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中国临床药理学与治疗学 ›› 2015, Vol. 20 ›› Issue (5): 571-577.

• 定量药理学 • 上一篇    下一篇

甲氨蝶呤及其活性代谢产物在类风湿关节炎患者体内的药动学模型研究

刘东阳1, 苏金梅2, 宋瀚麟1, 陈嘉1, 刘洋1, 幺雪婷1, 宋玲1, 江骥1, 胡蓓1   

  1. 1中国医学科学院北京协和医院临床药理中心,北京 100032;
    2中国医学科学院北京协和医院风湿免疫科,北京 100032
  • 收稿日期:2015-04-22 修回日期:2015-05-18 发布日期:2015-06-11
  • 作者简介:刘东阳,男,研究助理员,主要从事临床药动学/药效学研究。Tel: 010-69158356 E-mail: liudongyang@pumch.cn
  • 基金资助:
    国家自然科学基金(81403013);“重大新药创制”国家科技重大专项;糖尿病等新药临床评价研究技术平台建设(2012ZX09303006-002)

Pharmacokinetic modeling study on methotrexate and its active metabolites in rheumatoid arthritis patients

LIU Dong-yang1, SU Jing-mei2, SONG Han-lin1, CHEN Jia1, LIU Yang1, YAO Xue-ting1, SONG Lin1, JIANG Ji1, HU Pei1   

  1. 1 Clinical Pharmacology Research Center, Peking Union Medical College Hospital, Beijing 100032,China;
    2 Department of Rheumatology and Immunology School of Pharmaceutical Sciences,Peking Union Medical College Hospital, Beijing 100032,China
  • Received:2015-04-22 Revised:2015-05-18 Published:2015-06-11

摘要: 目的: 通过模型化方法定量研究甲氨蝶呤(MTX)及其活性代谢产物(多谷氨酸结合物,MTXPGn)在类风湿关节炎(RA)患者血浆、红细胞及骨关节液的药动学特征。方法: 使用Digitizer软件获取四篇文献中RA患者经口服、静脉、膝关节液注射三种方式给MTX后MTX及MTXPGn在血浆、红细胞、骨关节液的时间-浓度数据,由ADAPT 5软件使用最大似然算法对这些数据同时进行非线性拟合分析,通过一个基于半生理特征的药动学模型获取MTX及MTXPGn在RA患者体内的血液和骨关节液中的药动学参数。结果: 本文建立的PK模型可使用基本二房室模型较好描述血浆与关节液中MTX药动学特征,血浆与骨关节液中央室中的MTX可相互分布;可使用由血浆中央室以一级速率常数进入红细胞的五房室模型较好描述红细胞中MTXPG1-5的药动学特征,这五种物质之间可相互依次转化,并以相同速率常数被清除。其中的红细胞体积参数引用了健康人生理参数。上述估计的系统参数均接近于RA患者的生理、病理报告值,显示本模型估计值合理,并且除骨关节液中的分布清除率估计精密度略大于50%(56.7%)以外,其他参数均小于50%,显示参数估计可靠。结论: 本文成功建立了MTX及MTXPGn在RA患者体内的基于半生理的药动学模型,该模型有助于定量理解MTX及MTXPGn在RA患者循环系统以及骨关节液药效部位之间的药动学特征。

关键词: 甲氨蝶呤, 多谷氨酸结合物, 类风湿关节炎患者, 药动学模型

Abstract: AIM: To quantitatively investigate the pharmacokinetics of Methotrexate (MTX) and its active metabolites (MTXPGn) in plasma, red blood cells, and synovial fluid of rheumatoid arthritis (RA) patients using modeling approach.METHODS: Concentration values of MTX and MTXPGn in plasma, red blood cells, and synovial fluid of rheumatoid arthritis patients after orally, intravenously, and intra-synovial fluid dosing of MTX were digitized from 4 published literatures. ADAPT5 was utilized to conduct nonlinear regression analysis using maximum likelihood algorism for fitting these pharmacokinetic data using semi-physiologically-based PK model simultaneously.RESULTS: A semi-physiologically-based PK model was developed. This model could capture pharmacokinetic characteristics of MTX in plasma and synovial fluid using standard two compartment model separately, where MTX in central compartment system and synovial fluid could be distributed to each other. The PK model also could capture MTXPG1-5 concentrations using 5 compartments with first order distribution constant from system central compartment to red blood cell compartment, where MTXPG1-5 could be transformed to each other and be eliminated in same elimination rate constant. The physiological value of red blood cell was used as red blood cell compartment volume. Estimated parameters were close to physiological and pathological value, which were reasonable. The precision expressed in CV% of all parameters were less than 50% except for distribution clearance in synovial fluid (56.7%), which showed the estimation is robust.CONCLUSION: This study developed pharmacokinetic model of MTX and MTXPGn in RA patients, which could improve quantitative understanding on pharmacokinetics of MTX and MTXPGn in circulation and synovial fluid of RA patients.

Key words: methotrexate, poly-glutamate methotrexate, rheumatoid arthritis, pharmacokinetic model

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