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Clinical Pharmacokinetics

, Volume 51, Issue 8, pp 515–525 | Cite as

Fundamentals of Population Pharmacokinetic Modelling

Modelling and Software
  • Tony K. L. Kiang
  • Catherine M. T. Sherwin
  • Michael G. Spigarelli
  • Mary H. H. EnsomEmail author
Review Article

Abstract

Population pharmacokinetic modelling is widely used within the field of clinical pharmacology as it helps to define the sources and correlates of pharmacokinetic variability in target patient populations and their impact upon drug disposition. This review focuses on the fundamentals of population pharmacokinetic modelling and provides an overview of the commonly available software programs that perform these functions.

This review attempts to define the common, fundamental aspects of population pharmacokinetic modelling through a discussion of the literature describing the techniques and placing them in the appropriate context. An overview of the most commonly available software programs is also provided.

Population pharmacokinetic modelling is a powerful approach where sources and correlates of pharmacokinetic variability can be identified in a target patient population receiving a pharmacological agent. There is a need to further standardize and establish the best approaches in modelling so that any model created can be systematically evaluated and the results relied upon. Various nonlinear mixed-effects modelling methods, packaged in a variety of software programs, are available today. When selecting population pharmacokinetic software programs, the consumer needs to consider several factors, including usability (e.g. user interface, native platform, price, input and output specificity, as well as intuitiveness), content (e.g. algorithms and data output) and support (e.g. technical and clinical).

Keywords

Pharmacokinetic Parameter Population Pharmacokinetic Modelling Pharmacokinetic Variability Population Pharmacokinetic Study Target Patient Population 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

No sources of funding were used to assist in the preparation of this review. The authors have no potential conflicts of interest that are directly relevant to the content of this review to declare.

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Copyright information

© Springer International Publishing AG 2012

Authors and Affiliations

  • Tony K. L. Kiang
    • 1
  • Catherine M. T. Sherwin
    • 2
  • Michael G. Spigarelli
    • 2
  • Mary H. H. Ensom
    • 3
    Email author
  1. 1.Faculty of Pharmaceutical SciencesThe University of British ColumbiaVancouverCanada
  2. 2.Division of Clinical Pharmacology & Clinical Trials Office, Department of PediatricsUniversity of Utah School of MedicineSalt Lake CityUSA
  3. 3.Department of PharmacyChildren’s and Women’s Health Centre of British ColumbiaVancouverCanada

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