Saliner A Gallegos, Netzeva T I, Worth A P
European Chemicals Bureau (ECB), Institute for Health and Consumer Protection, Joint Research Centre, European Commission, 21020 Ispra (VA), Italy.
SAR QSAR Environ Res. 2006 Apr;17(2):195-223. doi: 10.1080/10659360600636022.
(Q)SAR models can be used to reduce animal testing as well as to minimise the testing costs. In particular, classification models have been widely used for estimating endpoints with binary activity. The aim of the present study was to develop and validate a classification-based quantitative structure-activity relationship (QSAR) model for endocrine disruption, based on interpretable mechanistic descriptors related to estrogenic gene activation. The model predicts the presence or absence of estrogenic activity according to a pre-defined cut-off in activity as determined in a recombinant yeast assay. The experimental data was obtained from the literature. A two-descriptor classification model was developed that has the form of a decision tree. The predictivity of the model was evaluated by using an external test set and by taking into account the limitations associated with the applicability domain (AD) of the model. The AD was determined as coverage of the model descriptor space. After removing the compounds present in the training set and the compounds outside of the AD, the overall accuracy of classification of the test chemicals was used to assess the predictivity of the model. In addition, the model was shown to meet the OECD Principles for (Q)SAR Validation, making it potentially useful for regulatory purposes.
（定量）构效关系（QSAR）模型可用于减少动物试验并将测试成本降至最低。特别是，分类模型已被广泛用于估计具有二元活性的终点。本研究的目的是基于与雌激素基因激活相关的可解释机制描述符，开发并验证一种基于分类的内分泌干扰定量构效关系（QSAR）模型。该模型根据重组酵母试验中确定的预定义活性截止值预测雌激素活性的存在与否。实验数据来自文献。开发了一种具有决策树形式的双描述符分类模型。通过使用外部测试集并考虑与模型适用域（AD）相关的局限性来评估模型的预测能力。AD被确定为模型描述符空间的覆盖范围。在去除训练集中存在的化合物和AD之外的化合物后，使用测试化学品分类的总体准确性来评估模型的预测能力。此外，该模型被证明符合经合组织（Q）SAR验证原则，使其在监管目的方面具有潜在用途。
