Cited by 377Computational Drug Discovery MethodsEffects and risks of endocrine disrupting chemicalsEstrogen and related hormone effects

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CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

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Mansouri, Kamel, Abdelaziz, Ahmed, Rybacka, Aleksandra, Roncaglioni, Alessandra, Tropsha, Alexander, Varnek, Alexandre, Zakharov, Alexey, Worth, Andrew, Richard, Ann M., Grulke, Christopher M., Trisciuzzi, Daniela, Fourches, Denis, Horvath, Dragos, Benfenati, Emilio, Muratov, Eugene, Wedebye, Eva Bay, Grisoni, Francesca, Mangiatordi, Giuseppe F., Incisivo, Giuseppina M., Hong, Huixiao, Ng, Hui W., Tetko, Igor V., Balabin, Ilya, Kancherla, Jayaram, Shen, Jie, Burton, Julien, Nicklaus, Marc, Cassotti, Matteo, Nikolov, Nikolai G., Nicolotti, Orazio, Andersson, Patrik L., Zang, Qingda, Politi, Regina, Beger, Richard D., Todeschini, Roberto, Huang, Ruili, Farag, Sherif, Rosenberg, Sine A., Slavov, Svetoslav, Hu, Xin, Judson, Richard S.·2016·Environmental Health Perspectives

DOI 10.1289/ehp.1510267PMC PMC4937869

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Abstract

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. 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. 2016. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. Environ Health Perspect 124:1023–1033; http://dx.doi.org/10.1289/ehp.1510267

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Figure 1 ROC curves of the categorical corrected consensus predictions for binding evaluated against different sets of the evaluation set with variable numbers of literature sources. The number of available chemicals in the evaluation set (between brackets) decreased with higher numbers of literature sources. The true and false positive rates are determined based on the number of actives in the different sets of the evaluation set.CC0 · PubMed Central

Figure 2 Box-plot of the positive class potency levels in the corrected quantitative consensus predictions for binding. The concordance between models is the fraction of the number of models that agrees on the prediction of a certain chemical. Boxes extend from the 25th to the 75th percentile, horizontal bars represent the median, whiskers indicate the 10th and 90th percentiles, and outliers are represented as points.CC0 · PubMed Central

Figure 3 Variation of the balanced accuracy of the corrected categorical consensus predictions for binding with positive concordance (agreement between models on predictions for active chemicals) threshold at different numbers of literature sources.CC0 · PubMed Central

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Additional file 1 of Free and open-source QSAR-ready workflow for automated standardization of chemical structures in support of QSAR modeling
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Additional file 2 of Free and open-source QSAR-ready workflow for automated standardization of chemical structures in support of QSAR modeling
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Navigating Molecular Complexity : a Comprehensive Molecular Modeling Study on Neonicotinoids, Sulfoxaflor, and Flupyradifurone Interacting with the Human α7 Nicotinic Acetylcholine Receptor
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Influential
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Authors: 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., Farag, S., Rosenberg, S. A., Slavov, S., Hu, X., & Judson, R. S.
Affiliations: Environmental Protection Agency (US)
Helmholtz Munich (DE)
Umeå University (SE)
Mario Negri Institute for Pharmacological Research (IT)
University of North Carolina at Chapel Hill (US)
Université de Strasbourg (FR)
National Institutes of Health (US)
European Commission (BE)
National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
University of Bari Aldo Moro (IT)
Technical University of Denmark (DK)
University of Milano-Bicocca (IT)
National Center for Toxicological Research (US)
Lockheed Martin (United States) (US)
Research Institute for Fragrance Materials (US)
Integrated Laboratory Systems, Inc. (US)
Corresponding author: Judson, Richard S.
ORCID: Mansouri, Kamel
Roncaglioni, Alessandra
Tropsha, Alexander
Varnek, Alexandre
Zakharov, Alexey
Worth, Andrew
Trisciuzzi, Daniela
Fourches, Denis
Horvath, Dragos
Benfenati, Emilio
Muratov, Eugene
Wedebye, Eva Bay
Grisoni, Francesca
Mangiatordi, Giuseppe F.
Hong, Huixiao
Tetko, Igor V.
Balabin, Ilya
Kancherla, Jayaram
Shen, Jie
Nicklaus, Marc
Cassotti, Matteo
Nikolov, Nikolai G.
Nicolotti, Orazio
Andersson, Patrik L.
Zang, Qingda
Beger, Richard D.
Todeschini, Roberto
Huang, Ruili
Rosenberg, Sine A.
Slavov, Svetoslav
Hu, Xin
Judson, Richard S.
Venue: Environmental Health Perspectives
Publisher: American Chemical Society (ACS)
Published: 2016-02-23
Volume: 124
Issue: 7
Pages: 1023-1033
ISSN: 0091-6765 (ISSN-L)
1552-9924
DOI: 10.1289/ehp.1510267
PMC: PMC4937869
OpenAlex: W2394108223
Cited by: 375
References: 85
Citations by year: 2026: 26 · 2025: 46 · 2024: 35 · 2023: 32 · 2022: 36 · 2021: 41 · 2020: 35 · 2019: 30 · 2018: 38 · 2017: 29 · 2016: 27
FWCI: 38.02
Type: journal article
Language: en
Open access: gold
doi.org
Licence: public-domain
creativecommons.org/licenses/by/4.0/ (vor, 675 d)
Locations: Environmental Health Perspectives (publishedVersion, public-domain)
PubMed Central (publishedVersion, public-domain)
PubMed (publishedVersion)
PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion, cc-by)
KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
HAL (submittedVersion, other-oa)
BOA (University of Milano-Bicocca) (submittedVersion)
Site cant be reached (submittedVersion, cc-by)
Joint Research Centre (European Commission) (submittedVersion)
Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, other-oa)
CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) (submittedVersion, other-oa)
Funders: National Institute of General Medical Sciences (T32GM067553)
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Effects and risks of endocrine disrupting chemicals
Life Sciences › Biochemistry, Genetics and Molecular Biology › Endocrinology › Estrogen and related hormone effects
Keywords: endocrine disruptors, estrogen receptor, QSAR, molecular docking, consensus model, chemical prioritization, high-throughput screening, chemical risk assessment
MeSH terms: Computer Simulation, Receptors, Estrogen, United States, Toxicity Tests, Quantitative Structure-Activity Relationship, Endocrine Disruptors, Environmental Policy

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