A quantitative structure–activity relationship study investigating the potential antidiabetic activity of aryl sulfonyl piperazines

Background: In Diabetes Mellitus, gluconeogenesis can contribute up to 90% of the increase in overall hepatic glucose production. Excessive concentration of glucocorticoid in the liver and adipose tissue can lead to glucose intolerance and insulin resistance. Hydroxysteroid Dehydrogenase is family of enzymes that regulate ligand binding to various steroid hormones to the inactive (precursor) forms & vice-versa. The type 1 isoform (11-β-HSD1), located primarily in the liver and adipose tissue, is a low affinity, NADPH-dependent bidirectional enzyme which acts predominantly as an oxidoreductase to form the active glucocorticoids, whereas the type 2 isoform (11-β-HSD2), located primarily in the kidney, is a high affinity, unidirectional enzyme functioning exclusively as a NAD-dependent dehydrogenase producing inactive 11-keto metabolites. Aims and Objectives: Quantitative structure-activity relationship (QSAR) is a prominent tool to explore the relationship between the structures of ligands and their binding affinities. QSAR methodologies save resources and expedite the process of the development of new molecules and drugs. QSAR increases the probability of success and reduce the time and cost involved in the drug discovery process. In view of this we decided to develop models from classical QSAR descriptors using multiple linear regressions and partial least square. Materials and Methods: The quantitative structure-activity relationship (QSAR) models were developed using multiple linear regression and partial least squares for a set of 29 molecules as 11-beta hydroxysteroid dehydrogenase type 1 (11-β-HSD1) Inhibitor. Results: The regression analysis demonstrated that the three descriptors namely total dipole moment (whole molecule), kier Chi4 (Path/Cluster) index (whole molecule), and VAMP polarization ZZ (whole molecule), have significant impact on anti-diabetic activity of the compounds. The best predictive QSAR model derived had r2cv of 0.7878, non cross-validation r2 of 0.8172, predictive r2 for test set 0.7855 and standard error of estimate 0.2433. Conclusions: The results clearly indicate that the anti-diabetic activity exhibited by the series is predominantly explained by the polarizability factor of the whole molecules, and hence modulation of the dipole could be used to optimize activity.