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Predictive Endocrine Testing in the 21st Century Using In Vitro Assays of Estrogen Receptor Signaling Responses
Citation:
Rotroff, D., M. Martin, D. Dix, D. Filer, K. Houck, T. Knudsen, N. Sipes, D. Reif, M. Xia, R. Huang, AND R. Judson. Predictive Endocrine Testing in the 21st Century Using In Vitro Assays of Estrogen Receptor Signaling Responses. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, 48:8706-8716, (2014).
Impact/Purpose:
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.
Description:
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 endpoints 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 omnibus 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 chemials (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.
