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      A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization

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      1 , 2 , 3 , 1 , 2 , 3 , 11 , 1 , 1 , 7 , 1 , 8 , 4 , 6 , 1 , 1 , 4 , 1 , 11 , 1 , 2 , 3 , 3 , 9 , 10 , 10 , 11 , 12 , 8 , 4 , 1 , 1 , 5 , 1 , 5 , 7 , 2 , 3 , 1 ,
      Scientific Reports
      Nature Publishing Group UK
      Computational models, Data integration, Virtual drug screening

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          Abstract

          The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific community’s massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2/COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.

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          Most cited references28

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          Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

          Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
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            Imbalanced Host Response to SARS-CoV-2 Drives Development of COVID-19

            Summary Viral pandemics, such as the one caused by SARS-CoV-2, pose an imminent threat to humanity. Because of its recent emergence, there is a paucity of information regarding viral behavior and host response following SARS-CoV-2 infection. Here we offer an in-depth analysis of the transcriptional response to SARS-CoV-2 compared with other respiratory viruses. Cell and animal models of SARS-CoV-2 infection, in addition to transcriptional and serum profiling of COVID-19 patients, consistently revealed a unique and inappropriate inflammatory response. This response is defined by low levels of type I and III interferons juxtaposed to elevated chemokines and high expression of IL-6. We propose that reduced innate antiviral defenses coupled with exuberant inflammatory cytokine production are the defining and driving features of COVID-19.
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              A SARS-CoV-2 Protein Interaction Map Reveals Targets for Drug-Repurposing

              SUMMARY The novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 2.3 million people, killed over 160,000, and caused worldwide social and economic disruption 1,2 . There are currently no antiviral drugs with proven clinical efficacy, nor are there vaccines for its prevention, and these efforts are hampered by limited knowledge of the molecular details of SARS-CoV-2 infection. To address this, we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), identifying 332 high-confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (29 FDA-approved drugs, 12 drugs in clinical trials, and 28 preclinical compounds). Screening a subset of these in multiple viral assays identified two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the Sigma1 and Sigma2 receptors. Further studies of these host factor targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19.
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                Author and article information

                Contributors
                martin.hofmann-apitius@scai.fraunhofer.de
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                26 May 2021
                26 May 2021
                2021
                : 11
                : 11049
                Affiliations
                [1 ]GRID grid.418688.b, ISNI 0000 0004 0494 1561, Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, , Institutszentrum Birlinghoven, ; 53754 Sankt Augustin, Germany
                [2 ]ScreeningPort, Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, 22525 Hamburg, Germany
                [3 ]Fraunhofer Cluster of Excellence for Immune Mediated Diseases, CIMD, External Partner Site, 22525 Hamburg, Germany
                [4 ]Unit 8B Bankside, PrecisionLife Ltd., Hanborough Business Park, Long Hanborough, Oxfordshire, OX29 8LJ UK
                [5 ]Philipp Morris International R&D, Biological Systems Research, R&D Innovation Cube T1517.07, Quai Jeanrenaud 5, 2000 Neuchâte, Switzerland
                [6 ]Causality BioModels Pvt Ltd., Kinfra Hi-Tech Park, Kerala Technology Innovation Zone- KTIZ, Kalamassery, Cochin, 683503 India
                [7 ]GRID grid.6363.0, ISNI 0000 0001 2218 4662, Center for Digital Health, Berlin Institute of Health (BIH), , Charité Universitätsmedizin Berlin, ; Berlin, Germany
                [8 ]GRID grid.47100.32, ISNI 0000000419368710, Center for Biomedical Data Science, Yale School of Medicine, , Yale University, ; 333 Cedar Street, New Haven, CT 06510 USA
                [9 ]GRID grid.411088.4, ISNI 0000 0004 0578 8220, Pharmazentrum Frankfurt/ZAFES, Institut Für Klinische Pharmakologie, , Klinikum Der Goethe-Universität Frankfurt, ; 60590 Frankfurt am Main, Germany
                [10 ]GRID grid.510864.e, Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, ; 60596 Frankfurt am Main, Germany
                [11 ]GRID grid.411088.4, ISNI 0000 0004 0578 8220, Institute for Medical Virology, , University Hospital Frankfurt, ; 60590 Frankfurt am Main, Germany
                [12 ]DZIF, German Centre for Infection Research, External Partner Site, 60596 Frankfurt am Main, Germany
                Article
                90296
                10.1038/s41598-021-90296-2
                8155020
                34040048
                65ae1fbf-2c8f-4656-9ed0-2ce526dc38ca
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 1 October 2020
                : 4 May 2021
                Funding
                Funded by: Zentrale der Fraunhofer-Gesellschaft (1050)
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                computational models,data integration,virtual drug screening
                Uncategorized
                computational models, data integration, virtual drug screening

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