Oritavancin for COVID-19

Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) caused an outbreak in 2002-2003, spreading to 29 countries with a mortality rate of about 10%. Strict quarantine and infection control methods quickly stopped the spread of the disease. Later research showed that SARS-CoV came from animals (zoonosis) and stressed the possibility of a similar spread from host to human, which was clearly shown by the COVID-19 outbreak. The COVID-19 pandemic, instigated by SARS-CoV-2, has affected 776 million confirmed cases and more than seven million deaths globally as of Sept 15, 2024. The existence of animal reservoirs of coronaviruses continues to pose a risk of re-emergence with improved fitness and virulence. Given the high death rate (up to 70 percent) and the high rate of severe sickness (up to 68.7 percent in long-COVID patients), it is even more critical to identify new therapies as soon as possible. This study combines research on antivirals that target SARS coronaviruses that have been conducted over the course of more than twenty years. It is a beneficial resource that might be useful in directing future studies.

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.