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 201–300 of 651
Guigal, N., Lockhart, B. P., Moser, T., & Heitzer, E. (2025) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-025-03328-4 · Cited by 16
“However, this model has demonstrated its limitations, particularly in this era of precision-based medicine, and is manifest to some extent by the high attrition rates in early development of oncology drugs where over 90% of investigational drugs in Phase I never achieve market authorization [2].”
Chataway, J., Williams, T. E., Blackstone, J., John, N. A., Braisher, M., Angelis, F. D., Bianchi, A., Calvi, A., Doshi, A., Mangion, S. A., et al. (2025) · The Lancet · doi:10.1016/s0140-6736(25)01039-6 · Cited by 23
Bhanot, A., & Sundriyal, S. (2021) · ACS Omega · doi:10.1021/acsomega.1c00104 · Cited by 18
Salvati, A. L., & Poelstra, K. (2022) · Pharmaceutics · doi:10.3390/pharmaceutics14010217 · Cited by 16
“In fact, more than 90% of all new active substances that enter clinical phase I fail in subsequent clinical trials [170] and 79% of these failures are due to safety or efficacy issues [170].”
BackgroundDiNuzzo, M. (2022) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2022.1019706 · Cited by 16
“Indeed, most drugs fail as a result of low ef fi cacy (45 – 65% of cases) or high toxicity (15 – 35% of cases), and seldom because of strategic, commercial, or operative reasons (Harrison, 2016; Takebe et al., 2018; Dowden and Munro, 2019).”
BackgroundFaisal, M., Aid, J., o‘g‘li, N. B. H., Lee, B., & Hickey, M. A. (2023) · PLoS ONE · doi:10.1371/journal.pone.0281003 · Cited by 16
“Although many possible treatments for AD are in development [5, 6], drug approval rates are typically low for the nervous system [7] and a recent approval of Aducanumab [8] has proven controversial [9, 10].”
BackgroundLeading the pack: Best practices in comparative canine cancer genomics to inform human oncology
London, C. A., Gardner, H. L., Zhao, S., Knapp, D. W., Utturkar, S. M., Duval, D. L., Chambers, M. R., Ostrander, E. A., Trent, J. M., & Kuffel, G. R. (2023) · Veterinary and Comparative Oncology · doi:10.1111/vco.12935 · Cited by 18
Fast and effective molecular property prediction with transferability map
Yao, S., Song, J., Jia, L., Cheng, L., Zhong, Z., Song, M., & Feng, Z. (2024) · Communications Chemistry · doi:10.1038/s42004-024-01169-4 · Cited by 17
“However, it is common that the experimental data size is small as producing labeled data requires time-consuming and expensive experiments 4,5 .”
BackgroundJung, O., Song, M. J., & Ferrer, M. (2021) · SLAS DISCOVERY · doi:10.1177/24725552211030903 · Cited by 16
“24–26 Choosing the appropriate CIVM assay platform for scientific research or a specific phase of the drug discovery and development process needs to be established through a fit-for-purpose approach:7 (1) the disease to model, (2) the disease stage, (3) the physiological complexity needed to model the overall disease…”
BackgroundInfluentialAllosteric Modulators of Serotonin Receptors: A Medicinal Chemistry Survey
Brunetti, L., Francavilla, F., Leopoldo, M., & Lacivita, E. (2024) · Pharmaceuticals · doi:10.3390/ph17060695 · Cited by 16
“These receptors have proven to be the most successful class of drug targets, with more than 700 drugs approved by the FDA, targeting 134 GPCRs [12].”
BackgroundSamantasinghar, A., Sunildutt, N., Ahmed, F., Memon, F. H., Kang, C.-U., & Choi, K. H. (2025) · ACS Omega · doi:10.1021/acsomega.4c11227 · Cited by 15
Tanaka, K., Mochizuki, T., Baba, S., Kawai, S., Nakano, K., Tachibana, T., Uchimura, K., Kato, A., Miyayama, T., YAMAGUCHI, T., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-95823-z · Cited by 16
Frost, T. S., Jiang, L., & Zohar, Y. (2020) · Micromachines · doi:10.3390/mi11050536 · Cited by 16
“This trend is noted as a decrease in the number of new compounds entering early stages of clinical trials, but an increase in the relative percentage of compounds progressing throughout clinical trials [6].”
