Lavielle Marc, Mentré France
Department of Mathematics, University Paris 5; University Paris 11, Orsay, France.
J Pharmacokinet Pharmacodyn. 2007 Apr;34(2):229-49. doi: 10.1007/s10928-006-9043-z. Epub 2007 Jan 9.
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.
在非线性混合效应模型中，基于似然线性化的估计方法虽存在若干方法学缺陷，但仍被广泛使用。库恩和拉维耶（《计算统计学与数据分析》49:1020 - 1038 (2005)）开发了一种估计方法，该方法将SAEM（随机近似期望最大化）算法与MCMC（马尔可夫链蒙特卡罗）程序相结合，用于非线性混合效应模型的最大似然估计，无需进行线性化。此方法在Matlab软件MONOLIX中实现，该软件可从http://www.math.u - psud.fr/~lavielle/monolix/logiciels获取。在本文中，我们将MONOLIX应用于分析沙奎那韦（一种蛋白酶抑制剂）的药代动力学，数据来自100名HIV患者单剂量给药后的血药浓度测量值，其中部分患者患有晚期疾病。我们还说明了如何使用MONOLIX，通过贝叶斯信息准则构建协变量模型。沙奎那韦的口服清除率（CL/F）估计为1. .26 L/h，且随体重指数增加，CL/F的患者间变异性为120%。目前正在进行多项方法学改进，以扩展SAEM，SAEM是群体药代动力学/药效学分析中一种非常有前景的估计方法。
