Gentamicin for COVID-19

AbstractDrug repurposing, also known as drug repositioning, is a currently tested approach by which new uses are being assigned for already tested drugs. In this case there are antibiotics that are used to combat bacterial infections. However, antibiotics are among the drugs that have been studied for possible antiviral activities. Therefore, the aim of this work is to carry out a review of the studies of antibiotics that could be repositioned for the treatment of viral infections. Among the main antibiotics that have demonstrated antiviral activity are macrolides and glycopeptides. In addition, several antibiotics from the group of tetracyclines, fluoroquinolones, cephalosporins and aminoglycosides have also been studied for their antiviral activity. These antibiotics have demonstrated antiviral activity against both RNA and DNA viruses, including the recent pandemic virus SARS-CoV-2. Some of these antibiotics were selected in addition to its antiviral activity for their immunomodulatory and anti-inflammatory properties. Of the antibiotics that present antiviral activity, in many cases the mechanisms of action are not exactly known. The use of these antibiotics to combat viral infections remains controversial and is not generally accepted, since clinical trials are required to prove its effectiveness. Therefore, there is currently no antibiotic approved as antiviral therapy. Hence is necessary to present the studies carried out on antibiotics that can be repositioned in the future as antiviral drugs.

Background: COVID-19 is a critical pandemic that has affected human communities worldwide. Although it is urgent to rapidly develop effective drugs, large number of candidate drug compounds may be useful for treating COVID-19, and evaluation of these drugs is time-consuming and costly. Thus, screening to identify potentially effective drugs prior to experimental validation is necessary. Method: In this study, we applied the recently proposed method tensor decomposition (TD)-based unsupervised feature extraction (FE) to gene expression profiles of multiple lung cancer cell lines infected with severe acute respiratory syndrome coronavirus 2. We identified drug candidate compounds that significantly altered the expression of the 163 genes selected by TD-based unsupervised FE. Results: Numerous drugs were successfully screened, including many known antiviral drug compounds. Conclusions: The drugs screened using our strategy may be effective candidates for treating patients with COVID-19.

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.