Literature DB >> 14992824

POPED, a software for optimal experiment design in population kinetics.

Marco Foracchia1, Andrew Hooker, Paolo Vicini, Alfredo Ruggeri.   

Abstract

Population kinetic analysis is the methodology used to quantify inter-subject variability in kinetic studies. It entails the collection of (possibly sparse) data from dynamic experiments in a group of subjects and their quantitative interpretation by means of a mathematical model. This methodology is widely used in the pharmaceutical industry (where it is termed "pharmacokinetic population analysis") and recently it is becoming increasingly used in other areas of biomedical research. Unlike traditional kinetic studies, where the number of subjects can be quite small, population kinetic studies require large numbers of subjects. It is, therefore, of great interest to design these studies in the most efficient manner possible, to maximize the information content provided by the data. In this paper we propose an algorithm and a computer program, POPED, for the optimal design of a population kinetic experiment. In particular, the number of samples for each subject and the design of the individual sampling strategies, i.e. the number and location of the time points at which the output variable is sampled, will be considered. Among the various criteria proposed in the literature, D and ED optimality are the ones implemented in our software program, since they are the most widely used. A brief description of the techniques employed to perform design optimization is given, together with some details on their actual implementation. Some examples are then presented to show the program usage and the results provided.

Mesh:

Year:  2004        PMID: 14992824     DOI: 10.1016/S0169-2607(03)00073-7

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  31 in total

1.  Rapid sample size calculations for a defined likelihood ratio test-based power in mixed-effects models.

Authors:  Camille Vong; Martin Bergstrand; Joakim Nyberg; Mats O Karlsson
Journal:  AAPS J       Date:  2012-02-17       Impact factor: 4.009

2.  Serial correlation in optimal design for nonlinear mixed effects models.

Authors:  Joakim Nyberg; Richard Höglund; Martin Bergstrand; Mats O Karlsson; Andrew C Hooker
Journal:  J Pharmacokinet Pharmacodyn       Date:  2012-03-14       Impact factor: 2.745

3.  Prediction of repeat-dose occupancy from single-dose data: characterisation of the relationship between plasma pharmacokinetics and brain target occupancy.

Authors:  Sergio Abanades; Jasper van der Aart; Julien A R Barletta; Carmine Marzano; Graham E Searle; Cristian A Salinas; Javaad J Ahmad; Richard R Reiley; Sabina Pampols-Maso; Stefano Zamuner; Vincent J Cunningham; Eugenii A Rabiner; Marc A Laruelle; Roger N Gunn
Journal:  J Cereb Blood Flow Metab       Date:  2010-10-13       Impact factor: 6.200

4.  Methods and software tools for design evaluation in population pharmacokinetics-pharmacodynamics studies.

Authors:  Joakim Nyberg; Caroline Bazzoli; Kay Ogungbenro; Alexander Aliev; Sergei Leonov; Stephen Duffull; Andrew C Hooker; France Mentré
Journal:  Br J Clin Pharmacol       Date:  2015-01       Impact factor: 4.335

5.  Robust population pharmacokinetic experiment design.

Authors:  Michael G Dodds; Andrew C Hooker; Paolo Vicini
Journal:  J Pharmacokinet Pharmacodyn       Date:  2005-02       Impact factor: 2.745

6.  Simultaneous population optimal design for pharmacokinetic-pharmacodynamic experiments.

Authors:  Andrew Hooker; Paolo Vicini
Journal:  AAPS J       Date:  2005-11-01       Impact factor: 4.009

7.  Non-Bayesian knowledge propagation using model-based analysis of data from multiple clinical studies.

Authors:  Jakob Ribbing; Andrew C Hooker; E Niclas Jonsson
Journal:  J Pharmacokinet Pharmacodyn       Date:  2007-11-08       Impact factor: 2.745

8.  Simultaneous optimal experimental design on dose and sample times.

Authors:  Joakim Nyberg; Mats O Karlsson; Andrew C Hooker
Journal:  J Pharmacokinet Pharmacodyn       Date:  2009-03-25       Impact factor: 2.745

9.  Simultaneous optimal experimental design for in vitro binding parameter estimation.

Authors:  C Steven Ernest; Mats O Karlsson; Andrew C Hooker
Journal:  J Pharmacokinet Pharmacodyn       Date:  2013-08-13       Impact factor: 2.745

10.  Optimizing disease progression study designs for drug effect discrimination.

Authors:  Sebastian Ueckert; Stefanie Hennig; Joakim Nyberg; Mats O Karlsson; Andrew C Hooker
Journal:  J Pharmacokinet Pharmacodyn       Date:  2013-08-27       Impact factor: 2.745

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