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

Authors:
James W. T. Yates
Affiliations:
Discovery Drug Metabolism and Pharmacokinetics, AstraZeneca, Alderley Park, Macclesfield, Cheshire SK10 4TG, United Kingdom
Venue:
Computer Methods and Programs in Biomedicine
Year:
2009

Citing 2
Cited 0

Numerical Recipes 3rd Edition: The Art of Scientific Computing

Numerical Recipes 3rd Edition: The Art of Scientific Computing
Maximum likelihood estimation in nonlinear mixed effects models

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.00

Visualization

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.