使用基于生理的药代动力学(PBPK)模型预测肝硬化对肝脏药物清除的影响,我们比较了酒精(n=27)或丙型肝炎(HCV,n=30)肝硬化与非肝硬化(对照)肝脏(n=25)。与对照肝脏相比,酒精或HCV肝硬化的S9总蛋白含量显着降低(即,38.3±8.3,32.3±12.8,vs.51.1±20.7毫克/克肝脏,分别)。总的来说,与HCV肝硬化相比,酒精性肝硬化与DME丰度下降更大相关;然而,只有丰富的UGT1A4,乙醇脱氢酶(ADH)1A,与HCV肝硬化肝相比,酒精肝中的ADH1B显着降低。当归一化为每克组织时,9个DME的丰度(UGT1A6,UGT1A4,CYP3A4,UGT2B7,CYP1A2,ADH1A,ADH1B,酒精性肝硬化和HCV肝硬化中的五个DME(UGT1A6,UGT1A4,CYP3A4,UGT2B7和CYP1A2)中的醛氧化酶(AOX)1和羧酸酯酶(CES)1)<对照肝脏中的25%。肝硬化肝脏中大多数DME的丰度为对照肝脏的25%至50%。CES2丰度不受肝硬化影响。将肝硬化肝脏中的UGT2B7丰度整合到Simcyp的肝硬化(ChildPughC)模型中,可以改善ChildPughC肝硬化受试者的齐多夫定和吗啡PK的预测。这些数据表明蛋白质丰度数据,结合PBPK建模和仿真,可以成为预测特殊人群药物配置的有力工具。
To predict the impact of liver cirrhosis on hepatic drug clearance using physiologically based pharmacokinetic (PBPK) modeling, we compared the protein abundance of various phase 1 and phase 2 drug-metabolizing enzymes (DMEs) in S9 fractions of alcoholic (n = 27) or hepatitis C (HCV, n = 30) cirrhotic versus noncirrhotic (control) livers (n = 25). The S9 total protein content was significantly lower in alcoholic or HCV cirrhotic versus control livers (i.e., 38.3 ± 8.3, 32.3 ± 12.8, vs. 51.1 ± 20.7 mg/g liver, respectively). In general, alcoholic cirrhosis was associated with a larger decrease in the DME abundance than HCV cirrhosis; however, only the abundance of UGT1A4, alcohol dehydrogenase (ADH)1A, and ADH1B was significantly lower in alcoholic versus HCV cirrhotic livers. When normalized to per gram of tissue, the abundance of nine DMEs (UGT1A6, UGT1A4, CYP3A4, UGT2B7, CYP1A2, ADH1A, ADH1B, aldehyde oxidase (AOX)1, and
carboxylesterase (CES)1) in alcoholic cirrhosis and five DMEs (UGT1A6, UGT1A4, CYP3A4, UGT2B7, and CYP1A2) in HCV cirrhosis was <25% of that in control livers. The abundance of most DMEs in cirrhotic livers was 25% to 50% of control livers. CES2 abundance was not affected by cirrhosis. Integration of UGT2B7 abundance in cirrhotic livers into the liver cirrhosis (Child Pugh C) model of Simcyp improved the prediction of zidovudine and morphine PK in subjects with Child Pugh C liver cirrhosis. These data demonstrate that protein abundance data, combined with PBPK modeling and simulation, can be a powerful tool to predict drug disposition in special populations.