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Rationale: Liver cirrhosis is known to affect drug pharmacokinetics, but the functional assessment of the underlying pathophysiological alterations in drug metabolism is difficult.Methods: Cirrhosis in mice was induced by repeated treatment with carbon tetrachloride for 12 months. A cocktail of six drugs was administered, and parent compounds as well as phase I and II metabolites were quantified in blood, bile, and urine in a time-dependent manner. Pharmacokinetics were modeled in relation to the altered expression of metabolizing enzymes. In discrepancy with computational predictions, a strong increase of glucuronides in blood was observed in cirrhotic mice compared to vehicle controls.Results: The deviation between experimental findings and computational simulations observed by analyzing different hypotheses could be explained by increased sinusoidal export and corresponded to increased expression of export carriers (Abcc3 and Abcc4). Formation of phase I metabolites and clearance of the parent compounds were surprisingly robust in cirrhosis, although the phase I enzymes critical for the metabolism of the administered drugs in healthy mice, Cyp1a2 and Cyp2c29, were downregulated in cirrhotic livers. RNA-sequencing revealed the upregulation of numerous other phase I metabolizing enzymes which may compensate for the lost CYP isoenzymes. Comparison of genome-wide data of cirrhotic mouse and human liver tissue revealed similar features of expression changes, including increased sinusoidal export and reduced uptake carriers.Conclusion: Liver cirrhosis leads to increased blood concentrations of glucuronides because of increased export from hepatocytes into the sinusoidal blood. Although individual metabolic pathways are massively altered in cirrhosis, the overall clearance of the parent compounds was relatively robust due to compensatory mechanisms.

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.

Physiologically based pharmacokinetic (PBPK) models are increasingly used to support pediatric dose selection for small molecule drugs. In contrast, only a few pediatric PBPK models for therapeutic antibodies have been published recently, and the knowledge on the maturation of the processes relevant for antibody pharmacokinetics (PK) is limited compared to small molecules. The aim of this study was, thus, to evaluate predictions from antibody PBPK models for children which were scaled from PBPK models for adults in order to identify respective knowledge gaps. For this, we used the generic PBPK model implemented in PK-Sim without further modifications. Focusing on general clearance and distribution mechanisms, we selected palivizumab and bevacizumab as examples for this evaluation since they show simple, linear PK which is not governed by drug-specific target mediated disposition at usual therapeutic dosages, and their PK has been studied in pediatric populations after intravenous application. The evaluation showed that the PK of palivizumab was overall reasonably well predicted, while the clearance for bevacizumab seems to be underestimated. Without implementing additional ontogeny for antibody PK-specific processes into the PBPK model, bodyweight normalized clearance increases only moderately in young children compared to adults. If growth during aging at the time of the simulation was considered, the apparent clearance is approximately 20% higher compared to simulations for which growth was not considered for newborns due to the long half-life of antibodies. To fully understand the differences and similarities in the PK of antibodies between adults and children, further research is needed. By integrating available information and data, PBPK modeling can contribute to reveal the relevance of involved processes as well as to generate and test hypothesis.