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Pentamidine for COVID-19

Pentamidine has been reported as potentially beneficial for treatment of COVID-19. We have not reviewed these studies. See all other treatments.
Tomar et al., Blockers of the SARS-CoV-2 3a Channel Identified by Targeted Drug Repurposing, Viruses, doi:10.3390/v13030532
The etiological agent of the COVID-19 pandemic is SARS-CoV-2. As a member of the Coronaviridae, the enveloped pathogen has several membrane proteins, of which two, E and 3a, were suggested to function as ion channels. In an effort to increase our treatment options, alongside providing new research tools, we have sought to inhibit the 3a channel by targeted drug repurposing. To that end, using three bacteria-based assays, we screened a library of 2839 approved-for-human-use drugs and identified the following potential channel-blockers: Capreomycin, Pentamidine, Spectinomycin, Kasugamycin, Plerixafor, Flumatinib, Litronesib, Darapladib, Floxuridine and Fludarabine. The stage is now set for examining the activity of these compounds in detailed electrophysiological studies and their impact on the whole virus with appropriate biosafety measures.
Issac et al., Improved And Optimized Drug Repurposing For The SARS-CoV-2 Pandemic, bioRxiv, doi:10.1101/2022.03.24.485618
The active global SARS-CoV-2 pandemic caused more than 426 million cases and 5.8 million deaths worldwide. The development of completely new drugs for such a novel disease is a challenging, time intensive process. Despite researchers around the world working on this task, no effective treatments have been developed yet. This emphasizes the importance of drug repurposing, where treatments are found among existing drugs that are meant for different diseases. A common approach to this is based on \emph{knowledge graphs}, that condense relationships between entities like drugs, diseases and genes. Graph neural networks (GNNs) can then be used for the task at hand by predicting links in such knowledge graphs. Expanding on state-of-the-art GNN research, Doshi {\sl et al.} recently developed the \drcov \ model. We further extend their work using additional output interpretation strategies. The best aggregation strategy derives a top-100 ranking of 8,070 candidate drugs, 32 of which are currently being tested in COVID-19-related clinical trials. Moreover, we present an alternative application for the model, the generation of additional candidates based on a given pre-selection of drug candidates using collaborative filtering. In addition, we improved the implementation of the \drcov \ model by significantly shortening the inference and pre-processing time by exploiting data-parallelism. As drug repurposing is a task that requires high computation and memory resources, we further accelerate the post-processing phase using a new emerging hardware --- we propose a new approach to leverage the use of high-capacity Non-Volatile Memory for aggregate drug ranking.
Please send us corrections, updates, or comments. c19early involves the extraction of over 100,000 datapoints from thousands of papers. Community updates help ensure high accuracy. Vaccines and treatments are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. We do not provide medical advice. Before taking any medication, consult a qualified physician who can provide personalized advice and details of risks and benefits based on your medical history and situation. FLCCC and WCH provide treatment protocols.
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