基于非布司他生理药动学模型预测潜在药物相互作用
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摘要
目的:建立非布司他在健康人体内的生理药动学(PBPK)模型,计算人体内不同部位的药物浓度,预测非布司他对基于CYP2C8和CYP2D6介导代谢的药物的潜在相互作用(DDIs)。方法:通过文献收集和ADMET Predictor~(TM)软件预测获得非布司他建模的理化性质参数、生物药剂学参数、人体生理参数等;同时收集不同给药剂量非布司他在人体内的血药浓度(PK)数据,建立并验证PBPK模型。采用PBPK模型计算非布司他在肠道上皮细胞浓度、血浆达峰浓度、肝脏达峰浓度、肝脏入口游离药物浓度,预测非布司他与基于CYP2C8和CYP2D6代谢酶代谢药物的DDIs。结果:PBPK模型预测非布司他不同剂量下的药-时曲线与实测值拟合良好。预测的非布司他不同剂量下药动学参数(AUC,C_(max),T_(max))与实测值相近,倍数误差<2,模型准确可靠。DDIs预测结果显示采用肝脏达峰药物浓度与肝入口游离药物浓度来代替酶活性位点抑制剂浓度作出的DDIs预测结果更与临床结果相符合,即非布司他在健康人群日服用80 mg剂量下,基本不会对由CYP2C8和CYP2D6酶代谢的药物产生明显的DDIs。结论:所建立的PBPK模型可较好预测非布司他的体内药时曲线与机体不同部位的药物浓度,且能准确预测药物可能的相互作用。
OBJECTIVE To establish a physiological pharmacokinetic(PBPK) model of febuxostat in healthy individuals, and to calculate the concentration of drugs in different sites to predict DDIs mediated by CYP2C8 and CYP2D6. METHODS The characteristics of febuxostat including physicochemical properties, biopharmaceutical parameters, physiological parameters of human body were obtained through literature collection and prediction with ADMET Predictor~(TM) software. Meanwhile, the blood concentration(PK) data with regard to different doses of febuxostat were collected to establish and validate PBPK model. The PBPK model was used to calculate the concentrations of febuxostat in intestinal epithelial cells, peak drug concentrations, liver peak concentrations and liver inlet free drug concentrations to predict the DDIs of febuxostat as well as drugs based on metabolism of CYP2C8 and CYP2D6 metabolic enzymes. RESULTS The drug concentration-time curves of febuxostat at different doses predicted by PBPK model fitted well with measured values. The pharmacokinetic parameters(AUC, C_(max), T_(max)) at different doses of febuxostat were similar to the measured values with a fold error of < 2, which showed that the model was accurate and reliable.The results of DDIs prediction showed that compared to DDIs predicted by the enzyme activity site inhibitor concentration, in vitro DDIs predicted by hepatic peak drug concentration and liver inlet free drug concentrations are more consistent with the clinical results,thus, febuxostat does not produce significant DDIs with drugs metabolized by CYP2C8 and CYP2D6 at a dose of 80 mg daily among healthy individuals. CONCLUSION The well-established PBPK model can be a favorable tool to predict in vivo drug concentration-time curve of febuxostat and the drug concentration in different parts of the body as well as drug-drug interactions.
OBJECTIVE To establish a physiological pharmacokinetic(PBPK) model of febuxostat in healthy individuals, and to calculate the concentration of drugs in different sites to predict DDIs mediated by CYP2C8 and CYP2D6. METHODS The characteristics of febuxostat including physicochemical properties, biopharmaceutical parameters, physiological parameters of human body were obtained through literature collection and prediction with ADMET Predictor~(TM) software. Meanwhile, the blood concentration(PK) data with regard to different doses of febuxostat were collected to establish and validate PBPK model. The PBPK model was used to calculate the concentrations of febuxostat in intestinal epithelial cells, peak drug concentrations, liver peak concentrations and liver inlet free drug concentrations to predict the DDIs of febuxostat as well as drugs based on metabolism of CYP2C8 and CYP2D6 metabolic enzymes. RESULTS The drug concentration-time curves of febuxostat at different doses predicted by PBPK model fitted well with measured values. The pharmacokinetic parameters(AUC, C_(max), T_(max)) at different doses of febuxostat were similar to the measured values with a fold error of < 2, which showed that the model was accurate and reliable.The results of DDIs prediction showed that compared to DDIs predicted by the enzyme activity site inhibitor concentration, in vitro DDIs predicted by hepatic peak drug concentration and liver inlet free drug concentrations are more consistent with the clinical results,thus, febuxostat does not produce significant DDIs with drugs metabolized by CYP2C8 and CYP2D6 at a dose of 80 mg daily among healthy individuals. CONCLUSION The well-established PBPK model can be a favorable tool to predict in vivo drug concentration-time curve of febuxostat and the drug concentration in different parts of the body as well as drug-drug interactions.
