Acyclovir for COVID-19

The outbreak of the new coronavirus (COVID-19) has been transferred exponentially. There are many articles that have found the inhibitory effect of plant extracts or plant compounds on the coronavirus family. In this study, we want to use systematic review and meta-analysis to answer the question of which herbal compound can be more effective against the coronavirus. The present study is based on the guidelines for conducting meta-analyzes. An extensive search was conducted in the electronic database, and based on the inclusion and exclusion criteria, articles were selected and data screening was performed. Quality control of articles was performed. Data analysis was carried out in STATA software. The results showed that alkaloid compounds had a good effect in controlling the coronavirus and reducing viral titer. Trypthantrin, Sambucus extract, S. cusia extract, Boceprevir and Indigole B, dioica agglutinin urtica had a good effect on reducing the virus titer but their selectivity index has not been reported and it is recommended to determine for these compounds. Also among the compounds that had the greatest effect on virus inhibition, including Saikosaponins B2, SaikosaponinsD, SaikosaponinsA and Phillyrin, had an acceptable selectivity index greater than 10. Andrographolide showed the highest selectivity index on SARS-COV2, while virus titration and virus inhibition were not reported. The small number of studies that used alkaloid compounds was one of the limitations and it is suggested to investigate the effect of more alkaloid compounds against the coronavirus for verifying its effect.

COVID-19 is one of the deadliest respiratory diseases, and its emergence caught the pharmaceutical industry off guard. While vaccines have been rapidly developed, treatment options for infected people remain scarce, and COVID-19 poses a substantial global threat. This study presents a novel workflow to predict robust druggable targets against emerging RNA viruses using metabolic networks and information of the viral structure and its genome sequence. For this purpose, we implemented pymCADRE and PREDICATE to create tissue-specific metabolic models, construct viral biomass functions and predict host-based antiviral targets from more than one genome. We observed that pymCADRE reduces the computational time of flux variability analysis for internal optimizations. We applied these tools to create a new metabolic network of primary bronchial epithelial cells infected with SARS-CoV-2 and identified enzymatic reactions with inhibitory effects. The most promising reported targets were from the purine metabolism, while targeting the pyrimidine and carbohydrate metabolisms seemed to be promising approaches to enhance viral inhibition. Finally, we computationally tested the robustness of our targets in all known variants of concern, verifying our targets’ inhibitory effects. Since laboratory tests are time-consuming and involve complex readouts to track processes, our workflow focuses on metabolic fluxes within infected cells and is applicable for rapid hypothesis-driven identification of potentially exploitable antivirals concerning various viruses and host cell types.

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.