Quantitative Comparisons of in Vitro Assays for Estrogenic Activities

Abstract

Substances that may act as estrogens show a broad chem. structural diversity. To thoroughly address the question of possible adverse estrogenic effects, reliable methods are needed to detect and identify the chems. of these diverse structural classes. The authors compared three assays-in vitro estrogen receptor competitive binding assays (ER binding assays), yeast-based reporter gene assays (yeast assays), and the MCF-7 cell proliferation assay (E-SCREEN assay)-to det. their quant. agreement in identifying structurally diverse estrogens. The authors examd. assay performance for relative sensitivity, detection of active/inactive chems., and estrogen/antiestrogen activities. In this examn., the authors combined individual data sets in a specific, quant. data mining exercise. Data sets for at least 29 chems. from five labs. were analyzed pair-wise by X-Y plots. The ER binding assay was a good predictor for the other two assay results when the antiestrogens were excluded (r2 is 0.78 for the yeast assays and 0.85 for the E-SCREEN assays). Addnl., the examn. strongly suggests that biol. information that is not apparent from any of the individual assays can be discovered by quant. pair-wise comparisons among assays. Antiestrogens are identified as outliers in the ER binding/yeast assay, while complete antagonists are identified in the ER binding and E-SCREEN assays. Furthermore, the presence of outliers may be explained by different mechanisms that induce an endocrine response, different impurities in different batches of chems., different species sensitivity, or limitations of the assay techniques. Although these assays involve different levels of biol. complexity, the major conclusion is that they generally provided consistent information in quant. detg. estrogenic activity for the five data sets examd. The results should provide guidance for expanded data mining examns. and the selection of appropriate assays to screen estrogenic endocrine disruptors. [on SciFinder(R)]