Non-compartment model to compartment model pharmacokinetics transformation meta-analysis--a multivariate nonlinear mixed model.
Non-compartment model to compartment model pharmacokinetics transformation meta-analysis--a multivariate nonlinear mixed model.
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DOI：
10.1186/1752-0509-4-s1-s8
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发表时间：
2010-05-28
影响因子：
--
通讯作者：
Li L
中科院分区：
文献类型：
--
作者：
Wang Z;Kim S;Quinney SK;Zhou J;Li L
为了实现基于模型的药物开发，第一步通常是从已发表的文献中建立模型。药物动力学模型是基于模型的药物开发的核心。提出了一种将已发表的非房室模型药代动力学参数转换为房室模型药动学参数的重要方法。这项荟萃分析是用多变量非线性混合模型进行的。提出了一种用于统计估计和推断的条件一阶线性化方法。以MDZ为例，该方法成功地将10种出版物中的6个非房室模型PK参数转换为5个房室模型PK参数。在模拟研究中，我们发现，如果每个研究都报道了所有的非间隔PK参数，则该多元非线性混合模型在估计间隔模型PK参数时具有很小的相对偏差(1%)。如果一些已发表的文献中存在遗漏的非房室PK参数，则房室模型PK参数的相对偏差仍然很小(<；3%)。这些PK参数估计的95%覆盖概率都在85%以上。这种将非间隔模型PK参数转换为间隔模型的Meta分析方法具有有效的统计推断。它可以常规用于基于模型的药物开发。
To fulfill the model based drug development, the very first step is usually a model establishment from published literatures. Pharmacokinetics model is the central piece of model based drug development. This paper proposed an important approach to transform published non-compartment model pharmacokinetics (PK) parameters into compartment model PK parameters. This meta-analysis was performed with a multivariate nonlinear mixed model. A conditional first-order linearization approach was developed for statistical estimation and inference. Using MDZ as an example, we showed that this approach successfully transformed 6 non-compartment model PK parameters from 10 publications into 5 compartment model PK parameters. In simulation studies, we showed that this multivariate nonlinear mixed model had little relative bias (<1%) in estimating compartment model PK parameters if all non-compartment PK parameters were reported in every study. If there missing non-compartment PK parameters existed in some published literatures, the relative bias of compartment model PK parameter was still small (<3%). The 95% coverage probabilities of these PK parameter estimates were above 85%. This non-compartment model PK parameter transformation into compartment model meta-analysis approach possesses valid statistical inference. It can be routinely used for model based drug development.