Development, validation and integration of in silico models to identify androgen active chemicals. (April 2019)

• Record Type: Journal Article
• Authors: Manganelli, Serena; Roncaglioni, Alessandra; Mansouri, Kamel; Judson, Richard S.; Benfenati, Emilio; Manganaro, Alberto; Ruiz, Patricia
• Is Part Of: Chemosphere. Volume 220(2019)
• Page Start: 204
• Page End: 215
• Publication Date: 2019-04
• Subjects: Endocrine disrupting chemicals -- High-throughput screening -- Androgen receptor -- In silico -- Artificial neural networks -- Support vector machine -- Decision tree; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2018.12.131
• Languages: English
• ISSNs: 0045-6535

Abstract

Abstract: Humans are exposed to large numbers of environmental chemicals, some of which potentially interfere with the endocrine system. The identification of potential endocrine disrupting chemicals (EDCs) has gained increasing priority in the assessment of environmental hazards. The U.S. Environmental Protection Agency (U.S. EPA) has developed the Endocrine Disruptor Screening Program (EDSP) which aims to prioritize and screen potential EDCs. The Toxicity Forecaster (ToxCast) program has generated data using in vitro high-throughput screening (HTS) assays measuring activity of chemicals at multiple points along the androgen receptor (AR) activity pathway. In the present study, using a large and diverse data set of 1667 chemicals provided by the U.S. EPA from the combined ToxCast AR assays in the framework of the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA). Two models were built using ADMET Predictor™; one is based on Artificial Neural Networks (ANNs) technology and the other uses a Support Vector Machine (SVM) algorithm; one model is a Decision Tree (DT) developed in R; and two models make use of differently combined sets of structural alerts (SAs) automatically extracted by SARpy. We used two strategies to integrate predictions from single models; one is based on a majority vote approach and the other on prediction convergence. These strategies led to enhanced statistical performance in most cases. Moreover, the majority vote approach improved prediction coverage when one or more single models were not able to provide any estimations. This study integrates multiple in silico approaches as a virtual screening tool for use in risk assessment of endocrine disrupting chemicals. Highlights: Integration of in silico approaches as a virtual screening tool for use in risk assessment of endocrine disrupting chemicals. Five classification models for predicting Androgen receptor-binding activity potential were developed and evaluated. ANNs, SVM and DT modeling approaches are presented and validated for transparency and reliability. These results enhance our understanding of the Androgen receptor-binding activity of environmental chemicals.