Targeting the SARS-CoV-2 MPRO through covalent and non-covalent inhibition: molecular modeling and synthetic procedures for the development of new antiviral molecules

Background and rationale: The COVID-19 pandemic has underscored the urgent need for specific pharmacological treatments beyond existing vaccines. One of the most attractive targets for antiviral therapies development is the SARS-CoV-2 Main Protease (MPRO), a key enzyme in viral life. The lack of MPRO human homologs and its conservation rate among coronaviruses make this enzyme strategically important. Considering its mechanism of action, MPRO inhibition could prevent the maturation of viral proteins and halt viral replication. X-ray crystallographic structures revealed that both catalytic and allosteric regions represent crucial sites for modulating its activity. The inhibition of the catalytic site of MPRO is one of the most direct and effective strategies. Its high conservation across coronaviruses suggests that inhibitors could potentially offer broad-spectrum antiviral activity. On the other hand, allosteric inhibition presents a promising alternative strategy, targeting non-catalytic sites that are nevertheless crucial for enzyme function. Inhibition at these sites can occur through either covalent or non-covalent interactions. In silico approaches are gaining increasing importance in drug development and clinical research. These techniques allow researchers to evaluate treatments qualitatively and quantitatively, leading to more practical and cost-effective experimentation. In the search for effective COVID-19 treatments, computational methods have become essential for discovering and developing SARS-CoV-2 MPRO inhibitors. Leveraging various computational methodologies accelerates the drug discovery process, reduces experimental costs, and enhances the precision of target identification and lead optimization.Results: This PhD Thesis focuses on developing innovative in silico and synthetic protocols for identifying diverse anti-SARS-CoV-2 agents targeting MPRO. To this purpose, three different inhibition strategies were explored: non-covalent inhibition, covalent inhibition through non peptidomimetic inhibitors, and covalent inhibition through peptidomimetic inhibitors.For non-covalent inhibitors, a series benzo[b]thiophene 2 and benzo[b]furan 3 compounds were identified using a hierarchical and hybrid virtual screening approach. We used the ligand-based Biotarget Predictor Tool (BPT), available in DRUDIT, to filter a large in-house structure database and identify small molecules with high affinity against the SARS-CoV-2 MPRO catalytic site. ADME properties were investigated through the SwissADME tool and docking studies confirmed DRUDIT prediction. Moreover, aiming at evaluating the possibility of a dual binding mechanism of action, the identified hits were further investigated by means of statistical analysis and dockings studies into the dimerization site. Compounds 2i-l also exhibited promising IC50 when in vitro evaluated as inhibitors of the catalytic site, among them 2i (IC50 values of 70.4) and 2l (IC50 values of 45.9) are promising lead compounds for further development as antiviral agents with a dual binding activity. On the other hand, the covalent inhibition strategy has been extensively assessed through both peptidomimetic and non-peptidomimetic point of views.In the case of non-peptidomimetic inhibitors, we focused on the rational design and synthesis of ester derivatives 45a-m, with compound 45g showing the most promise with an IC50 value of 30 ± 6.6 µM. These esters offer the advantage of long-lasting inhibition, becoming a potent strategy for disrupting viral replication.Additionally, a combinatorial library of 450 peptidomimetic compounds with aldehydic warheads was generated, refined to 388 compounds through docking studies, and further evaluated for covalent binding capabilities, revealing that compounds 57-62, and 64 exhibited significantly higher affinities compared to known inhibitors, thus affirming the validity of the adopted design strategy. Conclusions: Our findings demonstrate that integrating advanced computational tools offers a strategic and promising avenue for identifying new antiviral drugs. My thesis took the advantages of our in-house ligand-based tool, the BPT, available in DRUDIT, which allowed us to screen enormous ligands libraries. This tool integrated with both structure-based techniques and, interestingly, multivariate statistical analysis, has been applied to evaluate potential new SARS-CoV-2 MPRO inhibitors. This research project plays the groundwork for future research and the design of selective antiviral agents aimed at combating COVID-19, and supports ongoing efforts to combat SARS-CoV-2 and related coronaviruses.