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 401–500 of 651

Computational and biophysical methods for the discovery and optimization of covalent drugs
Wang, G., Moitessier, N., & Mittermaier, A. (2023) · Chemical Communications · doi:10.1039/d3cc03285j · Cited by 6
Progress in using deep learning to treat cancer
Kamerlin, S. C. L. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00514-2 · Cited by 3
Keeping translational research grounded in human biology
Haldar, S. M. (2024) · Journal of Clinical Investigation · doi:10.1172/jci178332 · Cited by 4
“These hurdles contribute to the extremely low success rate of drug development programs progressing from target identification to approval (1).”
A multi-channel microfluidic platform based on human flavin-containing monooxygenase 3 for personalised medicine
Angelis, M. D., Schobesberger, S., Selinger, F., et al. (2024) · RSC Advances · doi:10.1039/d4ra01516a · Cited by 3
Shared genetics between breast cancer and predisposing diseases identifies novel breast cancer treatment candidates
Lalagkas, P. N., & Melamed, R. (2024) · Human Genomics · doi:10.1186/s40246-024-00688-4 · Cited by 5
GICL: A Cross-Modal Drug Property Prediction Framework Based on Knowledge Enhancement of Large Language Models
Li, N., Qiao, J., Gao, F., Wang, Y., Shi, H. W., Zhang, Z., Cui, F., Zhang, L., & Wei, L. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00895 · Cited by 4
Artificial intelligence in preclinical epilepsy research: Current state, potential, and challenges
Medel‐Matus, J., Santana‐Gomez, C., Escalante, R. G., Duncan, D., Viana, P. F., Cereda, G. S., Kuroda, N., & Galanopoulou, A. S. (2025) · Epilepsia Open · doi:10.1002/epi4.70137 · Cited by 3
“103 Given the intricate nature of biological systems, contemporary drug development requires the assimilation of diverse data sources.”
3D Radiation Biology for Identifying Radiosensitizers
Vehlow, A., Deville, S. S., & Cordes, N. (2020) · Cancer drug discovery and develogment/Cancer drug discovery and development · doi:10.1007/978-3-030-49701-9_6 · Cited by 4
Lessons From Adaptive Randomization: Spying the I-SPY2 Trial in Breast Cancer
Gyawali, B., & Niraula, S. (2020) · Journal of the National Comprehensive Cancer Network · doi:10.6004/jnccn.2020.7648 · Cited by 3
Integrated Systems for Continuous Synthesis and Biological Screenings
Gioiello, A., Moroni, G., & Cerra, B. (2022) · Methods and principles in medicinal chemistry · doi:10.1002/9783527824595.ch5 · Cited by 4
Integration of Dendrimer‐Based Delivery Technologies with Computational Pharmaceutics and Their Potential in the Era of Nanomedicine
Tupally, K. R., Seal, P., Pandey, P. M., Lohman, R., Smith, S. C., Ouyang, D., & Parekh, H. S. (2024) · Wiley eBooks · doi:10.1002/9781119987260.ch10 · Cited by 6
Transition to innovative, human-relevant pre-clinical cardiovascular research: a perspective
Daskalopoulos, E. P., Deceuninck, P., Whelan, M., & Gribaldo, L. (2024) · Cardiovascular Research · doi:10.1093/cvr/cvae080 · Cited by 2
Accelerating evidence generation: Addressing critical challenges and charting a path forward
Rim, J. G., Jackman, J. G., Hornik, C. P. V., Rutter, J. L., Warraich, H. J., Wittes, J. T., Fleisher, L. A., Anderson, B. S., Krofah, E., Kinter, E. T., et al. (2024) · Journal of Clinical and Translational Science · doi:10.1017/cts.2024.621 · Cited by 2
Structure–Tissue Exposure/Selectivity Relationship (STR) on Carbamates of Cannabidiol
Wang, S., Yang, J.-G., Rong, K., Li, H., Wu, C., & Tang, W. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252211888 · Cited by 2
Exploring Nitro‐functionalized Bis‐Schiff Bases: Synthesis, Structural Characterization, Antimicrobial, DFT, In‐silico ADME/T and Molecular Docking Studies
Munjapara, A., Parekh, J. H., Ibrahim, M., Bhatt, T. C., Patel, R. N., Socha, B. N., Jebaliya, H., Bishoyi, A., Pal, S., & Patra, S. (2024) · ChemistrySelect · doi:10.1002/slct.202404268 · Cited by 3
A Small‐Molecule Drug for the Self‐Checking of Mitophagy
Gao, Y., Bai, Q., Ren, Y., Shao, X., Zhang, M., Wu, L., Lewis, S. E., James, T. D., Chen, X. S., & Chen, Q. (2025) · Angewandte Chemie · doi:10.1002/ange.202421269 · Cited by 2
Geroscience in heart failure: the search for therapeutic targets in the shared pathobiology of human aging and heart failure
Castro, C., Delwarde, C., Shi, Y., & Roh, J. D. (2025) · The Journal of Cardiovascular Aging · doi:10.20517/jca.2024.15 · Cited by 3
“However, it is important to recognize that the success rate of a drug target identified in a preclinical study reaching phase II/III clinic trials is extremely low, especially for cardiovascular diseases [39,40] .”
