Tong W, Perkins R, Strelitz R, Collantes E R, Keenan S, Welsh W J, Branham W S, Sheehan D M
R.O.W. Sciences, Jefferson, AR 72079, USA.
Environ Health Perspect. 1997 Oct;105(10):1116-24. doi: 10.1289/ehp.971051116.
The recognition of adverse effects due to environmental endocrine disruptors in humans and wildlife has focused attention on the need for predictive tools to select the most likely estrogenic chemicals from a very large number of chemicals for subsequent screening and/or testing for potential environmental toxicity. A three-dimensional quantitative structure-activity relationship (QSAR) model using comparative molecular field analysis (CoMFA) was constructed based on relative binding affinity (RBA) data from an estrogen receptor (ER) binding assay using calf uterine cytosol. The model demonstrated significant correlation of the calculated steric and electrostatic fields with RBA and yielded predictions that agreed well with experimental values over the entire range of RBA values. Analysis of the CoMFA three-dimensional contour plots revealed a consistent picture of the structural features that are largely responsible for the observed variations in RBA. Importantly, we established a correlation between the predicted RBA values for calf ER and their actual RBA values for human ER. These findings suggest a means to begin to construct a more comprehensive estrogen knowledge base by combining RBA assay data from multiple species in 3D-QSAR based predictive models, which could then be used to screen untested chemicals for their potential to bind to the ER. Another QSAR model was developed based on classical physicochemical descriptors generated using the CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) program. The predictive ability of the CoMFA model was superior to the corresponding CODESSA model.
对人类和野生动物中环境内分泌干扰物所产生的不良反应的认识，已将注意力集中在需要有预测工具，以便从大量化学物质中筛选出最有可能具有雌激素活性的化学物质，用于后续的潜在环境毒性筛选和/或测试。基于使用小牛子宫胞质溶胶进行的雌激素受体（ER）结合试验的相对结合亲和力（RBA）数据，构建了一个使用比较分子场分析（CoMFA）的三维定量构效关系（QSAR）模型。该模型表明，计算出的空间场和静电场与RBA具有显著相关性，并且在整个RBA值范围内得出的预测结果与实验值吻合良好。对CoMFA三维等高线图的分析揭示了在很大程度上导致观察到的RBA变化的结构特征的一致情况。重要的是，我们建立了小牛ER的预测RBA值与其人类ER的实际RBA值之间的相关性。这些发现表明了一种通过在基于三维定量构效关系的预测模型中结合来自多个物种的RBA测定数据来开始构建更全面的雌激素知识库的方法，该知识库随后可用于筛选未测试的化学物质与ER结合的潜力。基于使用CODESSA（结构和统计分析综合描述符）程序生成的经典物理化学描述符，开发了另一个QSAR模型。CoMFA模型的预测能力优于相应的CODESSA模型。
