Ligand-based Modeling of CXC Chemokine Receptor 4 and Identification of Inhibitors of Novel Chemotypes as Potential Leads towards New Anti-COVID-19 Treatments

Background: Chemokines are involved in several human diseases and different stages of COVID-19 infection. They play a critical role in the pathophysiology of the associated acute respiratory disease syndrome, a major complication leading to death among COVID-19 patients. In particular, CXC chemokine receptor 4 (CXCR4) was found to be highly expressed in COVID-19 patients. Methods: We herein describe a computational workflow based on combining pharmacophore modeling and QSAR analysis towards the discovery of novel CXCR4 inhibitors. Subsequent virtual screening identified two promising CXCR4 inhibitors from the National Cancer Institute (NCI) list of compounds. The most active hit showed in vitro IC50 value of 24.4 mu M. Conclusion: These results proved the validity of the QSAR model and associated pharmacophore models as means to screen virtual databases for new CXCR4 inhibitors as leads for the development of new COVID-19 therapies.

Automated summary

This study employed computational methods combining pharmacophore modeling and QSAR analysis to discover a novel CXCR4 inhibitor for COVID-19 treatment.