Mechanistic Investigation of Hidden Flavonoids Isoorientin and Hyperin as Pancreatic Lipase Inhibitors Using Molecular Simulations and Machine Learning Correlation

Background

Phytochemicals exhibit diverse therapeutic properties with minimal adverse effects and documented their benefits against metabolic disorders. Inhibition of pancreatic lipase is a validated strategy for managing obesity and metabolic syndrome.

Objectives

To statistically analyze experimentally reported pancreatic lipase inhibitory activities of selected phytoconstituents and to evaluate the binding affinity and stability of isoorientin and hyperin using molecular docking and Molecular Dynamics (MD) simulations.

Materials and Methods

Using machine learning based tools such as correlation analysis and frequency distribution phytocompounds were ranked by occurrence frequency, and uncharted flavonoids Isoorientin and Hyperin were selected for in silico evaluation. Molecular docking was performed using Glide XP against Pancreatic Lipase (PDB: 1LPB). MD Simulations (100 ns) were conducted using the Desmond package with the OPLS force field and TIP4P water model. Structural stability was assessed using RMSD, RMSF, and interaction profiling.

Results

Isoorientin showed a low median IC₅₀ with narrow variability, indicating strong and reproducible inhibition, while hyperin demonstrated consistent moderate potency. Docking studies revealed favorable binding affinities of both the compounds with extensive hydrogen bonding, hydrophobic, and π-π interactions within the catalytic site, surpassing orlistat in docking scores. MD simulations confirmed stable complex formation with minimal conformational fluctuations and persistent intermolecular interactions.

Conclusion

Isoorientin and hyperin exhibit reliable pancreatic lipase inhibitory potential, supported by statistical and computational analyses, highlighting their promise as lead candidates for metabolic disorder management.