CERAPP: Collaborative estrogen receptor activity prediction project

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

Research output: Contribution to journalArticle

103 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1023-1033
Number of pages11
JournalEnvironmental Health Perspectives
Volume124
Issue number7
DOIs
Publication statusPublished - Jul 1 2016

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Estrogen Receptors
Endocrine Disruptors
Small Molecule Libraries
Chemical Models
Quantitative Structure-Activity Relationship
Computer Simulation
Estrogens
Hormones

ASJC Scopus subject areas

  • Health, Toxicology and Mutagenesis
  • Public Health, Environmental and Occupational Health

Cite this

Mansouri, K., Abdelaziz, A., Rybacka, A., Roncaglioni, A., Tropsha, A., Varnek, A., ... Judson, R. S. (2016). CERAPP: Collaborative estrogen receptor activity prediction project. Environmental Health Perspectives, 124(7), 1023-1033. https://doi.org/10.1289/ehp.1510267

CERAPP : Collaborative estrogen receptor activity prediction project. / 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.

In: Environmental Health Perspectives, Vol. 124, No. 7, 01.07.2016, p. 1023-1033.

Research output: Contribution to journalArticle

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', Environmental Health Perspectives, vol. 124, no. 7, pp. 1023-1033. https://doi.org/10.1289/ehp.1510267
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. / CERAPP : Collaborative estrogen receptor activity prediction project. In: Environmental Health Perspectives. 2016 ; Vol. 124, No. 7. pp. 1023-1033.
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title = "CERAPP: Collaborative estrogen receptor activity prediction project",
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.",
author = "Kamel Mansouri and Ahmed Abdelaziz and Aleksandra Rybacka and Alessandra Roncaglioni and Alexander Tropsha and Alexandre Varnek and Alexey Zakharov and Andrew Worth and Richard, {Ann M.} and Grulke, {Christopher M.} and Daniela Trisciuzzi and Denis Fourches and Dragos Horvath and Emilio Benfenati and Eugene Muratov and Wedebye, {Eva Bay} and Francesca Grisoni and Mangiatordi, {Giuseppe F.} and Incisivo, {Giuseppina M.} and Huixiao Hong and Ng, {Hui W.} and Tetko, {Igor V.} and Ilya Balabin and Jayaram Kancherla and Jie Shen and Julien Burton and Marc Nicklaus and Matteo Cassotti and Nikolov, {Nikolai G.} and Orazio Nicolotti and Andersson, {Patrik L.} and Qingda Zang and Regina Politi and Beger, {Richard D.} and Roberto Todeschini and Ruili Huang and Sherif Farag and Rosenberg, {Sine A.} and Svetoslav Slavov and Xin Hu and Judson, {Richard S.}",
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T1 - CERAPP

T2 - Collaborative estrogen receptor activity prediction project

AU - Mansouri, Kamel

AU - Abdelaziz, Ahmed

AU - Rybacka, Aleksandra

AU - Roncaglioni, Alessandra

AU - Tropsha, Alexander

AU - Varnek, Alexandre

AU - Zakharov, Alexey

AU - Worth, Andrew

AU - Richard, Ann M.

AU - Grulke, Christopher M.

AU - Trisciuzzi, Daniela

AU - Fourches, Denis

AU - Horvath, Dragos

AU - Benfenati, Emilio

AU - Muratov, Eugene

AU - Wedebye, Eva Bay

AU - Grisoni, Francesca

AU - Mangiatordi, Giuseppe F.

AU - Incisivo, Giuseppina M.

AU - Hong, Huixiao

AU - Ng, Hui W.

AU - Tetko, Igor V.

AU - Balabin, Ilya

AU - Kancherla, Jayaram

AU - Shen, Jie

AU - Burton, Julien

AU - Nicklaus, Marc

AU - Cassotti, Matteo

AU - Nikolov, Nikolai G.

AU - Nicolotti, Orazio

AU - Andersson, Patrik L.

AU - Zang, Qingda

AU - Politi, Regina

AU - Beger, Richard D.

AU - Todeschini, Roberto

AU - Huang, Ruili

AU - Farag, Sherif

AU - Rosenberg, Sine A.

AU - Slavov, Svetoslav

AU - Hu, Xin

AU - Judson, Richard S.

PY - 2016/7/1

Y1 - 2016/7/1

N2 - 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.

AB - 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.

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