引文
[1] Khosravan R, Grabowski B, Wu J T, et al. Effect of food or antacid on pharmacokinetics and pharmacodynamics of febuxostat in healthy subjects [J]. Br J Clin Pharmacol, 2008,65(3):355-63.
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[10] Li L, Yang JB. Application progress of physiologically-based pharmacokinetic model in clinical development of novel molecular entities [J]. Chin J Clin Pharmacol(中国临床药理学杂志),2017,33(17):1728-1732.
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[13] Xu W, Jiang J, Hu B, et al. Application of physiological based pharmacokinetic model in transport mediated drug-drug interactions [J]. Chin J Clin Pharmacol(中国临床药理学杂志), 2015,12(23):2370-2372.
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[16] Zhang M, Xiaohui DI, Lin XU, et al. Pharmacokinetics and pharmacodynamics of febuxostat under fasting conditions in healthy individuals [J]. Exp Ther Med, 2014,7(2):393-396.
[17] Qiu FR, Zhu LL, Jiang J, et al. Pharmacokinetics of oral administration of febuxostat tablet in Chinese healthy volunteers [J]. Chin J Clin Pharmacol(中国临床药理学杂志), 2015,31(3):199-202.
[18] Zhang YC, Hu ZD, Yang ZW, et al. Advance in clinical research of febuxostat in the treatment of gout [J]. World Clin Drugs(世界临床药物), 2014,35(9):519-522.
[19] Grabowski B A, Khosravan R, Vernillet L, et al. Metabolism and excretion of [14C] febuxostat, a novel nonpurine selective inhibitor of xanthine oxidase, in healthy male subjects. [J]. J Clin Pharmacol, 2013,51(2):189-201.
[20] Li G, Wang K, Chen R, et al. Simulation of the pharmacokinetics of bisoprolol in healthy adults and patients with impaired renal function using whole-body physiologically based pharmacokinetic modeling [J]. Pharmacol Acta, 2012,33(11):1359-1371.
[21] Niu XY, Wu JJ, Ge GB, et al. Progress in the application of physiological pharmacokinetic models in drug evaluation [J].Chin J Pharmacol Toxicol(中国药理学与毒理学杂志), 2015,9(6):993-1000.
[22] Zhong QW, Wang Q.Progress in the prediction of human exposure by physiological pharmacokinetics (PBPK) model——2016 (2nd) Toxicity Test Alternatives and Transformation Toxicology (International) Symposium and AOP and Risk Assessment Training Session[C]. Hangzhou: Chinese Toxicology(中国毒理学会), 2017:64-65.
[23] Chen Y, Zhao K, Liu F, et al. Prediction of Deoxypodophyllotoxin Disposition in Mouse, Rat, Monkey, and Dog by Physiologically Based Pharmacokinetic Model and the Extrapolation to Human [J]. Frontiers in Pharmacology, 2016,7(e63):488.
[24] Zhang Q, Gong Y, Lao JY, et al. Determination of equilibrium solubility, apparent oil/water partition coefficient and dissociation constant of febuxostat [J]. Chin J New Drugs(中国新药杂志), 2013,22(1):103-106.
[25] Pharmacology B, Kuttan G, Antitumor K. European Medicines Agency Evaluation of Medicines for Human Use[EB/OL]. 2008,55(11):0-6. https://www.mendeley.com/catalogue/european-medicines-agency-evaluation-medicines-human.
[26] Jiang ZG, Luo XY, Xu J, et al. Physiologically based pharmacokinetic model of escitalopram based on ACSL Math [J]. J Mod Med Health(现代医药卫生), 2013,29(22):3396-3398.
[27] Zhou Y, Hu B, Wang HY, et al. Characteristics and applications in drug-drug interaction of physiologically based on pharmacokinetic model [J]. Chin J Clin Pharmacol(中国临床药理学杂志), 2013,29(09):706-709.
