Literature DB >> 16139390

The use of a modified Fedorov exchange algorithm to optimise sampling times for population pharmacokinetic experiments.

Kayode Ogungbenro1, Gordon Graham, Ivelina Gueorguieva, Leon Aarons.   

Abstract

We propose a new algorithm for optimising sampling times for population pharmacokinetic experiments using D-optimality. The algorithm was used in conjunction with the population Fisher information matrix as implemented in MATLAB (PFIM 1.1 and 1.2) to evaluate population pharmacokinetic designs. The new algorithm based on the classical Fedorov exchange algorithm optimises the determinant of the population Fisher information matrix. The performance of the new algorithm has been compared with other existing algorithms including simplex, simulated annealing and adaptive random search. The new algorithm performed better especially when dealing with complex designs at the expense of longer computing times.

Mesh:

Year:  2005        PMID: 16139390     DOI: 10.1016/j.cmpb.2005.07.001

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


  12 in total

1.  Optimal blood sampling time windows for parameter estimation using a population approach: design of a phase II clinical trial.

Authors:  Marylore Chenel; Kayode Ogungbenro; Vincent Duval; Christian Laveille; Roeline Jochemsen; Leon Aarons
Journal:  J Pharmacokinet Pharmacodyn       Date:  2005-12       Impact factor: 2.745

2.  Optimal design for multivariate response pharmacokinetic models.

Authors:  Ivelina Gueorguieva; Leon Aarons; Kayode Ogungbenro; Karin M Jorga; Trudy Rodgers; Malcolm Rowland
Journal:  J Pharmacokinet Pharmacodyn       Date:  2006-03-21       Impact factor: 2.745

3.  Drug-drug interaction predictions with PBPK models and optimal multiresponse sampling time designs: application to midazolam and a phase I compound. Part 1: comparison of uniresponse and multiresponse designs using PopDes.

Authors:  Marylore Chenel; François Bouzom; Leon Aarons; Kayode Ogungbenro
Journal:  J Pharmacokinet Pharmacodyn       Date:  2009-01-07       Impact factor: 2.745

4.  Population Fisher information matrix and optimal design of discrete data responses in population pharmacodynamic experiments.

Authors:  Kayode Ogungbenro; Leon Aarons
Journal:  J Pharmacokinet Pharmacodyn       Date:  2011-06-10       Impact factor: 2.745

5.  Optimal design in population kinetic experiments by set-valued methods.

Authors:  Peter Gennemark; Alexander Danis; Joakim Nyberg; Andrew C Hooker; Warwick Tucker
Journal:  AAPS J       Date:  2011-07-15       Impact factor: 4.009

6.  Web-based tools for finding optimal designs in biomedical studies.

Authors:  Weng Kee Wong
Journal:  Comput Methods Programs Biomed       Date:  2013-06-24       Impact factor: 5.428

7.  FPCA-based method to select optimal sampling schedules that capture between-subject variability in longitudinal studies.

Authors:  Meihua Wu; Ana Diez-Roux; Trivellore E Raghunathan; Brisa N Sánchez
Journal:  Biometrics       Date:  2017-05-08       Impact factor: 2.571

8.  Population pharmacokinetics and optimal design of paediatric studies for famciclovir.

Authors:  Kayode Ogungbenro; Ivan Matthews; Michael Looby; Guenther Kaiser; Gordon Graham; Leon Aarons
Journal:  Br J Clin Pharmacol       Date:  2009-10       Impact factor: 4.335

9.  Optimal design for multiresponse pharmacokinetic-pharmacodynamic models - dealing with unbalanced designs.

Authors:  Kayode Ogungbenro; Ivelina Gueorguieva; Oneeb Majid; Gordon Graham; Leon Aarons
Journal:  J Pharmacokinet Pharmacodyn       Date:  2007-02-07       Impact factor: 2.410

10.  Intravenous anakinra can achieve experimentally effective concentrations in the central nervous system within a therapeutic time window: results of a dose-ranging study.

Authors:  James Galea; Kayode Ogungbenro; Sharon Hulme; Andrew Greenhalgh; Leon Aarons; Sylvia Scarth; Peter Hutchinson; Samantha Grainger; Andrew King; Stephen J Hopkins; Nancy Rothwell; Pippa Tyrrell
Journal:  J Cereb Blood Flow Metab       Date:  2010-07-14       Impact factor: 6.200

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