Cited by 651Science, Research, and MedicineHistorical Medical Research and TreatmentsGeneral Medicine and Therapeutics

The DOI matches its Crossref record.

Trends in clinical success rates and therapeutic focus

Dowden, Helen, Munro, Jamie·2019·Nature Reviews Drug Discovery

DOI 10.1038/d41573-019-00074-z

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1038/d41573-019-00074-z

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Sorted by citations

Items 501–600 of 651

DrugBank Online: A How‐to Guide
Klinger, C. M., Cox, J., So, D., Stauth, T., Wilson, M., Wilson, A., & Knox, C. (2023) · Methods and principles in medicinal chemistry · doi:10.1002/9783527830497.ch3 · Cited by 1
Exploring the Potential of Large Language Models in Molecular Tasks: An Insightful Evaluation with GPT‐4Preprint
Zhang, J., Fang, Y., Zhang, N., Shao, X., Chen, H., & Fan, X. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.28.568966 · Cited by 1
“However, traditional methods in molecular research are often challenged by the complexity of molecular structures, necessitating substantial time and resources[2][3][4].”
FedECA: Federated External Control Arms for Causal Inference with Time-To-Event Data in Distributed SettingsPreprint
du Terrail, J. O., Klopfenstein, Q., Li, H., Mayer, I., Loiseau, N., Hallal, M., Debouver, M., Camalon, T., Fouqueray, T., Castro, J. A., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.16984 · Cited by 1
“However, it was shown to be already detrimental to the statistical analysis (see Supplementary Figs.3 and 11 and Supplementary Table13).”
Dynamic Clinical Success Rates for Drugs in the 21st CenturyPreprint
Zhou, Y., Zhang, Y., Chen, Z., Huang, S., Li, Y., Fu, J., Zhang, H., Zhao, D., Lian, X., Zhou, Y., et al. (2024) · medRxiv · doi:10.1101/2024.02.26.24303388 · Cited by 1
Dynamic Clinical Success Rates for Drugs in the 21st CenturyPreprint
Zhu, F., Zhou, Y., Zhang, Y., Chen, Z., Huang, S., Li, Y., Fu, J., Zhang, H., Zhao, D., Lian, X., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-4053961/v1 · Cited by 1
Shared genetics between breast cancer and predisposing diseases identifies novel breast cancer treatment candidatesPreprint
Lalagkas, P. N., & Melamed, R. (2024) · Research Square · doi:10.21203/rs.3.rs-4536370/v1 · Cited by 1
“However, oncology clinical trials are slow, expensive and have up to 95% attrition rates, mainly due to lack of understanding of the mechanism of action of the tested drug prior to the clinical trial (8–10).”
Developing tumor microenvironment in rotating human melanoma cell cultures: study of novel preclinical modelPreprint
Wawrowicz, K., Durak-Kozica, M., Wierzbicki, M., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-4884972/v1 · Cited by 1
Virtual Clinical Trials of BMP4 Differentiation Therapy: Digital Twins to Aid Successful Glioblastoma Trial DesignPreprint
Harbour, N., Curtin, L. A., Michaelides, L., Hubbard, M. E., Jackson, P., Rani, V., Kenchappa, R. S., Farias, V., Carrano, A., Owen, M. R., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.22.609156 · Cited by 1
Regulation Flow Analysis discovers molecular mechanisms of action from large knowledge databasesPreprint
Roca, C. P., Sysoev, O., Eyre, E., Galan, S., Sinibaldi, D., Tedder, P. M. R., & Mangion, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.15.659785 · Cited by 1
“Less than 10% of new drugs in Phase 1 will eventually be launched, with the majority of failures happening in the transition from Phase 2 to Phase 3, because of toxicity or lack of efficacy [2, 3].”
Long-term daily light exposure boosts photoreceptor maturation in retinal organoidsPreprint
