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)
(1) Systems Pharmacology & Medicine, Bayer AG, Leverkusen, Germany, (2) Institute of Applied Microbiology (iAMB), Aachen Biology and Biotechnology (ABBt), RWTH Aachen University, Aachen, Germany, (3) Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany, (4) University of Tuebingen, Tuebingen, Germany, (5) Department of Cardiology I, University Hospital Muenster, Münster, Germany, (6) Depts. of Clinical Pharmacology and of Biochemistry and Pharmacy, University of Tuebingen, Tuebingen, Germany, (7) Institute for Systems medicine with focus on organ interactions, University Hospital Aachen, Aachen, Germany
Objectives: Physiologically based pharmacokinetic (PBPK) models typically rely on parameter values that represent the mean of the study cohort or population. In reality, persons that have the same properties as average individuals hardly exist. The goal of this study was to build personalized PBPK models and evaluate their predictive performance.
Methods: Pharmacokinetic data was obtained in a drug cocktail trial [1]. Healthy volunteers received a single subtherapeutic oral dose of caffeine, codeine, midazolam, pravastatin, talinolol and torsemide. Biometric data (height, weight, age, sex) and physiologic data (glomerular filtration rate, liver blood flow) of 48 volunteers were used for the development of a personalized caffeine PBPK model. The paraxanthine/caffeine plasma ratio at 4h was determined to inform CYP1A2 activity. PBPK models were simulated with PK-Sim, which is part of the open systems pharmacology suite (OSP) [2]. Personalization was carried out in MATLAB with the OSP toolbox.
Results: Before model personalization, the caffeine PBPK model was adjusted to the mean caffeine plasma concentration of the study cohort. The base model described the mean data very well and served as a reference for the evaluation of model personalization. When the base model was compared to the individual caffeine plasma concentrations, 45.8% of the predictions fell into the 1.25-fold range of the data.
Informing the PBPK model simulations with biometric data (age, height, weight, sex) increased the percentage in the 1.25-fold range to 57.8%. Addition of physiological information (glomerular filtration rate, liver blood flow, hematocrit) did not further improve the model prediction (59.1% in 1.25-fold range).
Caffeine metabolism by CYP1A2 has a large influence on plasma PK. Paraxanthine is the predominant caffeine metabolite and the paraxanthine/caffeine ratio showed a good correlation to caffeine clearance (R2=0.86). The enzyme activities in the PBPK model were informed with single point measurements of paraxanthine/caffeine ratio in plasma at 4 hours. The final personalized PBPK models contained biometric, physiologic and CYP1A2 phenotype information. The percentage within the 1.25-fold range increased to 66.2%.
Conclusions: We systematically analyzed the impact of biometric, physiologic and CYP1A2 phenotype data on personalized caffeine PBPK model simulations. Informing the caffeine PBPK model with of biometric data (height, weight, age, sex) of study individuals improved the performance of model simulations, inclusion of physiologic information (glomerular filtration rate, liver blood flow, hematocrit) did not. CYP1A2 phenotyping by the paraxanthine/caffeine ratio in plasma worked well and improved the model performance.
Furthermore, we concluded that the drug’s pharmacology determines which parameters are relevant for model personalization. Caffeine clearance is enzyme-limited and renal clearance is low, therefore the low impact of personalized liver blood flows and glomerular filtration rates on the model simulation was not surprising.
The presented approach demonstrates that personalized PBPK models can predict individual pharmacokinetic profiles more accurately and that they could be applied for model informed precision dosing in the future.
References:
[1] Kuepfer, L., R. Kerb, et al. (2014). “Clinical Translation in the Virtual Liver Network.” CPT Pharmacometrics Syst. Pharmacol. 3(7): e127.
[2] http://www.open-systems-pharmacology.org
Reference: PAGE 29 (2021) Abstr 9809 [www.page-meeting.org/?abstract=9809]
Poster: Drug/Disease Modelling - Absorption & PBPK