SynthMol: A Drug Safety Prediction Framework Integrating Graph Attention and Molecular Descriptors into Pre-Trained Geometric Models
Su, Z., Zhang, R., Fan, X., & Tian, B. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01320 · Cited by 2
Near‐Infrared Chemiluminescent Theranostics
Lin, Y., Huang, J., & Pu, K. (2025) · Angewandte Chemie · doi:10.1002/ange.202501681 · Cited by 4
Cost and Duration of Clinical Trials in Drug Development by Japanese Pharmaceutical Companies
Okada, N., & Takahashi, Y. (2025) · Pharmaceutical Medicine · doi:10.1007/s40290-025-00558-x · Cited by 2
“In drug development, the probability of success from the start of Phase 1 to approval is approximately 10% [1, 3].”
Utilization of Proteomic Measures for Early Detection of Drug Benefits and Adverse Effects
Chadwick, J., Berry, C., Carpenter, M. A., Gulati, G., Heck, S. L., Hinterberg, M., Holman, R. R., Lee, M. M. Y., Omland, T., Paterson, C., et al. (2025) · The Journal of Clinical Pharmacology · doi:10.1002/jcph.70077 · Cited by 3
Biased Signaling and Its Role in the Genesis of Short- and Long-Acting β2-Adrenoceptor Agonists
Phan, N. T. N., Roman, L. B., Hoare, B. L., Harwood, C. R., Geary, N., Guba, W., Han, Y., Zenko, V., Koers, E. J., Rufer, A. C., et al. (2025) · Biochemistry · doi:10.1021/acs.biochem.5c00148 · Cited by 2
Application of directed message-passing neural network to predict human oral bioavailability of pharmaceuticals
Lin, W., Ye, F., Chen, P., & Wei, Z. (2025) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-025-00649-6 · Cited by 2
Relinquishing experimental control to improve translation
Wallis, J. M. (2025) · Synthese · doi:10.1007/s11229-025-05219-9 · Cited by 2
“Consider the statistic that roughly 90% of the drugs and interventions which succeed in animal studies eventually fail in human trials (DiMasi et al., 2010; Dowden & Munro, 2019; Drucker, 2016; Hay et al., 2014; Kola & Landis, 2004; Marshall et al., 2023; Wong et al., 2019).”
