The DOI matches its Crossref record.
Trends in clinical success rates and therapeutic focus
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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?
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
Citations
Cited by
Sorted by citations
Items 601–651 of 651
Broster, J. H., Popovic, B., Kondinskaia, D. A., Deane, C. M., & Imrie, F. (2026) · Chemical Science · doi:10.1039/d6sc02655a
Bhagavatula, S. K., Maloney, E., Ploeger, H., Ahn, S. W., Martin, S., Mathew, S. A., Panikkanvalappil, S. R., Liu, G., & Jonas, O. H. (2026) · Annals of Biomedical Engineering · doi:10.1007/s10439-026-04113-w
General Principles of Psychopharmacology
Giakas, J. A., & Grossberg, G. T. (2026) · Springer eBooks · doi:10.1007/978-3-032-15772-0_2
Exploration of autophagy modulation in cancer and infectious diseases
Bestion, E. (2021) · theses.fr (ABES) · doi:10.70675/3663f1cdz2637z4b11za0afza7e39569a2fc
Evaluation of Epithelial Integrity in Human Precision‐Cut Kidney Slices
Pedersen, G. A., Green, B. B., Merrild, C., et al. (2026) · Apmis · doi:10.1111/apm.70205
Enhancing Drug Discovery and Development Through Artificial Intelligence
Espaillat, A. (2026) · Springer eBooks · doi:10.1007/978-3-032-19336-0_12
Tools of the trade: leveraging 3D in vitro models to advance silicosis research
Donohoe, E., Selvamani, S. P., Dharwal, V., Zelei, R., Linton, A., YATES, D., & Hosseini-Beheshti, E. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1782408
“Poor clinical success of proposed therapies for pulmonary fibrosis is in part attributed to ineffective pre-clinical testing models, and limited relevancy to human physiology when evaluating therapeutic potential (Dowden and Munro, 2019; Sun et al., 2022).”
Disentangling the contribution of disease genes to drug therapeutic and side effectsPreprint
Lalagkas, P. N., & Melamed, R. (2026) · medRxiv · doi:10.64898/2026.05.04.26352378
Statistical Methodology Groups in the Pharmaceutical Industry #
Devenport, J. N., Mielke, T., Akacha, M., et al. (2026) · Statistics in Biopharmaceutical Research · doi:10.1080/19466315.2026.2656249
“Success rates for new drug approvals have remained stagnant over time while attrition rates, development timelines, and costs per new drug have increased (DiMasi et al. 2016, Laermann-Nguyen & Backfisch 2021, Dowden & Munro 2019, Schuhmacher et al. 2025).”
Nejsum, L. N. (2026) · Acta Physiologica · doi:10.1111/apha.70237
Zhu, H., Du, S., Yang, Q., Xu, L., & Shi, W. (2026) · Pharmaceuticals · doi:10.3390/ph19050744
ERFA: Combining expression data with large knowledge graphs and regulation flow for MoA discovery
Sysoev, O., Roca, C. P., Eyre, E., et al. (2025) · International Conference on Biomedical and Bioinformatics Engineering (ICBBE) · doi:10.1145/3794209.3794278
Petrosyan, N., Matevosyan, H., Tirosyan, I., Gabrielyan, Y., Gevorgyan, S., Harutyunyan, V., Matevosyan, M., & Zakaryan, H. S. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02953
Danter, W. R. (2026) · Research Square · doi:10.21203/rs.3.rs-9473758/v1
Li, X., Liu, L., Luo, L., Deng, M., Jiang, Y., Ren, W., & Qian, H. (2026) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2026.113185 · Cited by 1
“Yet, the alarming high failure rate in clinical trials clearly indicates that the existing preclinical efficacy evaluation framework is inadequate and in need of refinement (11, 12).”
Riishede, A., Merrild, C., Hyun, S., Madsen, M. G., Keller, A. K., Mutsaers, H. A. M., Nørregaard, R., & Nejsum, L. N. (2026) · Apmis · doi:10.1111/apm.70226
“One of the major challenges in CKD drug development is that less than 10% of drugs entering Phase I clinical trials actually show clinical benefit [4].”
