Checkerboard: a Bayesian efficacy and toxicity interval design for phase I/II dose-finding trials

Optimal biological dose selection in dose-finding trials with model-assisted designs based on efficacy and toxicity: a simulation study

With the emergence of molecular targeted agents and immunotherapies in anti-cancer treatment, a concept of optimal biological dose (OBD), accounting for efficacy and toxicity in the framework of dose-finding, has been widely introduced into phase I oncology clinical trials. Various model-assisted designs with dose-escalation rules based jointly on toxicity and efficacy are now available to establish the OBD, where the OBD is generally selected at the end of the trial using all toxicity and efficacy data obtained from the entire cohort. Several measures to select the OBD and multiple methods to estimate the efficacy probability have been developed for the OBD selection, leading to many options in practice; however, their comparative performance is still uncertain, and practitioners need to take special care of which approaches would be the best for their applications. Therefore, we conducted a comprehensive simulation study to demonstrate the operating characteristics of the OBD selection approaches. The simulation study revealed key features of utility functions measuring the toxicity-efficacy trade-off and suggested that the measure used to select the OBD could vary depending on the choice of the dose-escalation procedure. Modelling the efficacy probability might lead to limited gains in OBD selection.

Shotgun-2: A Bayesian phase I/II basket trial design to identify indication-specific optimal biological doses

For novel molecularly targeted agents and immunotherapies, the objective of dose-finding is often to identify the optimal biological dose, rather than the maximum tolerated dose. However, optimal biological doses may not be the same for different indications, challenging the traditional dose-finding framework. Therefore, we proposed a Bayesian phase I/II basket trial design, named "shotgun-2," to identify indication-specific optimal biological doses. A dose-escalation part is conducted in stage I to identify the maximum tolerated dose and admissible dose sets. In stage II, dose optimization is performed incorporating both toxicity and efficacy for each indication. Simulation studies under both fixed and random scenarios show that, compared with the traditional "phase I  +  cohort expansion" design, the shotgun-2 design is robust and can improve the probability of correctly selecting the optimal biological doses. Furthermore, this study provides a useful tool for identifying indication-specific optimal biological doses and accelerating drug development.

How I treat pediatric acute myeloid leukemia

Treatment outcomes for pediatric patients with acute myeloid leukemia (AML) have continued to lag behind outcomes reported for children with acute lymphoblastic leukemia (ALL), in part because of the heterogeneity of the disease, a paucity of targeted therapies, and the relatively slow development of immunotherapy compared to ALL. In addition, we have reached the limits of treatment intensity and, even with outstanding supportive care, it is highly unlikely that further intensification of conventional chemotherapy alone will impact relapse rates. However, comprehensive genomic analyses and a more thorough characterization of the leukemic stem cell have provided insights that should lead to tailored and more effective therapies in the near future. In addition, new therapies are finally emerging, including the BCL-2 inhibitor venetoclax, CD33 and CD123-directed chimeric antigen receptor T cell therapy, CD123-directed antibody therapy, and menin inhibitors. Here we present four cases to illustrate some of the controversies regarding the optimal treatment of children with newly diagnosed or relapsed AML.