Abstract
Purpose: Drug repurposing is an approach successfully used for discovery of new therapeutic applications for the existing drugs. The current study was aimed to use the combination of in silico methods to identify FDA-approved drugs with possible S1P(1) agonistic activity useful in multiple sclerosis (MS). Methods: For this, a 3D-QSAR model for the known 21 S1P(1) agonists were generated based on 3D-QSAR approach and used to predict the possible S1P(1) agonistic activity of FDA-approved drugs. Then, the selected compounds were screened by docking into S1P(1) and S1P(3) receptors to select the S1P(1) potent and selective compounds. Further evaluation was carried out by molecular dynamics (MD) simulation studies where the S1P(1) binding energies of selected compounds were calculated. Results: The analyses resulted in identification of cobicistat, benzonatate and brigatinib as the selective and potent S1P(1) agonists with the binding energies of -85.93, -69.77 and -67.44 kcal. mol(-1), calculated using MM-GBSA algorithm based on 50 ns MD simulation trajectories. These values are better than that of siponimod (-59.35 kcal mol(-1)), an FDA approved S1P(1) agonist indicated for MS treatment. Furthermore, similarity network analysis revealed that cobicistat and brigatinib are the most structurally favorable compounds to interact with S1P(1). Conclusion: The findings in this study revealed that cobicistat and brigatinib can be evaluated in experimental studies as potential S1P(1) agonist candidates useful in the treatment of MS.