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Local identifiability for two and three-compartment pharmacokinetic models with time-lags
J A Merino1, J De Biasi, Y Plusquellec
1Equipe de cinétique des Xénobiotiques and Laboratoire de Pharmacocinétique et toxicologie clinique: CHU de Rangueil, Toulouse, France.
Medical Engineering & Physics
|September 5, 1998
Summary
This study explores pharmacokinetic models with time-delays, finding that while delays can be modeled, separate identification of parameters and delays is unsuitable. Identifiability of delayed models is also discussed.
Area of Science:
- Pharmacokinetics
- Mathematical Modeling
- Systems Biology
Background:
- Pharmacokinetic models are essential for understanding drug disposition.
- Time-delays between compartments can significantly impact model behavior.
- Assessing model identifiability is crucial for reliable parameter estimation.
Purpose of the Study:
- To investigate the identifiability of pharmacokinetic models incorporating time-delays.
- To determine the suitability of separate parameter and time-lag identification.
- To compare identifiability of delayed versus non-delayed models.
Main Methods:
- Analysis of 2 and 3 compartment pharmacokinetic models.
- Utilizing Laplace transformation for identifiability studies.
- Employing Jacobian matrices to assess parameter and time-lag identifiability.
- Investigating identifiability from amount (Q) or concentration (C) measures.
Main Results:
- Separate identification of model parameters and time-lags is not suitable.
- Time-lagged models can be locally identifiable when non-delayed models are not.
- For two-compartment delayed models with one observation, multiple inputs are not required.
- Identifiability of parameters and lags depends on the specific two-compartment model and measurement type (Q or C).
Conclusions:
- Time-delays in pharmacokinetic models can be accounted for, but require specific identifiability analysis.
- Delayed models offer unique identifiability properties compared to their non-delayed counterparts.
- The study provides insights into parameter and lag identifiability in various two-compartment models.