一种用于ACSLXTREME中群体药代动力学-药效学建模的期望最大化（EM）算法的实现。

An implementation of the Expectation-Maximisation (EM) algorithm for population pharmacokinetic-pharmacodynamic modelling in ACSLXTREME.

作者信息

Yates James W T

机构信息

Discovery Drug Metabolism and Pharmacokinetics, AstraZeneca, Macclesfield, Cheshire, United Kingdom.

出版信息

Comput Methods Programs Biomed. 2009 Oct;96(1):49-62. doi: 10.1016/j.cmpb.2009.03.011. Epub 2009 May 1.

DOI:10.1016/j.cmpb.2009.03.011

PMID:19410328

Abstract

An implementation of the Expectation-Maximisation (EM) algorithm in ACSLXTREME (AEGIS Technologies) for the analyses of population pharmacokinetic-pharmacodynamic (PKPD) data is demonstrated. The parameter estimation results are compared with those from NONMEM (Globomax) using the first order conditional estimate method. The estimates are comparable and it is concluded that the EM algorithm is a useful technique in population pharmacokinetic-pharmacodynamic modelling. The implementation also demonstrates the ease with which parameter estimation algorithms for population data can be implemented in simulation software packages.

摘要

展示了在ACSLXTREME（AEGIS技术公司）中用于群体药代动力学-药效学（PKPD）数据分析的期望最大化（EM）算法的一种实现。使用一阶条件估计方法将参数估计结果与来自NONMEM（Globomax）的结果进行比较。估计值具有可比性，得出结论：EM算法是群体药代动力学-药效学建模中的一种有用技术。该实现还展示了在模拟软件包中实现群体数据参数估计算法的简便性。