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      Network medicine for disease module identification and drug repurposing with the NeDRex platform

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          Abstract

          Traditional drug discovery faces a severe efficacy crisis. Repurposing of registered drugs provides an alternative with lower costs and faster drug development timelines. However, the data necessary for the identification of disease modules, i.e. pathways and sub-networks describing the mechanisms of complex diseases which contain potential drug targets, are scattered across independent databases. Moreover, existing studies are limited to predictions for specific diseases or non-translational algorithmic approaches. There is an unmet need for adaptable tools allowing biomedical researchers to employ network-based drug repurposing approaches for their individual use cases. We close this gap with NeDRex, an integrative and interactive platform for network-based drug repurposing and disease module discovery. NeDRex integrates ten different data sources covering genes, drugs, drug targets, disease annotations, and their relationships. NeDRex allows for constructing heterogeneous biological networks, mining them for disease modules, prioritizing drugs targeting disease mechanisms, and statistical validation. We demonstrate the utility of NeDRex in five specific use-cases.

          Abstract

          There is an unmet need for adaptable tools allowing biomedical researchers to employ network-based drug repurposing approaches for their individual use cases. Here, the authors close this gap with NeDRex, an integrative and interactive platform.

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          Most cited references60

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              DrugBank 5.0: a major update to the DrugBank database for 2018

              Abstract DrugBank (www.drugbank.ca) is a web-enabled database containing comprehensive molecular information about drugs, their mechanisms, their interactions and their targets. First described in 2006, DrugBank has continued to evolve over the past 12 years in response to marked improvements to web standards and changing needs for drug research and development. This year’s update, DrugBank 5.0, represents the most significant upgrade to the database in more than 10 years. In many cases, existing data content has grown by 100% or more over the last update. For instance, the total number of investigational drugs in the database has grown by almost 300%, the number of drug-drug interactions has grown by nearly 600% and the number of SNP-associated drug effects has grown more than 3000%. Significant improvements have been made to the quantity, quality and consistency of drug indications, drug binding data as well as drug-drug and drug-food interactions. A great deal of brand new data have also been added to DrugBank 5.0. This includes information on the influence of hundreds of drugs on metabolite levels (pharmacometabolomics), gene expression levels (pharmacotranscriptomics) and protein expression levels (pharmacoprotoemics). New data have also been added on the status of hundreds of new drug clinical trials and existing drug repurposing trials. Many other important improvements in the content, interface and performance of the DrugBank website have been made and these should greatly enhance its ease of use, utility and potential applications in many areas of pharmacological research, pharmaceutical science and drug education.
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                Author and article information

                Contributors
                sadegh@wzw.tum.de
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                25 November 2021
                25 November 2021
                2021
                : 12
                : 6848
                Affiliations
                [1 ]GRID grid.6936.a, ISNI 0000000123222966, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, ; Munich, Germany
                [2 ]GRID grid.9026.d, ISNI 0000 0001 2287 2617, Chair of Computational Systems Biology, University of Hamburg, ; Hamburg, Germany
                [3 ]GRID grid.1006.7, ISNI 0000 0001 0462 7212, School of Computing, Newcastle University, ; Newcastle upon Tyne, UK
                [4 ]GRID grid.5330.5, ISNI 0000 0001 2107 3311, Department Artificial Intelligence in Biomedical Engineering, , Friedrich-Alexander University Erlangen-Nürnberg, ; Erlangen, Germany
                [5 ]GRID grid.6738.a, ISNI 0000 0001 1090 0254, Division Data Science in Biomedicine, , Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, ; Braunschweig, Germany
                [6 ]GRID grid.6738.a, ISNI 0000 0001 1090 0254, Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, ; Braunschweig, Germany
                [7 ]GRID grid.7220.7, ISNI 0000 0001 2157 0393, Natural Sciences Department, , Universidad Autónoma Metropolitana-Cuajimalpa, ; Mexico City, Mexico
                [8 ]GRID grid.1006.7, ISNI 0000 0001 0462 7212, School of Biomedical, Nutrition and Sports Sciences, Faculty of Medical Sciences, Newcastle University, ; Newcastle upon Tyne, UK
                [9 ]GRID grid.5012.6, ISNI 0000 0001 0481 6099, Department of Pharmacology and Personalised Medicine, School for Mental Health and Neuroscience (MHeNs), , Maastricht University, ; Maastricht, the Netherlands
                [10 ]GRID grid.410718.b, ISNI 0000 0001 0262 7331, Department of Neurology, , University Hospital Essen, ; Essen, Germany
                [11 ]GRID grid.10825.3e, ISNI 0000 0001 0728 0170, Computational Biomedicine Lab, Department of Mathematics and Computer Science, University of Southern Denmark, ; Odense, Denmark
                Author information
                http://orcid.org/0000-0003-4851-8848
                http://orcid.org/0000-0001-5812-8013
                http://orcid.org/0000-0001-8651-750X
                http://orcid.org/0000-0003-0419-5549
                http://orcid.org/0000-0002-0282-0462
                http://orcid.org/0000-0002-5393-2413
                Article
                27138
                10.1038/s41467-021-27138-2
                8617287
                34824199
                1f7bf257-2d64-4aac-8812-d8b54682bef3
                © 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 28 July 2021
                : 4 November 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100010661, EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020);
                Award ID: 777111
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                computational platforms and environments,data integration,databases,target identification

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