Computational Investigation of Anti-inflammatory Potential of Fluticasone Propionate through Molecular Docking and Dynamics Analysis TargetingGlucocorticoid Receptors

Introduction: Molecular modelling serves as a cornerstone of contemporary drug discovery, enabling the visualization of molecular interactions and facilitating rational therapeutic design. This study explores the anti-inflammatory potential of fluticasone propionate, a corticosteroid, using computational approaches targeting glucocorticoid receptors. Methods: Four receptor structures (1M2Z, 4UDC, 3BQD, and 1P93) were retrieved from the Protein Data Bank and subjected to molecular docking using Schrödinger’s GLIDE module, followed by validation through AutoDock Vina. The protein–ligand complexes were further analyzed using 100 ns molecular dynamics simulations in GROMACS, and pharmacokinetic parameters were evaluated via SwissADME and DruLiTo tools. Results: Among the examined receptors, 1P93 exhibited the highest binding affinity with fluticasone propionate, showing a docking score of –11.94 kcal/mol. Molecular dynamics analysis confirmed stable binding with an average RMSD of 0.40 nm and RMSF of 0.15 nm, while ADME profiling indicated excellent oral absorption (92.67%) and compliance with Lipinski’s Rule of Five. Discussion: The high docking affinity and stable molecular dynamics trajectory suggest that fluticasone propionate efficiently interacts with key residues of the 1P93 receptor, potentially inhibiting NF-κB and AP-1 pathways to exert anti-inflammatory effects. Conclusion: This integrated computational study establishes 1P93 as a potent receptor target for fluticasone propionate, demonstrating its strong binding stability and favorable drug-likeness, thereby supporting its potential for repurposing in targeted anti-inflammatory therapy, pending further experimental validation.