How to combine non-compartmental analysis with the population pharmacokinetics? A study of tobacco smoke's influence on the bioavailability of racemic citalopram in rats.

BACKGROUND Citalopram (CIT) is an antidepressant drug from the group of selective serotonin reuptake inhibitors in which it is the most potent selective inhibitor of serotonin uptake currently available. Patients treated with CIT are often heavy cigarette smokers. Individual pharmacokinetic parameters cannot be directly estimated if full pharmacokinetic profiles are not available for each subject. Sparse sampling is common to experiments using small animals, such as the case that our study is concerned with. METHODS The aim of the study was to demonstrate how the two (non-compartmental analysis (NCA) and nonlinear mixed-effect (NLME)) approaches, used simultaneously, can help overcome specific limitations of these separate methods whilst at the same time preserve their respective benefits. RESULTS Despite the ultra-sparse design, the NLME approach enabled us to develop a pharmacostatistic model with the required covariate--exposition to the tobacco smoke. CONCLUSIONS A tobacco smoke slows down the absorption of the CIT and at the same time makes it more effective. The consistency of results obtained both with NCA and NLME decreased the risk of model misspecification and increased confidence in the final conclusions. Combining NLME with NCA may therefore be recommended for investigating pharmacokinetic properties of the drug in the sparse designs.