Evidence map›Paper›PMID 37226094›Full record

ArticleBMC bioinformatics2023

Drug mechanism enrichment analysis improves prioritization of therapeutics for repurposing.

Belinda B Garana, James H Joly, Alireza Delfarah, Hyunjun Hong, Nicholas A Graham

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Belinda B GaranaMork Family Department of Chemical Engineering and Materials Science, University of Southern California, 3710 McClintock Ave., RTH 509, Los Angeles, CA, 90089, USA.
James H JolyMork Family Department of Chemical Engineering and Materials Science, University of Southern California, 3710 McClintock Ave., RTH 509, Los Angeles, CA, 90089, USA.
Alireza DelfarahMork Family Department of Chemical Engineering and Materials Science, University of Southern California, 3710 McClintock Ave., RTH 509, Los Angeles, CA, 90089, USA.
Hyunjun HongDepartment of Computer Science, Information Systems, and Applications, Los Angeles City College, Los Angeles, CA, USA.
Nicholas A GrahamMork Family Department of Chemical Engineering and Materials Science, University of Southern California, 3710 McClintock Ave., RTH 509, Los Angeles, CA, 90089, USA. nagraham@usc.edu.

Funding

Developing novel affinity reagents for methyl-arginine proteomics using mRNA displayR21GM144910 · NIGMS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI GRAHAM, NICHOLAS ALEXANDER, TAKAHASHI, TERRY TORAO · 2022 to 2023
$436k
NIGMS NIH HHS R21 GM144910
6 · The paper itself

Abstract

backgroundThere 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.

resultsFirst, 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.

conclusionsDMEA 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 .

Indexed as

Drug RepositioningSenotherapeuticsCell LineComputational BiologyEthylaminesHumansEthylaminesN,N-dimethylethylamineSenotherapeuticsDrug repurposingEnrichment analysisGene expression analysisMechanism of actionPrecision medicineProteomic analysisSenescenceSenolyticTargeted therapeutics

Identifiers

PMID37226094
PMCPMC10207828

What Socratic holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.