Bayesian Workflow for Minimal PBPK Models: Case Study of Dapagliflozin.

Authors

Anna Mikhailova, Kirill Peskov, Gabriel Helmlinger, Victor Sokolov

Published in

CPT: pharmacometrics & systems pharmacology. Volume 15. Issue 8. Pages e70307.

Abstract

Numerous clinical trials (CTs) and population pharmacokinetic (PK) models have been published on dapagliflozin, an approved SGLT2 inhibitor used to treat type 2 diabetes, heart failure, and chronic kidney disease. This study proposes a Bayesian workflow for the development of minimal physiologically-based PK models (mPBPK) that integrates all available PK information, to support uncertainty quantification and informed drug development. First, a systematic collection of published dapagliflozin PK data and models was performed. A mPBPK model was then developed, and posterior parameter distributions were obtained based on data from parallel-design CTs. Three Bayesian modeling packages: NIMBLE (v1.1.0), MCSim (v6.2.0), and Torsten (v0.89.0), and three alternative prior specifications were evaluated. Model validation was performed using PK data from crossover CTs and urinary recovery data, followed by sensitivity analysis. 18 studies reporting PK data and 10 reporting PK models were identified. Posterior distributions were comparable across programs: 95% credible intervals overlapped. Torsten showed superior sampling efficiency, as compared to NIMBLE and MCSim (5.7 and 10.0 times higher in tails and central posterior regions, respectively); however, the average effective sample size per hour was comparable for Torsten and MCSim. The predicted urinary recovery was 2.2%-4.4% (mean 3.2%), while the observed values were in the 0.8%-4.0% range (mean 2.0%). Glomerular filtration rate and fraction unbound were the main contributors to inter-trial variability in urinary recovery, while volume of distribution and clearance had the highest influence on maximum concentration and area-under-the-concentration curve, respectively. The proposed Bayesian workflow is flexible and transferable to other mechanistic model types.

PMID:
42543489
Bibliographic data and abstract were imported from PubMed on 03 Aug 2026.

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