An Adaptive Bayesian Design for Personalized Dosing in a Cancer Prevention Trial

In biomarker-driven clinical trials, translational strategies typically involve moving findings from animal experiments to human trials. Typically, the translation is static, using a fixed model derived from animal experiments for the duration of the trial. Bayesian designs, capable of incorporating information external to the experiment, provide a dynamic translational strategy. This article demonstrates an example of such a dynamic Bayesian strategy in a clinical trial.

This study explored the effect of a personalized dose of fish oil for reducing prostaglandin E₂, an inflammatory marker linked to colorectal cancer. A Bayesian design was implemented for the dose-finding algorithm that adaptively updated a dose-response model derived from a previously completed animal study during the clinical trial. In the initial stages of the trial, the dose-response model parameters were estimated from the rodent data. The model was updated following a Bayesian algorithm after data on every 10‒15 subjects were obtained until the model stabilized. Subjects were enrolled in the study between 2013 and 2015, and the data analysis was carried out in 2016.

The 3 dosing models were used for groups of 16, 15, and 15 subjects. The mean target dose significantly decreased from 6.63 g/day (Model 1) to 4.06 g/day (Model 3) (p=0.001). Compared with the static strategy of dosing with a single model, the dynamic modeling reduced the dose significantly by about 1.38 g/day on average.

A Bayesian design was effective in adaptively revising the dosing algorithm, resulting in a lower pill burden.

This study is registered at www.clinicaltrials.gov NCT01860352.