Virtual screening of epalrestat mimicking selective ALR2 inhibitors from natural product database: auto pharmacophore, ADMET prediction and molecular dynamics approach

Abstract

Epalrestat is the only effective aldose reductase (ALR2) inhibitor available in the market for the treatment of diabetic neuropathy. Clinical effectiveness of epalrestat in diabetic neuropathy encouraged us to develop some more ALR2 inhibitors with a better therapeutic profile. Herein, we utilized the pharmacophoric features of epalrestat to search some novel ALR2 inhibitors from an InterBioScreen database of natural compounds. ADME and PAINS filters were applied to provide drug-likeness and to remove toxicophores from the screened hits. The pharmacophoric features of 4-hydroxy-2-nonenal (HNE), a well-known substrate of ALR1, were also explored to identify selective ALR2 inhibitors. The structure-based analysis was then adopted to find out the molecules showing interactions with ALR2 which are crucial for their therapeutic activity. These interaction patterns and binding modes were compared with that of epalrestat. Molecular dynamics (MD) analysis was also carried out to get more insight into the interactions of screened hits in the catalytic domain of ALR2. Additionally, the top hits were docked and simulated with aldehyde reductase (ALR1) to determine their selectivity for ALR2 over ALR1. Overall, five hits including STOCKIN-44771, STOCKIN-46041, STOCKIN-59369, STOCKIN-69620 and STOCKIN-88220 were found to possess a good therapeutic profile in terms of key interactions, binding energies and drug-likeness. Two hits, STOCKIN-46041 and STOCKIN-59369, were identified as the most selective ALR2 inhibitors when assessed their selectivity profile. Communicated by Ramaswamy H. Sarma