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      Network-based multi-omics-disease-drug associations reveal drug repurposing candidates for COVID-19 disease phases

      In review


            Background:The development and roll-out of vaccines, and the use of various drugs have contributed to controlling the COVID-19 pandemic. Nevertheless, challenges such as the inequitable distribution of vaccines, the influence of emerging viral lineages and immune evasive variants on vaccine efficacy, and the inadequate immune defense in subgroups of the population continue to motivate the development of new drugs to combat the disease.

            Aim:In this study, we sought to identify, prioritize, and characterize drug repurposing candidates appropriate for treating mild, moderate, or severe COVID-19 using a network-based integrative approach that systematically integrates drug-related data and multi-omics datasets.

            Methods: We leveraged drug data, and multi-omics data, and used a random walk restart algorithm to explore an integrated knowledge graph comprised of three sub-graphs: (i) a COVID-19 knowledge graph, (ii) a drug repurposing knowledge graph, and (iii) a COVID-19 disease-state specific omics graph.

            Results:We prioritized twenty FDA-approved agents as potential candidate drugs for mild, moderate, and severe COVID-19 disease phases. Specifically, drugs that could stimulate immune cell recruitment and activation including histamine, curcumin, and paclitaxel have potential utility in mild disease states to mitigate disease progression. Drugs like omacetaxine, crizotinib, and vorinostat that exhibit antiviral properties and have the potential to inhibit viral replication can be considered for mild to moderate COVID-19 disease states. Also, given the association between antioxidant deficiency and high inflammatory factors that trigger cytokine storms, antioxidants like glutathione can be considered for moderate disease states. Drugs that exhibit potent anti-inflammatory effects like (i) anti-inflammatory drugs (sarilumab and tocilizumab), (ii) corticosteroids (dexamethasone and hydrocortisone), and (iii) immunosuppressives (sirolimus and cyclosporine) are potential candidates for moderate to severe disease states that trigger a hyperinflammatory cascade of COVID-19.

            Conclusion:Our study demonstrates that the multi-omics data-driven integrative analysis within the drug data enables prioritizing drug candidates for COVID-19 disease phases, offering a comprehensive basis for therapeutic strategies that can be brought to market quickly given their established safety profiles. Importantly, the multi-omics data-driven integrative analysis within the drug data approach implemented here can be used to prioritize drug repurposing candidates appropriate for other diseases.


            Author and article information

            16 April 2024
            [1 ] Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa ( https://ror.org/03p74gp79)
            [2 ] Department of Medical BioSciences, Radboud University Medical Center Nijmegen, The Netherlands ( https://ror.org/05wg1m734)
            [3 ] Department of Applied Science, Faculty of Health and Life Sciences, Northumbria University, Newcastle, Tyne and Wear, NE1 8ST, UK ( https://ror.org/049e6bc10)
            Author notes
            Author information

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

            : 16 April 2024
            Funded by: funder-id , Dutch Organization of Scientific Research;
            Award ID: 184.034.019
            Funded by: funder-id , European Union to the EATRIS-Plus infrastructure project;
            Award ID: 871096

            The datasets generated during and/or analysed during the current study are available in the repository: https://github.com/francis-agamah/Network-based-multi-omics-disease-drug-associations_drugs-for-COVID-19-disease-phases
            Bioinformatics & Computational biology
            multi-omics,drug repurposing,random walk,COVID-19,networks


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