Fang H, Tong W, Perkins R, Soto A M, Prechtl N V, Sheehan D M
Division of Genetic and Reproductive Toxicology, National Center for Toxicological Research (NCTR), Jefferson, Arkansas, USA.
Environ Health Perspect. 2000 Aug;108(8):723-9. doi: 10.1289/ehp.00108723.
Substances that may act as estrogens show a broad chemical structural diversity. To thoroughly address the question of possible adverse estrogenic effects, reliable methods are needed to detect and identify the chemicals of these diverse structural classes. We 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 determine their quantitative agreement in identifying structurally diverse estrogens. We examined assay performance for relative sensitivity, detection of active/inactive chemicals, and estrogen/antiestrogen activities. In this examination, we combined individual data sets in a specific, quantitative data mining exercise. Data sets for at least 29 chemicals from five laboratories 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 (r(2) is 0.78 for the yeast assays and 0.85 for the E-SCREEN assays). Additionally, the examination strongly suggests that biologic information that is not apparent from any of the individual assays can be discovered by quantitative 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 chemicals, different species sensitivity, or limitations of the assay techniques. Although these assays involve different levels of biologic complexity, the major conclusion is that they generally provided consistent information in quantitatively determining estrogenic activity for the five data sets examined. The results should provide guidance for expanded data mining examinations and the selection of appropriate assays to screen estrogenic endocrine disruptors.
具有雌激素活性的物质呈现出广泛的化学结构多样性。为了全面探讨可能存在的不良雌激素效应问题，需要可靠的方法来检测和识别这些结构各异的化学物质。我们比较了三种检测方法——体外雌激素受体竞争性结合检测（ER结合检测）、基于酵母的报告基因检测（酵母检测）以及MCF-7细胞增殖检测（E-SCREEN检测）——以确定它们在识别结构多样的雌激素方面的定量一致性。我们考察了这些检测方法在相对灵敏度、活性/非活性化学物质检测以及雌激素/抗雌激素活性方面的性能。在此次考察中，我们通过特定的定量数据挖掘操作将各个数据集进行了合并。来自五个实验室的至少29种化学物质的数据集通过X-Y图进行了两两分析。当排除抗雌激素物质后，ER结合检测对于其他两种检测结果是一个良好的预测指标（酵母检测的r²为0.78，E-SCREEN检测的r²为0.85）。此外，此次考察强烈表明，通过检测方法之间的定量两两比较，可以发现任何单个检测中都不明显的生物学信息。在ER结合/酵母检测中，抗雌激素物质被识别为异常值，而在ER结合和E-SCREEN检测中识别出了完全拮抗剂。此外，异常值的存在可能由诱导内分泌反应的不同机制、不同批次化学物质中的不同杂质、不同物种的敏感性或检测技术的局限性来解释。尽管这些检测涉及不同水平的生物学复杂性，但主要结论是，它们在定量确定所检测的五个数据集的雌激素活性方面总体上提供了一致的信息。这些结果应为扩展数据挖掘考察以及选择合适的检测方法来筛选雌激素内分泌干扰物提供指导。
