Integration of data from multiple sources for simultaneous modelling analysis : experience from nevirapine population pharmacokinetics

• WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT Integrating individual data from multiple sources in one simultaneous population analysis (sometimes called a mega-model) can address novel research questions and add power for covariate detection without requiring new clinical studies. However, the development of this type of model can be challenging and time consuming. Nevirapine is a non-nucleoside reverse transcriptase inhibitor commonly used for treatment of human immunodeficiency virus infection in resource-limited settings.WHAT THIS STUDY ADDS This study outlines a strategy for integration of data from multiple sources for modelling analysis. It provides suggestions on handling of missing covariates in the context of several data sources and a starting point for development of a multinational nevirapine mega-model. AIMS To propose a modelling strategy to efficiently integrate data from different sources in one simultaneous analysis, using nevirapine population pharmacokinetic data as an example.METHODS Data from three studies including 115 human immunodeficiency virus-infected South African adults were used. Patients were on antiretroviral therapy regimens including 200 mg nevirapine twice daily and sampled at steady state. A development process was suggested, implemented in NONMEM7 and the final model evaluated with an external data set.RESULTS A stepwise approach proved efficient. Model development started with the intensively sampled data. Data were added sequentially, using visual predictive checks for inspecting their compatibility with the existing model. Covariate exploration was carried out, and auxiliary regression models were designed for imputation of missing covariates. Nevirapine pharmacokinetics was described by a one-compartment model with absorption through two transit compartments. Body size was accounted for using allometric scaling. The model included a mixture of two subpopulations with different typical values of clearance, namely fast (3.12 l h-1) and slow metabolizers (1.45 l h-1), with 17% probability of belonging to the latter. Absorption displayed large between-occasion variability, and food slowed the absorption mean transit time from 0.6 to 2.5 h. Concomitant antitubercular treatment including rifampicin typically decreased bioavailability by 39%, with significant between-subject variability. Visual predictive checks of external validation data indicated good predictive performance.CONCLUSIONS The development strategy succeeded in integrating data from different sources to produce a model with robust parameter estimates. This work paves the way for the creation of a nevirapine mega-model, including additional data from numerous diverse sources.

Keyword

missing covariates

nevirapine

NONMEM

population pharmacokinetics

prediction and variability corrected visual predictive check

simultaneous modelling analysis

Publication and Content Type

ref (subject category)

art (subject category)