BackgroundLi, X., Zhou, Y., Xu, B., Qin, Y., Zhao, J., Li, M., Xu, J., & Li, G. (2024) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2023-007959 · Cited by 17
Virtanen, P. S., Ortiz, K. J., Patel, A., et al. (2024) · Current Oncology Reports · doi:10.1007/s11912-024-01497-7 · Cited by 15
“However, this barrier is counterproductive in the treatment of brain tumors by restricting penetration of therapeutics to the site of disease [2, 3].”
Simão, D. F., Gomes, C. M., Alves, P. M., & Brito, C. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107883 · Cited by 15
The promise and reality of therapeutic discovery from large cohorts
Melamud, E., Taylor, D. L., Sethi, A., Cule, M., Baryshnikova, A., Saleheen, D., van Bruggen, N., & FitzGerald, G. A. (2020) · Journal of Clinical Investigation · doi:10.1172/jci129196 · Cited by 14
“Among drugs entering clinical development, only about 10% will ultimately pass the stringent regulatory requirements necessary for a new-drug approval (7).”
BackgroundMalamatari, M. (2023) · Processes · doi:10.3390/pr11102919 · Cited by 16
Progress and application of lung-on-a-chip for lung cancer
Li, L., Bo, W., Wang, G., Juan, X., Xue, H., & Zhang, H. (2024) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2024.1378299 · Cited by 14
“These models have poor predictive performance for the key features to lung because it is dif fi cult to replicate biological functions (Vlachogiannis et al., 2018), which contributes to high failure rates in new drug development (Ainslie et al., 2019; Dowden and Munro, 2019; Gao et al., 2022).”
BackgroundTheory of errors in innovation failure and strategic management of winning by failing
Coccia, M. (2024) · Technology Analysis and Strategic Management · doi:10.1080/09537325.2024.2383604 · Cited by 15
“%) and poor strategic planning 10% (Dowden and Munro 2019).”
Umbricht, D., Kas, M. J., & Dawson, G. R. (2024) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2024.08.511 · Cited by 15
“Despite a prevailing view that drug development in neuropsychiatric disorders is particularly prone to failure, attrition rates in disease areas such as oncology are comparably low (Dowden and Munro, 2019; Pangalos et al., 2007; Wong et al., 2019).”
BackgroundCan Caenorhabditis elegans Serve as a Reliable Model for Drug and Nutraceutical Discovery?
Deji-Oloruntoba, O. O., Elufioye, T. O., Adefegha, S. A., & Jang, M. (2025) · Applied Biosciences · doi:10.3390/applbiosci4020023 · Cited by 13
“Neurodegenerative disorders (NDs) refer to a group of age-related diseases characterized by the breakdown of cellular proteostasis and the misfolding of various proteins, leading to the formation of toxic neuronal aggregates [156,157].”
Drug Discovery Firms and Business Alliances for Sustainable Innovation
Harada, Y., Wang, H., Kodama, K., et al. (2021) · Sustainability · doi:10.3390/su13073599 · Cited by 14
Harley, I. T. W., Allison, K., & Scofield, R. H. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.953439 · Cited by 15
QuantumTox: Utilizing quantum chemistry with ensemble learning for molecular toxicity prediction
Wang, X., Wang, L., Wang, S., Ren, Y., Chen, W., Li, X., Han, P., & Song, T. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106744 · Cited by 14
Innovations to improve the efficiency of phase II IBD clinical trials
Noor, N. M., & Raine, T. (2023) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-023-00805-z · Cited by 14
Austin, C. P. (2021) · Nature Reviews Drug Discovery · doi:10.1038/d41573-021-00008-8 · Cited by 14
Genomics of drug target prioritization for complex diseases
Chen, R. K., Duffy, Á., & Do, R. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00904-4 · Cited by 28
“Approximately 50% of these failures result from drug inef-ficacy, and 30% of trials are halted owing to safety concerns 2,3 , both of which are often attributable to poor target selection during preclinical stages 4 .”
Chen, H., Shi, Y., Yuan, M., Li, H., & Liu, X. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108785 · Cited by 18
Murali, V., Muralidhar, Y. P., Königs, C., Nair, M., Madhu, S., Nedungadi, P., Srinivasa, G., & Athri, P. (2022) · Chemical Biology & Drug Design · doi:10.1111/cbdd.14092 · Cited by 16
An Evolved Transformer Model for ADME/Tox Prediction
Shao, C., Shao, F., Huang, S., Sun, R., & Zhang, T. (2024) · Electronics · doi:10.3390/electronics13030624 · Cited by 16
Duan, Y., Yang, X., Zeng, X., Wang, W., Deng, Y., & Cao, D. (2024) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c00692 · Cited by 14
Song, W. H., Lim, Y. S., Kim, J., Kang, H. Y., Lee, C., Rajbongshi, L., Hwang, S.-Y., Oh, S., Kim, B. S., Lee, D., et al. (2024) · Marine Drugs · doi:10.3390/md22070295 · Cited by 15
Use of Drug Sensitisers to Improve Therapeutic Index in Cancer
Chen, Y.-S., Jin, E., & Day, P. J. (2024) · Pharmaceutics · doi:10.3390/pharmaceutics16070928 · Cited by 16
“[1–4].”
