A comparative study of adaptive trial designs for dose optimization

Abstract

Recently, the US Food and Drug Administration Oncology Center of Excellence initiated Project Optimus to reform the dose optimization and dose selection paradigm in oncology drug development. The agency pointed out that the current paradigm for dose selection—based on the maximum tolerated dose (MTD)—is not sufficient for molecularly targeted therapies and immunotherapies, for which efficacy may not increase after the dose reaches a certain level. In these cases, it is more appropriate to identify the optimal biological dose (OBD) that optimizes the risk–benefit tradeoff of the drug. Project Optimus has spurred tremendous interest and urgent need for guidance on designing dose optimization trials. In this article, we review several representative dose optimization designs, including model‐based and model‐assisted designs, and compare their operating characteristics based on 10,000 randomly generated scenarios with various dose‐toxicity and dose‐efficacy curves and some fixed representative scenarios. The results show that, compared with model‐based designs, model‐assisted methods have advantages of easy‐to‐implement, robustness, and high accuracy to identify OBD. Some guidance is provided to help biostatisticians and clinicians to choose appropriate dose optimization methods in practice.