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Parameter Identifiability of Fundamental Pharmacodynamic Models.
David L I Janzén1, Linnéa Bergenholm2, Mats Jirstrand3
1Biomedical 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.
Frontiers in Physiology
|December 21, 2016
Summary
This study analyzes parameter identifiability in pharmacodynamic models. Most models are identifiable if specific parameters are fixed, but data quality and initial analysis are crucial for accurate estimation.
Area of Science:
- Pharmacometrics
- Systems Biology
- Mathematical Modeling
Background:
- Parameter identifiability is crucial for reliable pharmacodynamic (PD) model analysis.
- Routinely used PD models often face challenges with parameter identifiability.
- Both fixed-effects and mixed-effects models require careful identifiability assessment.
Purpose of the Study:
- To analytically assess the structural identifiability of 16 common PD model structures.
- To demonstrate the impact of data quality on parameter estimation.
- To highlight the benefits of incorporating identifiability analysis into model development.
Main Methods:
- Analytical assessment of structural global identifiability using the input-output approach.
- Simulation studies with artificial data of varying quality to test parameter estimation.
- Case study applying an unidentifiable model to real experimental data.
Main Results:
- All 16 analyzed PD model structures were found to be structurally globally identifiable when specific parameters were fixed.
- Structural identifiability is necessary but not sufficient for successful parameter estimation; data quality is critical.
- Identifiability analysis prior to estimation improved model performance and revealed potential issues masked by standard errors.
Conclusions:
- Structural identifiability analysis is a vital step in PD model development.
- Fixing specific parameters can resolve identifiability issues in many common PD models.
- Careful consideration of identifiability and data quality is essential for robust parameter estimation in pharmacodynamics.
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