Repurposing of anisomycin and oleandomycin as a potential anti-(SARS-CoV-2) virus targeting key enzymes using virtual computational approaches.

Despite the accelerated emerging of vaccines, development against the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) drugs discovery is still in demand. Repurposing the existing drugs is an ideal time/cost-effective strategy to tackle the clinical impact of SARS CoV-2. Thereby, the present study is a promising strategy that proposes the repurposing of approved drugs against pivotal proteins that are responsible for the viral propagation of SARS-CoV-2 virus Angiotensin-converting enzyme-2 (ACE2; 2AJF), 3CL-protease: main protease (6LU7), Papain-like protease (6W9C), Receptor Binding Domain of Spike protein (6VW1), Transmembrane protease serine 2 (TMPRSS-2; 5AFW) and Furin (5MIM) by in silico methods. Molecular docking results were analyzed based on the binding energy and active site interactions accomplished with pharmacokinetic analysis. It was observed that both anisomycin and oleandomycin bind to all selected target proteins with good binding energy, achieving the most favorable interactions. Considering the results of binding affinity, pharmacokinetics and toxicity of anisomycin and oleandomycin, it is proposed that they can act as potential drugs against the SARS CoV-2 infection. Further clinical testing of the reported drugs is essential for their use in the treatment of SARS CoV-2 infection.

Integration of genetically regulated gene expression and pharmacological library provides therapeutic drug candidates.

Approaches toward new therapeutics using disease genomics, such as genome-wide association study (GWAS), are anticipated. Here, we developed Trans-Phar [integration of transcriptome-wide association study (TWAS) and pharmacological database], achieving in silico screening of compounds from a large-scale pharmacological database (L1000 Connectivity Map), which have inverse expression profiles compared with tissue-specific genetically regulated gene expression. Firstly we confirmed the statistical robustness by the application of the null GWAS data and enrichment in the true-positive drug-disease relationships by the application of UK-Biobank GWAS summary statistics in broad disease categories, then we applied the GWAS summary statistics of large-scale European meta-analysis (17 traits; naverage = 201 849) and the hospitalized COVID-19 (n = 900 687), which has urgent need for drug development. We detected potential therapeutic compounds as well as anisomycin in schizophrenia (false discovery rate (FDR)-q = 0.056) and verapamil in hospitalized COVID-19 (FDR-q = 0.068) as top-associated compounds. This approach could be effective in disease genomics-driven drug development.