FDA News: Issue 2, January 2022

Author: [AUTHOR] Published on 1/21/2022 6:00:00 AM

FDA Designates a New Bayesian Optimal Interval Design as ‘Fit-For-Purpose’ Under Certain Conditions as a Statistical Methodology for Phase 1 Dose-Finding Clinical Trials

On December 10, 2021, the US Food and Drug Administration (FDA) designated a Bayesian Optimal Interval (BOIN) design as a statistical methodology for phase 1 dose-finding clinical trials as ‘fit-for-purpose’ (FFP) in the context described below. The BOIN design is a statistical methodology for phase 1 dose-finding clinical trials where the goal is to find the maximum tolerated dose (MTD) of a new drug. Here the MTD is defined as the dose with a dose-limiting toxicity probability closest to a pre-specified target value. The FDA’s FFP determination applies only to the local BOIN design under the non-informative prior and does not preclude the availability and use of other methods for phase 1 dose-finding clinical trials.

The FDA’s FFP Initiative provides a pathway for the regulatory acceptance of dynamic tools for use in drug development programs. Due to the evolving nature of these types of drug development tools and the inability to provide formal qualification, a designation of FFP has been established. A drug development tool is deemed FFP based on the acceptance of the proposed tool following a thorough evaluation of the information provided. The FFP determination is made publicly available in an effort to facilitate greater utilization of these tools in drug development programs. The FDA’s Office of Clinical Pharmacology leads the FFP Initiative and reviews submissions in multidisciplinary teams with representation from other Offices in the Center for Drug Evaluation and Research.

For more information about this FFP designation as well as other drug development tools designated as FFP, please refer to the FDA’s website: https://go.usa.gov/xt2F3

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This communication was prepared by Office of Clinical Pharmacology, Office of Translational Sciences, CDER, FDA.

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