Literature DB >> 28612141

Deterministic identifiability of population pharmacokinetic and pharmacokinetic-pharmacodynamic models.

Vijay K Siripuram1, Daniel F B Wright2, Murray L Barclay3, Stephen B Duffull2.   

Abstract

Identifiability is an important component of pharmacokinetic-pharmacodynamic (PKPD) model development. Structural identifiability is concerned with the uniqueness of the model parameters for a set of perfect input-output data and deterministic identifiability with the precision of parameter estimation given imperfect input-output data. We introduce two subcategories of deterministic identifiability, external and internal, and consider factors that distinguish between these forms. We define external deterministic identifiability as a function of externally controllable variables, i.e., the design, and internal deterministic identifiability as a function of the model and its parameter values. The concepts are explored using three common PK and PKPD models, and verified for their precision for the selected set of parameter values under optimal design.

Keywords:  Identifiability; NONMEM; Optimal study design; PKPD models; Parameter precision; Population analysis

Mesh:

Year:  2017        PMID: 28612141     DOI: 10.1007/s10928-017-9530-4

Source DB:  PubMed          Journal:  J Pharmacokinet Pharmacodyn        ISSN: 1567-567X            Impact factor:   2.745


  21 in total

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Authors:  N D Evans; K R Godfrey; M J Chapman; M J Chappell; L Aarons; S B Duffull
Journal:  J Pharmacokinet Pharmacodyn       Date:  2001-02       Impact factor: 2.745

2.  Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood.

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Journal:  Bioinformatics       Date:  2009-06-08       Impact factor: 6.937

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Journal:  JPEN J Parenter Enteral Nutr       Date:  1991 May-Jun       Impact factor: 4.016

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Authors:  James W T Yates; R D Owen Jones; Mike Walker; S Y Amy Cheung
Journal:  Expert Opin Drug Metab Toxicol       Date:  2009-03       Impact factor: 4.481

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Journal:  Math Biosci       Date:  1990-11       Impact factor: 2.144

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Journal:  Br J Clin Pharmacol       Date:  1998-03       Impact factor: 4.335

7.  The design and analysis of parallel experiments to produce structurally identifiable models.

Authors:  S Y Amy Cheung; James W T Yates; Leon Aarons
Journal:  J Pharmacokinet Pharmacodyn       Date:  2013-01-09       Impact factor: 2.745

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Authors:  J A Jacquez
Journal:  Fed Proc       Date:  1987-06

9.  Some considerations on the design of population pharmacokinetic studies.

Authors:  Stephen Duffull; Tim Waterhouse; John Eccleston
Journal:  J Pharmacokinet Pharmacodyn       Date:  2005-08       Impact factor: 2.745

10.  What do we mean by identifiability in mixed effects models?

Authors:  Marc Lavielle; Leon Aarons
Journal:  J Pharmacokinet Pharmacodyn       Date:  2015-12-10       Impact factor: 2.745

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Journal:  J Pharmacokinet Pharmacodyn       Date:  2018-02-12       Impact factor: 2.745

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Authors:  Neil D Evans; S Y Amy Cheung; James W T Yates
Journal:  J Pharmacokinet Pharmacodyn       Date:  2018-02-02       Impact factor: 2.745

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4.  Model-Informed Optimization of a Pediatric Clinical Pharmacokinetic Trial of a New Spironolactone Liquid Formulation.

Authors:  Manasa Tatipalli; Vijay Kumar Siripuram; Tao Long; Diana Shuster; Galina Bernstein; Pierre Martineau; Kim A Cook; Rodrigo Cristofoletti; Stephan Schmidt; Valvanera Vozmediano
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5.  Automated Scale Reduction of Nonlinear QSP Models With an Illustrative Application to a Bone Biology System.

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