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人类长期肝脏模型的代谢谱分析以及使用非线性混合效应模型根据体外数据进行肝脏清除率预测。

Metabolic Profiling of Human Long-Term Liver Models and Hepatic Clearance Predictions from In Vitro Data Using Nonlinear Mixed-Effects Modeling.

作者信息

Kratochwil Nicole A, Meille Christophe, Fowler Stephen, Klammers Florian, Ekiciler Aynur, Molitor Birgit, Simon Sandrine, Walter Isabelle, McGinnis Claudia, Walther Johanna, Leonard Brian, Triyatni Miriam, Javanbakht Hassan, Funk Christoph, Schuler Franz, Lavé Thierry, Parrott Neil J

机构信息

Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070, Basel, Switzerland.

出版信息

AAPS J. 2017 Mar;19(2):534-550. doi: 10.1208/s12248-016-0019-7. Epub 2017 Jan 3.

DOI:10.1208/s12248-016-0019-7
PMID:28050713
Abstract

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.

摘要

人体清除率的早期预测往往具有挑战性,尤其是对于越来越多的低清除率化合物而言。人们已经开发出长期体外模型,可用于复杂的肝脏药物处置研究并改善清除率预测。在此,将细胞系HepG2、诱导多能干细胞衍生的肝细胞(iCell®)、肝干细胞系HepaRG™以及人肝细胞共培养物(HμREL™和HepatoPac®)与原代肝细胞悬浮培养物在关键代谢活性方面进行了比较。当对所有11种酶标志物进行平均时,发现长期模型HepaRG™、HμREL™和HepatoPac®与短期悬浮培养物具有相似的代谢活性,尽管在CYP2D6和非CYP酶的活性方面存在差异。对于iCell®和HepG2,代谢活性降低了十倍以上。对微图案化的HepatoPac®模型在清除率预测方面进行了进一步评估。为了评估体外参数,应用了药代动力学建模。通过长期模型中的非线性混合效应建模来确定内在清除率,显著提高了参数估计的可信度,并将敏感范围扩展至肝血流量的3%,即与悬浮培养物相比降低了10倍以上。对于体外到体内的外推,使用了充分搅拌模型。对于大多数低清除率化合物,微图案化模型在人体中的清除率预测误差在两倍以内。需要进一步研究以了解转运体活性以及非CYP酶(如UGT、SULT、AO和FMO)的药物代谢在这些长期培养模型中是否与体内情况相当。

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