van Oosten, E., Hoogendoorn, A. D. M., Kieboom, W., Berendsen, S. L., Haerkens, J., Maden, M., Veerman, D., Bufe, A., Almedawar, S., van der Meer, A. D., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.17.665292 · Cited by 1
Robust human genetic evidence supporting causal effects of FGF21 on reducing alcohol consuming behavioursPreprint
Cronjé, H. T., Hu, S., Gurrell, R., Vujković, M., Larsson, S. C., Mason, R., Butt, R., & Gill, D. (2025) · medRxiv · doi:10.1101/2025.11.15.25340305 · Cited by 1
How the Right In Vitro Biological Model Can Drive Success in Drug Discovery
Hoeck, K. (2020) · Genetic Engineering & Biotechnology News · doi:10.1089/gen.40.11.05
Tamizado virtual aplicado a la búsqueda de nuevos fármacos antiepilépticos activos en el modelo de 6 Hz
Goicoechea, S. (2020) · doi:10.35537/10915/100890
In silico cancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcomePreprint
Creemers, J. H. A., Ankan, A., Roes, K. C. B., Schröder, G., Mehra, N., Figdor, C. G., de Vries, I. J. M., & Textor, J. C. (2021) · medRxiv · doi:10.1101/2021.09.09.21263319
“the trial, and (2) if a trial does not adhere to a PHA, what will be the shape of the survival kinetics? 316 Especially in an era where treatment and control arm regimens are becoming increasingly complex, 317”
BackgroundMethodInfluential
Repositioning of a Novel GABA-B Receptor Agonist, AZD3355 (Lesogaberan), for the Treatment of Non-alcoholic SteatohepatitisPreprint
Bhattacharya, D., Becker, C., Readhead, B. P., Goossens, N., Buros, J. L., Fiel, M. I., Cousens, L. P., Magnusson, B., Backmark, A., Hicks, R., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-386482/v1
Highly Parallelized, Multicolor Optogenetic Recordings of Cellular Activity for Therapeutic Discovery Applications in Ion Channels and Disease-associated Excitable CellsPreprint
Borja, G. B., Zhang, H., Harwood, B. N., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.11.487888
Combinations of Drug Candidate Properties Affecting Development Success and Discontinuation for 5 Diseases: Lymphoma, Non–Small Cell Lung Cancer, Arthritis, Depression, and Alzheimer Disease
Yamaguchi, S., & Narukawa, M. (2022) · The Journal of Clinical Pharmacology · doi:10.1002/jcph.2063
Búsqueda de nuevos fármacos antichagásicos inhibidores de la Tripanotión sintetasa
Alice, J. I. (2022) · doi:10.35537/10915/137336
Drug approval prediction based on the discrepancy between gene perturbation effects in cells and humansPreprint
Kim, S., Park, M., Kim, D., Kim, I., & Im, S. (2022) · Research Square · doi:10.21203/rs.3.rs-1932224/v1
Beyond Inhibition
Kostić, M., & Jones, L. H. (2022) · Wiley eBooks · doi:10.1002/9781119774198.ch15
Automated STED microscopy for cell-biological high-throughput assays
Bierbaum, M. S. (2022) · doi:10.53846/goediss-9583
Selection of animal models for drug efficacy
Veening-Griffioen, D. H. (2023) · Utrecht University Repository (Utrecht University) · doi:10.33540/1605
Leveraging ADME/PK information to enable knowledge-driven decisions in drug discovery and development
Wienkers, L. C. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-817134-9.00021-0
Integrated pharmacokinetic/pharmacodynamic/efficacy analysis in oncology: importance of pharmacodynamic/efficacy relationships
Wong, H. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-817134-9.00015-5
As pharma is to people, so infrastructure is to cities
Erdem, F. A., & Varga, L. (2023) · Proceedings of the International Conference on Evolving Cities · doi:10.55066/proc-icec.2022.45
The Emergence of a Pharma Repurposing Ecosystem: Evidence of a New Innovation Strategy?Preprint
Barlow, J. G., Andriani, P., & Spyridonidis, D. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4483037
