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Cited by 12Computational Drug Discovery MethodsBioinformatics and Genomic NetworksBiomedical Text Mining and Ontologies

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Drug mechanism enrichment analysis improves prioritization of therapeutics for repurposing

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Garana, Belinda B., Joly, James H., Delfarah, Alireza, Hong, Hyunjun, Graham, Nicholas A.·2023·BMC Bioinformatics
DOI 10.1186/s12859-023-05343-8PMC PMC10207828

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Abstract

Background: There is a pressing need for improved methods to identify effective therapeutics for diseases. Many computational approaches have been developed to repurpose existing drugs to meet this need. However, these tools often output long lists of candidate drugs that are difficult to interpret, and individual drug candidates may suffer from unknown off-target effects. We reasoned that an approach which aggregates information from multiple drugs that share a common mechanism of action (MOA) would increase on-target signal compared to evaluating drugs on an individual basis. In this study, we present drug mechanism enrichment analysis (DMEA), an adaptation of gene set enrichment analysis (GSEA), which groups drugs with shared MOAs to improve the prioritization of drug repurposing candidates. Results: First, we tested DMEA on simulated data and showed that it can sensitively and robustly identify an enriched drug MOA. Next, we used DMEA on three types of rank-ordered drug lists: (1) perturbagen signatures based on gene expression data, (2) drug sensitivity scores based on high-throughput cancer cell line screening, and (3) molecular classification scores of intrinsic and acquired drug resistance. In each case, DMEA detected the expected MOA as well as other relevant MOAs. Furthermore, the rankings of MOAs generated by DMEA were better than the original single-drug rankings in all tested data sets. Finally, in a drug discovery experiment, we identified potential senescence-inducing and senolytic drug MOAs for primary human mammary epithelial cells and then experimentally validated the senolytic effects of EGFR inhibitors. Conclusions: DMEA is a versatile bioinformatic tool that can improve the prioritization of candidates for drug repurposing. By grouping drugs with a shared MOA, DMEA increases on-target signal and reduces off-target effects compared to analysis of individual drugs. DMEA is publicly available as both a web application and an R package at https://belindabgarana.github.io/DMEA .

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Metadata

Authors
Garana, B. B., Joly, J. H., Delfarah, A., Hong, H., & Graham, N. A.
Affiliations
  • University of Southern California (US)
  • Los Angeles City College (US)
Corresponding author
Graham, Nicholas A.
ORCID
Venue
BMC Bioinformatics
Publisher
Springer Science and Business Media LLC
Published
2023-05-24
Volume
24
Issue
1
Pages
215-215
ISSN
1471-2105 (ISSN-L)
DOI
10.1186/s12859-023-05343-8
PMC
PMC10207828
OpenAlex
W4378071768
Cited by
12
References
11
Citations by year
2026: 5 · 2025: 7
FWCI
1.71
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Bioinformatics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Science Foundation (1842487)
  • National Institutes of Health
  • Viterbi School of Engineering, University of Southern California
  • National Institute of General Medical Sciences (R21GM144910)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
Keywords
drug repurposing, mechanism of action, drug prioritization, gene set enrichment analysis, drug resistance, senolytics, senescence, EGFR inhibitors, mammary epithelial cells
MeSH terms
Senotherapeutics, Cell Line, Ethylamines, Humans, Computational Biology, Drug Repositioning

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