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CONTEXT Key Objective: Improvement of patient selection in early-phase oncology trials is a key step towards optimization of patients' care and reduction of clinical development associated costs. Knowledge Generated: We designed a machine learning model that significantly reduced by 68% the rate of screen fail among an independent cohort of 264 phase I or II cancer patients.
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.
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