Arginine for COVID-19

Phytochemicals from plant extracts are becoming increasingly popular in the world of food science and technology because they have positive effects on human health. In particular, several bioactive foods and dietary supplements are being investigated as potential treatments for chronic COVID. Hydroxytyrosol (HXT) is a natural antioxidant, found in olive oil, with antioxidant anti-inflammatory properties that has been consumed by humans for centuries without reported adverse effects. Its use was approved by the European Food Safety Authority as a protective agent for the cardiovascular system. Similarly, arginine is a natural amino acid with anti-inflammatory properties that can modulate the activity of immune cells, reducing the production of pro-inflammatory cytokines such as IL-6 and TNF-α. The properties of both substances may be particularly beneficial in the context of COVID-19 and long COVID, which are characterised by inflammation and oxidative stress. While l-arginine promotes the formation of •NO, HXT prevents oxidative stress and inflammation in infected cells. This combination could prevent the formation of harmful peroxynitrite, a potent pro-inflammatory substance implicated in pneumonia and COVID-19-associated organ dysfunction, as well as reduce inflammation, improve immune function, protect against free radical damage and prevent blood vessel injury. Further research is needed to fully understand the potential benefits of HXT and arginine in the context of COVID-19.

COVID-19 pandemic caused by the novel SARS-CoV-2 has impacted human livelihood globally. Strenuous efforts have been employed for its control and prevention; however, with recent reports on mutated strains with much higher infectivity, transmissibility, and ability to evade immunity developed from previous SARS-CoV-2 infections, prevention alternatives must be prepared beforehand in case. We have perused over 128 recent works (found on Google Scholar, PubMed, and ScienceDirect as of February 2023) on medicinal plants and their compounds for anti-SARS-CoV-2 activity and eventually reviewed 102 of them. The clinical application and the curative effect were reported high in China and in India. Accordingly, this review highlights the unprecedented opportunities offered by medicinal plants and their compounds, candidates as the therapeutic agent, against COVID-19 by acting as viral protein inhibitors and immunomodulator in (32 clinical trials and hundreds of in silico experiments) conjecture with modern science. Moreover, the associated foreseeable challenges for their viral outbreak management were discussed in comparison to synthetic drugs.

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.