Genetic insights into the therapeutic potential of hydrogen sulfide
Brady, J. J. R., & Reay, W. R. (2025) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2025.178135 · Cited by 2
Intracellular Binding of Novel Fluorinated Compounds to Carbonic Anhydrase Isoforms Explored by In-Cell 19F NMR
Costantino, A., Barbieri, L., Giovannuzzi, S., Nocentini, A., Supuran, C. T., Raitelaitis, M., Nordlund, P., Banci, L., & Luchinat, E. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5c02227 · Cited by 2
Bridging the digital divide in the pharmaceutical industry: A future research agenda
Miozza, M., Lombardo, S. M., Galvagno, M., & Magni, L. (2025) · Social Science & Medicine · doi:10.1016/j.socscimed.2025.118832 · Cited by 7
A biomaterial-cell model of inflammation-induced neurodegeneration of Parkinson’s disease
Yu, Y., Xue, H., Wang, H., Fu, Y., Zhang, Y., Yu, W., Yu, L. C., Wang, J., Lu, B., & Ding, J. (2025) · Cell Biomaterials · doi:10.1016/j.celbio.2025.100298 · Cited by 3
Recent progress of novel strategies of small molecule drugs to treat inflammatory bowel diseases
Huang, X., & Huang, Y. (2026) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2026.109535 · Cited by 2
Human protein-based biomaterials in preclinical modelling
Dias, M., Custódio, C. A., & Mano, J. F. (2026) · Nature Reviews Bioengineering · doi:10.1038/s44222-026-00425-7 · Cited by 2
Translating Pharmacology from Animal Models to Organoids: Advancing Predictive Human Biology in Drug Discovery
Patel, G., & Amin, H. (2026) · Stem Cell Reviews and Reports · doi:10.1007/s12015-026-11096-7 · Cited by 2
Life Cycle Thinking in Early Drug Discovery: Reducing Environmental Impact and Animal Testing via Interpretable Machine Learning- and Chemometrics-Based Oral Acute Toxicity Screening
Jianing, X. U., Ren, T., Chen, S., Li, F., Zhang, N., Zhao, L., 钟儒刚, & Sun, G. (2026) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.6c03787 · Cited by 3
Drug discovery: Chaos can be your friend or your enemy
Miller, S. M., Moos, W. H., Munk, B. H., Munk, S. A., Hart, C. P., & Spellmeyer, D. C. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-824304-6.00012-2 · Cited by 3
Molecular Docking and Computational In Silico Investigations of Metal-Based Drug Agents
Arjmand, F., Tabassum, S., & Khan, H. Y. (2024) · Springer eBooks · doi:10.1007/978-981-97-0146-9_6 · Cited by 4
In silico screening of Fyn kinase inhibitors using classification-based QSAR model, molecular docking, molecular dynamics and ADME study
Hằng, N. T., Anh, T. D. H., Le, T. N., et al. (2024) · Molecular Diversity · doi:10.1007/s11030-024-10905-w · Cited by 2
“One of the key determinants of whether a drug can be used clinically is its pharmacokinetic properties (absorption, distribution, metabolism, and elimination—ADME) [32, 33].”
Drug Repurposing Using Molecular Network Analysis Identifies Jak as Targetable Driver in Necrobiosis Lipoidica
Hughes, A. N., Li, X., Lehman, J. S., et al. (2024) · JID Innovations · doi:10.1016/j.xjidi.2024.100296 · Cited by 2
A gravity-driven tissue chip to study the efficacy and toxicity of cancer therapeutics
Nejad, P. R., Lamichhane, A., Guragain, P., Luker, G. D., & Tavana, H. (2024) · Lab on a Chip · doi:10.1039/d4lc00404c · Cited by 3
Challenges in Sustainable Pharmaceutical Development
Nguyen, T. T., & Nguyen, T. P. T. (2025) · Springer eBooks · doi:10.1007/978-981-97-9707-3_16 · Cited by 2
Use of Bayesian approaches in oncology clinical trials: A cross-sectional analysis
Lopez-Rey, B. G., Carot-Sans, G., Ouchi, D., Torres, F., & Pontes, C. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1548997 · Cited by 3
“…oncology, where clinical trials, on average, take twice as long to complete compared to non-oncology trials (Wong et al., 2019), with lower success rates (Thomas et al., 2016; Dowden and Munro, 2019; Wong et al., 2019) and greater complexity due to the heterogeneity of cancer (Wang et al., 2017).”
Developing a Tumor Microenvironment in Rotating Human Melanoma Cell Cultures: Study of a Novel Preclinical Model
Wawrowicz, K., Durak-Kozica, M., Wierzbicki, M., & Stępień, E. Ł. (2025) · ACS Omega · doi:10.1021/acsomega.5c02682 · Cited by 5
CoMPO-GPT: Cross-Attention Conditioning for Multi-target Molecular Design in Generative Models
Cerveira, A., Kremer, F. S., Gomes, G., & Corrêa, U. B. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227286 · Cited by 2
“Recent estimates suggest that developing a new therapeutic agent can take up to 10 years, with less than 10% of drug candidates advancing beyond the initial stages of the DDP [2].”
