Flexible and efficient Bayesian pharmacometrics modeling using Stan and Torsten, Part I

Margossian, Charles C.; Zhang, Yi; Gillespie, William R.

doi:10.48550/arxiv.2109.10184

Cited by 2 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(1)) was solved analytically as provided by the Torsten library ready-to-use solver. Torsten package (Zhang et al, 2021) is based on Stan software (v2.27.0) and provides functions that facilitate the analysis of pharmacometrics data (Margossian et al, 2021). The library handles clinical event schedule data written in NONMEM (Beal et al, 2009) conventions and the computation of steady-state dosing 8 .…”

Section: Methodsmentioning

confidence: 99%

Bayesian Pharmacometrics Analysis of Baclofen for Alcohol Use Disorder

Baldy

Hashemi

Simon

et al. 2022

Preprint

View full text Add to dashboard Cite

Alcohol use disorder (AUD) also called alcohol dependence is a major public health problem, which affects almost 10% of the world's population. Baclofen as a selective GABA_B 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.

show abstract

Section: Methodsmentioning

confidence: 99%

Bayesian Pharmacometrics Analysis of Baclofen for Alcohol Use Disorder

Baldy

Hashemi

Simon

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The one-compartment linear ODE system (equation ( 1)) was solved analytically as provided by the Torsten library ready-to-use solver. Torsten package (Zhang et al 2021) is based on Stan software (v2.27.0) and provides functions that facilitate the analysis of pharmacometrics data (Margossian et al 2021). The library handles clinical event schedule data written in NONMEM (Beal et al 2009) conventions and the computation of steady-state dosing 11 .…”

Section: Solving Ode Systems In Stanmentioning

confidence: 99%

Hierarchical Bayesian pharmacometrics analysis of Baclofen for alcohol use disorder

Baldy,

Simon,

Jirsa

et al. 2023

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

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 G A B A 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.

show abstract

Flexible and efficient Bayesian pharmacometrics modeling using Stan and Torsten, Part I

Cited by 2 publications

References 15 publications

Bayesian Pharmacometrics Analysis of Baclofen for Alcohol Use Disorder

Bayesian Pharmacometrics Analysis of Baclofen for Alcohol Use Disorder

Hierarchical Bayesian pharmacometrics analysis of Baclofen for alcohol use disorder

Contact Info

Product

Resources

About