An Evolutionary Search Algorithm for Covariate Models in Population Pharmacokinetic Analysis
An Evolutionary Search Algorithm for Covariate Models in Population Pharmacokinetic Analysis
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DOI：
10.1016/j.xphs.2017.04.029
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发表时间：
2017-09-01
影响因子：
3.8
通讯作者：
Hashida, Mitsuru
中科院分区：
文献类型：
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作者：
Yamashita, Fumiyoshi;Fujita, Atsuto;Hashida, Mitsuru
协变量模型的建立是群体药代动力学研究中的一项重要任务。本研究提出了一种基于基因表达编程（gene expression programming， GEP）的自动协变量建模新方法，该方法不仅可以实现协变量选择，还可以构建药代动力学参数与协变量之间的非多项式关系。为了将GEP应用于扩展的非线性最小二乘分析，进一步开发并实现了参数固结和初始参数值估计算法。整个程序是用Java编写的。建立的协变量模型对妥布霉素的群体药代动力学数据进行了评价。与已建立的协变量模型相比，仅增加2个可调参数即可大大提高实测数据的拟合优度。10次测试运行产生了相同的解决方案。总之，系统探索方法是群体药代动力学分析中预筛选协变量模型的潜在有力工具。(C) 2017美国药剂师协会(R)。Elsevier Inc.出版。版权所有。
Building a covariate model is a crucial task in population pharmacokinetics. This study develops a novel method for automated covariate modeling based on gene expression programming (GEP), which not only enables covariate selection, but also the construction of nonpolynomial relationships between pharmacokinetic parameters and covariates. To apply GEP to the extended nonlinear least squares analysis, the parameter consolidation and initial parameter value estimation algorithms were further developed and implemented. The entire program was coded in Java. The performance of the developed covariate model was evaluated for the population pharmacokinetic data of tobramycin. In comparison with the established covariate model, goodness-of-fit of the measured data was greatly improved by using only 2 additional adjustable parameters. Ten test runs yielded the same solution. In conclusion, the systematic exploration method is a potentially powerful tool for prescreening covariate models in population pharmacokinetic analysis. (C) 2017 American Pharmacists Association (R). Published by Elsevier Inc. All rights reserved.