A network pharmacology- and molecular dynamics simulation-based bioprospection of <i>Khaya grandifoliola</i> C. DC. for diabetes care

Abstract

<i>Khaya grandifoliola</i> is a common medicinal plant with diverse uses. Despite its reported antidiabetic activity<i>,</i> stduies on the exact mechanism of action is limited. This study investigated the mechanism of action of <i>K. grandifoliola</i> metabolites in the management of type 2 diabetes mellitus (T2DM) using network pharmacology and molecular dynamics simulation methods. Findings from this study revealed that of the total 290 overlapping genes retrieved from 100 <i>K. grandifoliola</i> metabolites, 249 are linked to T2DM genes. A further KEGG analysis revealed AGE-RAGE as the most enriched signaling pathway with PRKCA and MMP2 as its most enriched genes as they interacted with 24 and 22 metabolites of <i>K. grandifoliola</i>, respectively. The top-five interacting metabolites of <i>K. grandifoliola</i> had higher negative docking scores, binding free energies, stability and interaction for PRKCA and MMP2 genes than metformin and the respective gene inhibitors. Cholestane-3,26-diol-22-one (-57.17 ± 5.07 kcal/mol) and linolelaidic acid (-37.96 ± 4.66 kcal/mol) had the highest negative binding free energies with PRKCA and MMP2, respectively. Conclusively, the identified lead metabolites of <i>K. grandifoliola</i> probably exhibit their antidiabetic activity <i>via</i> the downregulation of the profiled T2DM genes and can be further explored as potential antidiabetic drug candidates. Further studies to validate the degree of modulation of the profiled T2DM signaling pathways by the lead metabolites in the management of T2DM are required and efforts are underway in this direction.