Prediction of Drug Approval After Phase I Clinical Trials in Oncology: RESOLVED2.
Journal:
JCO clinical cancer informatics
PMID:
31539266
Abstract
PURPOSE: Drug development in oncology currently is facing a conjunction of an increasing number of antineoplastic agents (ANAs) candidate for phase I clinical trials (P1CTs) and an important attrition rate for final approval. We aimed to develop a machine learning algorithm (RESOLVED2) to predict drug development outcome, which could support early go/no-go decisions after P1CTs by better selection of drugs suitable for further development.