PKBOIN-12: A Bayesian optimal interval Phase I/II design incorporating pharmacokinetics outcomes to find the optimal biological dose
Hao Sun, Jieqi Tu

TL;DR
This paper introduces PKBOIN-12, a Bayesian model-assisted design that incorporates pharmacokinetics data into early phase clinical trials to more accurately identify the optimal biological dose, improving dose selection and patient safety.
Contribution
The paper presents a novel PKBOIN-12 design that integrates PK outcomes into dose-finding algorithms, extending existing methods to better identify the OBD in early phase trials.
Findings
PKBOIN-12 more effectively identifies the OBD than BOIN12.
PKBOIN-12 allocates more patients to the true OBD.
The design reduces the chance of selecting ineffective doses.
Abstract
Immunotherapies and targeted therapies have gained popularity due to their promising therapeutic effects across multiple treatment areas. The focus of early phase dose-finding clinical trials has shifted from finding the maximum tolerated dose (MTD) to identifying the optimal biological dose (OBD), which aims to balance the toxicity and efficacy outcomes, thereby optimizing the risk-benefit trade-off. These trials often collect multiple pharmacokinetics (PK) outcomes to assess drug exposure, which has shown correlations with toxicity and efficacy outcomes but has not been utilized in the current dose-finding designs for OBD selection. Moreover, PK outcomes are usually available within days after initial treatment, much faster than toxicity and efficacy outcomes. To bridge this gap, we introduce the innovative model-assisted PKBOIN-12 design, which enhances the BOIN12 design by…
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Taxonomy
TopicsStatistical Methods in Clinical Trials · Biosimilars and Bioanalytical Methods · Computational Drug Discovery Methods
