Amikacin for COVID-19

Background: Osteoporosis is a progressive bone disease characterized by reduced bone density and deterioration of bone microarchitecture, predominantly affecting the elderly population. The ongoing COVID‐19 pandemic has introduced additional challenges in osteoporosis management, potentially due to systemic inflammation and direct viral impacts on bone metabolism. This study aims to identify common differentially expressed genes (DEGs) and key molecular pathways shared between osteoporosis and COVID‐19, with the goal of uncovering potential therapeutic targets through bioinformatics analysis.Methods: Publicly available gene expression datasets GSE164805 (osteoporosis) and GSE230665 (COVID‐19) were analyzed to identify overlapping DEGs. Functional enrichment analysis using Gene Ontology (GO), pathway analysis, protein–protein interaction (PPI) network construction, and transcription factor (TF)–hub gene regulatory network analysis were performed to explore the biological significance and regulatory mechanisms of these DEGs.Results: A total of 325 common DEGs were identified between osteoporosis and COVID‐19. GO enrichment analysis revealed significant involvement in signal transduction and plasma membrane components. Pathway analysis highlighted the “cytokine–cytokine receptor interaction” pathway as a central player. PPI network analysis identified a module of 193 genes with 397 interactions, from which 10 key hub genes were prioritized: ACTB, CDH1, RPS8, IFNG, RPL17, UBC, RPL36, RPS4Y1, GSK3B, and FGF13. Furthermore, 76 TFs were found to regulate these hub genes, and 15 existing drugs targeting four of these hub genes were identified.Conclusion: This integrative bioinformatics study reveals 15 candidate therapeutic agents that target key regulatory genes shared between osteoporosis and COVID‐19, offering promising treatment strategies for osteoporotic patients, especially those impacted by or at risk of SARS‐CoV‐2​ infection.

Abstract Using as a template the crystal structure of the SARS-CoV-2 main protease, we developed a pharmacophore model of functional centers of the protease inhibitor-binding pocket. With this model, we conducted data mining of the conformational database of FDA-approved drugs. This search brought 64 compounds that can be potential inhibitors of the SARS-CoV-2 protease. The conformations of these compounds undergone 3D fingerprint similarity clusterization. Then we conducted docking of possible conformers of these drugs to the binding pocket of the protease. We also conducted the same docking of random compounds. Free energies of the docking interaction for the selected compounds were clearly lower than random compounds. Three of the selected compounds were carfilzomib, cyclosporine A, and azithromycin—the drugs that already are tested for COVID-19 treatment. Among the selected compounds are two HIV protease inhibitors and two hepatitis C protease inhibitors. We recommend testing of the selected compounds for treatment of COVID-19.

Coronavirus (CoV) disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has claimed many lives worldwide and is still spreading since December 2019. The 3C-like protease (3CL pro ) and papain-like protease (PL pro ) are essential for maturation of viral polyproteins in SARS-CoV-2 life cycle and thus regarded as key drug targets for the disease.