[28] Huang S M, Temple R, Throckmorton D C, et al. Drug interaction studies: study design, data analysis, and implications for dosing and labeling [J]. Clini Pharmacol Ther, 2007,81(2):298.
[29] Shardlow C E, Generaux G T, Maclauchlin C C, et al. Utilizing drug-drug interaction prediction tools during drug development: enhanced decision making based on clinical risk [J]. Drug Metab Dispos, 2011,39(11):2076-2084.
[30] Zhang QH, Li Y. Quantitative prediction of in vivo drug-drug interactions based on in vitro inhibition or/and induction data for CYP3A4 [J]. Acta Pharm Sin(药学学报), 2010,45(8):952-959.
[31] Becker MA, Kisicki J, Khosravan R, et al. Febuxostat(TMX-67), a novel, nonpurine, selective inhibitor oxanthine oxidase, is safe and decreases serum urate in healthy volunteers [J]. Nucleos Nucleot Nucl, 2004,23(8): 1111-1116.
[2] Shen Y, Bo Y. Progress in clinical research and application of febuxostat in the treatment of gout and hyperuricemia [J]. Chin J Ctrl Endem Dis(中国地方病防治杂志), 2016,31(10):1107-1108.
[3] Ma WX, Tang CY, Liu QM, et al. Comparative study on the dissolution curves of Febuxostat Tablet [J]. J Pharm Res(药学研究), 2017,36(2):84-87.
[4] Naik H, Wu J, Palmer R, et al. The effects of febuxostat on the pharmacokinetic parameters of rosiglitazone, a CYP2C8 substrate [J]. Bri J Clin Pharmacol, 2012,74(2):327-335.
[5] Kamel B, Graham GG, Williams KM, et al. Clinical pharmacokinetics and Pharmacodynamics of febuxostat [J]. Clini Pharmacokinet, 2017,56(5):1-17.
[6] Mukoyoshi M, Nishimura S, Hoshide S, et al. In vitro drug-drug interaction studies with febuxostat, a novel non-purine selective inhibitor of xanthine oxidase:plasma protein binding, identification of metabolic enzymes and cytochrome P450 inhibition [J]. Xenobiotica, 2008,38(5):496-510.
[7] Huang L, Jiang XH, Zhang Q, et al. Study on physiologically based pharmacokinetic model for Lidocaine administrated by intravenous infusion [J]. Chin Pharm J(中国药学杂志), 2006,41(8):585-588.
[8] Song L, Wang L, Jiang XH, et al. Predicting of physiological pharmacokinetic model on pharmacokinetics process of oral agomelatine in humans [J]. J China Pharm(中国药房),2015,26(8):1069-1073.
[9] Yu MM, Gao ZW, Chen XY, et al. Predicting pharmacokinetics of anti-cancer drug, famitinib in human using physiologically based pharmacokinetic model [J]. Acta Pharm Sin(药学学报), 2014,49(12):1684-1688.
[10] Li L, Yang JB. Application progress of physiologically-based pharmacokinetic model in clinical development of novel molecular entities [J]. Chin J Clin Pharmacol(中国临床药理学杂志),2017,33(17):1728-1732.
[11] Yu G, Li GF, Zheng QS, et al. Utility of physiologically based pharmacokinetic modeling in FDA regulatory review [J]. Chin J Clin Pharmacol(中国临床药理学杂志), 2014,30(1):55-57.
[12] Cheng LJ, Jiang J, Hu B, et al. A preliminary study on the physiological pharmacokinetic model of Chinese medicine Xianmao[D]. Qufu Normal Universit, 2011.
[13] Xu W, Jiang J, Hu B, et al. Application of physiological based pharmacokinetic model in transport mediated drug-drug interactions [J]. Chin J Clin Pharmacol(中国临床药理学杂志), 2015,12(23):2370-2372.
[14] Pharmceuticals Medical Devices Agency(PMDA): フェブリク錠10 mg/フェブリク錠20 mg/フェブリク錠40 mg[EB/OL]. http://www.pmda.go.jp/PmdaSearch/iyakuDetail/ResultDataSetPDF/470310_3949003F1023_1_09.
