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中国临床药理学与治疗学 ›› 2011, Vol. 16 ›› Issue (10): 1121-1125.

• 定量药理学 • 上一篇    下一篇

权重配方模型的非线性混合效应分析:一个药物相互作用研究实例

黄继汉1, 张密1, 王民2, 郑青山1   

  1. 1上海中医药大学药物临床研究中心,上海 201203;
    2山西皮尔复临床医药研究所,太原 030006,山西
  • 收稿日期:2011-07-06 修回日期:2011-09-07 出版日期:2011-10-26 发布日期:2011-11-02
  • 通讯作者: 郑青山,男,教授,博导,研究方向:定量药理学和生物统计学。Tel: 021-51323006, E-mail: zheng.zqs@gmail.com
  • 作者简介:黄继汉,男,博士,研究方向:临床药理学和定量药理学。Tel: 021-51322420, E-mail: whhuangjh@163.com
  • 基金资助:
    国家科技支撑计划项目(2008BAI51B03);上海市教委重点学科项目(J50303);上海市高校E 研究院项目 (E03008)

Weighted modification model for analyzing drug interactions by the nonlinear mixed effect model: A case study

HUANG Ji-han1, ZHANG Mi1, WANG Min2, ZHENG Qing-shan1   

  1. 1Center of Drug Clinical Research, Shanghai University of Chinese Medicine, Shanghai 201203, China;
    2Pierfu Institute of Clinical Medicine, Taiyuan 030006, Shanxi, China
  • Received:2011-07-06 Revised:2011-09-07 Online:2011-10-26 Published:2011-11-02

摘要: 目的: 基于权重配方模型,采用非线性混合效应分析(NONMEM),对尿囊素、甲硝唑及地塞米松联合抗炎作用的组方合理性进行定量评价,结果与原算法比较。方法: 实验数据为昆明种小鼠组织纤溶酶原激活物(t-PA)样品于 405 nm 处的吸光度值,以权重配方模型为基本模型,组方间相互作用为固定效应,并考虑随机效应,通过NONMEM法建立最终模型,定量评价尿囊素、甲硝唑和地塞米松的联合抗炎作用及其交互作用。结果: 基于整体效应的权重配方模型建立成功(P<0.001),X1X2(X1尿囊素,X2地塞米松)作为固定效应加入模型,最终模型中甲硝唑对整体药效作用不明显,尿囊素与地塞米松存在较强协同作用,都是主要起效组分,最优剂量配比尿囊素∶甲硝唑∶地塞米松=400∶131∶8.0 (mg/kg, i.p.)。结论: 采用NONMEM法,可全面考虑各组分间交互作用及个体间和个体内误差等随机效应,使权重配方模型更加严谨,与原算法比较,提供的信息量更大。

关键词: 权重配方法, 药物相互作用, 非线性混合效应, 组方优化

Abstract: AIM: To apply the weighted modification model with the nonlinear mixed effect model (NONMEM) to quantitatively assess the optimal combination of allantoin (X1), metronidazole (X2), and dexamethasone (X3) for anti-inflammatory effects in mice. METHODS: The activity of t-PA in peritoneum was taken as index for the analysis in Kunming mice. The weighted modification model was selected as basic model and NONMEM was used to build final model with drug interaction as fixed effect and random effects. RESULTS: The weighted modification model was built with X1X2 as fixed effect successfully (P<0.001). In the final model, the combination of X1 and X3 had a strong synergism, the both components were main effective ones, and the optimal dose ratio of X1, X2 and X3 was 400∶131∶8.0 (mg/kg, i.p.). CONCLUSION: The weighted modification model can provide more information for a combination design in using NONMEM, and the interactions among components and variability of inter-subject and intra-subject can be fully evaluated.

Key words: Weighted modification method, Drug interactions, Nonlinear mixed effect model, Formula optimization

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