Decellularized Tissue-Derived Materials for 3D In Vitro Cancer Models Development
Cardoso, A. P., & Marques-Magalhães, Â. (2023) · Springer eBooks · doi:10.1007/978-3-030-92090-6_45-1
RNA interference: from target validation to therapeutics
Zhu, W. (2023) · Theoretical and Natural Science · doi:10.54254/2753-8818/6/20230167
JoVE Video Dataset
Wolf, J., Chemudupati, T., Kumar, A., et al. (2023) · doi:10.3791/65804-v
Therapeutic Potential of Traditional Medicines and Natural Products for COVID-19: A Review
Sharma, R. K., Kaur, R., Khushwah, A. S., Sharma, S., Sharma, N., & Gaba, P. (2023) · Current Traditional Medicine · doi:10.2174/0122150838264297231026111437
Wird KI neue Medikamente gegen Herzkrankheiten hervorbringen?
Glaser, M., Ritterhoff, J., Most, P., & Wade, R. C. (2023) · Aktuelle Kardiologie · doi:10.1055/a-2131-2843
BIOCARVÃO ATIVADO: FONTES DE OBTENÇÃO, CARACTERÍSTICAS E APLICAÇÕES
Júnior, E. A. D. O., do Nascimento, A. K., Silva, G. C. S., Silva, F. M. D. S. E., de Carvalho, R. B. F., & Nunes, L. C. C. (2023) · doi:10.22533/at.ed.6652311123
PRODUTOS NATURAIS E CICLODEXTRINAS: EXPLORANDO ÓLEOS ESSENCIAIS E COMPLEXOS DE INCLUSÃO NA PESQUISA E DESENVOLVIMENTO DE FORMULAÇÕES FARMACÊUTICA
de Carvalho, R. B. F., de Almeida, A. A. C., de Medeiros, M. D. G. F., & Nunes, L. C. C. (2023) · doi:10.22533/at.ed.6652311127
Biotecnologia e farmacologia: abordagens interdisciplinares na terapêutica com recursos naturais
de Carvalho, R. B. F., Silva, G. C. S., Silva, F. M. D. S. E., & Nunes, L. C. C. (2023) · doi:10.22533/at.ed.665231112
POLISSACARÍDEOS NATURAIS: ANACARDIUM OCCIDENTALE L. E SUAS APLICAÇÕES NA OBTENÇÃO DE SCAFFOLDS PARA LIBERAÇÃO DE FÁRMACOS
Marques, M. C. A., Soares, G. B., Barud, H. D. S., & Nunes, L. C. C. (2023) · doi:10.22533/at.ed.6652311122
SPIRULINA EM FOCO: CARACTERÍSTICAS, PROPRIEDADES E APLICAÇÕES
Silva, G. C. S., de Carvalho, R. B. F., Silva, F. M. D. S. E., Magalhães, C. F. C. B., Rolim, H. M. L., & Nunes, L. C. C. (2023) · doi:10.22533/at.ed.6652311121
MICROAGULHAS POLIMÉRICAS COMO SISTEMA PARA VETORIZAÇÃO DE FÁRMACOS
Silva, F. M. D. S. E., Ferreira, E. D. S., Lima, S. D. S., Júnior, J. C. V. D. C., Silva, G. C. S., de Almeida, A. A. C., & Nunes, L. C. C. (2023) · doi:10.22533/at.ed.6652311125
Market Incentives and the Drug Development Pipeline: Evidence from AntibioticsPreprint
Kong, E. L., & Zhao, O. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4676384
Drug development for movement disorders
Nobbs, D., & Leyens, L. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-99494-1.00011-3
Translationale Forschung in der Onkologie: Hohe Präzision mit kombinierten Omics
Bergamini, G., & Neubauer, G. (2024) · Deutsches Ärzteblatt · doi:10.3238/personko.2024.02.09.04
Untangling the Intangible
van der Waal, M. B. (2024) · doi:10.5463/thesis.613
Drug Development Using Human iPSC-Derived Cardiomyocytes and Future Perspectives
Kawagishi, H., Yanagida, S., & Kanda, Y. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95488-4.00078-4
Artificial Intelligence for Drug Toxicity and Safety
Joshi-Barr, S., & Wampole, M. E. (2023) · Springer eBooks · doi:10.1007/978-3-030-73317-9_134-1
Advanced In Vitro Screening of New Drugs for Proarrhythmic Activity
Jenkinson, S. (2024) · Genetic Engineering & Biotechnology News · doi:10.1089/gen.44.05.20
Biokataliza u doba zelene revolucije
Glavinić, L., Milčić, N., Blažević, Z. F., et al. (2024) · Kemija u industriji · doi:10.15255/kui.2023.033
The GPS for drug development: navigating with evidence from human populations
Buergel, T., & Steinfeldt, J. (2024) · Trends in biotechnology · doi:10.1016/j.tibtech.2024.05.003
