In Silico Molecular Docking and ADMET Evaluation of Bimatoprost and Minoxidil Targeting Key Molecular Pathways in Alopecia
Saravanan, K. M. Sudha, A. Renuka, A. Subashree, S. SuganeshwariAbstract
Introduction:
Alopecia is a progressive multi-factorial disorder which results in hair loss, driven by reduced follicular vascularization, dysregulation of Wnt/β-catenin signaling, and androgen imbalance. The present study was designed to evaluate the pharmacokinetic profiles and molecular interactions of bimatoprost and minoxidil against key target proteins such as 5α-reductase, β-catenin, glycogen synthase kinase-3β (GSK3β), prostaglandin F2α (PGF2α), tyrosinase, and vascular endothelial growth factor (VEGF) involved in hair growth using AutoDock 3.0.
Materials and Methods:
The protein sequence and drug compound structure were retrieved from the Protein Data Bank and PubChem database. The sequences were docked using the software AutoDock 3.0. The postdocking analysis was done using PyMOL software. The score can be calculated according to hydrogen bonding and hydrophobic interactions. The data were analyzed.
Results:
According to docking data, the drug bimatoprost showed strong binding affinity against GSK3β and 5α-reductase with a dock score of −7.5 kcal/mol and −7.96 kcal/mol, respectively, which indicates the prevention of dihydrotestosterone production. The moderate interactions with PGF2α, VEGF, and tyrosinase indicate roles in angiogenesis, pigmentation, and anagen phase induction. Similarly, minoxidil has excellent affinity toward GSK3β with −7.46 kcal/mol.
Conclusion:
The ADMET analysis also showed that bimatoprost has excellent drug-likeness and physicochemical qualities. To summarize, minoxidil shows pathway-specific effects, whereas bimatoprost shows multi-target action across important alopecia-related pathways. These