Mpro Inhibitor(A2A Pharmaceuticals)

The study aims to identify potential SARS-CoV-2 inhibitors and investigate the mechanism of action on the viral ACE2 receptor and main protease (Mpro), using chemo- and bioinformatics approaches. Ligand-based virtual screening was performed in the Molport database (∼4.79 million compounds), and after applying physicochemical filters, 313 molecules with characteristics such as hydroxychloroquine were obtained. After obtaining bioactive conformations, the molecular structures were subjected to the study of pharmacokinetic predictions, in which 106 molecules presented properties for oral bioavailability, penetration of the BBB, PPB, and solubility (average). The toxicological property predictions proved plausible for the molecules, as they did not present warnings of hepatotoxicity, mutagenicity, potential risk of carcinogenicity, and LC₅₀ and LD₅₀ values higher than the controls. Subsequently, 81 structures were subjected to a molecular docking study of ACE2 receptor/Spike and Mpro. In the ACE2 receptor, four (4) ligands showed high binding affinity value, in which the molecule MolPort-010-778-422 had the best ΔG value of -9.414 kcal/mol, followed by MolPort-009-093-282 with ΔG = -8.978 kcal/mol. In the Mpro receptor, four (4) ligands showed high binding affinity values compared to control 11b, with emphasis on molecule MolPort-005-766-143 with ΔG = -8.829 kcal/mol, followed by molecule MolPort-046-186-743. To study the antiviral effects of the molecules in vitro, TopHits8 molecules were tested against the SARS-CoV-2 virus. MolPort-010-778-422 had the best result on the screening and presented an IC₅₀ of 8.9 nM.

The emergence of SARS-CoV-2 and the COVID-19 pandemic highlighted the urgent need for novel antiviral therapies. The main protease (Mpro) of SARS-CoV-2 is a key enzyme in viral replication and a promising therapeutic target.

This study employed virtual screening approaches to identify potential Mpro inhibitors, leveraging both structure- and ligand-based methods.

Two optimum pharmacophore models were built from hundreds of crystallographic structures of Mpro, validated through ROC curve analysis and Dynophores dynamic simulations. These models captured ≈ 60K hits from six diverse compound libraries made of more than 3 million compounds. Additionally, a ligandbased similarity search using ROCS software identified 1024 potential hits based on shape and atom-based comparisons with co-crystallized ligands. Subsequent molecular docking and filtering based on physicochemical properties and structural diversity yielded 16 and 6 hits from structure- and ligand-based screening, respectively. Molecular dynamics simulations were conducted on the top-scoring hits to assess their binding stability within the Mpro active site. SCR00943 demonstrated stable binding, interacting favorably with key residues, including the catalytic dyad, resulting in a binding affinity of -61.2 kcal/mol.

This virtual screening campaign identified promising Mpro inhibitors, showcasing the potential of computational approaches to accelerate drug discovery efforts against COVID-19.