Wang, ZhipingKim, SeonghoQuinney, Sara K.Zhou, JihaoLi, Lang2020-05-182020-05-182010-05-28Wang, Z., Kim, S., Quinney, S.K. et al. Non-compartment model to compartment model pharmacokinetics transformation meta-analysis – a multivariate nonlinear mixed model. BMC Syst Biol 4, S8 (2010). https://doi.org/10.1186/1752-0509-4-S1-S8https://hdl.handle.net/1805/22789Background 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.en-USAttribution 4.0 InternationalCompartment ModelCoverage ProbabilityRelative BiasNonlinear Mixed ModelModel Base Drug DevelopmentArticle