Physiologically based pharmacokinetic modeling of propofol: Impact of liver cirrhosis on systemic exposure and...

Propofol is a widely used intravenous (IV) anesthetic primarily eliminated through hepatic metabolism. However, quantitative prediction of pharmacokinetic (PK) alterations and dose adjustment requirements in patients with liver cirrhosis remains limited. This study aimed to develop a physiologically-based-pharmacokinetic (PBPK) model of propofol and evaluate impact of liver cirrhosis on systemic exposure and dosing requirements according to Child-Pugh (CP) classification. A PBPK model was developed using published clinical PK data from healthy adults and implemented in PK-Sim®. Drug-specific physicochemical properties and CYP2B6-, CYP2C9-, and UGT1A9-mediated metabolic pathways were incorporated into model. Subsequently, disease-specific physiological and biochemical changes associated with CP-A, CP-B, and CP-C cirrhosis were integrated to construct cirrhosis PBPK models. Healthy PBPK model adequately reproduced observed plasma concentration-time profiles across multiple IV dosing scenarios. Predicted-to-observed ratios ranged from 0.77 to 1.80 for AUC_all and 0.51-1.31 for C_max, with all datasets meeting conventional two-fold acceptance criterion. Cirrhosis PBPK models also demonstrated acceptable predictive performance, with AUC_all and C_max fold-errors ranging from 0.65 to 1.27 and 0.54-0.87, respectively. Simulations indicated progressive increases in propofol exposure with worsening liver cirrhosis severity. Simulation-based exposure matching predicted no maintenance infusion adjustment for CP-A, whereas reductions of approximately 6-7% and 12-14% were estimated for CP-B and CP-C, respectively, to approximate exposure in healthy subjects. Developed PBPK model characterized propofol PK in healthy and cirrhotic populations and provides a quantitative framework for simulation-based, exposure-matching dose adjustment according to CP classification. Prospective clinical validation is required before these model-derived dosing strategies can inform clinical practice.