Innovative ‘organs-on-a-chip’ technology for marine drug development
Mangalathettu, B. T., Pramitha, V. S., & Panicker, S. P. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13782-2.00011-5
Synthesis and Identification of Some New Heterocyclic Compounds Derived from N-(6-Hydrazinyl-6-oxohexanoyl)phthalimide
Ali, R., Al-Tamimi, E. O., & Alrecabi, Z. G. (2024) · Russian Journal of Organic Chemistry · doi:10.1134/s1070428024040201
When Do Firms Provide Early Access to Investigational Drugs? Evidence from Expanded Access in the Oncology Drug Market 1990-2020Preprint
Chang, S., & Kang, S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4885005
Advances in the Application of Mendelian Randomization in Neurological Research
吴, 含. (2024) · Advances in Clinical Medicine · doi:10.12677/acm.2024.1472025
Decellularized Tissue-Derived Materials for 3D In Vitro Cancer Models Development
Cardoso, A. P., & Marques-Magalhães, Â. (2024) · Springer eBooks · doi:10.1007/978-3-031-56363-8_45
Application of Virtual Drug Study to New Drug Research and Development: Challenges and Opportunity
Li, X., Liu, S., Liu, D., Yu, M., Wu, X., & Wang, H. (2024) · Clinical Pharmacokinetics · doi:10.1007/s40262-024-01416-w
“…showed that the success rate of Phase II clinical trials was about 25%, the success rate of Phase III trials was about 67%, and the success rate of the whole process from the beginning of Phase I clinical trials to marketing was less than 10%, mainly due to the lack of efficacy and safety [3].”
In vitro ADME, mouse pharmacokinetics of LD14b, and bioanalysis of a novel aβ 17β-HSD10 modulator for the treatment of Alzheimer’s disease
Daria, S., Kumar, D., Gautam, N., Alamoudi, J. A., Dow, L. F., Trippier, P. C., & Alnouti, Y. (2024) · Xenobiotica · doi:10.1080/00498254.2024.2402033
“…remains as one of the major reasons for drug development failures preventing new candidate compounds from reaching the clinic (Harrison 2016; Dowden and Munro 2019) Therefore, we sought to characterise the in vitro absorption, distribution, metabolism, and excretion (ADME) and in vivo PK…”
The Road from Laboratory to Patient
Zhang, Y., & Chen, Y. (2024) · Springer eBooks · doi:10.1007/978-981-97-7274-2_11
New Insights on Success and Failed Drugs
Sun, Z., & Zhou, Y. (2024) · Springer eBooks · doi:10.1007/978-981-97-7274-2_10
Preclinical: Drug Target Identification and Validation in Humans
Singh, H., & Kaur, R. P. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00145-7
Exploring Drug Repurposing for Interstitial Cystitis/Bladder Pain Syndrome: Defining Novel Therapeutic Targets
Gültekin, G. İ., Çetin, Z., & Mangır, N. (2024) · Neurourology and Urodynamics · doi:10.1002/nau.25651
“Repurposing is a cost ‐ efficient method that reduces the time committed to around 3 – 12 years and minimizes research and development expenses [6, 27, 28].”
Safety and efficacy assessments using human iPS cell-derived cardiomyocytes
Kawagishi, H., & Kanda, Y. (2024) · Folia Pharmacologica Japonica · doi:10.1254/fpj.24043
When PACE-Gate Meets Sample Size Calculations
Sepúlveda, N. (2025) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-031-68949-9_12
Genetics of Depression
Levey, D. F., & Gelernter, J. E. (2025) · Oxford University Press eBooks · doi:10.1093/med/9780197640654.003.0028
3R-Compliant Murine Ex Vivo Platform for Scalable Brain Cancer Modeling and Drug ScreeningPreprint
Wolfram, A., Arnold, V., Moskalchuk, A., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.03.641148
Slashing NIH Funding: Trump's Gamble on Science and HealthPreprint
Jalali, M. S., & Hasgül, Z. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5177987
