Estimation of Population Pharmacokinetic Parameters of Saquinavir in HIV Patients with the MONOLIX Software
- 448 Downloads
In nonlinear mixed-effects models, estimation methods based on a linearization of the likelihood are widely used although they have several methodological drawbacks. Kuhn and Lavielle (Comput. Statist. Data Anal. 49:1020–1038 (2005)) developed an estimation method which combines the SAEM (Stochastic Approximation EM) algorithm, with a MCMC (Markov Chain Monte Carlo) procedure for maximum likelihood estimation in nonlinear mixed-effects models without linearization. This method is implemented in the Matlab software MONOLIX which is available at http://www.math.u-psud.fr/~lavielle/monolix/logiciels. In this paper we apply MONOLIX to the analysis of the pharmacokinetics of saquinavir, a protease inhibitor, from concentrations measured after single dose administration in 100 HIV patients, some with advance disease. We also illustrate how to use MONOLIX to build the covariate model using the Bayesian Information Criterion. Saquinavir oral clearance (CL/F) was estimated to be 1.26 L/h and to increase with body mass index, the inter-patient variability for CL/F being 120%. Several methodological developments are ongoing to extend SAEM which is a very promising estimation method for population pharmacockinetic/pharmacodynamic analyses.
KeywordsEM algorithm nonlinear mixed-effects models stochastic approximation importance sampling model selection pharmacokinetics saquinavir
Unable to display preview. Download preview PDF.
- Dempster A.P., Laird N.M., Rubin D.B. (1977). Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B. Stat. Methodol. 1:1–38Google Scholar
- Trout H., Mentré F., Panhard X., Kodjo A., Escaut L., Pernet P., Gobert J.G., Vittecoq D., Knellwolf A.L., Caulin C., Bergmann J.F. (2004). Enhanced saquinavir exposure in HIV1-infected patients with diarrhea and/or wasting syndrome. Antimicrob. Agents Chemother. 48:538–545PubMedCrossRefGoogle Scholar
- P. Girard and F. Mentré. A comparison of estimation methods in nonlinear mixed effects models using a blind analysis. PAGE 14 (2005); Abstr 834 [www.page-meeting. org/?abstract=834].Google Scholar
- Louis T.A. (1982). Finding the observed information matrix when using EM algorithm. J. R. Stat. Soc. B. 44:226–233Google Scholar
- D. O. Stram and J. W. Lee. Variance components testing in the longitudinal mixed effects model. Biometrics 50(4):1171–1177 (1994). Erratum in: Biometrics 51(3):1196 (1995).Google Scholar
- Verbeke G., Molenberghs G. (2004). Linear Mixed Effect Models for Longitudinal Data. New York, SpringerGoogle Scholar
- S. Donnet and A. Samson. Estimation of parameters in incomplete data models defined by dynamical systems. J. Stat. Plan. Infer. to appear (2007).Google Scholar
- S. Retout, E. Comets, A. Samson, and F. Mentré. Designs in nonlinear mixed effects models: application to HIV viral load decrease with evaluation, optimization and determination of the power of the test of a treatment effect. PAGE 14 (2005); Abstr 775 [.page-meeting.org/?abstract=775]Google Scholar