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

Background: citalopram(cit) is an anlidepressant drug from lie group of selective serotonin reuptake inhibitors in which it is the most potent selective inhibitor of serotonin uptake currently available. patients treated with cit arc 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 (he two (non-compartmental analysis (nca) and nonlinear mixed-effect (n.lme)) 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,thenlme approach enabled us to develop a pharmacostatistic model with the required eo-variate - exposition to the tobacco smoke. conclusions: a tobacco smoke slows down the absorption of the cit and at the same lime makes it more effective. the consistency of results obtained both with nca andnlme 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.