Hierarchical Bayesian pharmacometrics analysis of Baclofen for alcohol use disorder

Abstract

Alcohol use disorder (AUD), also called alcohol dependence, is a major public health problem, affecting almost $10 \%$ of the world’s population. Baclofen, as a selective $\mathrm{GABA}_\mathrm{B}$ receptor agonist, has emerged as a promising drug for the treatment of AUD. However, the inter-trial, inter-individual and residual variability in drug concentration over time in a population of patients with AUD is unknown. In this study, we use a hierarchical 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 provide accurate and decisive predictions on Baclofen plasma concentration at both individual and group levels. Importantly, leveraging the information in prior provides faster computation, better convergence diagnostics, and substantially higher out-of-sample prediction accuracy. Moreover, the root mean squared error as a measure of within-sample predictive accuracy can be misleading for model evaluation, whereas the fully Bayesian information criteria correctly select the true data generating parameters. This study points out the capability of non-parametric Bayesian estimation using adaptive HMC sampling methods for easy and reliable estimation in clinical settings to optimize dosing regimens and efficiently treat AUD.

Keywords

• Hierarchical Bayesian inference
• adaptive Monte Carlo sampling
• convergence diagnostics
• prior information
• model selection and validation
• population pharmacokinetic analysis