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ADME Evaluation in Drug Discovery.7.Prediction of Oral Absorption by Correlation and Classification

机译:药物发现中的ADME评价7.通过相关性和分类预测口服吸收

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A critically evaluated database of human intestinal absorption for 648 chemical compounds is reported in this study, among which 579 are believed to be transported by passive diffusion. The correlation analysis between the intestinal absorption and several important molecular properties demonstrated that no single molecular property could be used as a good discriminator to efficiently distinguish the poorly absorbed compounds from those that are well absorbed. The theoretical correlation models for a training set of 455 compounds were proposed by using the genetic function approximation technique. The best prediction model contains four molecular descriptors: topoiogical polar surface area, the predicted distribution coefficient at pH =6.5, the number of violations of the Lipinski's rule-of-five, and the square of the number of hydrogenbond donors. The model was able to predict the fractional absorption with an r=0.84 and a prediction error (absolute mean error) of 11.2% for the training set. Moreover, it achieves an r=0.90 and a prediction error of 7.8% for a 98-compound test set. The recursive partitioning technique was applied to find the simple hierarchical rules to classify the compounds into poor (%FA≤30%) and good (%FA > 30%) intestinal absorption classes. The high quality of the classification model was validated by the satisfactory predictions on the training set (correctly identifying 95.9% of the compounds in the poor-absorption class and 96.1% of the compounds in the good-absorption class) and on the test set (correctly identifying 100% of the compounds in the poor-absorption class and 96.8% of the compounds in the good-absorption class). We expect that, in the future, the rules for the prediction of carrier-mediated transporting and first pass metabolism can be integrated into the current hierarchical rules, and the classification model may become more powerful in the prediction of intestinal absorption or even human bioavailability. The databases of human intestinal absorption reported here are available for download from the supporting Web site: http:// modern ucsd.edu/adme.
机译:这项研究报告了对人体对648种化合物的肠道吸收进行了严格评估的数据库,其中579种被认为是通过被动扩散转运的。肠道吸收与几种重要分子特性之间的相关性分析表明,没有一种分子特性可以用作有效区分吸收不好的化合物和吸收好的化合物的良好区分剂。通过使用遗传函数逼近技术,提出了455种化合物的训练集的理论相关模型。最佳预测模型包含四个分子描述符:拓扑极性表面积,pH = 6.5时的预测分布系数,违反Lipinski五法则的次数以及氢键供体数量的平方。对于训练集,该模型能够以r = 0.84预测分数吸收,预测误差(绝对平均误差)为11.2%。此外,对于98个化合物的测试集,它的r = 0.90和7.8%的预测误差。应用递归分配技术找到简单的分级规则,将化合物分类为不良(%FA≤30%)和良好(%FA> 30%)肠道吸收类别。分类模型的高质量通过训练集上的令人满意的预测(正确地识别了95.9%的低吸收性化合物和96.1%的高吸收性化合物)和测试集得到了满意的预测。正确识别100%吸收不良的化合物和96.8%吸收良好的化合物)。我们希望,将来,可以将用于预测载体介导的运输和首过代谢的规则整合到当前的分层规则中,并且分类模型在预测肠道吸收甚至人类生物利用度方面可能会变得更加强大。此处报告的人体肠道吸收数据库可从支持网站下载:http:// modern ucsd.edu/adme。

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