González‐Fierro, A., Chávez-Blanco, A. D., Domínguez-Gómez, G., Romo‐Pérez, A., Pastrana-Peralta, P., Gutiérrez-Delgado, F., Kast, R. E., Correa‐Basurto, J., Algara, A. L. C., & Dueñas‐González, A. (2026) · Expert Review of Anticancer Therapy · doi:10.1080/14737140.2026.2682866
Temporal Biodynamics: An AI Platform for Identification of Stage-Relevant Targets and BiomarkersPreprint
Natekar, P., Yao, B., Mohammad-Taheri, S., Rusnak, A., Gort-Freitas, N. A., Fillâtre, J., Raymond, J. J., Saksena, S. D., Lipnick, S., & Sokolov, A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.03.729984
Macrophage-derived FN1 orchestrates the radiation-induced abscopal effect in breast cancerPreprint
Jin, X., Li, L., Yu, B., Zhang, J., Feng, X., Jing, J., Tian, J., Gao, F., Di, C., Chen, W., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-9662129/v1
Zhu, H., Du, S., Yang, Q., Xu, L., & Shi, W. (2026) · Drug Design Development and Therapy · doi:10.2147/dddt.s595430
Demir, S., Bentrop, M. F., Hotes, A., Schmid, T., Indersie, É., Branchereau, S., Vokuhl, C., Haeberle, B., Schmid, I., Cairo, S., et al. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27125237
“Clinical trials remain among the most complex and costly stages of drug development, and despite substantial advances in oncology, the failure rate of candidate therapeutics in clinical trials is still unacceptably high [1].”
Yeadon, S., Salazar‐Villacorta, A., Kurian, M. A., & Barral, S. (2026) · Frontiers in Neurology · doi:10.3389/fneur.2026.1810863
“These shifts acknowledge concerns surrounding the limited value of animal models to predict human efficacy and toxicity and, importantly, support the establishment of regulatory pathways that allow patient-derived systems to be used as translational evidence (304).”
Biomaterials for Treatments of Neurodegenerative Disorders
Yu, Y. P., & Ding, J. (2026) · Engineering · doi:10.1016/j.eng.2026.06.006 · Cited by 1
Multi-branch molecular feature fusion for molecular property prediction
Sun, C., Li, N., Lyu, K., et al. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.115501
Clarke, R., Palazzo, G., Ruzicka, J., Ferla, S., & Bassetto, M. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1866562
Bhattacharya, R., Bose, D., Rodriguez, R. V., et al. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-27781-8.00019-1
Humanized mouse models for drug metabolism and drug transport: a systematic review
Hassan, I. Z., Pheiffer, W., & Hayeshi, R. (2026) · Drug Metabolism Reviews · doi:10.1080/03602532.2026.2697751
Chen, F., Wang, L., Liu, H., Khan, Q., Tang, B., & Bao, N. (2026) · Journal of Nanobiotechnology · doi:10.1186/s12951-026-04551-7
“Nearly nine out of ten drug candidates that enter clinical development fail during phase I, II, or III trials or at the regulatory approval stage [38, 39].”
Wong, D. Y. K., Fu, W., Uhm, H., Jiang, Y., Yip, Y. C. C., Mok, V., Kwok, T. C. Y., Ouyang, L., Fu, A. K. Y., & Ip, N. Y. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2536489123
Cerveira, A., Kremer, F., de Araújo, R. M., et al. (2026) · doi:10.5753/sbcas_estendido.2026.19660
“Developing a new drug can take up to 10 years, with fewer than 10% of candidates advancing beyond early pipeline stages [Dowden and Munro 2019].”
Long-term daily light exposure boosts human retinal organoid differentiation
van Oosten, E., Kieboom, W., Hoogendoorn, A. D. M., Berendsen, S. L., Haerkens, J., van der Maden, M. M., Hrubá, E., Veerman, D., Bufe, A., Almedawar, S., et al. (2026) · Communications Biology · doi:10.1038/s42003-026-10216-w
“One major limitation in therapy development is the poor translation of pre-clinical results into clinical trials, as there are substantial differences between the preclinical models and the human eye 1 – 3 .”