First-in-Class Small Molecule Degrader of Pregnane X Receptor Enhances Chemotherapy Efficacy
Huber, A. D., Jung, K., Li, Y., Lin, W., Wu, J., Poudel, S., Carrigan, A., Mishra, A., High, A. A., & Chen, T. (2024) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c01926 · Cited by 13
Cannarile, M. A., Gomes, B., Cañamero, M., Reis, B., Byrd, A. L., Charo, J., Yadav, M., & Karanikas, V. (2021) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-20-2345 · Cited by 14
“Exemplified by the high attrition rate of molecules in early clinical development, there is refueled interest to develop cancer immunotherapeutics capable of combating the biological environment in which the tumor grows and survives (2).”
BackgroundSong, C., Kendi, A. T., Shim, J. Y., Jung, D., Kang, P. S., Lowe, V. J., & Lee, S. (2023) · Breast Cancer · doi:10.1007/s12282-023-01437-6 · Cited by 14
“Currently, the drug discovery procedure includes 2D cell culture-based tests, then animal model tests, and finally clinical trials of which only 10% are successful [36].”
BackgroundEnhancing molecular property prediction through data integration and consistency assessment
Parrondo-Pizarro, R., Menestrina, L., García-Serna, R., Fernández‐Torras, A., & Mestres, J. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-01103-3 · Cited by 16
GM-Pep: A High Efficiency Strategy to De Novo Design Functional Peptide Sequences
Chen, Q., Yang, C., Xie, Y., Wang, Y., Li, X., Wang, K., Huang, J., & Yan, W. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00089 · Cited by 12
Park, M., Kim, D., Kim, I., Im, S., & Kim, S. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104705 · Cited by 12
Torres, J. E., Meng, F., Bhattacharya, S., Buno, K., Ahmadzadegan, A., Madduri, S., Babiak, P. M., Vlachos, P. P., Solorio, L., Yeo, Y., et al. (2023) · Biomacromolecules · doi:10.1021/acs.biomac.3c00448 · Cited by 14
Artificial Intelligence in ADME Property Prediction
Siramshetty, V. B., Xu, X., & Shah, P. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-3441-7_17 · Cited by 13
Okuyama, R. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103866 · Cited by 12
Tan, P. K., Ostertag, T. M., Rosenthal, S. B., Chilin-Fuentes, D., Aidnik, H., Linker, S. B., Murphy, K., Miner, J. N., & Brenner, D. A. (2023) · American Journal Of Pathology · doi:10.1016/j.ajpath.2023.12.005 · Cited by 12
Chandraghatgi, R., Ji, H., ROSEN, G. L., & Sokhansanj, B. A. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00234 · Cited by 16
Tarek, M. T., El-Gogary, R. I., & Kamel, A. O. (2025) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2025.125385 · Cited by 15
van den Maagdenberg, H. W., van Otterloo, J. D. M., van Hasselt, J. G. C., van der Graaf, P. H., & van Westen, G. J. P. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00107 · Cited by 13
The Opportunities and Use of Imaging to Measure Target Engagement
Maynard, J., & Hart, P. J. (2019) · SLAS DISCOVERY · doi:10.1177/2472555219897270 · Cited by 12
Human performance augmentation: the importance of integrative physiological quantification
Ryan, B. J., Charkoudian, N., & Joyner, M. J. (2022) · The Journal of Physiology · doi:10.1113/jp283975 · Cited by 12
Lien, S.-T., Lin, T. E., Hsieh, J., Sung, T.-Y., Chen, J., & Hsu, K. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106722 · Cited by 14
Single cell variant to enhancer to gene map for coronary artery diseasePreprint
Amrute, J., Lee, P. C., Eres, I. E., Lee, C. J. M., Bredemeyer, A. L., Sheth, M. U., Yamawaki, T., Gurung, R., Anene-Nzelu, C. G., Qiu, W., et al. (2024) · medRxiv · doi:10.1101/2024.11.13.24317257 · Cited by 12
“Pseudobulk caQTLs for the major cell types can be found in Supplementary Tables 14, 16, 18, and 20 at the FDR 5% cut-off.”
