated Laboratory Systems, Inc., Research Triangle Park, North Carolina, USA; 18Division of Systems Biology, National Center for Toxicological Research, USDA, Jefferson, Arizona, USA; 19National Center for Advancing Translational Sciences, NIH, DHHS, Bethesda, Maryland, USA

Recommended Citation:
Kamel Mansouri,1,2 Ahmed Abdelaziz,et al. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project[J]. Environmental Health Perspectives,2016-01-01,Volume 124(Issue 7):1023
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