Regression analysis: Identifying molecular descriptors for HIA, MDCK and caco-2

Oral bioavailability depends on many physiological, physiochemical and formulation factors. Poor oral bioavailability is an important parameter accounting for the failure of the drug candidates. 50% of drug failure is because of unfavorable bioavailability. Two important properties that govern oral absorption are in vitro permeability and solubility, which are commonly used as indicators of Human Intestinal Absorption (HIA) and Colon epithelial cancer cell line (Caco-2) and Madin-Darby Canine Kidney cells (MDCK) for permeability. In silico prediction of oral bioavailability based on physiochemical properties are highly needed. Although many computational models have been developed to predict absorption and permeability, their accuracy remains low with a significant number of false positives. In this study, we present model based on systems biological approach, using regression analysis of predictions coupled with physiochemical descriptors. A large dataset of HIA, Caco2, MDCK predictions was collated along with physiochemical descriptors for the chosen chemical structures. The descriptors found common in three regression analysis showed good relation with rule of five descriptors. Nevertheless, the study captures the fundamental molecular descriptors, which can be used as an entity to facilitate increase in oral bioavailability.