Literature DB >> 8875349

Comparison of some practical sampling strategies for population pharmacokinetic studies.

E N Jonsson1, J R Wade, M O Karlsson.   

Abstract

Using population analysis, sparsely sampled Phase 3 clinical data can be utilized to determine the pharmacokinetic characteristics of the target population. Data arising from such studies are likely to be constrained to certain sampling windows, i.e., the visiting hours at the study clinic. When the sampling window is narrow compared to the half-life of the drug, the advantage of taking more than one sample is not obvious. Study designs with one or two samples per visit have been compared with respect to (i) precision and bias of the population parameter estimates, (ii) the ability to identify the underlying pharmacokinetic model, and (iii) the estimation of individual parameter values. The first point was assessed using simulated data while the latter two were studied using a real data set. Results show: (i) Parameter estimates are more biased and imprecise when only one sample is taken compared to when two samples are obtained, this is true irrespective of the time span between the two samples. (ii) Ability to identify a more complex model is increased if two samples are taken. Specifically, the variability between occasions can be quantified. (iii) Two-sample designs are generally better with respect to prediction of individual parameter values. Even minor changes to commonly employed study designs, in this case the addition of one sample at each study occasion, can improve quality and quantity of the information obtained.

Mesh:

Year:  1996        PMID: 8875349     DOI: 10.1007/bf02353491

Source DB:  PubMed          Journal:  J Pharmacokinet Biopharm        ISSN: 0090-466X


  12 in total

1.  Comparison of methods to calculate cyclosporine A bioavailability from consecutive oral and intravenous doses.

Authors:  M O Karlsson; A Lindberg-Freijs
Journal:  J Pharmacokinet Biopharm       Date:  1990-08

2.  Experimental design and efficient parameter estimation in population pharmacokinetics.

Authors:  M K al-Banna; A W Kelman; B Whiting
Journal:  J Pharmacokinet Biopharm       Date:  1990-08

3.  A three-step approach combining Bayesian regression and NONMEM population analysis: application to midazolam.

Authors:  P O Maitre; M Bührer; D Thomson; D R Stanski
Journal:  J Pharmacokinet Biopharm       Date:  1991-08

4.  Effect of misspecification of the absorption process on subsequent parameter estimation in population analysis.

Authors:  J R Wade; A W Kelman; C A Howie; B Whiting
Journal:  J Pharmacokinet Biopharm       Date:  1993-04

Review 5.  Sparse data analysis.

Authors:  L Aarons
Journal:  Eur J Drug Metab Pharmacokinet       Date:  1993 Jan-Mar       Impact factor: 2.441

6.  Evaluation of methods for estimating population pharmacokinetic parameters. III. Monoexponential model: routine clinical pharmacokinetic data.

Authors:  L B Sheiner; S L Beal
Journal:  J Pharmacokinet Biopharm       Date:  1983-06

7.  The importance of modeling interoccasion variability in population pharmacokinetic analyses.

Authors:  M O Karlsson; L B Sheiner
Journal:  J Pharmacokinet Biopharm       Date:  1993-12

8.  Some suggestions for measuring predictive performance.

Authors:  L B Sheiner; S L Beal
Journal:  J Pharmacokinet Biopharm       Date:  1981-08

9.  Designs for population pharmacodynamics: value of pharmacokinetic data and population analysis.

Authors:  Y Hashimoto; L B Sheiner
Journal:  J Pharmacokinet Biopharm       Date:  1991-06

10.  Implications of intraindividual variability in bioavailability studies of furosemide.

Authors:  A Grahnén; M Hammarlund; T Lundqvist
Journal:  Eur J Clin Pharmacol       Date:  1984       Impact factor: 2.953

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  29 in total

1.  Robust optimal design for the estimation of hyperparameters in population pharmacokinetics.

Authors:  M Tod; F Mentré; Y Merlé; A Mallet
Journal:  J Pharmacokinet Biopharm       Date:  1998-12

2.  Impact of pharmacokinetic-pharmacodynamic model linearization on the accuracy of population information matrix and optimal design.

Authors:  Y Merlé; M Tod
Journal:  J Pharmacokinet Pharmacodyn       Date:  2001-08       Impact factor: 2.745

3.  Designing population pharmacokinetic studies: performance of mixed designs.

Authors:  E O Fadiran; C D Jones; E I Ette
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2000 Jul-Dec       Impact factor: 2.441

4.  Power, selection bias and predictive performance of the Population Pharmacokinetic Covariate Model.

Authors:  Jakob Ribbing; E Niclas Jonsson
Journal:  J Pharmacokinet Pharmacodyn       Date:  2004-04       Impact factor: 2.745

5.  Optimization of individual and population designs using Splus.

Authors:  Sylvie Retout; France Mentré
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6.  A limited sampling strategy based on maximum a posteriori Bayesian estimation for a five-probe phenotyping cocktail.

Authors:  Thu Thuy Nguyen; Henri Bénech; Alain Pruvost; Natacha Lenuzza
Journal:  Eur J Clin Pharmacol       Date:  2016-01       Impact factor: 2.953

7.  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

Review 8.  A pragmatic approach to the design of population pharmacokinetic studies.

Authors:  Amit Roy; Ene I Ette
Journal:  AAPS J       Date:  2005-10-05       Impact factor: 4.009

Review 9.  Pharmacodynamic parameter estimation: population size versus number of samples.

Authors:  Suzette Girgis; Sudhakar M Pai; Ihab G Girgis; Vijay K Batra
Journal:  AAPS J       Date:  2005-10-05       Impact factor: 4.009

10.  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

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