Prioritization and functional validation of target genes from single-cell transcriptomics studies
Sokol, L., Cuypers, A., Truong, A.-C. K., Bouché, A., Brepoels, K., Souffreau, J., Rohlenová, K., Vinckier, S., Schoonjans, L., Eelen, G., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-05006-7 · Cited by 12
“Indeed, previous studies showed that insuf fi cient target validation at an early stage has been linked to costly clinical failures 18,19 and low drug approval rates 20,21 .”
Recent advances in nano-based drug delivery systems for treatment of liver cancer
Hefnawy, A., Abdelhamid, A. S., Abdelaziz, M. M., Elzoghby, A. O., & Khalil, I. A. H. (2024) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2024.08.012 · Cited by 12
Sun, D., Macedonia, C. R., Chen, Z., Chandrasekaran, S., Najarian, K., Zhou, S. Y., Cernak, T., Ellingrod, V. L., Jagadish, H. V., Marini, B. L., et al. (2024) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c01684 · Cited by 14
A Novel Tumor on Chip Mimicking the Breast Cancer Microenvironment for Dynamic Drug Screening
Testa, M., Gaggianesi, M., D’Accardo, C., Porcelli, G., Turdo, A., Marco, C. D., Patella, B., Franco, S. D., Modica, C., Bella, S. D., et al. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26031028 · Cited by 11
Tajammal, S. A., Coffey, A. B., & Tan, S. P. (2025) · Processes · doi:10.3390/pr13030653 · Cited by 12
Living myocardial slices: walking the path towards standardization
van der Geest, J. S. A., de Boer, T. P., Terracciano, C. M., et al. (2025) · Cardiovascular Research · doi:10.1093/cvr/cvaf079 · Cited by 11
Bottom-up Biomaterial strategies for creating tailored stem cells in regenerative medicine
Cruz-Gonzalez, B., Johandes, E., Gramm, D., & Hanjaya‐Putra, D. (2025) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2025.1581292 · Cited by 11
“Much of this time and cost is due to a 90% failure rate during clinical trials (Dowden and Munro, 2019).”
Wright, M. R. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1787-8_21 · Cited by 12
Challenges in the clinical development of non-D2 compounds for schizophrenia
Hopkins, S. C., Lew, R., Zeni, C. M., & Koblan, K. S. (2022) · Current Medical Research and Opinion · doi:10.1080/03007995.2022.2147342 · Cited by 12
Stephenson, D. T., Ollivier, C., Brinton, R. D., & Barrett, J. S. (2022) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2528 · Cited by 11
Transform Drug Discovery and Development With Generative Artificial Intelligence
Lavecchia, A. (2025) · Wiley eBooks · doi:10.1002/9781394280735.ch25 · Cited by 14
OriGene: A Self-Evolving Virtual Disease Biologist Automating Therapeutic Target DiscoveryPreprint
Zhang, Z.-Y., Qiu, Z.-J., Wu, Y.-C., Li, S., Wang, D.-Y., Liu, Y., Zhou, Z., Hu, Y.-S., Chen, Y., An, D., et al. (2026) · bioRxiv · doi:10.1101/2025.06.03.657658 · Cited by 33
“Conventional target discovery has relied on expert disease biologists who generate hypotheses by synthesizing diverse, often disparate, sources of knowledge, including genetic associations, signaling pathways, and pathological observations[11].”
Breaking down the barriers to animal-free research
Hope, L., & Bailey, J. (2025) · Alternatives to Laboratory Animals · doi:10.1177/02611929251349465 · Cited by 12
Didier, C. M., Fox, D. W., Pollard, K. J., et al. (2023) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.3c05788 · Cited by 10
International regulatory and publicly-funded initiatives to advance drug repurposing
Spin, E. L., Mantel-Teeuwisse, A. K., & Pasmooij, A. M. G. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1387517 · Cited by 10
Wang, R., Xiao, Y., Zhang, Z., et al. (2024) · Advanced Healthcare Materials · doi:10.1002/adhm.202401950 · Cited by 10
Phenogenomic resources immortalized in a panel of wild-derived strains of five species of house mice
Piálek, J., Ďureje, Ľ., Hiadlovská, Z., Kreisinger, J., Aghová, T., Bryjová, A., Čížková, D., de Bellocq, J. G., Hejlová, H., Janotová, K., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-86505-x · Cited by 10
“Despite rigorous optimization of drug candidates during the preclinical stage, nine of ten candidates allowed to proceed to clinical studies fail during one of three phases of clinical trials in the drug approval process 106 .”
