Literature DB >> 10783805

The combination of population pharmacokinetic studies.

J Wakefield1, N Rahman.   

Abstract

Pharmacokinetic data consist of drug concentrations with associated known sampling times and are collected following the administration of known dosage regimens. Population pharmacokinetic data consist of such data on a number of individuals, possibly along with individual-specific characteristics. During drug development, a number of population pharmacokinetic studies are typically carried out and the combination of such studies is of great importance for characterizing the drug and, in particular, for the design of future studies. In this paper, we describe a model that may be used to combine population pharmacokinetic data. The model is illustrated using six phase I studies of the antiasthmatic drug fluticasone propionate. Our approach is Bayesian and computation is carried out using Markov chain Monte Carlo. We provide a number of simplifications to the model that may be made in order to ease simulation from the posterior distribution.

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Year:  2000        PMID: 10783805     DOI: 10.1111/j.0006-341x.2000.00263.x

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  9 in total

1.  Integration of data from multiple sources for simultaneous modelling analysis: experience from nevirapine population pharmacokinetics.

Authors:  Elin Svensson; Jan-Stefan van der Walt; Karen I Barnes; Karen Cohen; Tamara Kredo; Alwin Huitema; Jean B Nachega; Mats O Karlsson; Paolo Denti
Journal:  Br J Clin Pharmacol       Date:  2012-09       Impact factor: 4.335

2.  Propagation of population pharmacokinetic information using a Bayesian approach: comparison with meta-analysis.

Authors:  Aristides Dokoumetzidis; Leon Aarons
Journal:  J Pharmacokinet Pharmacodyn       Date:  2005-08       Impact factor: 2.745

3.  Propagation of population PK and PD information using a Bayesian approach: dealing with non-exchangeability.

Authors:  Aristides Dokoumetzidis; Leon Aarons
Journal:  J Pharmacokinet Pharmacodyn       Date:  2005-12-12       Impact factor: 2.745

4.  Non-Bayesian knowledge propagation using model-based analysis of data from multiple clinical studies.

Authors:  Jakob Ribbing; Andrew C Hooker; E Niclas Jonsson
Journal:  J Pharmacokinet Pharmacodyn       Date:  2007-11-08       Impact factor: 2.745

5.  A Bayesian meta-analysis on published sample mean and variance pharmacokinetic data with application to drug-drug interaction prediction.

Authors:  Menggang Yu; Seongho Kim; Zhiping Wang; Stephen Hall; Lang Li
Journal:  J Biopharm Stat       Date:  2008       Impact factor: 1.051

6.  Population pharmacokinetics of mycophenolic acid and its main glucuronide metabolite: a comparison between healthy Chinese and Caucasian subjects receiving mycophenolate mofetil.

Authors:  Jing Ling; Jun Shi; Qiudi Jiang; Zheng Jiao
Journal:  Eur J Clin Pharmacol       Date:  2014-10-21       Impact factor: 2.953

7.  Non-compartment model to compartment model pharmacokinetics transformation meta-analysis--a multivariate nonlinear mixed model.

Authors:  Zhiping Wang; Seongho Kim; Sara K Quinney; Jihao Zhou; Lang Li
Journal:  BMC Syst Biol       Date:  2010-05-28

8.  Population pharmacokinetic modelling of darifenacin and its hydroxylated metabolite using pooled data, incorporating saturable first-pass metabolism, CYP2D6 genotype and formulation-dependent bioavailability.

Authors:  Thomas Kerbusch; Ulrika Wählby; Peter A Milligan; Mats O Karlsson
Journal:  Br J Clin Pharmacol       Date:  2003-12       Impact factor: 4.335

9.  A new probabilistic rule for drug-dug interaction prediction.

Authors:  Jihao Zhou; Zhaohui Qin; Sara K Quinney; Seongho Kim; Zhiping Wang; Menggang Yu; Jenny Y Chien; Aroonrut Lucksiri; Stephen D Hall; Lang Li
Journal:  J Pharmacokinet Pharmacodyn       Date:  2009-01-21       Impact factor: 2.745

  9 in total

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