Evidence map›Paper›PMID 27080087›Full record

ReviewWiley interdisciplinary reviews. Systems biology and medicine2016

In silico methods for drug repurposing and pharmacology.

Rachel A Hodos, Brian A Kidd, Khader Shameer, Ben P Readhead, Joel T Dudley

Abstract readReview
In one paragraph

Review in Wiley interdisciplinary reviews. Systems biology and medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 135 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
135citing papers in PubMed, 2 pooled it
–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

135 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Repurposing Birabresib to target Gram‑positive bacteria.npj antimicrobials and resistance · 2026
    Article
  4. Article
  5. Article
  6. Review
  7. Review
  8. Review
  9. Article
  10. Drug repurposing in oncology: a path beyond the bottleneck.Medical oncology (Northwood, London, England) · 2025
    Review
  11. Review
  12. Article
  13. Review
  14. Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

75 more citing papers are in PubMed but not listed here.

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.

Rachel A HodosDepartment of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, New York, NY, USA.
Brian A KiddDepartment of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, New York, NY, USA.
Khader ShameerDepartment of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, New York, NY, USA.
Ben P ReadheadDepartment of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, New York, NY, USA.
Joel T DudleyDepartment of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, New York, NY, USA.

Funding

MSHS Translational Science HubUL1TR001433 · NCATS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI WRIGHT, ROSALIND J · 2015 to 2019
$26.6M
Mount Sinai Institutes for Clinical and Translational SciencesUL1TR000067 · NCATS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI SAMPSON, HUGH A · 2012 to 2014
$16.5M
User Interface PortalU54CA189201 · NCI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI DUDLEY, JOEL THOMAS, MA'AYAN, AVI · 2014 to 2017
$1.9M
Methods for Evolutionary Informed Network Analysis to Discover Disease VariationR01DK098242 · NIDDK · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI DUDLEY, JOEL THOMAS · 2013 to 2016
$1.7M
NCATS NIH HHS UL1 TR000067NCATS NIH HHS UL1 TR001433NCI NIH HHS U54 CA189201NIDDK NIH HHS R01 DK098242
6 · The paper itself

Abstract

Data in the biological, chemical, and clinical domains are accumulating at ever-increasing rates and have the potential to accelerate and inform drug development in new ways. Challenges and opportunities now lie in developing analytic tools to transform these often complex and heterogeneous data into testable hypotheses and actionable insights. This is the aim of computational pharmacology, which uses in silico techniques to better understand and predict how drugs affect biological systems, which can in turn improve clinical use, avoid unwanted side effects, and guide selection and development of better treatments. One exciting application of computational pharmacology is drug repurposing-finding new uses for existing drugs. Already yielding many promising candidates, this strategy has the potential to improve the efficiency of the drug development process and reach patient populations with previously unmet needs such as those with rare diseases. While current techniques in computational pharmacology and drug repurposing often focus on just a single data modality such as gene expression or drug-target interactions, we argue that methods such as matrix factorization that can integrate data within and across diverse data types have the potential to improve predictive performance and provide a fuller picture of a drug's pharmacological action. WIREs Syst Biol Med 2016, 8:186-210. doi: 10.1002/wsbm.1337 For further resources related to this article, please visit the WIREs website.

Indexed as

AnimalsDatabases, FactualDrug InteractionsDrug-Related Side Effects and Adverse ReactionsDrug RepositioningGene ExpressionHumansMolecular Docking SimulationPharmaceutical PreparationsProteinsPharmaceutical PreparationsProteins

Identifiers

PMID27080087
PMCPMC4845762

What OpenQuestion holds

Textmetadata
LicenceTDM
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.