Subramanian Govindan, Kitchen Douglas B
Computer-Aided Drug Discovery Department, Albany Molecular Research, Inc., 21 Corporate Circle, P.O. Box 15098, Albany, NY 12212-5098, USA.
J Mol Model. 2006 Jul;12(5):577-89. doi: 10.1007/s00894-005-0065-z. Epub 2006 Apr 1.
Human intestinal absorption (HIA) is an important roadblock in the formulation of new drug substances. Computational models are needed for the rapid estimation of this property. The measurements are determined via in vivo experiments or in vitro permeability studies. We present several computational models that are able to predict the absorption of drugs by the human intestine and the permeability through human Caco-2 cells. The training and prediction sets were derived from literature sources and carefully examined to eliminate compounds that are actively transported. We compare our results to models derived by other methods and find that the statistical quality is similar. We believe that models derived from both sources of experimental data would provide greater consistency in predictions. The performance of several QSPR models that we investigated to predict outside the training set for either experimental property clearly indicates that caution should be exercised while applying any of the models for quantitative predictions. However, we are able to show that the qualitative predictions can be obtained with close to a 70% success rate.
人体肠道吸收（HIA）是新药物质制剂研发中的一个重要障碍。需要计算模型来快速估算这一特性。该特性通过体内实验或体外通透性研究来测定。我们提出了几种能够预测药物在人体肠道中的吸收以及通过人Caco-2细胞的通透性的计算模型。训练集和预测集来源于文献资料，并经过仔细筛选以排除主动转运的化合物。我们将我们的结果与其他方法得出的模型进行比较，发现统计质量相似。我们认为，来自两种实验数据来源的模型将在预测中提供更高的一致性。我们研究的几个用于预测训练集之外任何一种实验特性的QSPR模型的性能清楚地表明，在应用任何模型进行定量预测时都应谨慎。然而，我们能够表明，定性预测的成功率接近70%。
