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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'abstract': '<jats:p><jats:bold>Purpose:</jats:bold> This study introduces a sophisticated computational '
'pipeline, <jats:italic>eVir</jats:italic>, 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., COVID-19), particularly in '
'resource-limited 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.</jats:p><jats:p><jats:bold>Methods:</jats:bold> The '
'<jats:italic>eVir</jats:italic> AI software pipeline processes drug-protein and '
'protein-protein interaction networks generated from open-source datasets. '
'<jats:italic>eVir</jats:italic> 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.</jats:p><jats:p><jats:bold>Computational Predictions:</jats:bold> '
'The SNet confidently differentiated between similar and dissimilar drug pairs with an '
'accuracy of 97.28% and AUC of 99.47%. Key compounds identified through these networks '
'included Zinc, Mebendazole, Levomenol, Gefitinib, Niclosamide, and Imatinib. Notably, '
'Mebendazole and Zinc showcased the highest similarity scores, while Imatinib, Levemenol, and '
'Gefitinib also ranked within the top 20, suggesting their significant pharmacological '
'potentials. Further examination of protein binding analysis using explainable AI focused on '
'reverse engineering the causality of the networks. Protein interaction scores for Mebendazole '
'and Imatinib revealed their effects on notable proteins such as CDPK1, VEGF2, ABL1, and '
'several tyrosine protein kinases.</jats:p><jats:p><jats:bold>Laboratory Studies:</jats:bold> '
'This study determined that Mebendazole, Gefitinib, Topotecan and to some extent Carfilzomib '
'showed conventional drug-response curves, with IC50 values near or below that of Remdesivir '
'with excellent confidence all above R2&gt;0.91, and no cytotoxicity at the IC50 '
'concentration in Calu-3 cells. Cyclosporine A showed antiviral activity, but also '
'unconventional drug-response curves and low R2 which are explained by the non-dose dependent '
'toxicity of the compound. Additionally, Niclosamide demonstrated a conventional drug-response '
'curve with high confidence; however, its inherent cytotoxicity may be a confounding element '
'that misrepresents true antiviral efficacy, by reflecting cellular damage rather than a '
'genuine antiviral action. Remdesivir was used as a control compound and was evaluated in '
'parallel with the submitted test article and had conventional drug-response curves validating '
'the overall results of the assay. Mebendazole was identified from the cell studies to have '
'efficacy at non-toxic concentrations and were further evaluated in mice infected with '
'SARS-CoV-2. Mebendazole administered to K18-hACE2 mice infected with SARS-CoV-2, resulted in '
'a 44.2% reduction in lung viral load compared to non-treated placebo control respectively. '
'There were no significant differences in body weight and all clinical chemistry '
'determinations evaluated (i.e., kidney and liver enzymes) between the different treatment '
'groups.</jats:p><jats:p><jats:bold>Conclusion:</jats:bold> This research underscores the '
'potential of repurposing existing compounds for treating COVID-19. Our preliminary findings '
'underscore the therapeutic promise of several compounds, notably Mebendazole, in both '
'<jats:italic>in vitro</jats:italic> and <jats:italic>in vivo</jats:italic> settings against '
'SARS-CoV-2. Several of the drugs explored, especially Mebendazole, are off-label medication; '
'their cost-effectiveness position them as economical therapies against SARS-CoV-2.</jats:p>',
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