基于生理学的药代动力学模型
药理学
药代动力学
小檗碱
博舒替尼
伊马替尼
化学
P-糖蛋白
医学
达沙替尼
内科学
生物化学
髓系白血病
多重耐药
抗生素
作者
Jeffry Adiwidjaja,Alan V. Boddy,Andrew J. McLachlan
标识
DOI:10.1007/s00228-021-03266-y
摘要
This study implements a physiologically based pharmacokinetic (PBPK) modelling approach to predict the effect of hydrastine and berberine, two major alkaloids present in goldenseal extract, on pharmacokinetics of imatinib and bosutinib.PBPK models of hydrastine and berberine were developed in the Simcyp Simulator (version 17), integrating prior in vitro knowledge and published clinical pharmacokinetic data. The models account for reversible and irreversible (mechanism-based) inhibition of CYP3A enzymes as well as inhibition of the P-glycoprotein transporter. Inhibitory potencies of hydrastine and berberine on imatinib and bosutinib were estimated based on in vitro inhibition of metabolite formation.The PBPK models provided reliable estimates on the magnitude of interactions due to co-administration of goldenseal extract or high-dose berberine on substrates of CYP3A enzymes (midazolam, indinavir and cyclosporine) and P-glycoprotein (digoxin). PBPK simulations predicted a moderate twofold increase (5th to 95th percentiles of prediction of 1.4-3.1) in systemic exposure (AUC) of bosutinib when co-administered with clinically relevant doses of goldenseal extract. A high dose of berberine (300 mg thrice daily) was also expected to affect bosutinib exposure, albeit to a lesser extent than that predicted with goldenseal (AUC ratio of 1.3, 5th to 95th percentile: 1.1-1.6). Conversely, the corresponding effects on imatinib exposure are unlikely to be of clinical importance (predicted AUC ratios of 1.0-1.2).PBPK model-based predictions highlighted potential clinically significant interactions between goldenseal extract and bosutinib, but not imatinib. Dose adjustment may need to be considered if co-administration is desirable. These findings should be confirmed with optimally designed controlled drug interaction studies.
科研通智能强力驱动
Strongly Powered by AbleSci AI