Yang, J., Yang, Y., Xu, P., Yao, Y., Tian, H., Wang, X., Fang, H., Ge, S., Yao, Y., Wang, Y., et al. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000002530 · Cited by 10
Neural Microphysiological Systems for In Vitro Modeling of Peripheral Nervous System Disorders
Pollard, K. J., Sharma, A. D., & Moore, M. J. (2019) · Bioelectronics in Medicine · doi:10.2217/bem-2019-0018 · Cited by 10
Akrimajirachoote, N., Satitsri, S., Sommart, U., et al. (2020) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2020.173393 · Cited by 10
A proof-of-concept study poised to remodel the drug development process
Nahle, Z. A. (2022) · Frontiers in Medical Technology · doi:10.3389/fmedt.2022.1053588 · Cited by 11
Sayour, N., Toth, V., Nagy, R., Vörös, I., Gergely, T., Onódi, Z., Nagy, N., Bödör, C., Váradi, B., Ruppert, M., et al. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241813826 · Cited by 10
Lewis, M. A., Patil, K., Ettayebi, K., Estes, M. K., Atmar, R. L., & Ramani, S. (2024) · PLoS ONE · doi:10.1371/journal.pone.0304526 · Cited by 11
“Yet, the journey from bench to bedside is plagued with challenges, as evidenced by a success rate of less than 10% for drugs moving from Phase I trials to market approval [1].”
BackgroundClinical trials in the pandemic age: What is fit for purpose?
Hartman, D., Heaton, P. M., Cammack, N., Hudson, I., Dolley, S., Netsi, E., Norman, T., & Mundel, T. (2020) · Gates Open Research · doi:10.12688/gatesopenres.13146.1 · Cited by 10
“The failure rate for a drug progressing from a phase 1 study to launch hovers around 90%, and evidence abounds that poor trial design and/or data quality are significant contributing factors 8,9 .”
BackgroundManzanares, J., Sala, F., García‐Gutiérrez, M. S., & Rueda, F. N. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820472-6.00060-8 · Cited by 12
Wei, C., Du, J., Shen, Y., Wang, Z., Lin, Q., Chen, J., Fuming, Z., Lin, W., Wang, Z., Yang, Z., et al. (2023) · Oncology Letters · doi:10.3892/ol.2023.13804 · Cited by 9
“Notably, ~90% of drug candidates fail during the various phases of clinical trials and drug approval (14).”
BackgroundSilva, P. J., Janjan, N. A., Ramos, K. S., Udeani, G., Zhong, L., Ory, M. G., & Smith, M. L. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1198088 · Cited by 9
Limeta, A., Gatto, F., Herrgård, M. J., Ji, B., & Nielsen, J. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.07.032 · Cited by 9
“Studies investigating the clinical trial data collected between 2016 and 2018 attribute the reasons for these failures to: insufficient clinical efficacy or excessive toxicity (79%), strategic realignment (13%), commercial reasons (7%) and operational or technical shortcomings (1%) [3].”
BackgroundOpen-access database of literature derived drug-related Torsade de Pointes cases
Krumpholz, L., Wiśniowska, B., & Polak, S. (2022) · BMC Pharmacology and Toxicology · doi:10.1186/s40360-021-00548-0 · Cited by 9
“The total probability of achieving the FDA approval for a new drug candidate in phase 1 of clinical trials is estimated as under 10% [1].”
BackgroundMuliaditan, M., & Sepp, A. (2022) · Clinical and Translational Science · doi:10.1111/cts.13278 · Cited by 9
Drennan, P. G., Karponis, D., Richards, D. B., Coles, M. C., & Fullerton, J. N. (2022) · Clinical and Translational Science · doi:10.1111/cts.13457 · Cited by 8
Biobanking of Human Aqueous and Vitreous Liquid Biopsies for Molecular Analyses
Wolf, J., Chemudupati, T., Kumar, A., et al. (2023) · Journal of Visualized Experiments · doi:10.3791/65804 · Cited by 9
Clinical trials of probiotics: Current outlook
Al‐Madhagi, H., & Alramo, A. (2023) · Food Science & Nutrition · doi:10.1002/fsn3.3884 · Cited by 8
“However, the duration from drug approval to market launch is usually about 1.5 years on average (Dowden & Munro, 2019).”
