Optimal designs for pharmacokinetic and viral dynamic nonlinear mixed effect models in HIV treatment
Seminar Room 1, Newton Institute
Background: Nonlinear mixed effects models (NLMEM) are increasingly used for analysis of dose-exposure-response models. Methods for “population” designs evaluation/ optimisation are needed for complex models to limit the number of samples in each patient. Approaches for population designs optimisation based on the Fisher information matrix for NLMEM are developed, using mostly first order approximation of the model. Antiretroviral treatment in patients with HIV infection is complex and show large inter -individual variability. Pharmacokinetic and viral dynamic models are available to describe evolution of concentrations, viral loads and CD4 counts. Parameters of these models are estimated through NLMEM.
Objectives: 1) to evaluate and optimise designs in patients for the pharmacokinetic study of an antiretroviral drug (zidovudine) and its active metabolite using cost functions, 2) to evaluate and optimise designs for viral dynamic response and study power to compare treatments efficacy.
Methods: We used the models and estimated parameters from data of patients of the COPHAR 2 - ANRS 111 trial. Measuring active metabolite concentration of zidovudine is costly, as they are intracellular, and we explored D-optimal population designs using various cost functions. The viral dynamic model is a complex model written in ordinary differential equations. We proposed sparse designs with limited number of visits per patient during the one year follow up. We studied the predicted power to compare two treatments. These analyses were performed using PFIM3.2, an R function that we developed for population designs.
Results: We found a design with only three samples for zidovudine and two samples for its active metabolite and showed that optimal designs varied with cost functions. For the viral dynamic model, we showed that a design with 6 visits, if optimally located, can provide good information on response. We evaluated the power to compare two treatments and computed the number of subject needed to get adequate power.
Conclusion: We showed that population design optimisation provides efficient designs respecting clinical constraints in multi responses nonlinear mixed effects models.