Magni P, Bellazzi R, De Nicolao G, Poggesi I, Rocchetti M
Dipartimento di Informatica e Sistemistica, Università degli Studi di Pavia, Pavia, Italy, Pharmacia & Upjohn, Nerviano, Italy.
J Pharmacokinet Pharmacodyn. 2002 Dec;29(5-6):445-71. doi: 10.1023/a:1022920403166.
The estimation of the AUC in a population without frequent and/or fixed individual samplings is of interest because the number of plasma samples can often be limited due to technical, ethical and cost reasons. Non-linear mixed effect models can provide both population and individual estimates of AUC based on sparse sampling protocols; however, appropriate structural models for the description of the pharmacokinetics are required. Nonparametric solutions have also been proposed to estimate the population AUC and the associated error when particular sampling protocols are adopted. However, they do not estimate the individual AUCs and lack flexibility. Also a semiparametric method has been proposed for addressing the problem of sparse sampling in reasonably well designed studies. In this work, we propose and evaluate a nonparametric Bayesian scheme for AUC estimation in population studies with arbitrary sampling protocols. In the stochastic model representing the whole population, the individual plasma concentration curves and the "mean" population curve are described by random walk processes, allowing the application of the method to the reconstruction of any kind of "regular" curves. Population and individual AUC estimation are performed by numerically computing the posterior expectation through a Markov chain Monte Carlo algorithm.
在没有频繁和/或固定个体采样的人群中估计AUC很有意义，因为由于技术、伦理和成本原因，血浆样本数量往往会受到限制。非线性混合效应模型可以基于稀疏采样方案提供人群和个体的AUC估计；然而，需要合适的结构模型来描述药代动力学。当采用特定采样方案时，也有人提出了非参数解决方案来估计人群AUC及相关误差。然而，它们无法估计个体AUC且缺乏灵活性。此外，还提出了一种半参数方法来解决设计合理的研究中的稀疏采样问题。在这项工作中，我们提出并评估了一种用于在具有任意采样方案的人群研究中估计AUC的非参数贝叶斯方案。在代表整个人群的随机模型中，个体血浆浓度曲线和“平均”人群曲线由随机游走过程描述，从而使该方法能够应用于任何类型“规则”曲线的重建。通过马尔可夫链蒙特卡罗算法对后验期望进行数值计算，从而进行人群和个体AUC估计。
