Key indicators of phase transition for clinical trials through machine learning
Felipe Feijoo1, Michele Palopoli2, Jen Bernstein3
1School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362854, Chile.
Drug Discovery Today
|January 12, 2020
Summary
Machine learning accurately predicts clinical trial success, identifying high-risk trials early. This helps drug sponsors make informed decisions about modifying or halting trials based on common success factors like endpoints and eligibility criteria.
Area of Science:
- Drug development
- Clinical trial analysis
- Computational pharmacology
Background:
- Many drugs fail during clinical testing, leading to significant financial and time losses.
- Understanding factors contributing to clinical trial attrition is crucial for improving drug development efficiency.
Purpose of the Study:
- To investigate factors influencing clinical trial failure in Phases II and III using machine learning (ML) and natural language processing (NLP).
- To predict clinical trial phase transitions and identify common characteristics associated with trial success.
Main Methods:
- Review and advancement of ML and NLP algorithms.
- Analysis of factors linked to drug trial failure in Phases II and III.
- Identification of common protocol characteristics correlating with phase success.
Main Results:
- Clinical trial phase transitions were predicted with an average accuracy of 80%.
- Identified key protocol characteristics associated with phase success, such as the number of endpoints and eligibility criteria complexity.
- Provided insights for sponsors to make informed decisions on high-risk trials.
Conclusions:
- ML and NLP can effectively predict clinical trial outcomes, aiding in risk assessment.
- Understanding specific protocol elements can guide the design of more successful clinical trials.
- Early identification of at-risk trials allows for timely intervention, potentially saving resources and accelerating drug development.
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