Evidence mapPaperPMID 31034926Full record

ReviewBiochimica et biophysica acta. Reviews on cancer2019

Drug repurposing in oncology: Compounds, pathways, phenotypes and computational approaches for colorectal cancer.

Patrycja Nowak-Sliwinska, Leonardo Scapozza, Ariel Ruiz i Altaba

Abstract readReview
In one paragraph

Review in Biochimica et biophysica acta. Reviews on cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
74citing papers in PubMed, 1 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

74 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Pan-cancer analysis and oncogenic implications ofJournal of cell communication and signaling · 2025
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14 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

3 authors.

Patrycja Nowak-SliwinskaSchool of Pharmaceutical Sciences, University of Geneva and University of Lausanne, Geneva, Switzerland; Translational Research Center in Oncohaematology, University of Geneva, Rue Michel Servet 1, 1211 Geneva 4, Switzerland. Electronic address: Patrycja.Nowak-Sliwinska@unige.ch.
Leonardo ScapozzaSchool of Pharmaceutical Sciences, University of Geneva and University of Lausanne, Geneva, Switzerland.
Ariel Ruiz i AltabaDepartment of Genetic Medicine and Development, Faculty of Medicine, University of Geneva, Rue Michel Servet 1, 1211 Geneva 4, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The strategy of using existing drugs originally developed for one disease to treat other indications has found success across medical fields. Such drug repurposing promises faster access of drugs to patients while reducing costs in the long and difficult process of drug development. However, the number of existing drugs and diseases, together with the heterogeneity of patients and diseases, notably including cancers, can make repurposing time consuming and inefficient. The key question we address is how to efficiently repurpose an existing drug to treat a given indication. As drug efficacy remains the main bottleneck for overall success, we discuss the need for machine-learning computational methods in combination with specific phenotypic studies along with mechanistic studies, chemical genetics and omics assays to successfully predict disease-drug pairs. Such a pipeline could be particularly important to cancer patients who face heterogeneous, recurrent and metastatic disease and need fast and personalized treatments. Here we focus on drug repurposing for colorectal cancer and describe selected therapeutics already repositioned for its prevention and/or treatment as well as potential candidates. We consider this review as a selective compilation of approaches and methodologies, and argue how, taken together, they could bring drug repurposing to the next level.

Indexed as

Machine LearningAnimalsAntineoplastic AgentsColorectal NeoplasmsDrug RepositioningHumansMedical OncologyPhenotypeAntineoplastic AgentsColorectal cancerComputational approachesDrug repositioningMechanism of actionOmicsOn/off-target effectsPhenotypesPolypharmacologyRepurposing in oncologySide effectsSignaling pathways

Identifiers

PMID31034926
PMCPMC6528778

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.