Experimental Approaches to Identify Selective Picomolar Inhibitors for Carbonic Anhydrase IX
Kazokaitė-Adomaitienė, J., Becker, H. M., Smirnovienė, J., Dubois, L. J., & Matulis, D. (2020) · Current Medicinal Chemistry · doi:10.2174/0929867327666201102112841 · Cited by 2
Engineering the Next Generation of Matched Models for Chemical Translational Biology
Downes, D. P. (2020) · ChemBioChem · doi:10.1002/cbic.202000520 · Cited by 2
Hochpreisige Arzneimittel: Mehr Perspektive als Herausforderung – Anmerkungen zu Preisen, Gewinnen und Fortschritten in der Arzneimitteltherapie
Steutel, H. (2021) · Springer eBooks · doi:10.1007/978-3-662-63929-0_14 · Cited by 2
Teaching old drugs new tricks to treat COVID-19
Notman, N. (2022) · Nature Synthesis · doi:10.1038/s44160-021-00013-0 · Cited by 2
Targeting RNA opens therapeutic avenues for Timothy syndrome
Velasco, S. (2024) · Nature · doi:10.1038/d41586-024-00911-1 · Cited by 2
Causes of failures in project management: Analysis and inductive evidence based on case study researchPreprint
Coccia, M. (2023) · Qeios · doi:10.32388/8sgx71 · Cited by 3
Bio-Mol:Pretraining Multimodality Bioactivity Profile for Enhancing Small Molecule Property PredictionPreprint
Yip, H. F., Wei, X., Li, Z., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.02.565401 · Cited by 2
“[4, 9].”
Background
Implications of the Inflation Reduction Act for the biotechnology industry; sensitivity of investment and valuation to drug price indices and market conditions
Hyman, C., Dao, H., Vaughan, G., et al. (2024) · doi:10.36687/inetwp223 · Cited by 2
Editorial: Machine learning in computer-aided drug design
Perišić, O., Bayrak, Ç. S., & Gunady, M. K. (2025) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2025.1568437 · Cited by 2
The Virtual Biotech: A Multi-Agent AI Framework for Therapeutic Discovery and DevelopmentPreprint
Zhang, H. G., Eckmann, P., Miao, J.-C., Mahon, A. B., & Zou, J. (2026) · bioRxiv · doi:10.64898/2026.02.23.707551 · Cited by 17
“Drug discovery programs face substantial attrition, with lack of efficacy and safety concerns accounting for approximately 79% of clinical trial failures [32].”
Case Study: Natural Language Processing (NLP) with Open Data for Drug Repositioning in Glioblastoma Therapy
Marxer, C., Rölke, H., Alfieri, A., & Halatsch, M. (2023) · IEEE Swiss Conference on Data Science (SDS) · doi:10.1109/sds57534.2023.00009 · Cited by 1
“Other high risk factors such as high drug toxicity [1], [2], poor drug-like properties like bad absorption, distribution, metabolism or excretion (ADME) [3] and lack of commercial”
Background
Patient-Derived Xenografts: A Valuable Preclinical Model for Drug Development and Biomarker Discovery
Weng, T., Jenkins, B. J., & Saad, M. I. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-3858-3_3 · Cited by 1
Nontraditional models as research tools: the road not taken
Kiaris, H. (2024) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2024.07.005 · Cited by 1
Artificial Intelligence to Enhance Future Clinical Trials in Vascular Surgery
Goffart, S., Chierici, A. P., Guzzi, L., Delingette, H., Alouane, A., Lareyre, F., & Raffort, J. (2024) · Annals of Vascular Surgery · doi:10.1016/j.avsg.2024.11.019 · Cited by 1
3D Hepatocyte Model with Composite Nanofibers That Reproduced Human In Vivo Drug Clearance Profiles
Park, R., & Chen, C. (2025) · ACS Pharmacology & Translational Science · doi:10.1021/acsptsci.5c00149 · Cited by 1
Methodology and Practice for Studying Crosstalk Between 3D Human Tissue Models in Pneumatically Actuated Multi-Organ-on-Chip Systems
Keulen, J., Kemas, A. M., Youhanna, S. C., Shafagh, R. Z., & Lauschke, V. M. (2025) · Methods in molecular biology · doi:10.1007/978-1-0716-4530-7_14 · Cited by 1
From theory to practice: Simple rules for improving clinical trial confidence with covariate adjustment
Ooghe, A., Pereira, Á. R., Jr., Colloca, L., Stannard, E., Ambrose, C., Lissin, D., & Branders, S. (2025) · Journal of Pain · doi:10.1016/j.jpain.2025.105489 · Cited by 1
DFusMol: predicting molecular properties based on dual-channel attention
Liu, X., Du, W., Tang, H., Gu, Y., Li, Z., & Fu, X. (2025) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2025.1623620 · Cited by 1
“In the domain of drug discovery, the development pipeline is notoriously lengthy and complex, characterized by substantial costs and high attrition rates (Hay et al., 2014; Dowden and Munro, 2019).”