[15] Pharmceuticals Medical Devices Agency(PMDA). Febuxostat 2016[EB/OL].https://db.yaozh.com/pmda?comprehensivesearchcontent=%E9%9D%9E%E5%B8%83%E5%8F%B8%E4%BB%96&470310_3949003F1023_1_009_1F.pdf.
[16] Zhang M, Xiaohui DI, Lin XU, et al. Pharmacokinetics and pharmacodynamics of febuxostat under fasting conditions in healthy individuals [J]. Exp Ther Med, 2014,7(2):393-396.
[17] Qiu FR, Zhu LL, Jiang J, et al. Pharmacokinetics of oral administration of febuxostat tablet in Chinese healthy volunteers [J]. Chin J Clin Pharmacol(中国临床药理学杂志), 2015,31(3):199-202.
[18] Zhang YC, Hu ZD, Yang ZW, et al. Advance in clinical research of febuxostat in the treatment of gout [J]. World Clin Drugs(世界临床药物), 2014,35(9):519-522.
[19] Grabowski B A, Khosravan R, Vernillet L, et al. Metabolism and excretion of [14C] febuxostat, a novel nonpurine selective inhibitor of xanthine oxidase, in healthy male subjects. [J]. J Clin Pharmacol, 2013,51(2):189-201.
[20] Li G, Wang K, Chen R, et al. Simulation of the pharmacokinetics of bisoprolol in healthy adults and patients with impaired renal function using whole-body physiologically based pharmacokinetic modeling [J]. Pharmacol Acta, 2012,33(11):1359-1371.
[21] Niu XY, Wu JJ, Ge GB, et al. Progress in the application of physiological pharmacokinetic models in drug evaluation [J].Chin J Pharmacol Toxicol(中国药理学与毒理学杂志), 2015,9(6):993-1000.
[22] Zhong QW, Wang Q.Progress in the prediction of human exposure by physiological pharmacokinetics (PBPK) model——2016 (2nd) Toxicity Test Alternatives and Transformation Toxicology (International) Symposium and AOP and Risk Assessment Training Session[C]. Hangzhou: Chinese Toxicology(中国毒理学会), 2017:64-65.
[23] Chen Y, Zhao K, Liu F, et al. Prediction of Deoxypodophyllotoxin Disposition in Mouse, Rat, Monkey, and Dog by Physiologically Based Pharmacokinetic Model and the Extrapolation to Human [J]. Frontiers in Pharmacology, 2016,7(e63):488.
[24] Zhang Q, Gong Y, Lao JY, et al. Determination of equilibrium solubility, apparent oil/water partition coefficient and dissociation constant of febuxostat [J]. Chin J New Drugs(中国新药杂志), 2013,22(1):103-106.
[25] Pharmacology B, Kuttan G, Antitumor K. European Medicines Agency Evaluation of Medicines for Human Use[EB/OL]. 2008,55(11):0-6. https://www.mendeley.com/catalogue/european-medicines-agency-evaluation-medicines-human.
[26] Jiang ZG, Luo XY, Xu J, et al. Physiologically based pharmacokinetic model of escitalopram based on ACSL Math [J]. J Mod Med Health(现代医药卫生), 2013,29(22):3396-3398.
[27] Zhou Y, Hu B, Wang HY, et al. Characteristics and applications in drug-drug interaction of physiologically based on pharmacokinetic model [J]. Chin J Clin Pharmacol(中国临床药理学杂志), 2013,29(09):706-709.
[28] Huang S M, Temple R, Throckmorton D C, et al. Drug interaction studies: study design, data analysis, and implications for dosing and labeling [J]. Clini Pharmacol Ther, 2007,81(2):298.
[29] Shardlow C E, Generaux G T, Maclauchlin C C, et al. Utilizing drug-drug interaction prediction tools during drug development: enhanced decision making based on clinical risk [J]. Drug Metab Dispos, 2011,39(11):2076-2084.
[30] Zhang QH, Li Y. Quantitative prediction of in vivo drug-drug interactions based on in vitro inhibition or/and induction data for CYP3A4 [J]. Acta Pharm Sin(药学学报), 2010,45(8):952-959.
[31] Becker MA, Kisicki J, Khosravan R, et al. Febuxostat(TMX-67), a novel, nonpurine, selective inhibitor oxanthine oxidase, is safe and decreases serum urate in healthy volunteers [J]. Nucleos Nucleot Nucl, 2004,23(8): 1111-1116.