Methods and software tools for design evaluation in population pharmacokinetics–pharmacodynamics studies. (January 2015)

• Record Type: Journal Article
• Authors: Nyberg, Joakim; Bazzoli, Caroline; Ogungbenro, Kay; Aliev, Alexander; Leonov, Sergei; Duffull, Stephen; Hooker, Andrew C.; Mentré, France
• Is Part Of: British journal of clinical pharmacology. Volume 79:Number 1(2015:Jan.)
• Page Start: 6
• Page End: 17
• Publication Date: 2015-01
• Subjects: Pharmacology -- Periodicals; Drugs -- Periodicals; 615.1
• DOI: 10.1111/bcp.12352
• Languages: English
• ISSNs: 0306-5251

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Population pharmacokinetic (PK)–pharmacodynamic (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‐effects 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 the following five software tools: PFIM, PkStaMp, PopDes, PopED and POPT. The comparisons were performed using two models, a simple‐one compartment warfarin PK model and a more complex PKPD model for pegylated interferon, with data on both concentration and response of viral load of hepatitis C virus. The results of the software were compared in terms of the standard error (SE) values of the parameters predicted from the software and the empirical SE values obtained via replicated clinical trial simulation and estimation. For the warfarin PK model and the pegylated interferon PKPD model, all software gave similar results. Interestingly, 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.</p> </abstract>