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Structure and parameterization of pharmacokinetic models: their impact on model predictions
T J Woodruff1, F Y Bois, D Auslander
1Bioengineering and School of Public Health, University of California, Berkeley 94720.
Summary
Physiologically based pharmacokinetic (PBPK) models show variability. Model predictions depend more on the data used for fitting than the model structure or parameter count.
Area of Science:
- Pharmacokinetics and Toxicological Risk Assessment
Background:
- Physiologically based pharmacokinetic (PBPK) models are increasingly used in risk assessment.
- Key issues include PBPK model prediction accuracy and variability influenced by model structure and parameters.
Purpose of the Study:
- To compare the predictive performance and variability of different PBPK models for benzene pharmacokinetics.
- To assess the impact of model structure, parameter count, and data set on model predictions.
Main Methods:
- Compared five-compartment PBPK, three-compartment PBPK, and nonphysiological models for benzene.
- Utilized Monte Carlo simulations to account for parameter variability.
- Fitted models to three experimental data sets and simulated a hypothetical experiment for comparison.
Main Results:
- Model prediction differences were larger between models fitted to different data sets than between different models fitted to the same data.
- Data type used for model fitting had a greater impact on prediction variability than model type or parameter count.
Conclusions:
- The choice and quality of experimental data significantly influence PBPK model predictions in risk assessment.
- Model structure and parameter number have a lesser impact on prediction variability compared to data fitting.