Literature DB >> 19319484

Simultaneous optimal experimental design on dose and sample times.

Joakim Nyberg1, Mats O Karlsson, Andrew C Hooker.   

Abstract

Optimal experimental design can be used for optimizing new pharmacokinetic (PK)-pharmacodynamic (PD) studies to increase the parameter precision. Several methods for optimizing non-linear mixed effect models has been proposed previously but the impact of optimizing other continuous design parameters, e.g. the dose, has not been investigated to a large extent. Moreover, the optimization method (sequential or simultaneous) for optimizing several continuous design parameters can have an impact on the optimal design. In the sequential approach the time and dose where optimized in sequence and in the simultaneous approach the dose and time points where optimized at the same time. To investigate the sequential approach and the simultaneous approach; three different PK-PD models where considered. In most of the cases the optimization method did change the optimal design and furthermore the precision was improved with the simultaneous approach.

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Year:  2009        PMID: 19319484     DOI: 10.1007/s10928-009-9114-z

Source DB:  PubMed          Journal:  J Pharmacokinet Pharmacodyn        ISSN: 1567-567X            Impact factor:   2.745


  15 in total

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

2.  Further developments of the Fisher information matrix in nonlinear mixed effects models with evaluation in population pharmacokinetics.

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

3.  Simultaneous vs. sequential analysis for population PK/PD data I: best-case performance.

Authors:  Liping Zhang; Stuart L Beal; Lewis B Sheiner
Journal:  J Pharmacokinet Pharmacodyn       Date:  2003-12       Impact factor: 2.745

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

Authors:  Marco Foracchia; Andrew Hooker; Paolo Vicini; Alfredo Ruggeri
Journal:  Comput Methods Programs Biomed       Date:  2004-04       Impact factor: 5.428

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.  A program for individual and population optimal design for univariate and multivariate response pharmacokinetic-pharmacodynamic models.

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Journal:  Comput Methods Programs Biomed       Date:  2007-02-09       Impact factor: 5.428

7.  Bayesian optimal designs for pharmacokinetic models: sensitivity to uncertainty.

Authors:  Aristides Dokoumetzidis; Leon Aarons
Journal:  J Biopharm Stat       Date:  2007       Impact factor: 1.051

8.  Derivation of various NONMEM estimation methods.

Authors:  Yaning Wang
Journal:  J Pharmacokinet Pharmacodyn       Date:  2007-07-10       Impact factor: 2.745

9.  Using disease progression models as a tool to detect drug effect.

Authors:  D R Mould; N G Denman; S Duffull
Journal:  Clin Pharmacol Ther       Date:  2007-05-16       Impact factor: 6.875

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Journal:  J Pharmacokinet Biopharm       Date:  1983-12
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  16 in total

1.  A general model-based design of experiments approach to achieve practical identifiability of pharmacokinetic and pharmacodynamic models.

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Journal:  J Pharmacokinet Pharmacodyn       Date:  2013-06-04       Impact factor: 2.745

2.  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

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4.  Optimization of the intravenous glucose tolerance test in T2DM patients using optimal experimental design.

Authors:  Hanna E Silber; Joakim Nyberg; Andrew C Hooker; Mats O Karlsson
Journal:  J Pharmacokinet Pharmacodyn       Date:  2009-06-25       Impact factor: 2.745

5.  D-optimal designs for parameter estimation for indirect pharmacodynamic response models.

Authors:  Leonid A Khinkis; Wojciech Krzyzanski; William J Jusko; William R Greco
Journal:  J Pharmacokinet Pharmacodyn       Date:  2009-11-11       Impact factor: 2.745

6.  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

7.  Optimizing Dose-Finding Studies for Drug Combinations Based on Exposure-Response Models.

Authors:  Theodoros Papathanasiou; Anders Strathe; Rune Viig Overgaard; Trine Meldgaard Lund; Andrew C Hooker
Journal:  AAPS J       Date:  2019-07-29       Impact factor: 4.009

8.  Analysis of opioid consumption in clinical trials: a simulation based analysis of power of four approaches.

Authors:  Rasmus Vestergaard Juul; Joakim Nyberg; Mads Kreilgaard; Lona Louring Christrup; Ulrika S H Simonsson; Trine Meldgaard Lund
Journal:  J Pharmacokinet Pharmacodyn       Date:  2017-04-07       Impact factor: 2.745

9.  Optimised protocol design for the screening of analgesic compounds in neuropathic pain.

Authors:  A Taneja; J Nyberg; M Danhof; O Della Pasqua
Journal:  J Pharmacokinet Pharmacodyn       Date:  2012-11-30       Impact factor: 2.745

10.  Model-based evaluation of drug-induced QTc prolongation for compounds in early development.

Authors:  Vincent F S Dubois; Huixin Yu; Meindert Danhof; Oscar Della Pasqua
Journal:  Br J Clin Pharmacol       Date:  2015-01       Impact factor: 4.335

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