Estimation of clinical trial success rates and related parameters
Open accessAbstract
Previous estimates of drug development success rates rely on relatively small samples from databases curated by the pharmaceutical industry and are subject to potential selection biases. Using a sample of 406 038 entries of clinical trial data for over 21 143 compounds from January 1, 2000 to October 31, 2015, we estimate aggregate clinical trial success rates and durations. We also compute disaggregated estimates across several trial features including disease type, clinical phase, industry or academic sponsor, biomarker presence, lead indication status, and time. In several cases, our results differ significantly in detail from widely cited statistics. For example, oncology has a 3.4% success rate in our sample vs. 5.1% in prior studies. However, after declining to 1.7% in 2012, this rate has improved to 2.5% and 8.3% in 2014 and 2015, respectively. In addition, trials that use biomarkers in patient-selection have higher overall success probabilities than trials without biomarkers.
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Less than 4% of anticancer drug candidates pass clinical trials to obtain FDA approval, mainly due to side effects and deficiency in efficacy.
“The success rate of anticancer drug candidates during clinical screening is extremely low, with less than 4% passing through clinical trials to obtain FDA approval, mainly due to their side effects and deficiency in efficacy.[ 2 ]”
From 3D Biomimetic Models to Reconstitute Tumor Microenvironment In Vitro: Spheroids, Organoids, and Tumor‐on‐a‐Chip (2023) · cites this paper as [2]
Table 2 in Results §4.1 reports a 3.4% Phase 1-to-approval success rate for oncology drug development paths, supporting the ‘less than 4%’ figure for the paper’s all-indications industry sample. The paper does not establish that side effects and inadequate efficacy are the main reasons for this low rate; its discussion of side effects in Results §4.5 concerns post-marketing studies of approved drugs. (LLM, conf=0.88)
Experimental — accuracy not yet measuredFew novel AI-discovered or AI-designed drugs have reached human clinical trials.
“Despite substantial progress in artificial intelligence (AI) for generative chemistry, few novel AI-discovered or AI-designed drugs have reached human clinical trials.”
From A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial (2025) · cites this paper as [20]
The Introduction describes a dataset of clinical trials involving more than 21,000 compounds, but does not identify which, if any, were AI-discovered or AI-designed. Searches for AI-designed drugs and generative chemistry found no passage addressing how many such drugs reached human trials. (LLM, conf=0.76)
Experimental — accuracy not yet measuredAI-discovered drugs have experienced similar levels of phase 2 trial failure as non-AI-discovered drugs.
“AI-discovered drugs have experienced similar levels of phase 2 trial failure as non-AI-discovered drugs20,21”
From A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial (2025) · cites this paper as [20]
Results §4.1 reports aggregate Phase 2-to-Phase 3 success rates, but does not distinguish AI-discovered from non-AI-discovered drugs. Searches for AI-discovered drugs and artificial-intelligence drug discovery found no passage comparing their Phase 2 failure rates. (LLM, conf=0.75)
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- Authors
- Wong, C. H., Siah, K. W., & Lo, A. W.
- Affiliations
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Corresponding author
- Lo, Andrew W
- Venue
- Biostatistics
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-01-31
- Volume
- 20
- Issue
- 2
- Pages
- 273-286
- ISSN
- 1465-4644 (ISSN-L)
- 1468-4357
- PMC
- PMC6409418
- OpenAlex
- W2789816271
- Cited by
- 1,585
- References
- 1
- Citations by year
- 2026: 210 · 2025: 199 · 2024: 230 · 2023: 211 · 2022: 226 · 2021: 208 · 2020: 196 · 2019: 79 · 2018: 25 · 2017: 1
- FWCI
- 57.24
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Biostatistics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- MIT Laboratory for Financial Engineering
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Pharmaceutical Economics and Policy
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmaceutical industry and healthcare
- Keywords
- clinical trial success rates, drug development, clinical trial duration, anticancer drug development, biomarker-based patient selection, clinical trial phases, pharmaceutical industry, selection bias
- MeSH terms
- Drug Development, Clinical Trials as Topic, Humans, Models, Statistical, Biomarkers, Databases, Factual, Outcome Assessment, Health Care
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