Liu Huanxiang, Papa Ester, Gramatica Paola
Department of Structural and Functional Biology, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, University of Insubria, via Dunant 3, 21100 Varese, Italy.
Chem Res Toxicol. 2006 Nov;19(11):1540-8. doi: 10.1021/tx0601509.
A large number of environmental chemicals, known as endocrine-disrupting chemicals, are suspected of disrupting endocrine functions by mimicking or antagonizing natural hormones, and such chemicals may pose a serious threat to the health of humans and wildlife. They are thought to act through a variety of mechanisms, mainly estrogen-receptor-mediated mechanisms of toxicity. However, it is practically impossible to perform thorough toxicological tests on all potential xenoestrogens, and thus, the quantitative structure--activity relationship (QSAR) provides a promising method for the estimation of a compound's estrogenic activity. Here, QSAR models of the estrogen receptor binding affinity of a large data set of heterogeneous chemicals have been built using theoretical molecular descriptors, giving full consideration to the new OECD principles in regulation for QSAR acceptability, during model construction and assessment. An unambiguous multiple linear regression (MLR) algorithm was used to build the models, and model predictive ability was validated by both internal and external validation. The applicability domain was checked by the leverage approach to verify prediction reliability. The results obtained using several validation paths indicate that the proposed QSAR model is robust and satisfactory, and can provide a feasible and practical tool for the rapid screening of the estrogen activity of organic compounds.
大量被称为内分泌干扰化学物的环境化学物质，被怀疑通过模拟或拮抗天然激素来干扰内分泌功能，并且这类化学物质可能对人类和野生动物的健康构成严重威胁。它们被认为通过多种机制起作用，主要是雌激素受体介导的毒性机制。然而，对所有潜在的外源性雌激素进行全面的毒理学测试几乎是不可能的，因此，定量构效关系（QSAR）为估计化合物的雌激素活性提供了一种很有前景的方法。在此，利用理论分子描述符建立了一大组异类化学物质的雌激素受体结合亲和力的QSAR模型，在模型构建和评估过程中充分考虑了经合组织关于QSAR可接受性的新规定。使用明确的多元线性回归（MLR）算法构建模型，并通过内部和外部验证来验证模型的预测能力。通过杠杆率方法检查适用域以验证预测可靠性。使用多种验证途径获得的结果表明，所提出的QSAR模型是稳健且令人满意的，并且可以为快速筛选有机化合物的雌激素活性提供一种可行且实用的工具。
