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Activity Number: 410 - Bayesian Adaptive Designs and Novel Strategies for Dose Optimization in Cellular Therapy Drug Development
Type: Topic Contributed
Date/Time: Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract #320885
Title: GBOIN-ET: A Generalized Bayesian Optimal Interval Design for Optimal Dose-Finding Accounting for Ordinal Graded Efficacy and Toxicity in Early Clinical Trials
Author(s): Kentaro Takeda*
Companies: Astellas Pharma Global Development, Inc.
Keywords: Bayesian adaptive dose-finding design; Phase I-II clinical trial design; efficacy grade; toxicity grade; model-assisted design
Abstract:

One of the primary objectives of an oncology dose-finding trial for novel therapies, such as molecular targeted agents and immune-oncology therapies, is to identify an optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. These new therapeutic agents appear more likely to induce multiple low or moderate-grade toxicities than dose-limiting toxicities. Besides, efficacy should be evaluated as an overall response and stable disease in solid tumors and the difference between complete remission and partial remission in lymphoma. This paper proposes the generalized Bayesian optimal interval design for dose-finding accounting for efficacy and toxicity grades. The new design, named “gBOIN-ET” design, is model-assisted, simple, and straightforward to implement in actual oncology dose-finding trials than model-based approaches. These characteristics are quite valuable in practice. A simulation study shows that the gBOIN-ET design has advantages compared with the other designs considering binary outcomes in the percentage of correct OD selection and the average number of patients allocated to the ODs across various realistic settings.


Authors who are presenting talks have a * after their name.

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