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

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

Volume: 12 Issue: 5

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

Affiliation:

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

Abstract: 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.

Cite this article as:

Hou Tingjun, Li Youyong, Zhang Wei and Wang Junmei, Recent Developments of In Silico Predictions of Intestinal Absorption and Oral Bioavailability, Combinatorial Chemistry & High Throughput Screening 2009; 12 (5) . https://dx.doi.org/10.2174/138620709788489082