人类长期肝脏模型的代谢谱分析和使用非线性混合效应模型的体外数据的肝脏清除预测

N. Kratochwil, C. Meille, S. Fowler, F. Klammers, A. Ekiciler, Birgit Molitor, Sandrine Simon, I. Walter, C. McGinnis, J. Walther, Brian Leonard, M. Triyatni, H. Javanbakht, C. Funk, F. Schuler, T. Lavé, N. Parrott
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引用次数: 79

摘要

人体清除率的早期预测通常具有挑战性,特别是对于越来越多的低清除率化合物。长期体外模型已经开发,使复杂的肝脏药物处置研究和改进的清除率预测。在这里,细胞系HepG2、ipsc衍生的肝细胞(iCell®)、肝干细胞系HepaRG™和人肝细胞共培养(HμREL™和HepatoPac®)与原代肝细胞悬浮培养的关键代谢活性进行了比较。尽管CYP2D6和非cyp酶的活性存在差异,但在所有11种酶标记物的平均水平上,长期模型HepaRG™、HμREL™和HepatoPac®和短期悬浮培养物的代谢活性相似。对于iCell®和HepG2,代谢活性降低了10倍以上。micropatterned HepatoPac®模型在清除率预测方面得到进一步评估。采用体外药代动力学模型评价其体外参数。在长期模型中,通过非线性混合效应建模来确定固有清除率,显著提高了参数估计的置信度,并将敏感范围扩展到肝脏血流量的3%,即与悬浮培养相比降低了>10倍。对于体外到体内的外推,采用搅拌良好的模型。微模式模型对大多数低清除率化合物的清除率预测误差为两倍。在这些长期培养模型中,转运体活性和非cyp酶(如UGTs、SULTs、AO和FMO)的药物代谢是否与体内情况相当,还需要进一步研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Metabolic Profiling of Human Long-Term Liver Models and Hepatic Clearance Predictions from In Vitro Data Using Nonlinear Mixed-Effects Modeling
Early prediction of human clearance is often challenging, in particular for the growing number of low-clearance compounds. Long-term in vitro models have been developed which enable sophisticated hepatic drug disposition studies and improved clearance predictions. Here, the cell line HepG2, iPSC-derived hepatocytes (iCell®), the hepatic stem cell line HepaRG™, and human hepatocyte co-cultures (HμREL™ and HepatoPac®) were compared to primary hepatocyte suspension cultures with respect to their key metabolic activities. Similar metabolic activities were found for the long-term models HepaRG™, HμREL™, and HepatoPac® and the short-term suspension cultures when averaged across all 11 enzyme markers, although differences were seen in the activities of CYP2D6 and non-CYP enzymes. For iCell® and HepG2, the metabolic activity was more than tenfold lower. The micropatterned HepatoPac® model was further evaluated with respect to clearance prediction. To assess the in vitro parameters, pharmacokinetic modeling was applied. The determination of intrinsic clearance by nonlinear mixed-effects modeling in a long-term model significantly increased the confidence in the parameter estimation and extended the sensitive range towards 3% of liver blood flow, i.e., >10-fold lower as compared to suspension cultures. For in vitro to in vivo extrapolation, the well-stirred model was used. The micropatterned model gave rise to clearance prediction in man within a twofold error for the majority of low-clearance compounds. Further research is needed to understand whether transporter activity and drug metabolism by non-CYP enzymes, such as UGTs, SULTs, AO, and FMO, is comparable to the in vivo situation in these long-term culture models.
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