Search

We address the problem of design optimization using cost functions in nonlinear mixed effects models with multiple responses. We focus on the relative feasibility of the optimized designs, in term of sampling times and of number of subjects. To do that, we extend the Fedorov-Wynn algorithm—a dedicated design optimization algorithm—to include a cost function that penalizes less feasible designs as well as to take into account multiple responses. We apply this extension to the design optimization of a joint pharmacokinetic model of infliximab and methotrexate administered in rheumatoid arthritis. We show the benefit of such an approach when substantial constraints on the design are imposed. [ABSTRACT FROM AUTHOR]

We evaluate by simulation three model-based methods to test the influence of a single nucleotide polymorphism on a pharmacokinetic parameter of a drug: analysis of variance (ANOVA) on the empirical Bayes estimates of the individual parameters, likelihood ratio test between models with and without genetic covariate, and Wald tests on the parameters of the model with covariate. Analyses are performed using the FO and FOCE method implemented in the NONMEM software. We compare several approaches for model selection based on tests and global criteria. We illustrate the results with pharmacokinetic data on indinavir from HIV-positive patients included in COPHAR 2-ANRS 111 to study the gene effect prospectively. Only the tests based on the EBE obtain an empirical type I error close to the expected 5%. The approximation made with the FO algorithm results in a significant inflation of the type I error of the LRT and Wald tests. [ABSTRACT FROM AUTHOR]