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Combinatorial Chemistry & High Throughput Screening


ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

Recent Developments of In Silico Predictions of Intestinal Absorption and Oral Bioavailability

Author(s): Tingjun Hou, Youyong Li, Wei Zhang and Junmei Wang

Volume 12, Issue 5, 2009

Page: [497 - 506] Pages: 10

DOI: 10.2174/138620709788489082

Price: $65


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.

Keywords: ADMET, intestinal absorption, bioavailability, in silico prediction, machine learning

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