Science Inventory

Development and Validation of a Computational Model for Androgen Receptor Activity


Kleinstreuer, N., P. Ceger, Eric Watt, M. Martin, K. Houck, P. Browne, R. Thomas, W. Casey, D. Dix, D. Allen, S. Sakamuru, M. Xia, R. Huang, AND R. Judson. Development and Validation of a Computational Model for Androgen Receptor Activity. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, 30(4):946-964, (2017).


• Agency Problem: The EPA’s Endocrine Disruptor Screening Program (EDSP) screens chemicals for effects on hormones. To predict chemical perturbation of androgen activity, the EDSP has traditionally relied upon an animal assay. The cost and time of this animal assay limits the number of chemicals that can be tested. As a result, the EDSP has sought predictive models of androgen activity to increase throughput. • Approach: A series of high throughput models for androgen activity were combined to build a predictive computational model for androgen activity. To evaluate this computational model, chemicals with known effects on androgen activity were selected from a database of ~3200 chemicals. • Results: Overall agreement between the control data and computational model was 66% (19/29). Most discrepancies between the computational model and the ieference chemicals could be explained by differences in dosimetry. The computational model had 100% positive predictive performance for the in vivo responses, meaning that chemicals predicted by the computational model to disrupt androgen activity were observed to impact androgen in the animal assay. • Impact The impact of the research in the companion papers is in two primary areas: 1) identification of a set of reference chemicals for androgen-related endocrine disruption and 2) evaluation of the performance of a previously published computational model for androgen receptor activity using the reference chemicals. Screening and identification of chemicals that disrupt androgen activity is a priority for EPA’s Endocrine Disruptor Screening Program.


Testing thousands of chemicals to identify potential androgen receptor (AR) agonists or antagonists would cost millions of dollars and take decades to complete using current validated methods. High-throughput in vitro screening (HTS) and computational toxicology approaches can more rapidly and inexpensively identify potential androgen-active chemicals. We integrated eleven HTS ToxCast/Tox21 in vitro assays into a computational network model to distinguish true AR pathway activity from technology-specific assay interference. The in vitro HTS assays probed perturbations of the AR pathway at multiple points (receptor binding, coregulator recruitment, gene transcription and protein production) and multiple cell types. Confirmatory in vitro antagonist assay data and cytotoxicity information were used as additional flags for potential non-specific activity. Validating such alternative testing strategies requires high-quality reference data. We compiled 158 putative androgen-active and inactive chemicals from a combination of international test method validation efforts and semi-automated systematic literature reviews. Detailed in vitro assay information and results were compiled into a single database using a standardized ontology. Reference chemical concentrations that activated or inhibited AR pathway activity were identified to establish a range of potencies with reproducible reference chemical results. Comparison with existing Tier 1 AR binding data from the U.S. EPA Endocrine Disruptor Screening Program revealed that the model identified binders at relevant test concentrations (<100 µM), and was more sensitive to antagonist activity. The AR pathway model based on the ToxCast/Tox21 assays had balanced accuracies of 95.2% for agonist (n=29) and 97.5% for antagonist (n=28) reference chemicals. Out of 1855 chemicals screened in the AR pathway model, 220 chemicals demonstrated AR agonist or antagonist activity, and an additional 174 chemicals were predicted to have potential weak AR pathway activity. This work does not reflect the official policy of any federal agency.

URLs/Downloads:   Exit

Record Details:

Product Published Date: 04/17/2017
Record Last Revised: 05/11/2018
OMB Category: Other
Record ID: 337507