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Cited by 22Computational Drug Discovery MethodsBioinformatics and Genomic NetworksBiomedical Text Mining and Ontologies

DrugGenEx-net: A novel computational platform for systems pharmacology and gene expression-based drug repurposing

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Issa, Naiem T., Kruger, Jordan, Wathieu, Henri, Raja, Rajarajan, Byers, Stephen W., Dakshanamurthy, Sivanesan·2016·BMC Bioinformatics
DOI 10.1186/s12859-016-1065-yPMC PMC4857427
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Abstract

Background: The targeting of disease-related proteins is important for drug discovery, and yet target-based discovery has not been fruitful. Contextualizing overall biological processes is critical to formulating successful drug-disease hypotheses. Network pharmacology helps to overcome target-based bottlenecks through systems biology analytics, such as protein-protein interaction (PPI) networks and pathway regulation. Results: We present a systems polypharmacology platform entitled DrugGenEx-Net (DGE-NET). DGE-NET predicts empirical drug-target (DT) interactions, integrates interaction pairs into a multi-tiered network analysis, and ultimately predicts disease-specific drug polypharmacology through systems-based gene expression analysis. Incorporation of established biological network annotations for protein target-disease, −signaling pathway, −molecular function, and protein-protein interactions enhances predicted DT effects on disease pathophysiology. Over 50 drug-disease and 100 drug-pathway predictions are validated. For example, the predicted systems pharmacology of the cholesterol-lowering agent ezetimibe corroborates its potential carcinogenicity. When disease-specific gene expression analysis is integrated, DGE-NET prioritizes known therapeutics/experimental drugs as well as their contra-indications. Proof-of-concept is established for immune-related rheumatoid arthritis and inflammatory bowel disease, as well as neuro-degenerative Alzheimer’s and Parkinson’s diseases. Conclusions: DGE-NET is a novel computational method that predicting drug therapeutic and counter-therapeutic indications by uniquely integrating systems pharmacology with gene expression analysis. DGE-NET correctly predicts various drug-disease indications by linking the biological activity of drugs and diseases at multiple tiers of biological action, and is therefore a useful approach to identifying drug candidates for re-purposing.

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  • Clozapine was initially proposed by a study published in 2016, with subsequent support in five studies published between 2017 and 2022.

    “Clozapine was initially proposed by a study published in 2016 (Issa et al., 2016), with subsequent support in five studies published between 2017 and 2022 (Choi et al., 2017;Chen et al., 2021;Chrétien et al., 2021;Gerring et al., 2021;Soleimani Zakeri et al., 2021).”

    From Drug repurposing for Alzheimer’s disease from 2012–2022—a 10-year literature review (2023) · cites this paper as Issa et al., 2016

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Cited by

Sorted by citations

Authors
Issa, N. T., Kruger, J., Wathieu, H., Raja, R., Byers, S. W., & Dakshanamurthy, S.
Affiliations
  • Georgetown University Medical Center (US)
  • Georgetown University (US)
  • George Mason University (US)
Corresponding author
Dakshanamurthy, Sivanesan
ORCID
Venue
BMC Bioinformatics
Publisher
Springer Science and Business Media LLC
Published
2016-05-05
Volume
17
Issue
1
Pages
202-202
ISSN
1471-2105 (ISSN-L)
DOI
10.1186/s12859-016-1065-y
PMC
PMC4857427
OpenAlex
W2347089309
Cited by
22
References
1
Citations by year
2025: 2 · 2023: 3 · 2022: 2 · 2021: 1 · 2020: 2 · 2019: 5 · 2018: 2 · 2017: 4 · 2016: 1
FWCI
3.68
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Bioinformatics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • U.S. Department of Defense (#CA140882)
  • National Institutes of Health (CA51008)
  • Lombardi Comprehensive Cancer Center, Georgetown University
  • National Cancer Institute (R01CA170653, P30CA051008)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
Keywords
drug repurposing, drug-target interactions, systems pharmacology, gene expression analysis, protein-protein interaction network, signaling pathways, rheumatoid arthritis, inflammatory bowel disease, Alzheimer's disease, Parkinson's disease
MeSH terms
Disease, Drug Interactions, Gene Expression Regulation, Humans, Proteins, Software, Reproducibility of Results, Computational Biology, Databases as Topic, Systems Biology, Drug Repositioning, Protein Interaction Maps

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