Physiologically based pharmacokinetic modelling in health and disease for the support of pharmacotherapy
Fendt, Rebekka Konstanze; Blank, Lars M. (Thesis advisor); Küpfer, Lars (Thesis advisor)
Aachen : RWTH Aachen University (2022, 2023)
Dissertation / PhD Thesis
Dissertation, RWTH Aachen University, 2022
The liver is the central organ for detoxification and metabolizes xenobiotics such as drugs. Liver cirrhosis is a disease that impairs liver function and alters many processes involved in the absorption, distribution, and excretion (ADME) of drugs. Such functional changes influence drug plasma concentrations in liver cirrhosis patients and may reduce the efficacy and safety of drug treatment. Model-informed precision dosing (MIPD) is a personalized medicine approach and could support the choice of the right dose for the right patient. In my dissertation, I investigated how physiologically based pharmacokinetic (PBPK) models could contribute to MIPD for liver cirrhosis patients in the future. Currently, an incomplete understanding of pharmacokinetics (PK) in liver cirrhosis patients hampers accurate predictions. Separate PBPK modelling studies with individual objectives will contribute to this overarching goal. First, the impact of liver cirrhosis on PK was studied in a mouse model. The initial PBPK model predictions for mice with liver cirrhosis were based on expression data. Contrary to the model predictions, the drug metabolism of mice with liver cirrhosis was surprisingly robust, although the mice suffered from liver damage. In addition, plasma concentrations of glucuronidated metabolites were increased. PBPK simulations and experiments pointed towards increased glucuronide transport from liver cells to plasma as the most probable mechanism. Both findings from the liver cirrhosis mouse model, the robustness of drug metabolism and altered glucuronide disposition, may be relevant for PK in cirrhosis patients and should be further investigated. Second, the suitability of PBPK models for MIPD was investigated. An important prerequisite is that PBPK models that reflect individual patients' characteristics simulate PK more accurately than PBPK models based on average data. Increased accuracy of personalized PBPK simulations could be shown for caffeine. Consideration of demographic parameters (sex, height, weight, age) and the cytochrome P450 (CYP) 1A2 phenotype improved the PBPK prediction. The CYP1A2 phenotype had been determined by the plasma concentration ratio of the metabolite paraxanthine to the parent drug caffeine 4h after drug administration. In contrast, consideration of physiological parameters (glomerular filtration rate [GFR], haematocrit, liver blood flow) did not improve the PBPK model prediction. The results demonstrate that data needed for accurate and personalized PBPK model predictions depends on the drug of interest. Future studies should consider these findings during the planning and data collection phase. The dissertation results demonstrate the potential benefits of PBPK modelling for personalized medicine approaches like MIPD. PBPK Models provide the framework to integrate various kinds of patient data, and they can predict PK on this basis accurately. Personalized PBPK models could be especially advantageous for simulating complex diseases such as liver cirrhosis which affect the whole organism. Optimized drug dosing can improve therapeutic efficacy and reduce the risk of adverse effects.
- Department of Biology 
- Chair of Applied Microbiology