Literature DB >> 8882748

Estimation of population pharmacokinetics using the Gibbs sampler.

N G Best1, K K Tan, W R Gilks, D J Spiegelhalter.   

Abstract

Quantification of the average and interindividual variation in pharmacokinetic behavior within the patient population is an important aspect of drug development. Population pharmacokinetic models typically involve large numbers of parameters related nonlinearly to sparse, observational data, which creates difficulties for conventional methods of analysis. The nonlinear mixed-effects method implemented in the computer program NONMEM is a widely used approach to the estimation of population parameters. However, the method relies on somewhat restrictive modeling assumptions to enable efficient parameter estimation. In this paper we describe a Bayesian approach to population pharmacokinetic analysis which used a technique known as Gibbs sampling to simulate values for each model parameter. We provide details of how to implement the method in the context of population pharmacokinetic analysis, and illustrate this via an application to gentamicin population pharmacokinetics in neonates.

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Year:  1995        PMID: 8882748     DOI: 10.1007/bf02353641

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


  18 in total

1.  Smooth nonparametric maximum likelihood estimation for population pharmacokinetics, with application to quinidine.

Authors:  M Davidian; A R Gallant
Journal:  J Pharmacokinet Biopharm       Date:  1992-10

2.  Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.

Authors:  S Geman; D Geman
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  1984-06       Impact factor: 6.226

3.  An evaluation of point and interval estimates in population pharmacokinetics using NONMEM analysis.

Authors:  D B White; C A Walawander; Y Tung; T H Grasela
Journal:  J Pharmacokinet Biopharm       Date:  1991-02

4.  Mixed-effects nonlinear regression for unbalanced repeated measures.

Authors:  E F Vonesh; R L Carter
Journal:  Biometrics       Date:  1992-03       Impact factor: 2.571

5.  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

6.  Population pharmacokinetics of tobramycin.

Authors:  L Aarons; S Vozeh; M Wenk; P Weiss; F Follath
Journal:  Br J Clin Pharmacol       Date:  1989-09       Impact factor: 4.335

7.  Alternative approaches to estimation of population pharmacokinetic parameters: comparison with the nonlinear mixed-effect model.

Authors:  J L Steimer; A Mallet; J L Golmard; J F Boisvieux
Journal:  Drug Metab Rev       Date:  1984       Impact factor: 4.518

8.  Postconceptional age and gentamicin elimination half-life.

Authors:  J W Kasik; S Jenkins; M P Leuschen; R M Nelson
Journal:  J Pediatr       Date:  1985-03       Impact factor: 4.406

9.  Estimation of gentamicin clearance and volume of distribution in neonates and young children.

Authors:  A W Kelman; A H Thomson; B Whiting; S M Bryson; D A Steedman; G E Mawer; L A Samba-Donga
Journal:  Br J Clin Pharmacol       Date:  1984-11       Impact factor: 4.335

10.  Evaluation of methods for estimating population pharmacokinetics parameters. I. Michaelis-Menten model: routine clinical pharmacokinetic data.

Authors:  L B Sheiner; S L Beal
Journal:  J Pharmacokinet Biopharm       Date:  1980-12
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  17 in total

1.  Bayesian analysis of population PK/PD models: general concepts and software.

Authors:  David J Lunn; Nicky Best; Andrew Thomas; Jon Wakefield; David Spiegelhalter
Journal:  J Pharmacokinet Pharmacodyn       Date:  2002-06       Impact factor: 2.745

2.  Prediction discrepancies for the evaluation of nonlinear mixed-effects models.

Authors:  France Mentré; Sylvie Escolano
Journal:  J Pharmacokinet Pharmacodyn       Date:  2005-11-13       Impact factor: 2.745

Review 3.  Non-linear mixed effects modeling - from methodology and software development to driving implementation in drug development science.

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Journal:  J Pharmacokinet Pharmacodyn       Date:  2005-11-07       Impact factor: 2.745

Review 4.  A survey of population analysis methods and software for complex pharmacokinetic and pharmacodynamic models with examples.

Authors:  Robert J Bauer; Serge Guzy; Chee Ng
Journal:  AAPS J       Date:  2007-03-02       Impact factor: 4.009

5.  Combining MCMC with 'sequential' PKPD modelling.

Authors:  David Lunn; Nicky Best; David Spiegelhalter; Gordon Graham; Beat Neuenschwander
Journal:  J Pharmacokinet Pharmacodyn       Date:  2009-01-09       Impact factor: 2.745

6.  Bayesian individualization via sampling-based methods.

Authors:  J Wakefield
Journal:  J Pharmacokinet Biopharm       Date:  1996-02

7.  Validation of a decision support system for use in drug development: pharmacokinetic data.

Authors:  S Guzy; C A Hunt
Journal:  Pharm Res       Date:  1997-10       Impact factor: 4.200

8.  A comparison of a Bayesian population method with two methods as implemented in commercially available software.

Authors:  J E Bennett; J C Wakefield
Journal:  J Pharmacokinet Biopharm       Date:  1996-08

9.  Population pharmacokinetic analysis of meloxicam in rheumatoid arthritis patients.

Authors:  Ingolf Meineke; Dietrich Türck
Journal:  Br J Clin Pharmacol       Date:  2003-01       Impact factor: 4.335

Review 10.  Use of pathway information in molecular epidemiology.

Authors:  Duncan C Thomas; David V Conti; James Baurley; Frederik Nijhout; Michael Reed; Cornelia M Ulrich
Journal:  Hum Genomics       Date:  2009-10       Impact factor: 4.639

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