Literature DB >> 24548174

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

Joakim Nyberg1, Caroline Bazzoli, Kay Ogungbenro, Alexander Aliev, Sergei Leonov, Stephen Duffull, Andrew C Hooker, France Mentré.   

Abstract

Population pharmacokinetic (PK)-pharmacodynamic (PKPD) models are increasingly used in drug development and in academic research; hence, designing efficient studies is an important task. Following the first theoretical work on optimal design for nonlinear mixed-effects models, this research theme has grown rapidly. There are now several different software tools that implement an evaluation of the Fisher information matrix for population PKPD. We compared and evaluated the following five software tools: PFIM, PkStaMp, PopDes, PopED and POPT. The comparisons were performed using two models, a simple-one compartment warfarin PK model and a more complex PKPD model for pegylated interferon, with data on both concentration and response of viral load of hepatitis C virus. The results of the software were compared in terms of the standard error (SE) values of the parameters predicted from the software and the empirical SE values obtained via replicated clinical trial simulation and estimation. For the warfarin PK model and the pegylated interferon PKPD model, all software gave similar results. Interestingly, it was seen, for all software, that the simpler approximation to the Fisher information matrix, using the block diagonal matrix, provided predicted SE values that were closer to the empirical SE values than when the more complicated approximation was used (the full matrix). For most PKPD models, using any of the available software tools will provide meaningful results, avoiding cumbersome simulation and allowing design optimization.
© 2014 The British Pharmacological Society.

Entities:  

Keywords:  Fisher information matrix; nonlinear mixed effect models; optimal design; population design; population pharmacokinetics-pharmacodynamics

Mesh:

Year:  2015        PMID: 24548174      PMCID: PMC4294071          DOI: 10.1111/bcp.12352

Source DB:  PubMed          Journal:  Br J Clin Pharmacol        ISSN: 0306-5251            Impact factor:   4.335


  24 in total

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Journal:  Comput Methods Programs Biomed       Date:  2001-05       Impact factor: 5.428

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Authors:  Sylvie Retout; France Mentré
Journal:  J Biopharm Stat       Date:  2003-05       Impact factor: 1.051

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Journal:  Comput Methods Programs Biomed       Date:  2004-04       Impact factor: 5.428

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Journal:  Comput Methods Programs Biomed       Date:  2012-05-27       Impact factor: 5.428

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Journal:  J Pharmacokinet Biopharm       Date:  1983-06

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Journal:  J Pharmacokinet Biopharm       Date:  1981-12

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Authors:  N R Draper; W G Hunter
Journal:  Biometrika       Date:  1967-12       Impact factor: 2.445

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  27 in total

1.  Simplification of a pharmacokinetic model for red blood cell methotrexate disposition.

Authors:  Shan Pan; Julia Korell; Lisa K Stamp; Stephen B Duffull
Journal:  Eur J Clin Pharmacol       Date:  2015-09-26       Impact factor: 2.953

2.  Individual Bayesian Information Matrix for Predicting Estimation Error and Shrinkage of Individual Parameters Accounting for Data Below the Limit of Quantification.

Authors:  Thi Huyen Tram Nguyen; Thu Thuy Nguyen; France Mentré
Journal:  Pharm Res       Date:  2017-06-28       Impact factor: 4.200

3.  Deterministic identifiability of population pharmacokinetic and pharmacokinetic-pharmacodynamic models.

Authors:  Vijay K Siripuram; Daniel F B Wright; Murray L Barclay; Stephen B Duffull
Journal:  J Pharmacokinet Pharmacodyn       Date:  2017-06-13       Impact factor: 2.745

4.  Study design and population pharmacokinetic analysis of a phase II dose-ranging study of interleukin-1 receptor antagonist.

Authors:  Kayode Ogungbenro; Sharon Hulme; Nancy Rothwell; Stephen Hopkins; Pippa Tyrrell; James Galea
Journal:  J Pharmacokinet Pharmacodyn       Date:  2016-02       Impact factor: 2.745

5.  Pharmacometrics: so much mathematics and why planes achieve their destinations with almost perfect results ….

Authors:  Geoffrey K Isbister; Robert Bies
Journal:  Br J Clin Pharmacol       Date:  2015-01       Impact factor: 4.335

6.  Assessing robustness of designs for random effects parameters for nonlinear mixed-effects models.

Authors:  Stephen B Duffull; Andrew C Hooker
Journal:  J Pharmacokinet Pharmacodyn       Date:  2017-10-24       Impact factor: 2.745

7.  Model-Based Adaptive Optimal Design (MBAOD) Improves Combination Dose Finding Designs: an Example in Oncology.

Authors:  Philippe B Pierrillas; Sylvain Fouliard; Marylore Chenel; Andrew C Hooker; Lena E Friberg; Mats O Karlsson
Journal:  AAPS J       Date:  2018-03-07       Impact factor: 4.009

8.  Optimal Design for Informative Protocols in Xenograft Tumor Growth Inhibition Experiments in Mice.

Authors:  Giulia Lestini; France Mentré; Paolo Magni
Journal:  AAPS J       Date:  2016-06-15       Impact factor: 4.009

9.  Good Practices in Model-Informed Drug Discovery and Development: Practice, Application, and Documentation.

Authors:  S F Marshall; R Burghaus; V Cosson; S Y A Cheung; M Chenel; O DellaPasqua; N Frey; B Hamrén; L Harnisch; F Ivanow; T Kerbusch; J Lippert; P A Milligan; S Rohou; A Staab; J L Steimer; C Tornøe; S A G Visser
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2016-03-14

10.  Statistical power calculations for mixed pharmacokinetic study designs using a population approach.

Authors:  Frank Kloprogge; Julie A Simpson; Nicholas P J Day; Nicholas J White; Joel Tarning
Journal:  AAPS J       Date:  2014-07-11       Impact factor: 4.009

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