Licoisoflavone B and glabridin from Glycyrrhiza glabra as potent nucleoprotein antagonists of Lassa virus: insights from molecular docking, dynamics simulation, PCA, and DFT studies
Ahmed, S. F., Munna, M. M. R., Ahmed, M. H., et al. (2025) · Journal of Genetic Engineering and Biotechnology · doi:10.1016/j.jgeb.2025.100544 · Cited by 1
“43,44 Assessing toxicity is an important preliminary stage prior to subjecting a potential drug to clinical trials, and it helps enhance the selection of lead compounds.”
An Interventional Radiology Method for In Situ Assessments of Cancer Drug Response: Preclinical Development, Feasibility, and Safety
Bhagavatula, S. K., Reichert, R., Ahn, S. W., Foley, G., Tatárová, Z., Jakubík, J., Dominas, C. A., Marlin, C., Azzolini, N., Maloney, E., et al. (2025) · Radiology Imaging Cancer · doi:10.1148/rycan.250055 · Cited by 1
Structure-guided discovery of marine natural products as glucokinase activators for type 2 diabetes mellitus: A computational perspective
Krishnakumar, H., Jayaraman, M., Prabhu, D., & Jeyakanthan, J. (2025) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2025.109181 · Cited by 1
The computationally guided design of selective targeters of multiple proteins (STaMPs) as a new opportunity for small molecule drug discovery
Kaiser, T. M. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1691119 · Cited by 1
“While there are some recent positive improvements in clinical trial success rates for Phase II and Phase III, it is still the case that experimental drugs have only a 10% success rate on average when entering clinical development which is much lower than 4 – 5 decades ago (Dowden and Munro, 2019).”
DeepSeek-LLM with Adaptive RAG for Pharmaceutical Dissolution Prediction
Lin, L., Zhou, X., Yang, K., Yang, L., & Chen, X. (2025) · Pharmaceutical Research · doi:10.1007/s11095-025-03932-1 · Cited by 2
“This empirical dependency introduces variability; studies indicate that 25–40% of late-stage clinical failures stem from inconsistent dissolution profiles, necessitating costly mid-development recalibrations [3, 4].”
A Critical Reassessment of Tumor Metastasis Simulation in Murine Models: Insights Into Methodological Advances and Biological Relevance
Li, X., Ren, W., Liu, L., Liu, C., & Chen, X. (2025) · Advanced Therapeutics · doi:10.1002/adtp.202500277 · Cited by 2
Evaluating Complexity in Orthopedic Tissue‐on‐a‐Chip Systems
Zlotnick, H. M., Goddard, D. N., & Burdick, J. A. (2025) · Advanced Healthcare Materials · doi:10.1002/adhm.202504275 · Cited by 1
Informing development of brain cancer therapies within "preclinical trials" using ex vivo patient tumors
Adefolaju, A., Kram, D. E., Mann, B., Hingtgen, S., & Satterlee, A. (2025) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2025.115736 · Cited by 1
Update and ADMET Profile of the Latin American Natural Product Database: LANaPDB
Gómez‐García, A., Lavecchia, M. J., Olmedo, D. A., Solís, P. N., & Medina‐Franco, J. L. (2025) · Molecular Informatics · doi:10.1002/minf.70013 · Cited by 2
“In the clinical phases I, II, and III, over 75% of the drug candidates fail to pass to the next phase [14, 15], absorption, distribution, metabolism, and excretion properties are related to the failure of 40% of the candidates, and toxicity is responsible in 30% of the cases [14, 16].”
Cost-effective non-additive GWAS across 2329 diseases in 500,349 individuals
Molotkov, I., Kurki, M., FinnGen, Palotie, A. V., Daly, M. J., & Artomov, M. (2025) · Nature Communications · doi:10.1038/s41467-025-67277-4 · Cited by 2
“Translating basic scienti ﬁ c research into clinically tested drugs is challenging due to the high clinical trial failure rate 1 .”
