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中国临床药理学与治疗学 ›› 2023, Vol. 28 ›› Issue (5): 525-535.doi: 10.12092/j.issn.1009-2501.2023.05.006

• 定量药理学 • 上一篇    下一篇

五种群体药动学分析工具计算结果的比较

黄志伟1,李 熠2,徐晓勇3,张 蕾1,沈一峰1,李华芳1   

  1. 1上海交通大学医学院附属精神卫生中心临床研究中心,上海  200000;2复旦大学附属华山医院抗生素研究所,上海  200000;3复旦大学附属儿科医院药剂科,上海  200000

  • 收稿日期:2022-11-21 修回日期:2023-03-01 出版日期:2023-05-26 发布日期:2023-06-08
  • 通讯作者: 李华芳,女,博士,主任医师,博士生导师,研究方向:临床精神药理学和诊治相关标志物。 E-mail: lhlh_5@163.com
  • 作者简介:黄志伟,男,博士,研究方向:定量药理学。 E-mail: hzw_mail@163.com
  • 基金资助:
    上海市精神心理疾病临床医学研究中心(19MC1911100);上海市转化医学协同创新中心(TM202116PT)

Comparison of calculation results of five population pharmacokinetic analysis tools

HUANG Zhiwei1, LI Yi2, XU Xiaoyong3, ZHANG Lei1, SHEN Yifeng1, LI Huafang1   

  1. 1 Clinical Research Center of Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200000, China; 2 Institute of Antibiotics, Huashan Hospital, Fudan University, Shanghai 200000, China; 3 Department of Pharmacy, Children's Hospital of Fudan University, Shanghai 200000, China
  • Received:2022-11-21 Revised:2023-03-01 Online:2023-05-26 Published:2023-06-08

摘要:

目的:比较群体药动学分析工具Phoenix NLME、Monolix、R语言nlmixr包和CPhaMAS云平台所计算的结果与金标准NONMEM的符合程度。方法:基于一房室模型模拟50个稀疏采样数据集和二房室模型模拟50个密集采样数据集,分别使用上述五种分析工具计算,比较群体典型值、个体变异和个体药动学参数。结果:CPhaMAS和Phoenix NLME计算的群体典型值、个体变异与NONMEM的匹配程度最高,其次为nlmixr,Monolix最低,但Monolix和nlmixr的算法可能更稳健。清除率和分布容积的对应性优于吸收速率常数。除了Monolix计算的吸收速率常数和nlmixr计算的房室间清除率,所有分析工具计算的个体药动学参数相关系数均大于0.99。结论:上述四种群体药动学分析工具计算结果与NONMEM的结果高度相关。

关键词: 群体药动学, NONMEM, 房室模型

Abstract:

AIM: To compare the results calculated by population pharmacokinetic analysis tools Phoenix NLME, Monolix, R nlmixr package and CPhaMAS cloud platform with the gold standard sofeware NONMEM. METHODS: Fifty sparse sampling data sets based on a one-compartment model and fifty dense sampling data sets based on a two-compartment model were simulated, and the above five analysis tools were used to calculate the population typical value, individual variability and individual pharmacokinetic parameters. RESULTS: The population typical value and individual variability calculated by CPhaMAS and Phoenix NLME had the highest matching degree with NONMEM, followed by nlmixr. Monolix had the lowest matching degree, but Monolix and nlmixr might be more robust. The correspondence between clearance and distribution volume was better than the absorption rate constant. Except the absorption rate constant calculated by Monolix and intercompartmental clearance calculated by nlmixr, the correlation coefficients of individual pharmacokinetic parameters calculated by all analytical tools were greater than 0.99. CONCLUSION: The results calculated by the above four population pharmacokinetic analysis tools are highly correlated with that of NONMEM.

Key words: population pharmacokinetics, NONMEM, compartmental model

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