Yamashita Fumiyoshi, Fujita Atsuto, Sasa Yukako, Higuchi Yuriko, Tsuda Masahiro, Hashida Mitsuru
Graduate School of Pharmaceutical Sciences, Kyoto University, Yoshidashimoadachi-cho, Sakyo-ku, Kyoto 606-8501, Japan.
Graduate School of Pharmaceutical Sciences, Kyoto University, Yoshidashimoadachi-cho, Sakyo-ku, Kyoto 606-8501, Japan.
J Pharm Sci. 2017 Sep;106(9):2407-2411. doi: 10.1016/j.xphs.2017.04.029. Epub 2017 Apr 25.
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.
构建协变量模型是群体药代动力学中的一项关键任务。本研究基于基因表达式编程（GEP）开发了一种用于自动协变量建模的新方法，该方法不仅能够进行协变量选择，还能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合并和初始参数值估计算法。整个程序用Java编码。针对妥布霉素的群体药代动力学数据评估了所开发的协变量模型的性能。与已建立的协变量模型相比，仅使用另外2个可调参数就大大提高了实测数据的拟合优度。十次测试运行得出了相同的结果。总之，该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一种潜在强大工具。
