Data and supplemental files from COMPARA (A large-scale modeling project). COMPARA combined multiple models developed in collaboration with modelers and computational toxicology scientists from 25 international groups to predict androgen receptor activity of a common set of 55,450 chemical structures. Quantitative structure-activity relationship models and docking approaches were employed to build a total of 91 predictive models for binding, agonist, and antagonist. The consensus values for agonist and antagonist activity are being made public through the CompTox Chemicals Dashboard. This dataset is associated with the following publication: Mansouri, K., N. Kleinstreuer, R. Judson, A. Williams, I. Shah, and A. Richard. CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 128(2): 27002, (2020).

Screening certain environmental chemicals for their ability to interact with endocrine targets, including the androgen receptor (AR), is an important global concern. We previously developed a model using a battery of eleven in vitro AR assays to predict in vivo AR activity. Here we describe a revised mathematical modelling approach that also incorporates data from newly available assays and demonstrate that subsets of assays can provide close to the same level of predictivity. These subset models are evaluated against the full model using 1820 chemicals, as well as in vitro and in vivo reference chemicals from the literature. This dataset is associated with the following publication: Judson, R., K. Houck, K. Friedman, J. Brown, P. Browne, P. Johnston, D. Close, K. Mansouri, and N. Kleinstreuer. Selecting a Minimal set of Androgen Receptor Assays for Screening Chemicals. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 117(November 2020): 104764, (2020).

A diverse data set of 1667 chemicals with AR experimental activity were provided by the U.S. EPA from the oxicity Forecaster (ToxCast) program which generates data using in vitro high-throughput screening (HTS) assays measuring activity of chemicals at multiple points along the androgen receptor (AR) activity pathway. The Endocrine Disruptor Knowledgebase (EDKB) androgen receptor (AR) binding data set (Fang et al., 2003) was downloaded from the FDA website and was produced expressly as a training set designed for developing predictive models. The data is based on a validated assay using recombinant AR. The dataset contains 146 AR binders and 56 non-AR binders. These training set chemicals were selected for both chemical structure diversity and range of activity, both of which are essential to develop robust QSAR and other models (Perkins, 2003). This dataset is associated with the following publication: Manganelli, S., A. Roncaglioni, K. Mansouri, R. Judson, E. Benfenati, A. Manganaro, and P. Ruiz. Development, validation and integration of in silico models to identify androgen active chemicals. CHEMOSPHERE. Elsevier Science Ltd, New York, NY, USA, 220: 204-215, (2019).

Data sets include 1. Excel file with Hershberger assay protocols and data and summaries of in vivo antiandrogen studies 2. Figures of in vitro AR assay results from contract work and in house studies 3. Excel file with in house in vitro AR antagonism data. This dataset is associated with the following publication: Gray, L., J. Furr, C. Lambright, N. Evans, P. Hartig, M. Cardon, V. Wilson, A. Hotchkiss, and J. Conley. Quantification of uncertainties in extrapolating from in vitro androgen receptor (AR) antagonism to in vivo Hershberger Assay endpoints and adverse reproductive development in male rats. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 176(2): 297-311, (2020).

Data sets include 1. Excel file with Hershberger assay protocols and data and summaries of in vivo antiandrogen studies 2. Figures of in vitro AR assay results from contract work and in house studies 3. Excel file with in house in vitro AR antagonism data. This dataset is associated with the following publication: Gray, L., J. Furr, C. Lambright, N. Evans, P. Hartig, M. Cardon, V. Wilson, A. Hotchkiss, and J. Conley. Quantification of uncertainties in extrapolating from in vitro androgen receptor (AR) antagonism to in vivo Hershberger Assay endpoints and adverse reproductive development in male rats. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 176(2): 297-311, (2020).

ToxCast bioactivity data and model predictions for the estrogen receptor (ER) and androgen receptor (AR) pathways were obtained from the inks provided. This dataset is associated with the following publication: Lizarraga, L., J. Dean, J. Kaiser, S. Wesselkamper, J. Lambert, and J. Zhao. A Case Study on the Application of An Expert-driven Read-Across Approach in Support of Quantitative Risk Assessment of p,p’-Dichlorodiphenyldichloroethane. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 103: 301-313, (2019).

ToxCast bioactivity data and model predictions for the estrogen receptor (ER) and androgen receptor (AR) pathways were obtained from the inks provided. This dataset is associated with the following publication: Lizarraga, L., J. Dean, J. Kaiser, S. Wesselkamper, J. Lambert, and J. Zhao. A Case Study on the Application of An Expert-driven Read-Across Approach in Support of Quantitative Risk Assessment of p,p’-Dichlorodiphenyldichloroethane. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 103: 301-313, (2019).

Data from a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) demonstrating using predictive computational models on high-throughput screening data to screen thousands of chemicals against the estrogen receptor. This dataset is associated with the following publication: Mansouri , K., A. Abdelaziz, A. Rybacka, A. Roncaglioni, A. Tropsha, A. Varnek, A. Zakharov, A. Worth, A. Richard , C. Grulke , D. Trisciuzzi, D. Fourches, D. Horvath, E. Benfenati , E. Muratov, E.B. Wedebye, F. Grisoni, G.F. Mangiatordi, G.M. Incisivo, H. Hong, H.W. Ng, I.V. Tetko, I. Balabin, J. Kancherla , J. Shen, J. Burton, M. Nicklaus, M. Cassotti, N.G. Nikolov, O. Nicolotti, P.L. Andersson, Q. Zang, R. Politi, R.D. Beger , R. Todeschini, R. Huang, S. Farag, S.A. Rosenberg, S. Slavov, X. Hu, and R. Judson. (Environmental Health Perspectives) CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 1-49, (2016).

This is an original dataset generated at the U.S. EPA. Data was analyzed with the ToxCast Pipeline and deposited for release in invitroDB accessible via the U.S. EPA CompTox Chemicals Dashboard. Dataset is a zip file containing two Excel spreadsheets titled AIME-ERTA_384_Tables_All_Submission_v2 and README_AIME-ERTA_384_Manuscript_Sup_Data_v2. This dataset is associated with the following publication: Hopperstad, K., D. DeGroot, T. Zurlinden, C. Brinkman, R. Thomas, and C. Deisenroth. Chemical Screening in an Estrogen Receptor Transactivation Assay with Metabolic Competence. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 187(1): 112-126, (2022).

Microarray experiments examined in the study. This dataset is associated with the following publication: Rooney, J., N. Ryan, J. Liu, R. Houtman, R. van Beuningen, J. Hsieh, G. Chang, S. Chen, and J. Corton. A Gene Expression Biomarker Identifies Chemical Modulators of Estrogen Receptor α in an MCF-7 Microarray Compendium. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 34(2): 313-329, (2021).