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Identification of oral therapeutics using an AI platform against the virus responsible for COVID-19, SARS-CoV-2

Bess et al., Frontiers in Pharmacology, doi:10.3389/fphar.2023.1297924
Dec 2023  
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Zinc for COVID-19
2nd treatment shown to reduce risk in July 2020
*, now known with p = 0.0000027 from 43 studies, recognized in 10 countries.
No treatment is 100% effective. Protocols combine complementary and synergistic treatments. * >10% efficacy in meta analysis with ≥3 clinical studies.
3,800+ studies for 60+ treatments.
In Silico study showing potential benefit of zinc against SARS-CoV-2 according to an AI platform, eVir, designed to identify repurposed oral therapies. The software pipeline analyzes drug impacts on protein-protein networks involved in viral entry, fusion, and replication. Zinc obtained the one of the highest similarity scores to known antiviral peptides, suggesting significant potential as an oral therapeutic based on its pharmacological profile.
Bess et al., 22 Dec 2023, peer-reviewed, 7 authors. Contact:
In Silico studies are an important part of preclinical research, however results may be very different in vivo.
This PaperZincAll
Identification of oral therapeutics using an AI platform against the virus responsible for COVID-19, SARS-CoV-2
Adam Bess, Frej Berglind, Supratik Mukhopadhyay, Michal Brylinski, Chris Alvin, Fanan Fattah, Kishor M Wasan
Frontiers in Pharmacology, doi:10.3389/fphar.2023.1297924
Purpose: This study introduces a sophisticated computational pipeline, eVir, designed for the discovery of antiviral drugs based on their interactions within the human protein network. There is a pressing need for cost-effective therapeutics for infectious diseases (e.g., , particularly in resourcelimited countries. Therefore, our team devised an Artificial Intelligence (AI) system to explore repurposing opportunities for currently used oral therapies. The eVir system operates by identifying pharmaceutical compounds that mirror the effects of antiviral peptides (AVPs)-fragments of human proteins known to interfere with fundamental phases of the viral life cycle: entry, fusion, and replication. eVir extrapolates the probable antiviral efficacy of a given compound by analyzing its established and predicted impacts on the human protein-protein interaction network. This innovative approach provides a promising platform for drug repurposing against SARS-CoV-2 or any virus for which peptide data is available. Methods: The eVir AI software pipeline processes drug-protein and proteinprotein interaction networks generated from open-source datasets. eVir uses Node2Vec, a graph embedding technique, to understand the nuanced connections among drugs and proteins. The embeddings are input a Siamese Network (SNet) and MLPs, each tailored for the specific mechanisms of entry, fusion, and replication, to evaluate the similarity between drugs and AVPs. Scores generated from the SNet and MLPs undergo a Platt probability calibration and are combined into a unified score that gauges the potential antiviral efficacy of a drug. This integrated approach seeks to boost drug identification confidence, offering a potential solution for detecting therapeutic candidates with pronounced antiviral potency. Once identified a number of compounds were tested for efficacy and toxicity in lung carcinoma cells (Calu-3) infected with SARS-CoV-2. A lead compound was further identified to determine its efficacy and toxicity in K18-hACE2 mice infected with SARS-CoV-2. Computational Predictions: The SNet confidently differentiated between similar and dissimilar drug pairs with an accuracy of 97.28% and AUC of 99.47%.
Ethics statement Ethical approval was not required for the studies on humans in accordance with the local legislation and institutional requirements because only commercially available established cell lines were used. The animal study was approved by This animal work was done at IITRI CRO which has appropriate ethics approval. The study was conducted in accordance with the local legislation and institutional requirements. Author contributions Conflict of interest Author KW has stock options in Skymount Medical. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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