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

Sepideh Sadegh (), James Skelton, Elisa Anastasi, Judith Bernett, David B. Blumenthal, Gihanna Galindez, Marisol Salgado-Albarrán, Olga Lazareva, Keith Flanagan, Simon Cockell, Cristian Nogales, Ana I. Casas, Harald H. H. W. Schmidt, Jan Baumbach, Anil Wipat and Tim Kacprowski
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Sepideh Sadegh: Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich
James Skelton: School of Computing, Newcastle University
Elisa Anastasi: School of Computing, Newcastle University
Judith Bernett: Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich
David B. Blumenthal: Friedrich-Alexander University Erlangen-Nürnberg
Gihanna Galindez: Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School
Marisol Salgado-Albarrán: Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich
Olga Lazareva: Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich
Keith Flanagan: School of Computing, Newcastle University
Simon Cockell: School of Biomedical, Nutrition and Sports Sciences, Faculty of Medical Sciences, Newcastle University
Cristian Nogales: Maastricht University
Ana I. Casas: Maastricht University
Harald H. H. W. Schmidt: Maastricht University
Jan Baumbach: Chair of Computational Systems Biology, University of Hamburg
Anil Wipat: School of Computing, Newcastle University
Tim Kacprowski: Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41467-021-27138-2

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