Cangrelor and AVN-944 as repurposable candidate drugs for hMPV: analysis entailed by AI-driven in silico approach.

Abstract

Human metapneumovirus (hMPV) primarily causes respiratory tract infections in young children and older adults. According to the 2024 Human Pneumonia Etiology Research for Child Health (PERCH) study, hMPV is the second leading common cause of pneumonia in children under five in Asia and Africa. The virus encodes nine proteins, including the essential Fusion (F) and G glycoproteins, which facilitate entry to the host cells. Currently, there are no approved vaccines or antiviral treatments for hMPV; supportive care is the primary way it is managed. Hence, this study focuses on the F protein as a therapeutic target to find a repurposable drug to fight hMPV. Refolding of the F protein and its binding to heparan sulfate enable hMPV infection. Heparin sulfate is important for hMPV binding, and we have found that cangrelor and AVN 944 can prevent the fusion of membranes. We developed a deep learning-based pharmacophore to identify potential drugs targeting hMPV, from which we could narrowed a list of 2400 FDA-approved drugs and 255 antiviral drugs to 792 and 72 drugs, respectively. We then conducted quantitative validation using the ROC curve. Further virtual screening of the drugs was performed, leading us to select the one with the highest docking score. The validation of the deep learning prediction in virtual screening Pearson correlation was done. Further, the MD simulation of these drugs confirmed that the protein-drug complex stability remained in dynamic condition. Further, the stability of protein-drug complexes than unbound protein was confirmed by Free Energy Landscape and Dynamic Cross Correlation Matrices. Further in vitro and in vivo experiments need to determine the efficacy of the identified candidates.