Computational Evaluation of Phytocompounds From Selective Medicinal Plants as Potential Antidiabetic Agents

Area of Science:

• Pharmacology
• Computational Chemistry
• Biochemistry

Background:

• Diabetes mellitus (DM) is characterized by chronic hyperglycemia, necessitating novel and safer antidiabetic treatments.
• Phytochemicals from medicinal plants offer a rich source for drug discovery due to their diverse biological activities.

Purpose of the Study:

• To computationally investigate the antidiabetic potential of selected phytochemicals.
• To identify potent phytochemical inhibitors targeting key enzymes and receptors involved in diabetes.

Main Methods:

• Molecular docking simulations were performed against phosphorylated insulin receptor (IR) tyrosine kinase and human dipeptidyl peptidase IV (DPP-IV).
• Molecular dynamics simulations assessed the stability of phytochemical-protein complexes.
• Binding free energy calculations (MM/GBSA) determined the binding affinities.
• Pharmacokinetic properties and toxicity profiles were evaluated.

Main Results:

• Eriodictyol and petunidin were identified as potent inhibitors of IR and DPP-IV, respectively.
• Molecular dynamics simulations confirmed stable complexes with root mean square deviation values between 2.8-4.5 Å.
• High binding affinities were calculated: eriodictyol-IR (ΔGbinding = -44.63 ± 4.05 kcal/mol) and petunidin-DPP-IV (ΔGbinding = -49.86 ± 6.13 kcal/mol).
• Pharmacokinetic analysis indicated adherence to Lipinski's rule with no predicted hepatotoxicity or cytotoxicity.

Conclusions:

• Eriodictyol and petunidin demonstrate significant potential as natural antidiabetic drug candidates.
• These findings support further in vitro and in vivo studies to validate their therapeutic efficacy.