Ascorbic acid for COVID-19

Abstract Background The recent outbreak of the Coronavirus pandemic resulted in a successful vaccination program launched by the World Health Organization. However, a large population is still unvaccinated, leading to the emergence of mutated strains like alpha, beta, delta, and B.1.1.529 (Omicron). Recent reports from the World Health Organization raised concerns about the Omicron variant, which emerged in South Africa during a surge in COVID-19 cases in November 2021. Vaccines are not proven completely effective or safe against Omicron, leading to clinical trials for combating infection by the mutated virus. The absence of suitable pharmaceuticals has led scientists and clinicians to search for alternative and supplementary therapies, including dietary patterns, to reduce the effect of mutated strains. Main body This review analyzed Coronavirus aetiology, epidemiology, and natural products for combating Omicron. Although the literature search did not include keywords related to in silico or computational research, in silico investigations were emphasized in this study. Molecular docking was implemented to compare the interaction between natural products and Chloroquine with the ACE2 receptor protein amino acid residues of Omicron. The global Omicron infection proceeding SARS-CoV-2 vaccination was also elucidated. The docking results suggest that DGCG may bind to the ACE2 receptor three times more effectively than standard chloroquine. Conclusion The emergence of the Omicron variant has highlighted the need for alternative therapies to reduce the impact of mutated strains. The current review suggests that natural products such as DGCG may be effective in binding to the ACE2 receptor and combating the Omicron variant, however, further research is required to validate the results of this study and explore the potential of natural products to mitigate COVID-19. Graphical abstract

To better understand the genes with altered expression caused by infection with the novel coronavirus strain SARS-CoV-2 causing COVID-19 infectious disease, a tensor decomposition (TD)-based unsupervised feature extraction (FE) approach was applied to a gene expression profile dataset of the mouse liver and spleen with experimental infection of mouse hepatitis virus, which is regarded as a suitable model of human coronavirus infection. TD-based unsupervised FE selected 134 altered genes, which were enriched in protein-protein interactions with orf1ab, polyprotein, and 3C-like protease that are well known to play critical roles in coronavirus infection, suggesting that these 134 genes can represent the coronavirus infectious process. We then selected compounds targeting the expression of the 134 selected genes based on a public domain database. The identified drug compounds were mainly related to known antiviral drugs, several of which were also included in those previously screened with an in silico method to identify candidate drugs for treating COVID-19.

Background: COVID-19 is a critical pandemic that has affected human communities worldwide. Although it is urgent to rapidly develop effective drugs, large number of candidate drug compounds may be useful for treating COVID-19, and evaluation of these drugs is time-consuming and costly. Thus, screening to identify potentially effective drugs prior to experimental validation is necessary. Method: In this study, we applied the recently proposed method tensor decomposition (TD)-based unsupervised feature extraction (FE) to gene expression profiles of multiple lung cancer cell lines infected with severe acute respiratory syndrome coronavirus 2. We identified drug candidate compounds that significantly altered the expression of the 163 genes selected by TD-based unsupervised FE. Results: Numerous drugs were successfully screened, including many known antiviral drug compounds. Conclusions: The drugs screened using our strategy may be effective candidates for treating patients with COVID-19.

The current pandemic has highlighted the need for methodologies that can quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-CoV-2 infections. In the past decade, network medicine has developed and validated multiple predictive algorithms for drug repurposing, exploiting the sub-cellular network-based relationship between a drug's targets and disease genes. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs that had been experimentally screened in VeroE6 cells, and the list of drugs under clinical trial, that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that while most algorithms offer predictive power for these ground truth data, no single method offers consistently reliable outcomes across all datasets and metrics. This prompted us to develop a multimodal approach that fuses the predictions of all algorithms, showing that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We find that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these drugs rely on network-based actions that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.