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Mansouri, K.* ; Abdelaziz, A. ; Rybacka, A.* ; Roncaglioni, A.* ; Tropsha, A.* ; Varnek, A.* ; Zakharov, A.* ; Worth, A.* ; Richard, A.M.* ; Grulke, C.M.* ; Trisciuzzi, D.* ; Fourches, D.* ; Horvath, D.* ; Benfenati, E.* ; Muratov, E.* ; Wedebye, E.B.* ; Grisoni, F.* ; Mangiatordi, G.F.* ; Incisivo, G.M.* ; Hong, H.* ; Ng, H.W.* ; Tetko, I.V. ; Balabin, I.* ; Kancherla, J.* ; Shen, J.* ; Burton, J.* ; Nicklaus, M.* ; Cassotti, M.* ; Nikolov, N.G.* ; Nicolotti, O.* ; Andersson, P.L.* ; Zang, Q.* ; Politi, R.* ; Beger, R.D.* ; Todeschini, R.* ; Huang, R.J.* ; Farag, S.* ; Rosenberg, S.A.* ; Slavov, S.* ; Hu, X.* ; Judson, R.S.*

CERAPP: Collaborative estrogen receptor activity prediction project.

Environ. Health Perspect. 124, 1023-1033 (2016)
Verlagsversion Anhang DOI
Creative Commons Lizenzvertrag
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Endocrine-disrupting Chemicals; Computational Toxicology Resource; Quantitative Structure-activity; Throughput Screening Assays; Relationship Qsdar Models; Environmental Chemicals; Decision Forest; Partition-coefficients; Binary Classification; Biological-activity
ISSN (print) / ISBN 0091-6765
e-ISSN 1552-9924
Quellenangaben Band: 124, Heft: 7, Seiten: 1023-1033 Artikelnummer: , Supplement: ,
Verlag Research Triangle Park
Verlagsort NC [u.a.]
Begutachtungsstatus Peer reviewed