5. Animal Ethics and Animal Experimentation
Collective, C. (2025) · Bioethics · doi:10.11647/obp.0449.05
Calendar Period Estimation of Probabilities of Transition in Drug DevelopmentPreprint
Schirmacher, E., Chow, C. M., & Ledley, F. D. (2025) · medRxiv · doi:10.1101/2025.05.15.25327693
“Despite all the pre-clinical work done (approximately 5 years of research and development) to come up with a new drug candidate that is ready for human clinical trials, only about 10% of these candidates will eventually be approved for human use (Dowden and Munro 2019; Sun et al. 2022).”
The Importance of Pharmacodynamics in Early Drug Discovery
Chen, E. P. (2025) · doi:10.1002/9781119650263.ch09
An accessible, timely method for identifying medications for repurposing: hypothesis generating; testing and validation
Lange, M., Gogarty, E., Martyn, M., Braude, P., Fayez, F., Koutsouleris, N., Howes, O. D., Twumasi, R., & Carter, B. (2025) · BMC Digital Health · doi:10.1186/s44247-025-00170-8
“Moreover, clinical trial success rates are low [4, 5].”
Resources for Accelerating Medicine Discovery
Garcia‐Paredes, M. C., Ghanekar, P. G., Chambers, R. K., Schmitt, A. D., Cavanagh, K. L., & Woerly, E. M. (2025) · Wiley eBooks · doi:10.1002/0471266949.bmc319
Gentherapeutische Behandlungsmethoden für seltene ophthalmologische Erkrankungen im Lichte der Verteilungsgerechtigkeit – eine Übersicht
Fluck, C. (2025) · Die Ophthalmologie · doi:10.1007/s00347-025-02308-5
“Hinzu kommt, dass die Entwicklung der Thera-pien mit hohen Risiken von Misserfolgen, also dem Nicht-Eintreten der erwarteten Wirksamkeit einhergeht [8, 13].”
Aberrant Enhancer Regulation, Phase Separation, and Autoimmune Diseases
Hou, H., Qiu, Y., & Zhao, M. (2025) · Clinical Reviews in Allergy & Immunology · doi:10.1007/s12016-025-09096-5
“Autoimmune diseases currently face significant treatment challenges, with a clinical drug development success rate of only 10% [155].”
Advances in peptide-based drug discovery
Khatri, B., Velloso, J. P. L., Shambhawi, Kan, M., & Craik, D. J. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29808-0.00041-8
Macromolecular Crowding for Reparative Medicine and Drug Discovery Applications
Giuffredi, G., Rossoni, A., Cubillo, L. T., et al. (2025) · Sub-cellular biochemistry/Subcellular biochemistry · doi:10.1007/978-3-032-03370-3_19
GNNs in drug design
Ding, Y., Shen, Y., Yi, K., & Wang, Y. G. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-332908-1.00011-8
Artificial Intelligence: An Innovative Tool for the Drug Development and Drug Delivery System
Shukla, G., & Singh, D. (2025) · Biomedical Materials & Devices · doi:10.1007/s44174-025-00493-0 · Cited by 1
Healthcare and biopharmaceutical value chains
Williams, D. R. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-820597-6.00011-0
ToxAssay: a hierarchical model-driven tool for advanced toxicogenomics biomarker discovery
Rana, M. M., Mollah, M. N. H., Albujja, M. H., Hadi, S. S., & Liu, F. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf561
“Approximately 30% of drug candidates fail due to unwanted or unmanageable toxicity between 2010 and 2017 (Dowden and Munro 2019; Sun et al. 2022).”
Tümör-Bağışıklık Hücre Etkileşimlerini Araştırmak İçin Biyomühendisliksel Yöntemler
Topal, T., & Uzun, Z. E. (2025) · Gazi Üniversitesi Fen Fakültesi Dergisi · doi:10.63716/guffd.1768168
A Human Genetics Framework for De-risking Gene Editing Targets for Hematopoietic Cell and Gene TherapyPreprint
Dhall, A., Pyclik, A., Ung, M. H., Etchin, J., Keschner, Y., Ge, H. G., Chakraborty, T., & Lydeard, J. R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.15.694499
