Predictive Endocrine Testing in the 21st Century Usingin VitroAssays of Estrogen Receptor Signaling Responses
Author(s) -
Daniel M. Rotroff,
Matt Martin,
David J. Dix,
Dayne L. Filer,
Keith A. Houck,
Thomas B. Knudsen,
Nisha S. Sipes,
David M. Reif,
Menghang Xia,
Ruili Huang,
Richard Judson
Publication year - 2014
Publication title -
environmental science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.851
H-Index - 397
eISSN - 1520-5851
pISSN - 0013-936X
DOI - 10.1021/es502676e
Subject(s) - in vitro toxicology , estrogen receptor , in vitro , in vivo , agonist , chemistry , estrogen , estrogen receptor alpha , pharmacology , receptor , assay sensitivity , endocrine system , prioritization , computational biology , biology , hormone , biochemistry , medicine , endocrinology , microbiology and biotechnology , alternative medicine , pathology , cancer , breast cancer , management science , economics
Thousands of environmental chemicals are subject to regulatory review for their potential to be endocrine disruptors (ED). In vitro high-throughput screening (HTS) assays have emerged as a potential tool for prioritizing chemicals for ED-related whole-animal tests. In this study, 1814 chemicals including pesticide active and inert ingredients, industrial chemicals, food additives, and pharmaceuticals were evaluated in a panel of 13 in vitro HTS assays. The panel of in vitro assays interrogated multiple end points related to estrogen receptor (ER) signaling, namely binding, agonist, antagonist, and cell growth responses. The results from the in vitro assays were used to create an ER Interaction Score. For 36 reference chemicals, an ER Interaction Score >0 showed 100% sensitivity and 87.5% specificity for classifying potential ER activity. The magnitude of the ER Interaction Score was significantly related to the potency classification of the reference chemicals (p < 0.0001). ERα/ERβ selectivity was also evaluated, but relatively few chemicals showed significant selectivity for a specific isoform. When applied to a broader set of chemicals with in vivo uterotrophic data, the ER Interaction Scores showed 91% sensitivity and 65% specificity. Overall, this study provides a novel method for combining in vitro concentration response data from multiple assays and, when applied to a large set of ER data, accurately predicted estrogenic responses and demonstrated its utility for chemical prioritization.
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