CERAPP: Collaborative Estrogen Receptor Activity Prediction Project
Creators: name: Judson, Richard S.
name: Horvath, Dragos
name: Tropsha, Alexander
name: Farag, Sherif
name: Zakharov, Alexey
name: Cassotti, Matteo
name: Beger, Richard D.
name: Wedebye, Eva Bay
name: Hong, Huixiao
name: Mansouri, Kamel
name: Shen, Jie
name: Benfenati, Emilio
name: Slavov, Svetoslav
name: Abdelaziz, Ahmed
name: Nikolov, Nikolai G.
name: Trisciuzzi, Daniela
name: Grulke, Christopher M.
name: Nicklaus, Marc
name: Incisivo, Giuseppina M.
name: Worth, Andrew
name: Rosenberg, Sine A.
name: Hu, Xin
name: Varnek, Alexandre
name: Kancherla, Jayaram
name: Politi, Regina
name: Fourches, Denis
name: Todeschini, Roberto
name: Rybacka, Aleksandra
name: Zang, Qingda
name: Richard, Ann M.
name: Andersson, Patrik L.
name: Mangiatordi, Giuseppe F.
name: Ng, Hui W.
name: Roncaglioni, Alessandra
name: Nicolotti, Orazio
name: Muratov, Eugene
name: Huang, Ruili
name: Tetko, Igor V.
name: Burton, Julien
name: Grisoni, Francesca
name: Balabin, Ilya
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2016

description: 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.