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中国药学(英文版)

• 【研究论文】 • 上一篇    下一篇

基于meta分析的利培酮群体药代动力学研究及其在中国精神分裂症患者临床治疗药物监测中的预测应用

季双敏, 尚德为, 王曦培, 李安宁, 任宇鹏, 李良, 周田彦, 王传跃, 卢炜*   

  1. 1. 北京大学医学部 天然药物及仿生药物国家重点实验室, 北京 100191
    2. 北京大学医学部 药学院, 北京 100191
    3. 广州市精神病医院国家药品临床研究基地, 广州 510370
    4. 广东省人民医院 广东省心血管病研究所; 广东省医学科学院 临床药理部, 广州 510080
    5. 首都医科大学附属安定医院, 北京 100088
  • 收稿日期:2013-05-13 修回日期:2013-05-27 出版日期:2014-02-15 发布日期:2014-01-25
  • 通讯作者: 卢炜*
  • 作者简介:*Corresponding author. Tel.: 86-10-82801717; E-mail: luwei_pk@bjmu.edu.cn
  • 基金资助:

    Guangzhou Municipality Medical and Technology Project (Grant No. 20131A011087); Beijing Key Lab of Diagnostics and Therapeutics for Psychiatric disorders 2013 Open Foundation (Grant No. 2013JSJB01); Beijing Municipal Education Commission Science and Technology Development Program (Grant No. KM201110025025).

Population pharmacokinetics of risperidone based on meta-analysis and its application in therapeutic drug monitoring of Chinese schizophrenic patients

Shuangmin Ji, Dewei Shang, Xipei Wang, Anning Li, Yupeng Ren, Liang Li, Tianyan Zhou, Chuanyue Wang, Wei Lu*   

  1. 1. State Key Laboratory of Natural and Biomimetic Drugs, Peking University Health Science Center, Beijing 100191, China
    2. School of Pharmaceutical Sciences, Peking University Health Science Center, Beijing 100191, China
    3. Institution of National Drug Clinical Trials of Guangzhou Psychiatric Hospital, Guangzhou 510370, China
    4. Department of Clinical Pharmacology, Guangdong Cardiovascular Institute, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
    5. Beijing Anding Hospital, Capital Medical University, Beijing 100088, China
  • Received:2013-05-13 Revised:2013-05-27 Online:2014-02-15 Published:2014-01-25
  • Contact: Wei Lu*
  • About author:*Corresponding author. Tel.: 86-10-82801717; E-mail: luwei_pk@bjmu.edu.cn
  • Supported by:

    Guangzhou Municipality Medical and Technology Project (Grant No. 20131A011087); Beijing Key Lab of Diagnostics and Therapeutics for Psychiatric disorders 2013 Open Foundation (Grant No. 2013JSJB01); Beijing Municipal Education Commission Science and Technology Development Program (Grant No. KM201110025025).

摘要:

本文应用基于meta分析的群体药动学研究方法分析利培酮及其代谢产物的群体药物动力学特征。文中首筛选发表于19952011年的文献, 得到18篇符合录入排除标准的文献, 并在文献数据的基础上建立了利培酮及其活性代谢产物9-羟基利培酮的群体药动学模型。建立的模型用二室模型描述原药利培酮体内过程, 一室模型描述活性代谢产物9-羟基利培酮的体内过程, 并在药物的吸收过程中加入了原药的首过代谢过程。模型得到原药和代谢产物的系统清除率分别为7.66 L/h7.38 L/h, 表观分布体积分别为70.6 L117 L。建立的模型通过1000次仿真的可视化检验评价模型的拟合程度。本文还利用42例精神分裂症患者临床治疗药物监测数据来评价模型对于中国患者人群中利培酮血药浓度的预测性。本研究证明通过文献数据所建的模型是可靠的, 可以用作目标群体个体化治疗的依据。

关键词: Meta分析, 群体药代动力学, 利培酮, 9-羟基利培酮

Abstract:

Population pharmacokinetic meta-analysis method was used in order to obtain the pharmacokinetic characteristics of risperidone and its active metabolite. Eighteen studies were selected from published papers from 1995 to 2011. A model consisted of two compartments for parent drug and one compartment for its active metabolite combined with a flexible absorption process was developed based on the meta-dataset. The population-predicted apparent clearance for risperidone and 9-hydroxyrisperidone, the active metabolite was 7.66 L/h and 7.38 L/h, and the apparent volume of distribution in the central compartment was 70.6 L and 117 L, respectively. The final model was evaluated by visual predictive check (VPC) based on 1000 times model simulation. This model was adequately used to predict clinical therapeutic drug monitoring (TDM) data from 42 Chinese inpatients. Bias (mean prediction errors, MPE) and precision (root mean squared prediction errors, RMSE) were calculated to statistically analysis the population prediction error. It was demonstrated that the model developed from the meta-dataset was reliable and can be used to facilitate the individualized treatment for a target population.

Key words: Meta-analysis, Population pharmacokinetics, Risperidone, 9-Hydroxyrisperidone

Supporting:

Guangzhou Municipality Medical and Technology Project (Grant No. 20131A011087); Beijing Key Lab of Diagnostics and Therapeutics for Psychiatric disorders 2013 Open Foundation (Grant No. 2013JSJB01); Beijing Municipal Education Commission Science and Technology Development Program (Grant No. KM201110025025).