Molecular Docking Studies for the Evaluation of Cannabinoids as Multi-Target Inhibitors of Type 2 Diabetes

Type 2 Diabetes is due to dysregulation of glycemic control through multiple mechanisms. Cannabinoids from Cannabis sativa L. show promise as anti-diabetic agents, though currently their molecular mechanisms are unclear. This study carried out in silico molecular docking of cannabinoids (cannabidiol (CBD), tetrahydrocannabivarin (THCV), cannabichromene (CBC), cannabigerol (CBG), cannabinol (CBN), Δ9-tetrahydrocannabinol (THC)) against key diabetes drug targets namely: dipeptidyl peptidase-4 (DPP-4), α-glucosidase, α-amylase and invertase. Enzyme structures were obtained from the RCSB Protein Data Bank, while ligand structures were generated and optimized using ChemDraw. AutoDockTools-1.5.7 determined docking parameters, and Biovia Discovery Studio visualized interaction profiles. Normal mode analysis was performed for molecular dynamics simulations. Cannabinoids used in this study showed higher binding affinities (-5.02 to -7.85 kcal/mol) than reference drugs for the targets, with the exception of DPP-4. For DPP-4, CBN (-7.85 kcal/mol) showed best affinity followed by THC (-6.64 kcal/mol). All cannabinoids interacted with catalytic residues except cannabichromene. For α-amylase, THC (-7.14 kcal/mol) was strongest, with residues Asp197, Glu233, Asp300 involved. Against invertase, THC (-7.27 kcal/mol) had highest affinity interacting with Glu230. For α-glucosidase, THC (-7.86 kcal/mol) again dominated via key residues. Normal mode analysis revealed THC binding modulated complex dynamics differently for each enzyme. This study provides insights into cannabinoids' multi-target binding profiles against important T2D drug targets, supporting their potential as natural anti-diabetic agents requiring further research.