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Toxicol Sci. 2015 Nov;148(1):137-54. doi: 10.1093/toxsci/kfv168. Epub 2015 Aug 13.

Integrated Model of Chemical Perturbations of a Biological Pathway Using 18 In Vitro High-Throughput Screening Assays for the Estrogen Receptor.

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*U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711;
Department of Mathematics, University of Manitoba, Winnipeg, MB, Canada;
Division of Biology, California Institute of Technology, Pasadena, California 91125;
§Department of Mathematics, University of Southern California, Los Angeles, California 90089;
Department of Mathematics, Smith College, Northampton, Massachusetts 01063;
Courant Institute, New York University, New York New York 10012;
NIH Chemical Genomics Center, National Center for Advancing Translational Sciences, Rockville, Maryland 20892;
Department of Statistics and Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27607;
**ORISE Fellow at the U.S. EPA, Research Triangle Park, North Carolina 27711;
*U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711;
NIH National Toxicology Program, Research Triangle Park, North Carolina 27711.


We demonstrate a computational network model that integrates 18 in vitro, high-throughput screening assays measuring estrogen receptor (ER) binding, dimerization, chromatin binding, transcriptional activation, and ER-dependent cell proliferation. The network model uses activity patterns across the in vitro assays to predict whether a chemical is an ER agonist or antagonist, or is otherwise influencing the assays through a manner dependent on the physics and chemistry of the technology platform ("assay interference"). The method is applied to a library of 1812 commercial and environmental chemicals, including 45 ER positive and negative reference chemicals. Among the reference chemicals, the network model correctly identified the agonists and antagonists with the exception of very weak compounds whose activity was outside the concentration range tested. The model agonist score also correlated with the expected potency class of the active reference chemicals. Of the 1812 chemicals evaluated, 111 (6.1%) were predicted to be strongly ER active in agonist or antagonist mode. This dataset and model were also used to begin a systematic investigation of assay interference. The most prominent cause of false-positive activity (activity in an assay that is likely not due to interaction of the chemical with ER) is cytotoxicity. The model provides the ability to prioritize a large set of important environmental chemicals with human exposure potential for additional in vivo endocrine testing. Finally, this model is generalizable to any molecular pathway for which there are multiple upstream and downstream assays available.


EDSP; In vitro; biological modeling; estrogen receptor; high-throughput screening; prioritization

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