Additional file 6 of Standardized generation of human iPSC-derived hematopoietic organoids and macrophages utilizing a benchtop bioreactor platform under fully defined conditions
Ackermann, M., Saleh, F., Abdin, S. M., Hashtchin, A. R., Gensch, I., Golgath, J., Oliveira, C., Nguyen, A. H. H., Gaedcke, S., Fenske, A., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26735583
Additional file 7 of Standardized generation of human iPSC-derived hematopoietic organoids and macrophages utilizing a benchtop bioreactor platform under fully defined conditions
Ackermann, M., Saleh, F., Abdin, S. M., Hashtchin, A. R., Gensch, I., Golgath, J., Oliveira, C., Nguyen, A. H. H., Gaedcke, S., Fenske, A., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26735586
Additional file 1 of InterDILI: interpretable prediction of drug-induced liver injury through permutation feature importance and attention mechanism
Lee, S., & Yoo, S. (2024) · Figshare · doi:10.6084/m9.figshare.26660293
Additional file 5 of Standardized generation of human iPSC-derived hematopoietic organoids and macrophages utilizing a benchtop bioreactor platform under fully defined conditions
Ackermann, M., Saleh, F., Abdin, S. M., Hashtchin, A. R., Gensch, I., Golgath, J., Oliveira, C., Nguyen, A. H. H., Gaedcke, S., Fenske, A., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26735580
Additional file 2 of Standardized generation of human iPSC-derived hematopoietic organoids and macrophages utilizing a benchtop bioreactor platform under fully defined conditions
Ackermann, M., Saleh, F., Abdin, S. M., Hashtchin, A. R., Gensch, I., Golgath, J., Oliveira, C., Nguyen, A. H. H., Gaedcke, S., Fenske, A., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26054801
Additional file 4 of Standardized generation of human iPSC-derived hematopoietic organoids and macrophages utilizing a benchtop bioreactor platform under fully defined conditions
Ackermann, M., Saleh, F., Abdin, S. M., Hashtchin, A. R., Gensch, I., Golgath, J., Oliveira, C., Nguyen, A. H. H., Gaedcke, S., Fenske, A., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26735577
Additional file 2 of Prioritization of therapeutic targets for cancers using integrative multi-omics analysis
Jin, X., Mei, Y., Yang, P., Huang, R., Zhang, H., Wu, Y., Wang, M., He, X., Jiang, Z., Zhu, W., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25687585
Additional file 1 of Prioritization of therapeutic targets for cancers using integrative multi-omics analysis
Jin, X., Mei, Y., Yang, P., Huang, R., Zhang, H., Wu, Y., Wang, M., He, X., Jiang, Z., Zhu, W., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25687393
Additional file 1 of Standardized generation of human iPSC-derived hematopoietic organoids and macrophages utilizing a benchtop bioreactor platform under fully defined conditions
Ackermann, M., Saleh, F., Abdin, S. M., Hashtchin, A. R., Gensch, I., Golgath, J., Oliveira, C., Nguyen, A. H. H., Gaedcke, S., Fenske, A., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26054691
Additional file 8 of Standardized generation of human iPSC-derived hematopoietic organoids and macrophages utilizing a benchtop bioreactor platform under fully defined conditions
Ackermann, M., Saleh, F., Abdin, S. M., Hashtchin, A. R., Gensch, I., Golgath, J., Oliveira, C., Nguyen, A. H. H., Gaedcke, S., Fenske, A., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26735589
Additional file 3 of Standardized generation of human iPSC-derived hematopoietic organoids and macrophages utilizing a benchtop bioreactor platform under fully defined conditions
Ackermann, M., Saleh, F., Abdin, S. M., Hashtchin, A. R., Gensch, I., Golgath, J., Oliveira, C., Nguyen, A. H. H., Gaedcke, S., Fenske, A., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26735574
