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Clinical Trial
. 2021 Jul;10(7):782-793.
doi: 10.1002/psp4.12646. Epub 2021 Jun 26.

Data-driven personalization of a physiologically based pharmacokinetic model for caffeine: A systematic assessment

Rebekka Fendt  1   2 , Ute Hofmann  3   4 , Annika R P Schneider  1   2 , Elke Schaeffeler  3   4 , Rolf Burghaus  1 , Ali Yilmaz  5 , Lars Mathias Blank  2 , Reinhold Kerb  3   4 , Jörg Lippert  1 , Jan-Frederik Schlender  1 , Matthias Schwab  3   6 , Lars Kuepfer  1   7
Affiliations
Clinical Trial

Data-driven personalization of a physiologically based pharmacokinetic model for caffeine: A systematic assessment

Rebekka Fendt et al. CPT Pharmacometrics Syst Pharmacol. 2021 Jul.

Abstract

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.

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Conflict of interest statement

R.F., A.R.P.S., J.F.S., J.L., R.B., and L.K. were employed by Bayer AG during the manuscript's preparation and are potential stockholders of Bayer AG. All other authors declared no competing interests for this work.

Figures

FIGURE 1
FIGURE 1
Physiologically based pharmacokinetic model personalization workflow. Colored boxes indicate the names of the analyses. White boxes below indicate the respective information that was used to generate a personalized prediction. CYP, cytochrome P450
FIGURE 2
FIGURE 2
Basic goodness‐of‐fit plots. Rows show the result of the execution of the workflow steps: (a) Base model, (b) Step 1, (c) Step 2, (d) Step 3. Visual predictive check (left column): blue dots show observed caffeine plasma concentrations (C µM), and red dots show the mean of the observed plasma concentrations. The lines display the caffeine physiologically based pharmacokinetic model simulations for the respective cohort of virtual twins. Predicted versus observed concentrations (middle column): dots show the predicted concentrations that were plotted against the observed concentrations. The color of the dots corresponds to the timepoint of the measurement. The dashed black line is the line of unity; solid black lines indicate the twofold range. Residuals versus time (right column): dots show the residuals. The blue line marks the zero line. The black line is a cubic spline through the mean of the residuals
FIGURE 3
FIGURE 3
Boxplots of observed parameter values for demographic parameters, physiological parameters, and the paraxanthine/caffeine ratio. Blue dots depict the parameter values of the reference individual if applicable. GFR, glomerular filtration rate
FIGURE 4
FIGURE 4
Predicted versus observed pharmacokinetic parameter ratios. Boxplots show the pharmacokinetic parameter ratios for the respective modeling step. Horizontal solid black lines indicate the twofold range; dotted lines indicate the 1.25‐fold range, and the blue line marks a ratio of 1. The vertical line separates the workflow from the additional analyses. (a) Volume of distribution, (b) area under the curve from the start until the last measurement (8 h). AUC, area under the curve
FIGURE 5
FIGURE 5
Correlation of observed clearances and paraxanthine/caffeine ratio. (a) Linear regression model with the paraxanthine/caffeine ratio in plasma at 4 h as the explanatory variable and the observed caffeine clearance in plasma as the response variable. (b) Individual time courses of paraxanthine/caffeine ratios in plasma
FIGURE 6
FIGURE 6
Representative individual caffeine pharmacokinetics. Line colors indicate the respective workflow step, and numeric values in the legend display the corresponding root mean square error: (a) best prediction by base model (male, 42 years old, 179 cm, 83 kg), (b) best prediction by Step 1 (male, 31 years old, 170 cm, 67 kg), (c) best prediction by Step 2 (female, 31 years old, 163 cm, 82 kg), and (d) best prediction by Step 3 (female, 47 years old, 167 cm, 72 kg)
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