Rolitetracycline for COVID-19

Abstract We developed a computational framework that integrates Genome-Wide Association Studies (GWAS) and post-GWAS analyses, designed to facilitate drug repurposing for COVID-19 treatment. The comprehensive approach combines transcriptomic-wide associations, polygenic priority scoring, 3D genomics, viral-host protein-protein interactions, and small-molecule docking. Through GWAS, we identified nine druggable host genes associated with COVID-19 severity and SARS-CoV-2 infection, all of which show differential expression in COVID-19 patients. These genes include IFNAR1, IFNAR2, TYK2, IL10RB, CXCR6, CCR9, and OAS1. We performed an extensive molecular docking analysis of these targets using 553 small molecules derived from five therapeutically enriched categories, namely antibacterials, antivirals, antineoplastics, immunosuppressants, and anti-inflammatories. This analysis, which comprised over 20,000 individual docking analyses, enabled the identification of several promising drug candidates. All results are available via the DockCoV2 database (https://dockcov2.org/drugs/). The computational framework ultimately identified nine potential drug candidates: Peginterferon alfa-2b, Interferon alfa-2b, Interferon beta-1b, Ruxolitinib, Dactinomycin, Rolitetracycline, Irinotecan, Vinblastine, and Oritavancin. While its current focus is on COVID-19, our proposed computational framework can be applied more broadly to assist in drug repurposing efforts for a variety of diseases. Overall, this study underscores the potential of human genetic studies and the utility of a computational framework for drug repurposing in the context of COVID-19 treatment, providing a valuable resource for researchers in this field.

AbstractTo attain promising pharmacotherapies, researchers have applied drug repurposing (DR) techniques to discover the candidate medicines to combat the coronavirus disease 2019 (COVID-19) outbreak. Although many DR approaches have been introduced for treating different diseases, only structure-based DR (SBDR) methods can be employed as the first therapeutic option against the COVID-19 pandemic because they rely on the rudimentary information about the diseases such as the sequence of the severe acute respiratory syndrome coronavirus 2 genome. Hence, to try out new treatments for the disease, the first attempts have been made based on the SBDR methods which seem to be among the proper choices for discovering the potential medications against the emerging and re-emerging infectious diseases. Given the importance of SBDR approaches, in the present review, well-known SBDR methods are summarized, and their merits are investigated. Then, the databases and software applications, utilized for repurposing the drugs against COVID-19, are introduced. Besides, the identified drugs are categorized based on their targets. Finally, a comparison is made between the SBDR approaches and other DR methods, and some possible future directions are proposed.

Drug repurposing is a fast way to rapidly discover a drug for clinical use. In such circumstances of the spreading of the highly contagious COVID-19, searching for already known drugs is a worldwide demand. In this study, many drugs were evaluated by molecular docking. Among the test compounds, aliskiren (the best), dipyridamole, mopidamol and rosuvastatin showed higher energies of binding than that of the co-crystallized ligand N3 with COVID-19 main protease Mpro. Rolitetracycline showed the best binding with the catalytic center of the protease enzyme through binding with CYS 145 and HIS 41. Metamizole showed about 86 % of the binding energy of the ligand N3 while the protease inhibitor darunavir showed little bit lower binding energy than N3. These results are promising for using these drugs in the treatment and management of the spreading of COVID-19 virus. Also, it could stimulate clinical trials for the use of these drugs by systemic or inhalation route.The results stimulate the evaluation of these drugs as anti COVID-19 especially aliskiren which showed the highest score of binding with the binding site of N3. This will be added to its renin inhibition and advantage of renin inhibition and possibility of the reduced expression of ACE2[12]. Dipyridamole and mopidamol showed a potential to be more Mpro inhibitor than ligand N3 and darunavir. Also, dipyridamole has the property of antiviral activity beside its use to decrease the hypercoagulabilty that happens due to COVID infection in addition to the property of promoting type I interferon (IFN) responses and protect mice from viral pneumonia [30]. Rolitetracycling is an amazing in its binding mode in the active site of the protease pocket it seemed as it is tailored to be buried in that pocket. Mopidamol and rosuvastatin are slightly better than the co-crystallized ligand N3 and darunavir in binding mode which nominate the as COVID-19 protease inhibitors. Hopefully this study will help in the repurposing a drug for the treatment of 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.