Additional file 9 of Standardized generation of human iPSC-derived hematopoietic organoids and macrophages utilizing a benchtop bioreactor platform under fully defined conditions
Ackermann, M., Saleh, F., Abdin, S. M., Hashtchin, A. R., Gensch, I., Golgath, J., Oliveira, C., Nguyen, A. H. H., Gaedcke, S., Fenske, A., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26735592
Machine Learning in Computational Drug Repurposing
Gnanasekar, P., Singh, B., Singh, S., & Bhardwaj, A. (2025) · Springer handbooks · doi:10.1007/978-3-031-81728-1_33
Drug discovery guided by maximum drug likenessPreprint
Zhu, H.-Y., Xu, L., & Shi, W. F. (2025) · Research Square · doi:10.21203/rs.3.rs-8452302/v1
The OtR tumor recurrence and metastasis model reveals doxorubicin‐induced tumor shrinkage doesn't guarantee prolonged survival
Luo, L., Liu, L., Deng, M., et al. (2026) · Animal Models and Experimental Medicine · doi:10.1002/ame2.70127
Few-Shot Learning in Drug Discovery
Schimunek, J., & Klambauer, G. (2026) · Springer eBooks · doi:10.1007/978-3-031-98022-0_16 · Cited by 1
Drug target affinity prediction based on generative adversarial networks and convolutional networks
Wang, C., Wang, Z., Chen, C., et al. (2025) · doi:10.1109/icftic68075.2025.11324807
“The development of a new drug typically requires an average investment of $2.6 billion and take about 10 years, with a success rate of less than 10% in the initial phase of clinical trials [1].”
Influential
Computational Methods and Tools for the Design and Development of Small Molecule Inhibitors Targeting PPIs
Chowdhury, A., & Murru, S. (2026) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-3-032-07880-3_6
REACH: C—cause
Williams, S., Meran, L., McCartney, S. A., Harrow, P., & Lee, J. C. (2025) · Journal of Crohn s and Colitis · doi:10.1093/ecco-jcc/jjaf222 · Cited by 1
An attention-driven framework for drug repurposing against human metapneumovirus: Integrating predictive modeling with docking validation
Roudsari, A. M., Hooshmand, M., Majidifar, S., Danaeifar, M., & Rabiee, H. R. (2026) · Antiviral Research · doi:10.1016/j.antiviral.2026.106366
Informative alignment patterns between chemical structure and clinical signals support context-aware toxicity assessmentPreprint
Asano, H., Tian, Y., Hatabu, A., Fukuzawa, K., Takaya, D., & Ikeda, K. (2026) · Research Square · doi:10.21203/rs.3.rs-8923363/v1
Integrative machine learning and structure-based computational modeling reveal germination-driven enrichment of bioactive dipeptides in pigmented rice
Guzman, C. D. I. D., Perfinian, G. C., Alseekh, S., Fernie, A. R., Tiozon, R. N., & Sreenivasulu, N. (2026) · Food Bioscience · doi:10.1016/j.fbio.2026.108610
Cancer biology and the principles of targeted cancer drug discovery
Davies, A., Jones, G., Krishnan, S., Krishnamoorthy, M., Maruggi, M., Kaneda, M., & Marjon, K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-29808-0.00035-2

Authors: Dowden, H., & Munro, J.
Corresponding author: Munro, Jamie
Venue: Nature Reviews Drug Discovery
Publisher: Springer Science and Business Media LLC
Published: 2019-05-08
Volume: 18
Issue: 7
Pages: 495-496
ISSN: 1474-1776 (ISSN-L)
1474-1784
DOI: 10.1038/d41573-019-00074-z
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: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
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

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

Trends in clinical success rates and therapeutic focus

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cited by

Related

Metadata

Record

About these checks