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Chem Res Toxicol. 2017 Apr 17;30(4):946-964. doi: 10.1021/acs.chemrestox.6b00347. Epub 2016 Dec 9.

Development and Validation of a Computational Model for Androgen Receptor Activity.

Author information

1
NIH/NIEHS/DNTP/The NTP Interagency Center for the Evaluation of Alternative Toxicological Methods , Research Triangle Park, North Carolina 27713, United States.
2
Integrated Laboratory Systems, Inc. , Research Triangle Park, North Carolina 27560, United States.
3
EPA/ORD/National Center for Computational Toxicology , Research Triangle Park, North Carolina 27711, United States.
4
OECD Environment Directorate, Environment Health and Safety Division , Paris 75775, France.
5
EPA/OCSPP/Office of Science Coordination and Policy , Washington, DC, 20460, United States.
6
NIH/National Center for Advancing Translational Sciences , Bethesda, Maryland 20892, United States.

Abstract

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 11 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 nonspecific 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 semiautomated 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.

PMID:
27933809
PMCID:
PMC5396026
DOI:
10.1021/acs.chemrestox.6b00347
[Indexed for MEDLINE]
Free PMC Article

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