Authors
Felipe Feijoo, Michele Palopoli, Jen Bernstein, Sauleh Siddiqui, Tenley E Albright
Publication date
2020/2/1
Source
Drug discovery today
Volume
25
Issue
2
Pages
414-421
Publisher
Elsevier Current Trends
Description
Highlights
- Protocol features across therapeutic areas and trial phases linked to phase success.
- Supervised machine learning predicts drug transition across clinical trial phases.
- Clinical trials phase transitions predicted with an average accuracy of 80%.
- Natural language algorithms to study eligibility criteria role on phase success.
- Updated estimates for phase success and likelihood of approval.
A significant number of drugs fail during the clinical testing stage. To understand the attrition of drugs through the regulatory process, here we review and advance machine-learning (ML) and natural language-processing algorithms to investigate the importance of factors in clinical trials that are linked with failure in Phases II and III. We find that clinical trial phase transitions can be predicted with an average accuracy of 80%. Identifying these trials provides information to sponsors facing difficult decisions about whether these …
Scholar articles
F Feijoo, M Palopoli, J Bernstein, S Siddiqui… - Drug discovery today, 2020