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Methods and software tools for design evaluation for population pharmacokinetics-pharmacodynamics studies
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Pharmacometrics)
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2015 (English)In: British Journal of Clinical Pharmacology, ISSN 0306-5251, E-ISSN 1365-2125, Vol. 79, no 1, 6-17 p.Article in journal (Refereed) Published
Abstract [en]

Population Pharmacokinetic (PK)-Pharmacodynamic (PD) (PKPD) models are increasingly used in drug development and in academic research. Hence designing efficient studies is an important task. Following the first theoretical work on optimal design for nonlinear mixed effect models, this research theme has grown rapidly. There are now several different software tools that implement an evaluation of the Fisher information matrix for population PKPD. We compared and evaluated five software tools: PFIM, PkStaMP, PopDes, PopED, and POPT. The comparisons were performed using two models: i) a simple one compartment warfarin PK model; ii) a more complex PKPD model for Pegylated-interferon (peg-interferon) with both concentration and response of viral load of hepatitis C virus (HCV) data. The results of the software were compared in terms of the standard error values of the parameters (SE) predicted from the software and the empirical SE values obtained via replicated clinical trial simulation and estimation. For the warfarin PK model and the peg-interferon PKPD model all software gave similar results. Of interest it was seen, for all software, that the simpler approximation to the Fisher information matrix, using the block diagonal matrix, provided predicted SE values that were closer to the empirical SE values than when the more complicated approximation was used (the full matrix). For most PKPD models, using any of the available software tools will provide meaningful results, avoiding cumbersome simulation and allowing design optimization.

Place, publisher, year, edition, pages
2015. Vol. 79, no 1, 6-17 p.
National Category
Medical and Health Sciences Engineering and Technology Probability Theory and Statistics
URN: urn:nbn:se:uu:diva-224033DOI: 10.1111/bcp.12352ISI: 000346659100003PubMedID: 24548174OAI: oai:DiVA.org:uu-224033DiVA: diva2:714946
Available from: 2014-04-30 Created: 2014-04-30 Last updated: 2015-03-24Bibliographically approved

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Nyberg, JoakimHooker, Andrew C.
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