Bayesian Pharmacometrics Analysis of Baclofen for Alcohol Use Disorder

Abstract

Alcohol use disorder (AUD) also called alcohol dependence is a major public health problem, which affects almost 10% of the worlds population. Baclofen as a selective GABAB receptor agonist has emerged as a promising drug for the treatment of AUD, however, its optimal dosage varies according to individuals, and its exposure-response relationship has not been well established yet. In this study, we use a principled Bayesian workflow to estimate the parameters of a pharmacokinetic (PK) population model from Baclofen administration to patients with AUD. By monitoring various convergence diagnostics, the probabilistic methodology is first validated on synthetic longitudinal datasets and then, applied to infer the PK model parameters based on the clinical data that were retrospectively collected from outpatients treated with oral Baclofen. We show that state-of-the-art advances in automatic Bayesian inference using self-tuning Hamiltonian Monte Carlo (HMC) algorithms with a leveraged level of information in priors provide accurate predictions on Baclofen plasma concentration in individuals. This approach may pave the way to render non-parametric HMC sampling methods sufficiently easy and reliable to use in clinical schedules for personalized treatment of AUD.