Gaussian field-based comparative 3D QSAR modelling for the identification of favourable pharmacophoric features of chromene derivatives as selective inhibitors of ALR2 over ALR1

Abstract

Aldehyde reductase (ALR1) and aldose reductase (ALR2) are both oxo-reductase enzymes of aldo-keto reductase (AKR) superfamily involved in several cellular processes. ALR1 plays an important role in colorectal cancer, lungs, and hepatic carcinoma, while ALR2 is involved in diabetic complications like retinopathy, neuropathy, and nephropathy cataract. Both the enzymes take part in distinct physiological processes, however, share more > 70% structural homology. This is the major cause behind the unachieved target selectivity of molecules that entered the development pipeline as ALR2 inhibitors. Chromene analogues have been widely explored for diverse biological activities, including antioxidant and diabetic complication prevention potential. For the identification of spatial fingerprints of target-specific chromene bearing ALR2 inhibitors over ALR1, Gaussian field-based comparative 3D QSAR models were generated on a dataset having ALR1 and ALR2 inhibitory activity. Both the ALR1 and ALR2 3D QSAR models were statistically fit with good predictive ability concerning PLS generated validation constraints. The comparative steric, electrostatic, hydrophobic, HBA, and HBD features were elucidated using respective contour maps for selective target specific favourable activity against ALR2 over ALR1. In addition, the five-point pharmacophores for ALR1 and ALR2 favourable features were also generated using the DHHRR_1 hypothesis for better insight on the distinctive features of ALR2 inhibitors compared to ALR1.