Abstract
Background: Chronic Obstructive Pulmonary Disorder (COPD) is a chronic and progressive lung disease with a steady increase in prevalence over the recent years. Current treatment options of COPD are aimed at symptomatic relief without the ability to cure COPD, and certain corticosteroid treatments cause patients to be susceptible to infections. Newer studies have hinted that PDE3/4 dual inhibitors may produce a higher efficacy and better safety profile compared to current alternatives. These novel inhibitors may potentially improve the control of COPD exacerbation without increasing the risk of infections. Thus, our study aimed to identify and refine natural compounds with PDE3/4 dual inhibitory activities through molecular modelling techniques.
Methods: A two-sided approach through ligand-based and structure-based pharmacophore modelling was employed, followed by virtual screening and molecular docking to identify lead compounds with PDE3/4 dual inhibition activity.
Results: Pharmacophore-based screening of Universal Natural Products Database (UNPD) resulted in the identification of one compound for each pharmacophore model, namely UNPD1558 and UNPD139455, with high binding affinities towards both PDE3B and PDE4B. The two compounds were subsequently docked with PDE3B and PDE4B to study their interactions with the active site residues. Structural modifications of the compounds were proposed based on the docking results to optimise their binding affinity and physicochemical properties.
Conclusion: Compound 25a4 and compound 28, which were designed based on the structures of UNPD1558 and UNPD139455, respectively, showed an improved binding affinity for both PDE3B and PDE4B. These lead compounds showed promising results as drug candidates, and their PDE3/4 dual inhibitory properties should be further investigated through in vivo and in vivo studies.
Keywords: Phosphodiesterase, PDE3/4, chronic obstructive pulmonary disorder, shared feature pharmacophore, virtual screening, molecular docking, natural product database.
Graphical Abstract
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