Abstract

Liver cirrhosis is a complex pathophysiological condition that can affect the pharmacokinetics (PK) and hereby dosing of administered drugs. The physiologically based pharmacokinetic (PBPK) models are a valuable tool to explore PK of drugs in cirrhosis patients. The objective of this study was to develop and evaluate a PBPK-carvedilol-cirrhosis model with the available clinical data in liver cirrhosis patients and to recommend model-based drug dosing after exploring the underlying differences in unbound and total (bound and unbound) systemic carvedilol concentrations with the different disease stages. A whole body PBPK model was developed using the population-based PBPK simulator, Simcyp®. After model development and evaluation in healthy adults, system parameters were modified according to the pathophysiological changes that occur in liver cirrhosis, and predictions were compared to available experimental data from liver cirrhosis Child-Pugh [CP]-C patients. A two-fold error range for the observed/predicted ratios (ratioObs/Pred) of the pharmacokinetic parameters was used for model evaluation. Simulations were then extended to cirrhosis CP-A and CP-B populations were no experimental data that are available to explore changes in drug disposition in these patients. Finally, drug unbound and total (bound and unbound) exposure were predicted in cirrhotic patients of different disease severity, and the results were compared to those of healthy adults. The developed model has successfully described carvedilol PK in healthy and cirrhosis CP-C patients. The model predictions showed that, there was an ~13-fold increase in unbound and ~7-fold increase in total (bound and unbound) systemic exposure of carvedilol between healthy and CP-C populations. To have comparable predicted unbound drug exposure in cirrhosis CP-A, CP-B, and CP-C populations as in healthy subjects receiving a dose of 25mg, reductions of administered doses to 9.375mg in CP-A, 4.68mg in CP-B, and 2.34mg in CP-C population were recommended. The presented model-generated data can guide the optimization of carvedilol therapy on the basis of differences in unbound and total drug exposures with respect to disease severity and can help improve the design of some necessary clinical studies in the drug development process.

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