title: Parameter Identifiability of Fundamental Pharmacodynamic Models.
abstract: Issues of parameter identifiability of routinely used pharmacodynamics models are considered in this paper. The structural identifiability of 16 commonly applied pharmacodynamic model structures was analyzed analytically, using the input-output approach. Both fixed-effects versions (non-population, no between-subject variability) and mixed-effects versions (population, including between-subject variability) of each model structure were analyzed. All models were found to be structurally globally identifiable under conditions of fixing either one of two particular parameters. Furthermore, an example was constructed to illustrate the importance of sufficient data quality and show that structural identifiability is a prerequisite, but not a guarantee, for successful parameter estimation and practical parameter identifiability. This analysis was performed by generating artificial data of varying quality to a structurally identifiable model with known true parameter values, followed by re-estimation of the parameter values. In addition, to show the benefit of including structural identifiability as part of model development, a case study was performed applying an unidentifiable model to real experimental data. This case study shows how performing such an analysis prior to parameter estimation can improve the parameter estimation process and model performance. Finally, an unidentifiable model was fitted to simulated data using multiple initial parameter values, resulting in highly different estimated uncertainties. This example shows that although the standard errors of the parameter estimates often indicate a structural identifiability issue, reasonably "good" standard errors may sometimes mask unidentifiability issues.
authors.affiliation: Biomedical and Biological Systems Laboratory, School of Engineering, University of WarwickCoventry, UK; Drug Metabolism and Pharmacokinetics, Cardiovascular and Metabolic Diseases, iMED, AstraZenecaGothenburg, Sweden; Fraunhofer-Chalmers Centre, Chalmers Science ParkGothenburg, Sweden.
authors.forename: David L I
authors.initials: DL
authors.lastname: Janzén
authors.affiliation: Biomedical and Biological Systems Laboratory, School of Engineering, University of WarwickCoventry, UK; Drug Metabolism and Pharmacokinetics, Cardiovascular and Metabolic Diseases, iMED, AstraZenecaGothenburg, Sweden.
authors.forename: Linnéa
authors.initials: L
authors.lastname: Bergenholm
authors.affiliation: Fraunhofer-Chalmers Centre, Chalmers Science Park Gothenburg, Sweden.
authors.forename: Mats
authors.initials: M
authors.lastname: Jirstrand
authors.affiliation: Early Clinical Development, Quantitative Clinical Pharmacology, iMED, AstraZeneca Gothenburg, Sweden.
authors.forename: Joanna
authors.initials: J
authors.lastname: Parkinson
authors.affiliation: Oncology, iMED, AstraZeneca Cambridge, UK.
authors.forename: James
authors.initials: J
authors.lastname: Yates
authors.affiliation: Biomedical and Biological Systems Laboratory, School of Engineering, University of Warwick Coventry, UK.
authors.forename: Neil D
authors.initials: ND
authors.lastname: Evans
authors.affiliation: Biomedical and Biological Systems Laboratory, School of Engineering, University of Warwick Coventry, UK.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Chappell
keywords: fixed effects models; mixed effects models; pharmacodynamic models; practical parameter identifiability; structural identifiability