a Unité Modèles pour l'Ecotoxicologie et la Toxicologie (METO), Institut National de l'Environnement Industriel et des Risques (INERIS) , Verneuil-en-Halatte , France.
SAR QSAR Environ Res. 2012 Jan;23(1-2):37-57. doi: 10.1080/1062936X.2011.623325. Epub 2011 Oct 21.
The determination of binding affinities for the estrogen receptor (ER) is used extensively to assess potential hazards to human health and the environment arising from chemicals that can interfere with natural hormone homeostasis. Given the great number of chemicals to which humans and wildlife are exposed, (quantitative) structure-activity relationship (Q)SAR models for the characterization of ER disruptors represent a fast and cost-efficient alternative to experimental testing. In this toxicological context, the freely available Organisation for Economic Co-operation and Development (OECD) (Q)SAR Application Toolbox provides a profiler for the categorical profiling of chemicals according to their ER binding propensities. The aim of this study was to evaluate the predictive performances of this profiler. To achieve such a purpose, prediction results with the ER-profiler were compared with experimental binding affinities relative to two large datasets of chemicals (rat and human). The resulting Cooper statistics indicated that the binding affinities of the majority of chemicals included in the retained datasets could be correctly predicted.
测定雌激素受体 (ER) 的结合亲和力被广泛用于评估可能对人类健康和环境造成危害的化学物质,这些化学物质可能会干扰天然激素平衡。鉴于人类和野生动物接触的化学物质数量众多,用于描述 ER 干扰物的(定量)结构-活性关系 (QSAR) 模型是一种快速且具有成本效益的替代实验测试的方法。在这种毒理学背景下,经济合作与发展组织 (OECD) 提供的免费 (Q)SAR 应用工具包为根据 ER 结合倾向对化学品进行分类分析提供了一个分析器。本研究旨在评估该分析器的预测性能。为了达到这一目的,将 ER 分析器的预测结果与相对于两个大型化学品数据集(大鼠和人)的实验结合亲和力进行了比较。结果表明,保留数据集内的大多数化学品的结合亲和力可以被正确预测。