Development of QSAR models for microsomal stability: identification of good and bad structural features for rat, human and mouse microsomal stability
Development of QSAR models for microsomal stability: identification of good and bad structural features for rat, human and mouse microsomal stability
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DOI：
10.1007/s10822-009-9309-9
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发表时间：
2010-01-01
影响因子：
3.5
通讯作者：
Humblet, Christine
中科院分区：
文献类型：
--
作者：
Hu, Yongbo;Unwalla, Ray;Humblet, Christine
高通量微粒体稳定性测定已在药物发现中广泛应用，许多公司已经积累了数千种化合物的实验测量。此类数据集已用于开发计算机模型，以预测代谢稳定性并指导选择有希望的合成候选物。事实证明，在合成之前从建议的虚拟库中选择化合物时，这种方法最为有效。然而，这些模型在结构层面上不容易解释，因此几乎无法提供指导传统合成工作的见解。我们利用内部数据开发了大鼠、小鼠和人类肝微粒体稳定性的全球分类模型。这些模型是使用 Pipeline Pilot 中的朴素贝叶斯分类器通过 FCFP_6 指纹构建的。测试集被正确分类为稳定或不稳定，大鼠、人类和小鼠模型的准确率分别为 78%、77% 和 75%。使用贝叶斯评分分配预测置信度以评估模型的适用性。使用所得模型，我们开发了一种新颖的数据挖掘策略来识别与良好和不良微粒体稳定性相关的结构特征。我们还使用这种方法来识别对一个物种有利但对另一个物种不利的结构特征。有了这些发现，在药物发现中可能会更快、更早地理解结构-代谢关系。
High throughput microsomal stability assays have been widely implemented in drug discovery and many companies have accumulated experimental measurements for thousands of compounds. Such datasets have been used to develop in silico models to predict metabolic stability and guide the selection of promising candidates for synthesis. This approach has proven most effective when selecting compounds from proposed virtual libraries prior to synthesis. However, these models are not easily interpretable at the structural level, and thus provide little insight to guide traditional synthetic efforts. We have developed global classification models of rat, mouse and human liver microsomal stability using in-house data. These models were built with FCFP_6 fingerprints using a Na < ve Bayesian classifier within Pipeline Pilot. The test sets were correctly classified as stable or unstable with satisfying accuracies of 78, 77 and 75% for rat, human and mouse models, respectively. The prediction confidence was assigned using the Bayesian score to assess the applicability of the models. Using the resulting models, we developed a novel data mining strategy to identify structural features associated with good and bad microsomal stability. We also used this approach to identify structural features which are good for one species but bad for another. With these findings, the structure-metabolism relationships are likely to be understood faster and earlier in drug discovery.