Non-compartment model to compartment model pharmacokinetics transformation meta-analysis--a multivariate nonlinear mixed model.
BACKGROUND
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.


RESULTS
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%.


CONCLUSIONS
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.
Wang
Zhiping
Z
Division of Biostatistics, Department of Medicine, School of Medicine, Indiana University, Indianapolis, IN 46032, USA. zhipwang@iupui.edu
Kim
Seongho
S
Quinney
Sara K
SK
Zhou
Jihao
J
Li
Lang
L
D055808:Drug Discovery; D015999:Multivariate Analysis; D017711:Nonlinear Dynamics; D010599:Pharmacokinetics; D015203:Reproducibility of Results