TY - JOUR
T1 - Key indicators of phase transition for clinical trials through machine learning
AU - FEIJOO PALACIOS, FELIPE ANDRES
AU - Palopoli, Michele
AU - Bernstein, Jen
AU - Siddiqui, Sauleh
AU - Albright, Tenley E.
N1 - Funding Information:
Authors of this research received funding from Bloomberg Philanthropies , Argosy Foundation , and Blakely Investments . The research was in partnership with MIT Collaborative Initiatives. Appendix A
Publisher Copyright:
© 2020 Elsevier Ltd
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/2
Y1 - 2020/2
N2 - 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 higher risk trials should be modified or halted. We also find common protocol characteristics across therapeutic areas that are linked to phase success, including the number of endpoints and the complexity of the eligibility criteria.
AB - 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 higher risk trials should be modified or halted. We also find common protocol characteristics across therapeutic areas that are linked to phase success, including the number of endpoints and the complexity of the eligibility criteria.
UR - http://www.scopus.com/inward/record.url?scp=85077920813&partnerID=8YFLogxK
U2 - 10.1016/j.drudis.2019.12.014
DO - 10.1016/j.drudis.2019.12.014
M3 - Review article
C2 - 31926317
AN - SCOPUS:85077920813
VL - 25
SP - 414
EP - 421
JO - Drug Discovery Today
JF - Drug Discovery Today
SN - 1359-6446
IS - 2
ER -