Drug repurposing: progress, challenges and recommendations
Abstract
Given the high attrition rates, substantial costs and slow pace of new drug discovery and development, repurposing of 'old' drugs to treat both common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked compounds, with potentially lower overall development costs and shorter development timelines. Various data-driven and experimental approaches have been suggested for the identification of repurposable drug candidates; however, there are also major technological and regulatory challenges that need to be addressed. In this Review, we present approaches used for drug repurposing (also known as drug repositioning), discuss the challenges faced by the repurposing community and recommend innovative ways by which these challenges could be addressed to help realize the full potential of drug repurposing.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Drug repurposing offers several advantages compared to new drug development, including reduced development time frames, lower costs, and increased assurance of drug safety.
“Drug repurposing offers several advantages compared to new drug development, including reduced development time frames, lower costs, and importantly, increased assurance of drug safety (Pushpakom et al., 2019), making it an attractive approach for investigating potential AD treatments.”
From Drug repurposing for Alzheimer’s disease from 2012–2022—a 10-year literature review (2023) · cites this paper as Pushpakom et al., 2019
Drug repurposing leverages existing safety and efficacy data of approved drugs, allowing for faster translation to the clinic and reduced development costs compared to developing new drugs from scratch.
“Drug repurposing leverages existing safety and efficacy data of approved drugs, allowing for faster translation to the clinic and reduced development costs compared to developing new drugs from scratch4 (Fig. 1a).”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [4]
The citation lists haven’t been fetched yet
References 146 · Cited by 4,718
- Authors
- Pushpakom, S., Iorio, F., Eyers, P. A., Escott, K. J., Hopper, S., Wells, A., Doig, A., Guilliams, T., Latimer, J., McNamee, C., Norris, A., Sanseau, P., Cavalla, D., & Pirmohamed, M.
- Affiliations
- University of Liverpool (GB)
- Wellcome Sanger Institute (GB)
- AstraZeneca (United Kingdom) (GB)
- Medicines and Healthcare products Regulatory Agency (GB)
- University of Manchester (GB)
- Medical Research Council (GB)
- Numedicus (United Kingdom) (GB)
- Corresponding author
- Pirmohamed, Munir
- Venue
- Nature Reviews Drug Discovery
- Published
- 2018-10-12
- Volume
- 18
- Issue
- 1
- Pages
- 41-58
- ISSN
- 1474-1776 (ISSN-L)
- 1474-1784
- OpenAlex
- W2896002881
- Cited by
- 4,718
- References
- 146
- Citations by year
- 2026: 585 · 2025: 646 · 2024: 713 · 2023: 692 · 2022: 738 · 2021: 645 · 2020: 555 · 2019: 134 · 2018: 6
- FWCI
- 26.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Drug Discovery (publishedVersion)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion)
- Funders
- National Institute for Health and Care Research (NF-SI-0512-10064)
- Medical Research Council (MR/L006758/1)
- Biotechnology and Biological Sciences Research Council (BB/S018514/1)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Pharmaceutical Economics and Policy
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pharmaceutical studies and practices
- Keywords
- drug repurposing, drug development costs, drug attrition, rare diseases, regulatory challenges
- MeSH terms
- Drug Industry, Humans, Drug Discovery, Drug Repositioning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review