High drug attrition rates—where are we going wrong?
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Clinical trials in oncology have the highest failure rate compared with other therapeutic areas.
“Sadly, clinical trials in oncology have the highest failure rate compared with other therapeutic areas.”
From Raise standards for preclinical cancer research (2012) · cites this paper as [1]
The opening passage of our copy (labelled Abstract; page unavailable) says cancer-drug attrition is much higher than in other therapeutic areas, citing 5% of anticancer agents versus 20% of cardiovascular agents reaching licensure. That comparison covers development from preclinical activity through phase III, not failure rates specifically among clinical trials; it also does not explicitly establish a highest-rate ranking. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe disconnect between conventional monolayer models and the complexity of the in vivo tumor environment contributes to the high rate of failure observed during the transition from preclinical studies to clinical development.
“Together, this disconnect between conventional monolayer models and the complexity of the in vivo tumor environment contributes to the high rate of failure observed during the transition from preclinical studies to clinical development [11,12,13].”
From Modeling Tumor Microenvironment Complexity In Vitro: Spheroids as Physiologically Relevant Tumor Models and Strategies for Their Analysis (2025) · cites this paper as [12]
The indexed Abstract links high cancer-drug attrition to suboptimal preclinical models and explains that xenografts can misrepresent tumor heterogeneity and microenvironmental effects on drug response. It does not discuss conventional monolayer models, so it does not support the claim's specific attribution of the disconnect to those models. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe ability to translate cancer research to clinical success has been remarkably low.
“Although we in the cancer field hoped that this would lead to more effective drugs, historically, our ability to translate cancer research to clinical success has been remarkably low 1 .”
From Raise standards for preclinical cancer research (2012) · cites this paper as [1]
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Raise standards for preclinical cancer research
Begley, C. G., & Ellis, L. M. (2012) · Nature · doi:10.1038/483531a · Cited by 2,979 · see citing works
Vinci, M., Gowan, S., Boxall, F., Patterson, L., Zimmermann, M., Court, W., Lomas, C., Mendiola, M., Hardisson, D., & Eccles, S. A. (2012) · BMC Biology · doi:10.1186/1741-7007-10-29 · Cited by 1,024 · see citing works
Three-Dimensional Spheroids as In Vitro Preclinical Models for Cancer Research
Pinto, B., Henriques, A. C., Silva, P. M. A., & Bousbaa, H. (2020) · Pharmaceutics · doi:10.3390/pharmaceutics12121186 · Cited by 440 · see citing works
The production of 3D tumor spheroids for cancer drug discovery
Sant, S., & Johnston, P. A. (2017) · Drug Discovery Today: Technologies · doi:10.1016/j.ddtec.2017.03.002 · Cited by 404 · see citing works
3D Bioprinted in vitro metastatic models via reconstruction of tumor microenvironments
Meng, F., Meyer, C. M., Joung, D., Vallera, D. A., McAlpine, M. C., & Panoskaltsis‐Mortari, A. (2019) · Advanced Materials · doi:10.1002/adma.201806899 · Cited by 262 · see citing works
Modeling tumor phenotypes in vitro with three-dimensional bioprinting
Langer, E. M., Allen-Petersen, B. L., King, S. M., Kendsersky, N. D., Turnidge, M. A., Kuziel, G. M., Riggers, R., Samatham, R., Amery, T. S., Jacques, S. L., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2018.12.090 · Cited by 256 · see citing works
Genetically engineered pigs as models for human disease
Perleberg, C., Kind, A., & Schnieke, A. (2018) · Disease Models & Mechanisms · doi:10.1242/dmm.030783 · Cited by 222 · see citing works
3D-3-culture: a tool to unveil macrophage plasticity in the tumour microenvironment
Rebelo, S. P., Pinto, C., Martins, T. R., Harrer, N., Estrada, M. F., Loza-Alvarez, P., Cabeçadas, J., Alves, P. M., Gualda, E. J., Sommergruber, W., et al. (2018) · Biomaterials · doi:10.1016/j.biomaterials.2018.02.030 · Cited by 227 · see citing works
Khoo, B. L., Grenci, G., Lim, Y. B., Lee, S. C., Han, J., & Lim, C. T. (2018) · Nature Protocols · doi:10.1038/nprot.2017.125 · Cited by 172 · see citing works
Tzatzalos, E., Abilez, O. J., Shukla, P., & Wu, J. C. (2016) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2015.09.010 · Cited by 162 · see citing works
Drug development and clinical trials—the path to an approved cancer drug
Rubin, E. H., & Gilliland, D. G. (2012) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2012.22 · Cited by 135 · see citing works
Hachey, S. J., Movsesyan, S., Nguyen, Q. H., Burton-Sojo, G., Tankazyan, A., Wu, J., Hoang, T., Zhao, D., Wang, S., Hatch, M. M., et al. (2021) · Lab on a Chip · doi:10.1039/d0lc01216e · Cited by 124 · see citing works
Shah, S., & D’Souza, G. G. M. (2025) · Cells · doi:10.3390/cells14100732 · Cited by 66 · see citing works
ZEEBERG, K., CARDONE, R. A., GRECO, M. R., SACCOMANO, M., NØHR-NIELSEN, A., ALVES, F., PEDERSEN, S. F., & RESHKIN, S. J. (2016) · International Journal of Oncology · doi:10.3892/ijo.2016.3513 · Cited by 33 · see citing works
Recommendations toward a human pathway-based approach to disease research
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NeuralDock: Rapid and Conformation-Agnostic Docking of Small Molecules
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- Authors
- Hutchinson, L., & Kirk, R.
- ORCID
- Kirk, Rebecca
- Venue
- Nature Reviews Clinical Oncology
- Published
- 2011-03-30
- Volume
- 8
- Issue
- 4
- Pages
- 189-190
- ISSN
- 1759-4774 (ISSN-L)
- 1759-4782
- OpenAlex
- W2038448543
- Cited by
- 517
- References
- 0
- Citations by year
- 2026: 23 · 2025: 27 · 2024: 36 · 2023: 41 · 2022: 57 · 2021: 39 · 2020: 51 · 2019: 54 · 2018: 34 · 2017: 36 · 2016: 46 · 2015: 36 · 2014: 15 · 2013: 15 · 2012: 5
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Clinical Oncology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Cancer therapeutics and mechanisms
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Pharmaceutical Economics and Policy
- Keywords
- drug attrition, anticancer drug development, drug discontinuation, VEGF inhibitors, Aurora B kinase, targeted cancer therapy, drug target identification, preclinical models
- MeSH terms
- Animals, Drug Industry, Drug Evaluation, Preclinical, Humans, Neoplasms, Drug Design, Drug-Related Side Effects and Adverse Reactions
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