BackgroundInfluentialConsideration of the Root Causes in Candidate Attrition During Oncology Drug Development
Kuo, Y., & Barrett, J. S. (2024) · Clinical Pharmacology in Drug Development · doi:10.1002/cpdd.1464 · Cited by 8
“A lack of efficacy has been concluded in many studies 20,22,23 to be the primary reason for drug attrition.”
Refined ADME Profiles for ATC Drug Classes
Menestrina, L., Parrondo-Pizarro, R., Gómez, I., García-Serna, R., Boyer, S. K., & Mestres, J. (2025) · Pharmaceutics · doi:10.3390/pharmaceutics17030308 · Cited by 10
Tunçel, Ş. T., Sıcak, Y., Özbek, M. O., et al. (2025) · Chirality · doi:10.1002/chir.70041 · Cited by 8
RNA-based therapeutic opportunities for the treatment of kidney diseases
Prins, J., Biscans, A., van Zonneveld, A. J., Frazier, K. S., & van der Veer, E. P. (2025) · Nature Reviews Nephrology · doi:10.1038/s41581-025-01011-7 · Cited by 11
“Clinical trials are most often terminated owing to a failure to meet primary end points (futility), or because of unexpected toxicity or unfavourable drug characteristics 115,116 .”
Vaughan, G., Du, R., & Ledley, F. D. (2024) · Clinical Trials · doi:10.1177/17407745241259112 · Cited by 8
“14–16 Similarly, while the Congressional Budget Office’s econometric models focus on investment returns that may be consistent with strategic decision-making in large companies, our model focuses on the availability and allocation of capital resources.”
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 International Edition · doi:10.1002/anie.202421269 · Cited by 8
FDA-approved drugs in 2022: A brief outline
Al‐Madhagi, H. (2023) · Saudi Pharmaceutical Journal · doi:10.1016/j.jsps.2023.01.007 · Cited by 8
“Nevertheless, from drug approval to launch of marketing and distribution, it takes about 1.5 years on average (Dowden and Munro, 2019).”
InfluentialNeurologic orphan diseases: Emerging innovations and role for genetic treatments
Kioutchoukova, I. P., Foster, D. T., Thakkar, R. N., Foreman, M. A., Burgess, B. J., Toms, R., Valero, E., & Lucke-Wold, B. P. (2023) · World Journal of Experimental Medicine · doi:10.5493/wjem.v13.i4.59 · Cited by 11
Drug Screening in Human Cells by NMR Spectroscopy Allows the Early Assessment of Drug Potency
Luchinat, E., Barbieri, L., Cremonini, M., et al. (2020) · Angewandte Chemie · doi:10.1002/ange.201913436 · Cited by 8
Deng, J., Hou, W., Dong, X., Hajagos, J., Saltz, M. M., Saltz, J. H., & Wang, F. (2021) · Drugs - Real World Outcomes · doi:10.1007/s40801-021-00253-8 · Cited by 7
“In fact, new drug development is well known for its low success rate [28].”
BackgroundArtificial Intelligence in Drug Discovery: Applications and TechniquesPreprint
Deng, J., Yang, Z., Ojima, I., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.05386 · Cited by 8
Why Clinical Trials Stop: The Role of GeneticsPreprint
Razuvayevskaya, O., López, I., Dunham, I., & Ochoa, D. (2023) · medRxiv · doi:10.1101/2023.02.07.23285407 · Cited by 8
“Recent reports indicate that 79% of clinical studies fail because of the lack of efficacy or safety (Dowden and Munro, 2019).”
BackgroundInfluentialLow-Data Drug Design with Few-Shot Generative Domain Adaptation
Liu, P., Han, Y., Gong, Z., & Xu, H. (2023) · Bioengineering · doi:10.3390/bioengineering10091104 · Cited by 8
Sepp, A., & Muliaditan, M. (2024) · mAbs · doi:10.1080/19420862.2024.2324485 · Cited by 9
Screening for variable drug responses using human iPSC cohorts
Platani, M., Jiang, H., Davidson, L., Hariharan, S., Doyonnas, R., Lamond, A. I., & Swedlow, J. R. (2025) · PLoS ONE · doi:10.1371/journal.pone.0323953 · Cited by 7
“Unfortunately, these pipelines often have very high failure rates, with 80% or more drug candidates in major disease areas failing to pass clinical trials [4].”
Computational Strategies Reshaping Modern Drug Discovery
Tutone, M., & Almerico, A. M. (2026) · Molecules · doi:10.3390/molecules31020200 · Cited by 10
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
