The continuous emergence of SARS-CoV-2 variants has intensified the urgency to discover therapeutic agents with novel mechanisms of action. This study focused on validating the antiviral potential of Allium sativum (garlic) derivatives to disrupt the critical initial step of infection: the binding of the viral Spike protein's receptor-binding domain (RBD) to the human ACE2 enzyme. Through an integrated experimental and computational approach, the extraction process was optimized, identifying that the freeze-drying of the Tigre cultivar preserved the highest biological activity. Subsequent bio-guided fractionation led to an aqueous fraction that showed good inhibitory potency in ELISA assays, blocking the RBD-ACE2 interface with 57.26% inhibition at low concentration (0.01 ug/mL). The MS chemical profile of this fraction revealed a mixture rich in polar and sulfur compounds. Molecular docking analysis identified three candidate ligands (L36, L20, and L17) that bind strongly to the RBD-ACE2 interface, with predicted affinities (Gibbs free energy) in a favorable range of -7.5 to -6.9 kcal/mol}. A rigorous in silico pharmacokinetic and toxicological analysis determined that compound L17 is the most promising candidate. L17 is distinguished by exhibiting high gastrointestinal absorption and blood-brain barrier permeability, fully complying with all major drug-likeness rules, and, most importantly, showing no predicted inhibition of Cytochrome P450 isoforms or active risk of mutagenicity or nephrotoxicity.These findings suggest that the chemical scaffold of L17 is a viral entry inhibitor derived from a natural product, with a superior safety profile, prioritizing it for the preclinical development of new therapies against SARS-CoV-2.
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Allium sativum bioprospection reveals a potential anti-Sars-Cov-2 cell-entry inhibitor
Published:
09 March 2026
by MDPI
in Viruses 2026 – New Horizons in Virology
session Antiviral Therapeutics, Vaccines, and Host Defenses
Abstract:
Keywords: Allium sativum; Pharmacokinetic Profiling; Drug-Likeness; Bioactive Metabo-37 lite Identification; Computational drug discovery
