Gliptins in managing diabetes - Reviewing computational strategy

Abstract

The pace of anti-diabetic drug discovery is very slow in spite of increasing rate of prevalence of Type 2 Diabetes which remains a major public health concern. Though extensive research steps are taken in the past decade, yet craves for better new treatment strategies to overcome the current scenario. One such general finding is the evolution of gliptins which discriminately inhibits DPP4 (Dipeptidyl peptidase-4) enzyme. Although the mechanism of action of gliptin is highly target oriented and accurate, still its long-term use stands unknown. This step calls for a fast, flexible, and cost-effective strategies to meet the demands of producing arrays of high-content lead compounds with improved efficiency for better clinical success. The present review highlights the available gliptins in the market and also other naturally occurring DPP4 enzyme inhibitors. Along with describing the known inhibitors and their origin in this review, we attempted to identify a probable new lead compounds using advanced computational techniques. In this context, computational methods that integrate the knowledge of proteins and drug responses were utilized in prioritizing targets and designing drugs towards clinical trials with better efficacy. The compounds obtained as a result of virtual screening were compared with the commercially available gliptin in the market to have better efficiency in the identification and validation of the potential DPP4 inhibitors. The combinatorial computational methods used in the present study identified Compound 1: 25022354 as promising inhibitor.

Keywords: ADMET; DPP4 inhibitors; Gliptins; MM-PBSA; Molecular docking; Molecular dynamics; Virtual screening; in silico tools.