Repurposing non-oncology small-molecule drugs to improve cancer therapy: Current situation and future directions
Open accessAbstract
Drug repurposing or repositioning has been well-known to refer to the therapeutic applications of a drug for another indication other than it was originally approved for. Repurposing non-oncology small-molecule drugs has been increasingly becoming an attractive approach to improve cancer therapy, with potentially lower overall costs and shorter timelines. Several non-oncology drugs approved by FDA have been recently reported to treat different types of human cancers, with the aid of some new emerging technologies, such as omics sequencing and artificial intelligence to overcome the bottleneck of drug repurposing. Therefore, in this review, we focus on summarizing the therapeutic potential of non-oncology drugs, including cardiovascular drugs, microbiological drugs, small-molecule antibiotics, anti-viral drugs, anti-inflammatory drugs, anti-neurodegenerative drugs, antipsychotic drugs, antidepressants, and other drugs in human cancers. We also discuss their novel potential targets and relevant signaling pathways of these old non-oncology drugs in cancer therapies. Taken together, these inspiring findings will shed new light on repurposing more non-oncology small-molecule drugs with their intricate molecular mechanisms for future cancer drug discovery.
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It takes 10-15 years and costs $1-2 billion to produce a new drug approved for clinical use.
“As such, it takes 10–15 years and costs $1–2 billion to produce a new drug approved for clinical use.”
From Drug repurposing for cancer therapy (2024) · cites this paper as [13]
The Introduction reports that developing a new cancer drug takes an average of 10–17 years, close to the claimed 10–15 years. It gives a median cost of $648 million, however, not $1–2 billion; that higher figure approaches only the upper end of its reported cost range. The source also addresses cancer drugs rather than new drugs generally. (LLM, conf=0.88)
Experimental — accuracy not yet measuredLess than 1% of compounds are expected to enter clinical trials, let alone reach the market.
“Despite these investments, less than 1% of compounds are expected to enter clinical trials, let alone reach the market.13–15”
From Drug repurposing for cancer therapy (2024) · cites this paper as [13]
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- Authors
- Fu, L., Jin, W., Zhang, J., Zhu, L., Lu, J., Zhen, Y., Zhang, L., Ouyang, L., Liu, B., & Yu, H.
- Affiliations
- Southwest Jiaotong University (CN)
- Shenyang Pharmaceutical University (CN)
- Tianjin University of Traditional Chinese Medicine (CN)
- Sichuan University (CN)
- Corresponding author
- Zhang, Lan
- Liu, Bo
- Yu, Haiyang
- Venue
- Acta Pharmaceutica Sinica B
- Publisher
- Elsevier BV
- Published
- 2022-02
- Volume
- 12
- Issue
- 2
- Pages
- 532-557
- ISSN
- 2211-3835 (ISSN-L)
- PMC
- PMC8897051
- OpenAlex
- W3200369231
- Cited by
- 83
- References
- 147
- Citations by year
- 2026: 17 · 2025: 18 · 2024: 22 · 2023: 15 · 2022: 11
- FWCI
- 0.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Acta Pharmaceutica Sinica B (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (82172649, 81873089, 81803365, 31970374, 81873939, 2021YFE0203100)
- Department of Science and Technology of Sichuan Province (2020YJ0285)
- National Key Research and Development Program of China (2021YFE0203100)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Medicine › Pathology and Forensic Medicine › Cancer Mechanisms and Therapy
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolism, Diabetes, and Cancer
- Keywords
- drug repurposing, cancer therapy, cardiovascular drugs, antibiotics, antiviral agents, anti-inflammatory agents, antipsychotic drugs, antidepressants, signaling pathways, anticancer drug discovery, artificial intelligence in drug discovery
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