Wang Zhiping, Kim Seongho, Quinney Sara K, Zhou Jihao, Li Lang
Division of Biostatistics, Department of Medicine, School of Medicine, Indiana University, Indianapolis, IN 46032, USA.
BMC Syst Biol. 2010 May 28;4 Suppl 1(Suppl 1):S8. doi: 10.1186/1752-0509-4-S1-S8.
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.
为实现基于模型的药物研发，第一步通常是根据已发表的文献建立模型。药代动力学模型是基于模型的药物研发的核心部分。本文提出了一种将已发表的非房室模型药代动力学（PK）参数转换为房室模型PK参数的重要方法。本荟萃分析采用多元非线性混合模型进行。开发了一种条件一阶线性化方法用于统计估计和推断。
以MDZ为例，我们表明该方法成功地将10篇文献中的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，我们表明，如果每项研究都报告了所有非房室PK参数，那么这种多元非线性混合模型在估计房室模型PK参数时相对偏差很小（<1%）。如果某些已发表文献中存在缺失的非房室PK参数，房室模型PK参数的相对偏差仍然很小（<3%）。这些PK参数估计值的95%覆盖概率高于85%。
这种将非房室模型PK参数转换为房室模型的荟萃分析方法具有有效的统计推断。它可常规用于基于模型的药物研发。
