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Structural identifiability of PBPK models: practical consequences for modeling strategies and study designs
W Slob1, P H Janssen, J M van den Hof
1National Institute of Public Health and Environmental Protection (RIVM), Bilthoven, The Netherlands.
Critical Reviews in Toxicology
|May 1, 1997
Summary
Physiologically based pharmacokinetic (PBPK) models require parameter estimation. This study introduces structural identifiability to ensure meaningful parameter calibration from experimental data.
Area of Science:
- Pharmacokinetics
- Systems Biology
- Mathematical Modeling
Background:
- Physiologically based pharmacokinetic (PBPK) models often contain unknown parameters requiring estimation via calibration with in vivo experimental data.
- The number of parameters estimable through calibration is inherently limited, even with perfect data, depending on the specific model and experimental context.
Purpose of the Study:
- To introduce the concept of structural identifiability as a prerequisite for meaningful parameter estimation in PBPK models.
- To discuss systems analysis techniques for assessing model identifiability.
- To provide practical implications for PBPK modeling strategies and experimental design.
Main Methods:
- Introduction of structural identifiability as a key concept.
- Discussion of systems analysis techniques for identifiability assessment.
- Analysis of two uniqueness conditions: model equations/parameters and number of temporal observations.
- Derivation of general results for a specific class of PBPK models regarding the first uniqueness condition.
- Method for assessing the second uniqueness condition and determining the minimum required observations.
Main Results:
- Structural identifiability is essential for reliable parameter estimation in PBPK models.
- Identifiability analysis involves two uniqueness conditions: one mathematical and one data-driven.
- General results simplify the assessment of the first uniqueness condition for certain PBPK models.
- The second uniqueness condition is readily assessed, allowing determination of necessary data points.
Conclusions:
- Ensuring structural identifiability is crucial before attempting parameter calibration in PBPK models.
- Understanding identifiability guides the development of robust PBPK models and efficient experimental protocols.
- This work provides a framework for assessing and ensuring the meaningfulness of parameter estimation in PBPK modeling.