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Effects-based chemical category approach for prioritization of low affinity estrogenic chemicals
Citation:
Hornung, M., M. Tapper, J. Denny, Rick Kolanczyk, B. Sheedy, P. Hartig, H. Aladjov, T. Henry, AND P. Schmieder. Effects-based chemical category approach for prioritization of low affinity estrogenic chemicals. SAR AND QSAR IN ENVIRONMENTAL RESEARCH. Taylor & Francis, Inc., Philadelphia, PA, 25(4):289-323, (2014).
Impact/Purpose:
The data reported here is part of the larger rtER database used to build the ER expert system for chemical prioritization which has been extensively reviewed by two EPA FIFRA SAPs in 2009 and 2013.
Description:
Regulatory agencies are charged with addressing the endocrine disrupting potential of a large number of chemicals for which there is often little or no data on which to make decisions. Prioritizing the chemicals of greatest concern for potential hazard to humans and wildlife is an initial step in the process. One approach to selecting chemicals of highest priority for further testing is the development of effects-based chemical categories. This paper presents the collection of in vitro data optimized to detect low affinity estrogen receptor (ER) binding chemicals, and the use of that data to build effects-based chemical categories following QSAR approaches and principles pioneered by Veith and colleagues for application to environmental regulatory challenges. The categories were formulated based upon mechanistic hypotheses of how low affinity non-steroidal chemicals of seemingly dissimilar structure to 17ß-estradiol (E2) can interact with the ER via two distinct binding types. Chemicals within each binding type were organized by biological activity by measuring ER binding and gene activation within a chemical series: p-alkylphenols, o-alkylphenols, p-alkoxyphenols, phenylphenols, parabens, salicylates, p- alkylanilines, p-alkoxyanilines, and phthalates. A competitive rainbow trout ER (rtERáß) binding assay was used to determine binding affinity further confirmed by rtER-mediated endogenous vitellogenin (Vtg) gene expression in precision cut liver slice culture. Active chemicals were all very low affinity ligands compared to E2 with relative binding affinities (RBA) from 0.05% to 0.00001% of E2. Although RBAs were low, the efficacy in the Vtg assay ranged from a response of <1% of maximal Vtg mRNA production by E2 to an efficacy equal to that of E2. Effects-based categories built from the data are presented. A strong relationship between RBA and log Kow was observed for all chemicals categories where ER binding was observed.
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Effects-based chemical category approach for prioritization of low affinity estrogenic chemicals