Pokharel, B., Kulkarni, A., Drager, R., Muhammad, F. A., & Ziouzenkova, O. (2026) · Biomedicines · doi:10.3390/biomedicines14081834
“The epidemic rise in degenerative diseases demands new therapeutic strategies; however, up to 90% of drug candidates entering clinical trials (Phase I–III) ultimately fail to receive regulatory approval [90].”
APPLICATION OF MACHINE LEARNING METHODS IN EARLY DRUG DEVELOPMENT
Maksimov, A. S., & Дергачёва, Ж. М. (2026) · Vestnik Farmacii · doi:10.52540/2074-9457.2026.1.72
Nkosi, T., Molefe, L., Dlamini, S., Mokoena, A., & Ndlovu, K. (2026) · International Journal Of Pharmaceutical Research And Allied Sciences · doi:10.51847/fufncppkde
Okuyama, R. (2026) · Drug Discovery Today · doi:10.1016/j.drudis.2026.104782
Bioengineered In vitro bone marrow models of acute myeloid leukemia for novel drug discovery
Balanza, C. R., Clarke, L., Dalby, M. J., & Donnelly, H. (2026) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2026.2723800
“However, these models suffer from poor translatability to human disease and are thought to contribute to the widely cited ~90% failure rate of new candidate therapies during phase I/II clinical trials [9,10].”
Data-centric feedback loops for next-generation immunotherapy development
Shalita, R., & Amit, I. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-026-01785-6
Organoid intelligence: a promising paradigm for autism spectrum disorder research
Zuo, X., Zhang, X., Liu, Y., Li, X., Xia, M., Ma, Y., & Fan, X. (2026) · Molecular Psychiatry · doi:10.1038/s41380-026-03850-7
Integration of biological avatars and digital twins for “ex vivo clinical trials”
Howe, C. L. (2026) · EBioMedicine · doi:10.1016/j.ebiom.2026.106467
Wang, N., Zhang, R., & Pang, H. (2026) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.6c00594
Soheili, M., Kohan, B. G., Rastegar, L., Moradi, Y., & Kohan, H. G. (2026) · Journal of Pharmacy & Pharmaceutical Sciences · doi:10.3389/jpps.2026.16454
Translational Strategies for CNS Disorders
Sommer, B. (2026) · Springer eBooks · doi:10.1007/978-3-032-31497-0_9
Mason, G., & Weary, D. M. (2026) · Laboratory Animals · doi:10.1177/00236772261475086
Utility of 3D facial analysis as a biomarker in rare diseases exploration with hereditary angioedema
Jamuar, S. S., Palmer, R., Lee, H. Y., Chia, F. L.-A., Goh, C. B., Lee, S., Helmholz, P., Chan, S., & Baynam, G. S. (2026) · Frontiers in Medical Technology · doi:10.3389/fmedt.2026.1857918
Targeting extracellular matrix mechanobiology to suppress solid tumor progression
Boone, K., Kondapaneni, R. V., Libring, S., & Reinhart‐King, C. A. (2026) · Cancer Letters · doi:10.1016/j.canlet.2026.218856
McPherson, J. K., Sharma, S., Mishra, B., Shehadeh, F., & Mylonakis, E. E. (2026) · Frontiers in Bacteriology · doi:10.3389/fbrio.2026.1926678
Accelerating research to keep pace with the rising incidence of coccidioidomycosis
Homer, C. M. (2026) · mSphere · doi:10.1128/msphere.00082-26
“De novo drug design requires significant resource investment, and most candidate compounds do not ultimately become approved drugs (91).”
Vick, B., Jurinović, V., Kuhbandner, K., Lagally, L., Latzko, L., Arnreich, C., Hänel, G., Muth, A., Schönung, M., Kloos, A., et al. (2026) · HemaSphere · doi:10.1002/hem3.70497 · Cited by 1
“65 One promising strategy to overcome the limitations of conventional preclinical studies is conducting phase II ‐ like preclinical trials which enable adequately powered studies to assess drug efficacy across different tumor subgroups.”
Zohrevand, H., Afshar, S., & Ahmadi, M. (2026) · Iranian journal of pharmaceutical research · doi:10.5812/ijpr-172781
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
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
