Data on 1855 chemicals were generated during ToxCast Phases I and II and Tox21 screening using 11 AR-related in vitro assays to build a computational network model for AR pathway activity. This dataset is associated with the following publication: Kleinstreuer, N.C., P. Ceger, E. Watt, M. Martin, K. Houck, P. Browne, R. Thomas, W. Casey, D. Dix, D. Allen, S. Sakamuru, M. Xia, R. Huang, and R. Judson. (Chemical Research in Toxicology) Development and Validation of a Computational Model for Androgen Receptor Activity. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 30(4): 946-964, (2017).

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).

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 are summarized in a two-dimensional data matrix that was developed for each substance for hazard characterization (Tables S1–S3). In the horizontal direction of the matrix, read-across of the target phenol to the source analogues was performed for the purpose of data-gap filling, whereas in the vertical direction, data from different streams (traditional and NAM) were compared and contrasted, to evaluate concordance of orthogonal approaches for evaluating potential estrogenicity. The greater the degree of agreement in orthogonal approaches for determining bioactivity, the greater the confidence one has in using the collective results of such NAMs in hazard characterization of the target phenol. This dataset is associated with the following publication: Webster, F., M. Gagne, G. Patlewicz, P. Pradeep, N. Trefiak, R. Judson, and T. Barton-Maclaren. Predicting estrogen receptor activation by a group of substituted phenols: An integrated approach to testing and assessment case study. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 106: 278-291, (2019).

This data includes metabolite predictions for in vitro inactive chemicals, predictions of those metabolite's estrogen receptor binding activity, in vitro and in silico information regarding parent compound binding activities, linking of metabolite structures and routes to parent compounds, and estimates of binding activity obtained from literature when possible. This dataset is associated with the following publication: Leonard, J., C. Stevens, K. Mansouri, D. Chang, H. Pudukodu, S. Smith, and C. Tan. A Workflow for Identifying Metabolically Active Chemicals to Complement in vitro Toxicity Screening. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 6: 71-83, (2018).

This dataset provides a prediction for all Tox21 chemicals with available QSUR descriptors across all 41 valid QSUR models developed with FUse. This dataset is associated with the following publication: Phillips, K., J. Wambaugh, C. Grulke, K. Dionisio, and K. Isaacs. High-throughput screening of chemicals as functional substitutes using structure-based classification models. GREEN CHEMISTRY. Royal Society of Chemistry, Cambridge, UK, 19: 1063-1074, (2017).

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).

Phenones and hydroxy benzophenones are widely used as UV radiation filters, and in the manufacturing of insecticides and pharmaceuticals. Understanding the ability of these chemicals to mimic the effects of the steroid estrogen is of interest to the US Environmental Protection Agency and other international environmental organizations. The current study sequentially combined complementary in vitro (outside a living organism) rainbow trout estrogen receptor (rtER) binding and liver slice vitellogenin (Vtg) mRNA induction assays in the context of a defined ER-mediated adverse outcome pathway (AOP). Cyclic phenones, branched phenones, and hydroxybenzophenones bound to rtER with relative potency ranging from no affinity to high binding affinity of 0.11%, and many induced Vtg, an egg yolk protein, gene expression in rt liver slices. In addition, cyclohexylphenylketone which did not bind rtER binding in cytosol was biotransformed within liver tissue to a chemical that induced Vtg expression. Cyclic phenones, branched phenones and hydroxybenzophenones produced estrogen like responses in these rainbow trout in vitro assays. This dataset is associated with the following publication: Tapper, M., J. Denny, J. Serrano, R. Kolanczyk, B. Sheedy, G. Overland, M. Hornung, and P. Schmieder. Phenone, hydroxybenzophenone, and branched phenone estrogen receptor binding and vitellogenin agonism in rainbow trout in vitro models. Applied In Vitro Toxicology. Mary Ann Liebert, Inc., Larchmont, NY, USA, 5(1): 62-74, (2019).

Adverse outcome pathway (AOP) networks potentially provide a basis for predictive approaches to assess the toxicity of chemical mixtures. This study evaluated the utility of a simple AOP network to predict the interactive effects of a binary chemical mixture comprised of an inhibitor of the aromatase enzyme (fadrozole, a human pharmaceutical) and an agonist of the androgen receptor (trenbolone, a veterinary drug). Overall, prediction of interactive effects of the two chemicals based on the AOP network did not match actual observed effects. Rather, the two compounds seemed to interact in an independent manner in terms of their effects on the hypothalamic-pituitary-gonadal axis in the fish. This dataset is associated with the following publication: Ankley, G., B. Blackwell, J. Cavallin, J. Doering, D.J. Feifarek, K. Jensen, M. Kahl, C. Lalone, S. Poole, E. Randolph, T. Saari, and D. Villeneuve. Adverse outcome pathway network-based assessment of the interactive effects of an androgen receptor agonist and an aromatase inhibitor on fish endocrine function. AQUATIC TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 39(4): 913-922, (2020).