ABSTRACT
Diabetes is a progressive hyperglycemic condition triggered by either a total lack of insulin (type 1) or insufficient insulin that cannot be used efficiently (type 2). Despite numerous pharmacological and non-pharmacological interventions, diabetes still remains a silent epidemic. Therefore, natural medicinal therapy such as lichens and their bioactive phytochemicals were explored as a promising alternative to impede diabetes and its analogous comorbidities. To aid the process, bioinformatics tools were employed, as they expedite the process of drug candidate screening, reducing the onus on experimental animals. Taken together, an in silico analysis of the three metabolites, calcyin, stictic acid and physodic acid, and three standard anti-diabetic drugs, metformin, repaglinide and sitagliptin, against twelve known targets of diabetes were performed to assess their potential as anti-diabetic drugs. Autodockvina 1.1.2.was adopted to annotate ligand-target interactions and visually examined using Discovery Studio visualizer. Molecular properties and drug likeness were calculated using Molsoft prediction server, which uses Lipinski’s rule of five (Ro5) as the basis of selection. ADME/toxicity parameters were evaluated by AdmetSAR to predict the pharmacokinetic and pharmacodynamic properties. The test compounds exhibited favourable energies ranging from −5.4 to −8.4 kcal/mol for physodic acid, −7.2 to −8.9 kcal/mol for calycin and −6.8 kcal/mol to −8.8 kcal/mol for stictic acid. The highest binding energy was observed by calycin against 2QMJ (−8.4 kcal/mol). The results also showed that all three molecules abide by Lipinski’s rule, with one violation made by physodic acid. All compounds exhibited a positive result for HIA; however, mutagenicity was observed for stictic acid. Accordingly, calycin and physodic acid have the innate ability to be used as a potent anti-diabetic drugs.
