Key indicators of phase transition for clinical trials through machine learning.

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%.