Acetyl Salicylic Acid [KLH]

Introduction: 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. Methods: This study explored the effect of a personalized dose of fish oil for reducing prostaglandin E-2, an inflammatory marker linked to colorectal cancer. A Bayesian design was imple- mented 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. Results: 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). Com- pared 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. Conclusions: A Bayesian design was effective in adaptively revising the dosing algorithm, resulting in a lower pill burden. (C) 2020 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.