Abstract (Expand)
Physiologically based pharmacokinetic (PBPK) models have been proposed as a tool for more accurate individual pharmacokinetic (PK) predictions and model-informed precision dosing, but their application … in clinical practice is still rare. This study systematically assesses the benefit of using individual patient information to improve PK predictions. A PBPK model of caffeine was stepwise personalized by using individual data on (1) demography, (2) physiology, and (3) cytochrome P450 (CYP) 1A2 phenotype of 48 healthy volunteers participating in a single-dose clinical study. Model performance was benchmarked against a caffeine base model simulated with parameters of an average individual. In the first step, virtual twins were generated based on the study subjects' demography (height, weight, age, sex), which implicated the rescaling of average organ volumes and blood flows. The accuracy of PK simulations improved compared with the base model. The percentage of predictions within 0.8-fold to 1.25-fold of the observed values increased from 45.8% (base model) to 57.8% (Step 1). However, setting physiological parameters (liver blood flow determined by magnetic resonance imaging, glomerular filtration rate, hematocrit) to measured values in the second step did not further improve the simulation result (59.1% in the 1.25-fold range). In the third step, virtual twins matching individual demography, physiology, and CYP1A2 activity considerably improved the simulation results. The percentage of data within the 1.25-fold range was 66.15%. This case study shows that individual PK profiles can be predicted more accurately by considering individual attributes and that personalized PBPK models could be a valuable tool for model-informed precision dosing approaches in the future.
Authors: Rebekka Fendt, Ute Hofmann, Annika Schneider, Elke Schaeffeler, Rolf Burghaus, Ali Yilmaz, Lars Mathias Blank, Reinhold Kerb, Jan-Frederik Schlender, Matthias Schwab, Lars Kuepfer
Date Published: 30th May 2021
Publication Type: Journal
PubMed ID: 34053199
DOI: 10.1002/psp4.12646
Citation: Fendt R, Hofmann U, Schneider ARP, Schaeffeler E, Burghaus R, Yilmaz A, Blank LM, Kerb R, Lippert J, Schlender JF, Schwab M, Kuepfer L. Data-driven personalization of a physiologically based pharmacokinetic model for caffeine: A systematic assessment. CPT Pharmacometrics Syst Pharmacol. 2021 May 30. doi: 10.1002/psp4.12646. Epub ahead of print. PMID: 34053199.
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