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