药学学报, 2014, 49(12): 1684-1688
引用本文:
于明明, 高志伟, 陈笑艳, 钟大放. 采用生理药动学模型预测抗肿瘤新药法米替尼在人体中的药动学[J]. 药学学报, 2014, 49(12): 1684-1688.
YU Ming-ming, GAO Zhi-wei, CHEN Xiao-yan, ZHONG Da-fang. Predicting pharmacokinetics of anti-cancer drug, famitinib in human using physiologically based pharmacokinetic model[J]. Acta Pharmaceutica Sinica, 2014, 49(12): 1684-1688.

采用生理药动学模型预测抗肿瘤新药法米替尼在人体中的药动学
于明明, 高志伟, 陈笑艳, 钟大放
中国科学院上海药物研究所, 药物代谢研究中心, 上海 201203
摘要:
建立并优化法米替尼的大鼠和猴的生理药动学模型, 并且在该模型的基础上进行了种属外推至人, 预测法米替尼在人体中的药动学和组织分布.根据文献和本实验室此前的实验结果以及ACD/ADME suite和SimCYP软件预测法米替尼的理化性质, 采用GastroPlus软件的PBPK模块建立并优化法米替尼的大鼠和猴的生理药动学模型, 并且进行种属外推至人, 预测法米替尼在人体中的药动学和组织分布.优化好的大鼠和猴的生理药动学模型可以很好地预测大鼠和猴的药动学, 大鼠和猴的AUC0-∞实测值与计算值的比值分别为1.00和0.97.种属外推至人的AUC0-∞的实测值与预测值的比值分别为1.63 (大鼠→人) 和1.57 (猴→人).法米替尼的大鼠和猴的生理药动学模型成功建立, 并且在此基础上进行种属外推至人, 能够很好地预测人体内的药动学行为, 为今后的进一步研究药物-药物相互作用提供参考.
关键词:    法米替尼      生理药动学      种属外推      酪氨酸激酶抑制剂     
Predicting pharmacokinetics of anti-cancer drug, famitinib in human using physiologically based pharmacokinetic model
YU Ming-ming, GAO Zhi-wei, CHEN Xiao-yan, ZHONG Da-fang
Center for Drug Metabolism and Pharmacokinetics Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
Abstract:
This study is to establish physiologically based pharmacokinetic (PBPK) models of famitinib in rat and monkey, and then to predict the pharmacokinetics and tissue distribution of famitinib in human based on the PBPK models. According to published paper, previous studies and the chemical properties of famitinib predicted by ACD/ADME suite and SimCYP, the PBPK models of rat and monkey were established and optimized using GastroPlus. And then, the PBPK models were applied to predict the pharmacokinetic and tissue distribution of famitinib in human. The results showed that the PBPK models of rat and monkey can fit the observed data well, and the AUC0-∞ ratios of observed and calculated data in rat and monkey were 1.00 and 0.97, respectively. The AUC0-∞ ratios of observed and predicted data in human were 1.63 (rat to human) and 1.57 (monkey to human), respectively. The rat and monkey PBPK models of famitinib were well established, and the PBPK models were applied in predicting pharmacokinetic of famitinib in human successfully. Hence, the PBPK model of famitinib in human could be applied in future drug-drug interaction study.
Key words:    famitinib    physiologically based pharmacokinetic model    species extrapolation    tyrosine kinase inhibitor   
收稿日期: 2014-06-13
基金项目: 国家"重大新药创制"科技重大专项(2009ZX0930-001).
通讯作者: 钟大放
Email: dfzhong@mail.shcnc.ac.cn
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参考文献:
[1] Madhusudan S, Ganesan TS. Tyrosine kinase inhibitors in cancer therapy [J]. Clin Biochem, 2004, 37: 618-635.
[2] Arora A, Scholar E. Role of tyrosine kinase inhibitors in cancer therapy [J]. J Pharmacol Exp Ther, 2005, 315: 971- 979.
[3] Xie C, Zhou JL, Guo ZT, et al. Metabolism and bioactivation of famitinib, a novel inhibitor of receptor tyrosine kinase, in cancer patients [J]. Br J Pharmacol, 2013, 168: 1687-1706.
[4] Zhou AP, Zhang W, Chang CX, et al. Tolerability of famitinib malate: the preliminary results from phase I clinical trial [J]. Chin J New Drugs (中国新药杂志), 2011, 20: 1678-1683.
[5] Reddy MB, Clewell III HJ, Lave T, et al. Physiologically based pharmacokinetic modeling[M]: a tool for understanding ADMET properties and extrapolating to human [M]. NY, USA: InTech, 2013: 197-215.
[6] Jones H, Rowland K. Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development [J]. CPT Pharmacometrics Syst Pharmacol, 2013, 2: e63–75. doi: 10.1038/psp.2013.41
[7] Francis CP Law, He SX. The development and application of physiologically based pharmacokinetic modelling [J]. Acta Pharm Sin (药学学报), 1997, 32: 151-160.
[8] Toutain PL, Bousquet A. Plasma clearance [J]. J Vet Pharmacol Ther, 2004, 27: 415-425.
[9] Chiou WL, Robbie G, Chung SM, et al. Correlation of plasma clearance of 54 extensively metabolized drugs between humans and rats: mean allometric coefficient of 0.66 [J]. Pharm Res, 1998, 15: 1474-1479.
[10] Poulin P, Theil FP. Prediction of pharmacokinetics prior to in vivo studies. 1. Mechanism-based prediction of volume of distribution [J]. J Pharm Sci, 2002, 91:129-156.
[11] Zhang L, Zhang YD, Zhao P, et al. Predicting drug-drug interactions: an FDA perspective [J]. AAPS J, 2009, 11: 300-306.
[12] Rowland M, Peck C, Tucker G. Physiologically-based pharmacokinetics in drug development and regulatory science [J]. Annu Rev Pharmacol Toxicol, 2011, 51: 45-73.
[13] Zhao P, Rowland M, Huang SM. Best practice in the use of physiologically based pharmacokinetic modeling and simulation to address clinical pharmacology regulatory questions [J]. Clin Pharmacol Ther, 2012, 92: 17-20.
[14] Jones HM, Gardner IB, Watson KJ. Modelling and PBPK simulation in drug discovery [J]. AAPS J, 2009, 11: 155- 166.
[15] Buck SS, Sinha VK, Fenu LA, et al. Prediction of human pharmacokinetics using physiologically based modeling: a retrospective analysis of 26 clinically tested drugs [J]. Drug Metab Dispos, 2007, 35: 1766-1780.
[16] U.S. Food and Drug Administration. Guidance for Drug Interactions Study [S]. 2012.