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Computational Models Identify Several FDA Approved or Experimental Drugs as Putative Agents Against SARS-CoV-2.

ABSTRACT: The outbreak of a novel human coronavirus (SARS-CoV-2) has evolved into global health emergency, infecting hundreds of thousands of people worldwide. We have identified experimental data on the inhibitory activity of compounds tested against closely related (96% sequence identity, 100% active site conservation) protease of SARS-CoV and employed this data to build QSAR models for this dataset. We employed these models for virtual screening of all drugs from DrugBank, including compounds in clinical trials. Molecular docking and similarity search approaches were explored in parallel with QSAR modeling, but molecular docking failed to correctly discriminate between experimentally active and inactive compounds. As a result of our studies, we recommended 41 approved, experimental, or investigational drugs as potential agents against SARS-CoV-2 acting as putative inhibitors of Mpro. Ten compounds with feasible prices were purchased and are awaiting the experimental validation.
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SUBMITTER: Bobrowski T

PROVIDER: S-EPMC7252448 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Computational Models Identify Several FDA Approved or Experimental Drugs as Putative Agents Against SARS-CoV-2.

Bobrowski Tesia T Alves Vinicius M VM Melo-Filho Cleber C CC Korn Daniel D Auerbach Scott S Schmitt Charles C Muratov Eugene N EN Tropsha Alexander A

ChemRxiv : the preprint server for chemistry 20200422

The outbreak of a novel human coronavirus (SARS-CoV-2) has evolved into global health emergency, infecting hundreds of thousands of people worldwide. We have identified experimental data on the inhibitory activity of compounds tested against closely related (96% sequence identity, 100% active site conservation) protease of SARS-CoV and employed this data to build QSAR models for this dataset. We employed these models for virtual screening of all drugs from DrugBank, including compounds in clinic ...[more]

PMID: 32511287

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Project description:The emergence of antibiotic resistant bacterial pathogens is increasing at an unprecedented pace, calling for the development of new therapeutic options. Small molecules interfering with virulence processes rather than growth hold promise as an alternative to conventional antibiotics. Anti-virulence agents are expected to decrease bacterial virulence and to pose reduced selective pressure for the emergence of resistance. In the opportunistic pathogen Pseudomonas aeruginosa the expression of key virulence traits is controlled by quorum sensing (QS), an intercellular communication process that coordinates gene expression at the population level. Hence, QS inhibitors represent promising anti-virulence agents against P. aeruginosa. Virtual screenings allow fast and cost-effective selection of target ligands among vast libraries of molecules, thus accelerating the time and limiting the cost of conventional drug-discovery processes, while the drug-repurposing approach is based on the identification of off-target activity of FDA-approved drugs, likely endowed with low cytotoxicity and favorable pharmacological properties. This study aims at combining the advantages of virtual screening and drug-repurposing approaches to identify new QS inhibitors targeting the pqs QS system of P. aeruginosa. An in silico library of 1,467 FDA-approved drugs has been screened by molecular docking, and 5 hits showing the highest predicted binding affinity for the pqs QS receptor PqsR (also known as MvfR) have been selected. In vitro experiments have been performed by engineering ad hoc biosensor strains, which were used to verify the ability of hit compounds to decrease PqsR activity in P. aeruginosa. Phenotypic analyses confirmed the impact of the most promising hit, the antipsychotic drug pimozide, on the expression of P. aeruginosa PqsR-controlled virulence traits. Overall, this study highlights the potential of virtual screening campaigns of FDA-approved drugs to rapidly select new inhibitors of important bacterial functions.

Dataset Information

Computational Models Identify Several FDA Approved or Experimental Drugs as Putative Agents Against SARS-CoV-2.

Publications

Computational Models Identify Several FDA Approved or Experimental Drugs as Putative Agents Against SARS-CoV-2.

Similar Datasets

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