Recent Developments of In Silico Predictions of Intestinal Absorption and Oral Bioavailability
Recent Developments of In Silico Predictions of Intestinal Absorption and Oral Bioavailability
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DOI：
10.2174/138620709788489082
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发表时间：
2009-06-01
影响因子：
1.8
通讯作者：
Wang, Junmei
中科院分区：
文献类型：
--
作者：
Hou, Tingjun;Li, Youyong;Wang, Junmei
在吸收、分布、代谢、消除和毒性（ADMET）特性中，口服生物利用度不佳确实是停止候选药物进一步开发的重要原因。因此，口服生物利用度和生物利用度相关特性的预测，特别是肠道吸收，是需要取得进展的领域，以帮助药物开发。在本文中，我们回顾了被动肠道吸收和口服生物利用度预测的最新进展。综述了用于模型构建的数据集、分子描述符、预测模型和统计建模技术的进展。此外，我们比较了一种机器学习方法支持向量机（SVM）和一种传统分类方法递归划分（RP）在被动吸收预测上的性能。我们的比较表明，复杂的机器学习方法可以提供比传统方法更好的预测。最后，我们讨论了当前仍有待解决的挑战。
Among the absorption, distribution, metabolism, elimination, and toxicity properties (ADMET), unfavorable oral bioavailability is indeed an important reason for stopping further development of the drug candidates. Thus, predictions of oral bioavailability and bioavailability-related properties, especially intestinal absorption are areas in need of progress to aid pharmaceutical drug development. In this article, we review recent developments in the prediction of passive intestinal absorption and oral bioavailability. The advances in the datasets used for model building, the molecular descriptors, the prediction models, and the statistical modeling techniques, are summarized. Furthermore, we compared the performance of one machine learning method, support vector machines (SVM), and one traditional classification method, recursive partitioning (RP), on the predictions of passive absorption. Our comparisons demonstrate that the complex machine learning method could give better predictions than the traditional approach. Finally we discuss the current challenges that remain to be addressed.