Literature DB >> 21204908

Interpreting population pharmacokinetic-pharmacodynamic analyses - a clinical viewpoint.

Stephen B Duffull1, Daniel F B Wright, Helen R Winter.   

Abstract

The population analysis approach is an important tool for clinical pharmacology in aiding the dose individualization of medicines. However, due to their statistical complexity the clinical utility of population analyses is often overlooked. One of the key reasons to conduct a population analysis is to investigate the potential benefits of individualization of drug dosing based on patient characteristics (termed covariate identification). The purpose of this review is to provide a tool to interpret and extract information from publications that describe population analysis. The target audience is those readers who are aware of population analyses but have not conducted the technical aspects of an analysis themselves. Initially we introduce the general framework of population analysis and work through a simple example with visual plots. We then follow-up with specific details on how to interpret population analyses for the purpose of identifying covariates and how to interpret their likely importance for dose individualization.
© 2011 The Authors. British Journal of Clinical Pharmacology © 2011 The British Pharmacological Society.

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Year:  2011        PMID: 21204908      PMCID: PMC3099367          DOI: 10.1111/j.1365-2125.2010.03891.x

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


  36 in total

Review 1.  Population pharmacokinetics II: estimation methods.

Authors:  Ene I Ette; Paul J Williams
Journal:  Ann Pharmacother       Date:  2004-09-14       Impact factor: 3.154

2.  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

Review 3.  Population pharmacokinetics I: background, concepts, and models.

Authors:  Ene I Ette; Paul J Williams
Journal:  Ann Pharmacother       Date:  2004-08-24       Impact factor: 3.154

Review 4.  Understanding the time course of pharmacological effect: a PKPD approach.

Authors:  Daniel F B Wright; Helen R Winter; Stephen B Duffull
Journal:  Br J Clin Pharmacol       Date:  2011-06       Impact factor: 4.335

5.  Estimation of population characteristics of pharmacokinetic parameters from routine clinical data.

Authors:  L B Sheiner; B Rosenberg; V V Marathe
Journal:  J Pharmacokinet Biopharm       Date:  1977-10

6.  Modelling of individual pharmacokinetics for computer-aided drug dosage.

Authors:  L B Sheiner; B Rosenberg; K L Melmon
Journal:  Comput Biomed Res       Date:  1972-10

7.  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

8.  Evaluation of methods for estimating population pharmacokinetic parameters. II. Biexponential model and experimental pharmacokinetic data.

Authors:  L B Sheiner; S L Beal
Journal:  J Pharmacokinet Biopharm       Date:  1981-10

9.  Development of a dosing strategy for enoxaparin in obese patients.

Authors:  Bruce Green; Stephen B Duffull
Journal:  Br J Clin Pharmacol       Date:  2003-07       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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  51 in total

1.  A reduction in between subject variability is not mandatory for selecting a new covariate.

Authors:  Chakradhar V Lagishetty; Pavan Vajjah; Stephen B Duffull
Journal:  J Pharmacokinet Pharmacodyn       Date:  2012-07-06       Impact factor: 2.745

2.  Disease progress and response to treatment as predictors of survival, disability, cognitive impairment and depression in Parkinson's disease.

Authors:  Thuy C Vu; John G Nutt; Nicholas H G Holford
Journal:  Br J Clin Pharmacol       Date:  2012-08       Impact factor: 4.335

Review 3.  Covariate pharmacokinetic model building in oncology and its potential clinical relevance.

Authors:  Markus Joerger
Journal:  AAPS J       Date:  2012-01-25       Impact factor: 4.009

4.  Progression of motor and nonmotor features of Parkinson's disease and their response to treatment.

Authors:  Thuy C Vu; John G Nutt; Nicholas H G Holford
Journal:  Br J Clin Pharmacol       Date:  2012-08       Impact factor: 4.335

Review 5.  The role of infection models and PK/PD modelling for optimising care of critically ill patients with severe infections.

Authors:  T Tängdén; V Ramos Martín; T W Felton; E I Nielsen; S Marchand; R J Brüggemann; J B Bulitta; M Bassetti; U Theuretzbacher; B T Tsuji; D W Wareham; L E Friberg; J J De Waele; V H Tam; Jason A Roberts
Journal:  Intensive Care Med       Date:  2017-04-13       Impact factor: 17.440

Review 6.  Clinical pharmacology = disease progression + drug action.

Authors:  Nick Holford
Journal:  Br J Clin Pharmacol       Date:  2015-01       Impact factor: 4.335

Review 7.  What do we learn from repeated population analyses?

Authors:  Stephen B Duffull; Daniel F B Wright
Journal:  Br J Clin Pharmacol       Date:  2015-01       Impact factor: 4.335

Review 8.  Understanding the time course of pharmacological effect: a PKPD approach.

Authors:  Daniel F B Wright; Helen R Winter; Stephen B Duffull
Journal:  Br J Clin Pharmacol       Date:  2011-06       Impact factor: 4.335

9.  Population pharmacokinetic analysis of tacrolimus in Chinese myasthenia gravis patients.

Authors:  Yu-Si Chen; Zi-Qi Liu; Rong Chen; Lei Wang; Ling Huang; Xiao Zhu; Tian-Yan Zhou; Wei Lu; Ping Ma
Journal:  Acta Pharmacol Sin       Date:  2017-05-29       Impact factor: 6.150

10.  The impact of frailty on pharmacokinetics in older people: using gentamicin population pharmacokinetic modeling to investigate changes in renal drug clearance by glomerular filtration.

Authors:  Claire Johnston; Sarah N Hilmer; Andrew J McLachlan; Slade T Matthews; Peter R Carroll; Carl M Kirkpatrick
Journal:  Eur J Clin Pharmacol       Date:  2014-02-14       Impact factor: 2.953

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