Objectives: Hepatic impairment can impact drug concentration, dosing, and consequently drug labeling and prescribing. Cirrhosis patients undergo hepatic and extra-hepatic changes depending on severity of disease. PBPK modeling has shown utility for predicting the impact of hepatic impairment in a wide variety of drugs [ 1], and models have been reported using open-source model platforms accessible to all researchers [2,3]. However, access to accurate, harmonized PBPK models of hepatic impairment for use across multiple drugs, and disease severities, remains a challenge. We present an open-source PBPK model incorporating the key features of mild, moderate, and severe hepatically impaired patients, together with parameters consolidated across the available literature.
Methods: Hepatic impairment physiological parameters and CYP abundance data were obtained from literature [2,3,4]. Physiological parameters from PK-Sim (V11) were exported and a new physiology for hepatic impairment was calculated. This yielded three parameter sets one each for mild (Child-Pugh (CP) A), moderate (CP B), and severe (CP C) hepatic impairment. Each parameter set still conserved body volume and blood flow rate. Plasma protein binding and enzyme expression was adjusted. Observed PK profiles of propranolol, alfentanil, pazopanib, and cabozantinib in cirrhotic patients were used to validate the model.
Results: Predicted PK parameters for propranolol, alfentanil, pazopanib, and cabozatinib were within 2-fold of observed PK parameters for healthy controls and patients with liver impairment. Report clinical data for alfentanil was not divided by degree of liver impairment. Model predictions over estimated alfentanil PK in severe hepatic impairment group.
Conclusion: An open-source PBPK model, was developed, incorporating the key features of hepatically impaired patients, together with parameters consolidated across the literature. The model was validated for drugs across several drug classes. This model can be used prospectively to predict the impact of hepatic impairment and used to aid clinical trial design, support regulatory interactions and potentially labeling.
Citations: 1. Heimbach T et al. Physiologically Based Pharmacokinetic Modeling in Renal and Hepatic Impairment Populations: A Pharmaceutical Industry Perspective. Clin Pharmacol Ther. 2021;110(2):297-310. 2. Edginton A et al. Physiology-based simulations of a pathological condition: prediction of pharmacokinetics in patients with liver cirrhosis. Clin Pharmacokinet. 2008;47(11):743-752 3. Kalam M et al. Development and Evaluation of a Physiologically Based Pharmacokinetic Drug-Disease Model of Propranolol for Suggesting Model Informed Dosing in Liver Cirrhosis Patients. Drug Des Devel Ther. 2021;15:1195-1211. 4. Prasad B et al. Abundance of Phase 1 and 2 Drug-Metabolizing Enzymes in Alcoholic and Hepatitis C Cirrhotic Livers: A Quantitative Targeted Proteomics Study. Drug Metab Dispos. 2018;46(7):943-952
