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A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials
Open accessAbstract
Over the past decade, the rate of drug attrition due to clinical trial failures has risen substantially. Unfortunately it is difficult to identify compounds that have unfavorable toxicity properties before conducting clinical trials. Inspired by the effective use of sabermetrics in predicting successful baseball players, we sought to use a similar "moneyball" approach that analyzes overlooked features to predict clinical toxicity. We introduce a new data-driven approach (PrOCTOR) that directly predicts the likelihood of toxicity in clinical trials. PrOCTOR integrates the properties of a compound's targets and its structure to provide a new measure, the PrOCTOR score. Drug target network connectivity and expression levels, along with molecular weight, were identified as important indicators of adverse clinical events. Our method provides a data-driven, broadly applicable strategy to identify drugs likely to possess manageable toxicity in clinical trials and will help drive the design of therapeutic agents with less toxicity.
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“while efficacy is as complex to predict6.”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [6]
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“Gayvert et al. performed classified toxicity prediction on FDA-approved drugs and drugs that had failed to pass toxicity-tests, with the RF supervised learning algorithm. The learning was from multiple sources: chemical structure characterizations, the median value of the expression of the drug targeted genes from the transcriptome of various tissues, the frequency or possibility of functional”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [157]
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Metadata
- Authors
- Gayvert, K. M., Madhukar, N. S., & Elemento, O.
- Affiliations
- Memorial Sloan Kettering Cancer Center (US)
- Weill Cornell Medicine (US)
- Corresponding author
- Elemento, Olivier
- Venue
- Cell chemical biology
- Publisher
- Elsevier BV
- Published
- 2016-10
- Volume
- 23
- Issue
- 10
- Pages
- 1294-1301
- ISSN
- 2451-9448 (ISSN-L)
- 2451-9456
- PMC
- PMC5074862
- OpenAlex
- W2461620095
- Cited by
- 310
- References
- 30
- Citations by year
- 2026: 46 · 2025: 48 · 2024: 49 · 2023: 52 · 2022: 33 · 2021: 30 · 2020: 29 · 2019: 13 · 2018: 8 · 2017: 1 · 2016: 1
- FWCI
- 7.05
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell chemical biology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- CAREER grant from National Science Foundation (DB1054964)
- NIH (R01CA194547)
- Starr Cancer Foundation
- Institute for Computational Biomedicine
- PhRMA Foundation Pre Doctoral Informatics Fellowship
- Tri-Institutional Training Program in Computational Biology and Medicine
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- drug attrition, molecular weight, adverse clinical events, structure-property relationships, drug toxicity
- MeSH terms
- Clinical Trials as Topic, Humans, Models, Biological, Models, Molecular, Software, Likelihood Functions, Computational Biology, Drug Discovery, Drug-Related Side Effects and Adverse Reactions
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