Artificial Intelligence in Clinical Trials: From Protocol Design to Pharmacovigilance
Schulman, A., Fernández‐Torras, A., Gadiya, Y., Saeed, K., Mestres, J., & Tanoli, Z. (2026) · Springer eBooks · doi:10.1007/978-3-031-98022-0_24 · Cited by 1
Flüssigbiopsieproteomik in der Ophthalmologie
Ueffing, M., Lange, C., Schlunck, G., & Wolf, J. (2026) · Die Ophthalmologie · doi:10.1007/s00347-025-02376-7 · Cited by 1
The predictive edge: modeling and simulation in drug product development
Konagurthu, S., Ranathunga, D. T. S., Buchanan, S., Mehta, N. M., & Reynolds, T. (2026) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2026.115784 · Cited by 2
CAMA-DTI: A Cross-Domain Attention Empowered Mamba Architecture for Interpretable DTI Prediction
Wang, B., Ren, Y., Li, Y., et al. (2026) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2026.3661574 · Cited by 1
“billion [1].”
Structural Aspects of Proline Methanologues in Drug Design, Optimization, and Development
Hanessian, S., & Meanwell, N. A. (2026) · The Journal of Organic Chemistry · doi:10.1021/acs.joc.5c03173 · Cited by 1
Navigating new norms: The Tax Cuts and Jobs Act and its implications for innovation
Hong, Y. (2026) · Journal of Accounting and Public Policy · doi:10.1016/j.jaccpubpol.2026.107423 · Cited by 1
Preclinical validation of tetrahydroquinoline derivatives as EGFR inhibitor inducing glioblastoma cell death
Murugesan, A., Saravanan, K. M., Smirnov, A., et al. (2026) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2026.107503 · Cited by 1
Robust human genetic evidence supporting causal effects of FGF21 on reducing alcohol consuming behaviours
Cronjé, H. T., Hu, S., Gurrell, R., Vujković, M., Larsson, S. C., Mason, R., Butt, R. P., & Gill, D. (2026) · BMC Medicine · doi:10.1186/s12916-026-04807-x · Cited by 3
“Clinical drug development has a failure rate of approximately 90%, largely due to the pre-clinical evidence typically being drawn from animal experiments and traditional epidemiological studies, which are limited in their human relevance and ability to draw causal inferences [42, 43].”
Emergent bioengineering
Maull, V., Shpilkina, Y., de Lorenzo, V., & Solé, R. V. (2026) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2026.103520 · Cited by 1
3D bioprinting for cancer modeling and drug screening
Cadiou, A., Matera, E., Ellinger, N., Jordheim, L. P., Duruisseaux, M., Marquette, C. A., & Dumontet, C. (2026) · Biomarker Research · doi:10.1186/s40364-026-00951-x · Cited by 1
“Unfortunately, there are high attrition rates at every stage due to lack of efficacy and/or safety issues [4], and only a fraction of candidates selected by the preclinical phase actually succeed in clinical trials [2, 5, 6].”
