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.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

ToxCast Chemical Landscape: Paving the Road to 21st Century Toxicology

“…38 The CERAPP ER predictions are intended to aid in prioritizing chemicals for further testing and regulatory purposes. This CERAPP inventory includes a large fraction of all man-made chemicals to which humans are potentially exposed, spanning chemicals from a variety of use classes, including consumer products, food additives, and human and veterinary drugs.…”

“…Additionally, processes based on publicly available tools have been implemented to perform additional levels of structure standardization and validation, i.e., structure normalization, as well as desalting, to produce what are termed "QSARready" structure files (see "Modeling Representations" review step in Figure 4). 38 The latter files are suitable for calculating a wide range of physicochemical properties, substructure fingerprints, and structure-based descriptors that can serve as inputs into cheminformatics analyses and Quantitative…”

In Silico Study of In Vitro GPCR Assays by QSAR Modeling

“…A curation workflow was designed to process all chemical structures using the free and open-source data-mining environment KNIME [32]. The workflow performed the series of steps described below [33]:…”

A tiered, Bayesian approach to estimating of population variability for regulatory decision-making_suppl

“…The principle behind such a default distribution is the assumption that the chemicals for which data were previously collected are sufficiently representative of chemical space so that a new chemical can be reasonably considered a random draw from the same distribution. To check this assumption, the chemicals examined by Abdo et al (2015b) were compared with the over 32,000 chemicals in the CERAPP dataset (Mansouri et al, 2016), a virtual chemical library that has undergone stringent chemical structure processing and normalization for use in QSAR modeling. Chemical structures were mapped to chemical property space using DRAGON descriptors (DRAGON 6, http://www.…”

“…space as a measure of similarity (Zhu et al, 2009), greater than 97% of the CERAPP chemicals (Mansouri et al, 2016) are within 3 standard deviations of the nearest neighbor distances across the Abdo chemicals. Thus, the Abdo et al (2015b) chemicals represent a highly representative dataset from which to derive a data-derived prior distribution for population variability.…”