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Trends in clinical success rates and therapeutic focus
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“Human genetics can provide compelling evidence of causation through approaches such as Mendelian randomization (MR).”
From Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development (2021) · cites this paper as [9]
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“Human genetics can provide a tool by which the causal role of an exposure (such as a complex phenotype and/or a drug target) can be characterized 9 .”
From Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development (2021) · cites this paper as [9]
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“Performed well, such studies called Mendelian randomization (MR) analyses, can provide rigorous evidence of causation 9,10 .”
From Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development (2021) · cites this paper as [9]
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Muscle-on-a-chip devices: a new era for in vitro modelling of muscular dystrophies
Fernández‐Costa, J. M., Tejedera‐Villafranca, A., Fernández‐Garibay, X., & Ramón‐Azcón, J. (2023) · Disease Models & Mechanisms · doi:10.1242/dmm.050107 · Cited by 21
“Nevertheless, despite the effort and money invested in the preclinical phases, 90% of the candidate drugs in clinical trials fail (Dowden and Munro, 2019).”
Machine learning for metabolomics research in drug discovery
Martinelli, D. D. (2023) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2023.100101 · Cited by 19
Kim, J.-H., Shin, S.-A., Lee, C. S., & Chung, H. J. (2023) · Pharmaceutics · doi:10.3390/pharmaceutics16010048 · Cited by 19
“This limitation makes predicting the actual effects of drugs on humans challenging [3,4].”
BackgroundStructural perspectives on chemokine receptors
Kayastha, K., Zhou, Y., & Brünle, S. (2024) · Biochemical Society Transactions · doi:10.1042/bst20230358 · Cited by 18
Bridging the gap: From petri dish to patient - Advancements in translational drug discovery
El‐Tanani, M., Rabbani, S. A., El‐Tanani, Y., Matalka, I. I., & Khalil, I. A. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e41317 · Cited by 17
A 3D spheroid model for assessing nanocarrier-based drug delivery to solid tumors
Yadav, C., Evtushenko, A. S., Bistrović, A., Torres, B. L., Ahmed, I., Dvinskikh, L., Kaminski, C. F., & Fruk, L. (2025) · npj Biomedical Innovations. · doi:10.1038/s44385-025-00041-x · Cited by 24
“Over 90% of anti-cancer clinical trials fail 1 , predominantly due to the drugs investigated lacking clinical ef fi cacy or showing unmanageable toxicity 2 .”
Dynamic clinical trial success rates for drugs in the 21st century
Zhou, Y., Zhang, Y., Xu, H., Chen, Z., Huang, S., Li, Y., Fu, J., Zhang, H., Zhao, D., Lian, X., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-64552-2 · Cited by 20
“3 Theanswersto these questions play critical role in guiding scienti fi c and economic decisions for pharmaceutical company, investor and regulatory agency 4 .”
The human endogenous metabolome as a pharmacology baseline for drug discovery
Bofill, A., Jalencas, X., Oprea, T. I., & Mestres, J. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.06.007 · Cited by 17
Decoding kinase-adverse event associations for small molecule kinase inhibitors
Gong, X., Hu, M., Liu, J., Kim, G., Xu, J., McKee, A., Palmby, T. R., de Claro, R. A., & Zhao, L. Y. (2022) · Nature Communications · doi:10.1038/s41467-022-32033-5 · Cited by 20
Chen, L., Xu, J., & Yubin, Z. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107862 · Cited by 17
Artificial intelligence in clinical trials of lung cancer: Current and future prospects
Zhang, Y., You, P., Liu, R., Lu, Y., Li, J., Lei, Y., Wu, S., & Zhou, H. (2024) · Intelligent Oncology · doi:10.1016/j.intonc.2024.11.003 · Cited by 18
Chen, L., Lu, Y., Xu, J., et al. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06102-7 · Cited by 17
“Despite significant investments, the success rate of drug development programs remains low [2].”
Drug repurposing strategies and key challenges for COVID-19 management
Mule, S., Singh, A., Greish, K. F., Sahebkar, A., Kesharwani, P., & Shukla, R. (2021) · Journal of drug targeting · doi:10.1080/1061186x.2021.2013852 · Cited by 22
“New chemical entity discovery together with development is a challenging and complicated process that is frequently accompanied by a disappointing rate of failure in clinical phases II and III [80,81].”
BackgroundEguren‐Santamaría, I., de Piérola, E. F., Camps, G., Martín-Muñoz, P., Campos, M., Cuculescu, D., Aguilera-Buenosvinos, I., Rodríguez, I., Salido‐Vallejo, R., Alexandru, R., et al. (2024) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2023-008516 · Cited by 16
Metadata
- Authors
- Dowden, H., & Munro, J.
- Corresponding author
- Munro, Jamie
- Venue
- Nature Reviews Drug Discovery
- Published
- 2019-05-08
- Volume
- 18
- Issue
- 7
- Pages
- 495-496
- ISSN
- 1474-1776 (ISSN-L)
- 1474-1784
- OpenAlex
- W2944466104
- Cited by
- 649
- References
- 0
- Citations by year
- 2026: 101 · 2025: 129 · 2024: 140 · 2023: 114 · 2022: 57 · 2021: 57 · 2020: 43 · 2019: 7
- FWCI
- 39.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Reviews Drug Discovery (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Science, Research, and Medicine
- Health Sciences › Medicine › Pharmacology (medical) › Historical Medical Research and Treatments
- Health Sciences › Medicine › Internal Medicine › General Medicine and Therapeutics
- Keywords
- clinical success rate, therapeutic focus, drug development, clinical trial results, therapeutic areas
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