Venom-Derived Enzyme Inhibitors as Anticancer Agents: Structure–Activity Relationships, Molecular Targets and Mechanistic Insights
Ogundele, A. V., Nongthombam, G. S., Nwagu, A. D., Silva, H. H., & Fabiyi, O. A. (2026) · Molecules · doi:10.3390/molecules31132398 · Cited by 1
Advanced biomaterial strategies for 3D in vitro biomimetic neuroblastoma cell culture
Thibieroz, N., Picart, C., & Migliorini, E. (2026) · Biomaterials Science · doi:10.1039/d6bm00438e · Cited by 1
The Virtual Biotech: A multi-agent AI framework for therapeutic discovery and development
Zhang, H. G., Eckmann, P., Miao, J., Mahon, A. B., & Zou, J. (2026) · Science · doi:10.1126/science.aeg6779 · Cited by 9
Target Validation—Prosecuting the Target
Jones, L. H. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820472-6.00014-1 · Cited by 1
The potential of complex in vitro models in pharmaceutical toxicology
Enrich, M. V., & WALLACE, H. M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-85215-9.00027-1 · Cited by 1
The 2022 Ferno Award Address: CrEATE, an Efficient Crossover Evaluation of Addiction Treatment Efficacy
Perkins, K. A. (2022) · Nicotine & Tobacco Research · doi:10.1093/ntr/ntac139 · Cited by 1
Biogen’s Aduhelm controversy as a case study for accelerated approval biomarkers in Alzheimer’s and related diseases
Berg, A., Mayers, R. P., & Richards, S. (2022) · MIT Science Policy Review · doi:10.38105/spr.y9p3sxuqg1 · Cited by 1
Improving the predictive power of xenograft and syngeneic anti-tumour studies using mice humanised for pathways of drug metabolism
Henderson, C. J., McLaren, A. W., Kapelyukh, Y., et al. (2023) · F1000Research · doi:10.12688/f1000research.122987.2 · Cited by 1
Equilibrium and Nonequilibrium Methods for Free-Energy Calculations With Molecular Dynamics
Chipot, C. J., Gkeka, P., Tony Lelièvre, A., & Stoltz, G. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-821978-2.00112-4 · Cited by 1
Facts and Hopes on Biomarkers for Successful Early Clinical Immunotherapy Trials: Innovative Patient Enrichment Strategies
Cannarile, M. A., Karanikas, V., Reis, B., Mancao, C., Lagkadinou, E., Rüttinger, D., Rieder, N., Ribeiro, F. R., Kao, H., Dziadek, S., et al. (2023) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-23-1530 · Cited by 1
Evaluating Nanoparticles Penetration by a New Microfluidic Hydrogel-Based Approach
Goodarzi, S., Lux, F., & Rivière, C. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-3850-7_14 · Cited by 1
Translational pathology in drug discovery
Čužić, S., Antolić, M., Ognjenović, A., et al. (2024) · Frontiers in Pharmacology · doi:10.3389/fphar.2024.1409092 · Cited by 1
Noval insights and therapeutic strategies for tumor-induced kidney pathologies
Wang, M., Han, Y. K., & Zhang, C. (2024) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-024-03205-6 · Cited by 1
“However, drug development pipeline targeting TACR3 faces challenges similar to those of other GPCRs, such as low specificity and high toxicity, which limit their clinical application [22].”
Background
Multi-cell lines culturing system utilizing microfluidic three-dimensional additive manufactured polymeric scaffolds with controlled pumping
Romero, C. A., Lozano, L. M., Perfecto‐Avalos, Y., Cruz-Pavlovich, F. J. S., García‐Varela, R., & Chaírez, I. (2025) · Microchemical Journal · doi:10.1016/j.microc.2025.113193 · Cited by 1
The Importance of Publishing for Industrial Medicinal Chemists
Brown, D. G. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5c00842 · Cited by 1
Development of a protocol complexity tool: a framework designed to stimulate discussion and simplify study design
Willigers, B. J. A., Wiesiolek, A. E., Potter, B., Pszczółkowska, A., Kristensson, C., Presz, V., Saraiva, G. L., & Psallidas, I. (2025) · BMC Medical Research Methodology · doi:10.1186/s12874-025-02652-9 · Cited by 1
The GSK Bioanalytical Hub Model in Bioanalysis: Maximizing the Synergies of Co-Locating Various Platforms in Support of Regulated and Nonregulated Study end Points
Summerfield, S. G., Tang, H., Decman, V., Abberley, L., Gunn, G. R., & Yang, E. (2023) · Bioanalysis · doi:10.4155/bio-2022-0180 · Cited by 1
Genome-wide investigation of gene-cancer associations for the prediction of novel therapeutic targets in oncologyPreprint
Bazaga, A., Leggate, D., & Weisser, H. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.30.927285 · Cited by 1
The search for surrogacy in patient derived xenograft mouse trials: glass is less than half fullPreprint
Mistry, H. B. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.03.409730 · Cited by 1
“3 Therefore, generating enough evidence clinically to have faith in the surrogate biomarker is a difficult task.”
Background
PLUTONIUM FUEL DEVELOPMENT FOR FAST REACTORS IN THE U.S.
Kittel, J., Kelman, L. R., Handwerk, J. H., Zebroski, E. L., Baily, W. E., & Strasser, A. A. (1967) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · Cited by 1
Model-based intensification of CHO cell cultures: one-step strategy from fed-batch to perfusionPreprint
Richelle, A., Corbett, B., Agarwal, P., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.19.492635 · Cited by 1

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