Evidence map›Paper›PMID 28620943›Full record

ArticleAging cell2017

Drug repurposing for aging research using model organisms.

Matthias Ziehm, Satwant Kaur, Dobril K Ivanov, Pedro J Ballester, David Marcus, Linda Partridge, Janet M Thornton

Open access · goldAbstract read
In one paragraph

Article in Aging cell, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
2.5field-weighted citation impact, top 11% of its field
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

23 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.

  1. Pooled it
  2. Artificial Intelligence-Guided Phenotypic Drug Repurposing Against Streptococcus pneumoniae.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Pharmacology of Aging:Aging biology · 2024
    Article
  7. Article
  8. Article
  9. Review
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Identifying Potential Ageing-Modulating Drugs In Silico.Trends in endocrinology and metabolism: TEM · 2019
    Review
  17. Article
  18. microPublication biology · 2019
    Article
  19. Article
  20. 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

7 authors at 3 institutions in 2 countries.

Matthias ZiehmEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), The Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
Satwant KaurEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), The Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
Dobril K IvanovEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), The Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
Pedro J BallesterEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), The Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
David MarcusEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), The Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
Linda PartridgeDepartment of Genetics, Evolution and Environment, Institute of Healthy Ageing, University College London, Gower Street, London, WC1E 6BT, UK.
Janet M ThorntonEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), The Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
Wellcome Trust · GBUniversity College London · GBEuropean Bioinformatics Institute · GB

Funding

Wellcome Trust 098565/Z/12/Z
6 · The paper itself

Abstract

Many increasingly prevalent diseases share a common risk factor: age. However, little is known about pharmaceutical interventions against aging, despite many genes and pathways shown to be important in the aging process and numerous studies demonstrating that genetic interventions can lead to a healthier aging phenotype. An important challenge is to assess the potential to repurpose existing drugs for initial testing on model organisms, where such experiments are possible. To this end, we present a new approach to rank drug-like compounds with known mammalian targets according to their likelihood to modulate aging in the invertebrates Caenorhabditis elegans and Drosophila. Our approach combines information on genetic effects on aging, orthology relationships and sequence conservation, 3D protein structures, drug binding and bioavailability. Overall, we rank 743 different drug-like compounds for their likelihood to modulate aging. We provide various lines of evidence for the successful enrichment of our ranking for compounds modulating aging, despite sparse public data suitable for validation. The top ranked compounds are thus prime candidates for in vivo testing of their effects on lifespan in C. elegans or Drosophila. As such, these compounds are promising as research tools and ultimately a step towards identifying drugs for a healthier human aging.

Indexed as

AgingAnimalsCaenorhabditis elegansDatabases, PharmaceuticalDrosophila melanogasterDrug RepositioningDrugs, InvestigationalGene ExpressionHealthy AgingHigh-Throughput Screening AssaysHumansMiceMitogen-Activated Protein Kinase 14Molecular Docking SimulationRatsSmall Molecule LibrariesDrugs, InvestigationalMitogen-Activated Protein Kinase 14Small Molecule LibrariesagingC. eleganscomputational predictionsDrosophiladrug repurposinglifespan

Identifiers

PMID28620943
PMCPMC5595691
OpenAlexW2563262253

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 OpenQuestion graph.