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Structure and Parameterization of Pharmacokinetic Models: Their Impact on Model Predictions
Author(s) -
Woodruff Tracey J.,
Bois Frédéric Y.,
Auslander David,
Spear Robert C.
Publication year - 1992
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/j.1539-6924.1992.tb00667.x
Subject(s) - physiologically based pharmacokinetic modelling , monte carlo method , experimental data , mathematical model , pharmacokinetics , biological system , computer science , econometrics , statistical physics , mathematics , statistics , bioinformatics , physics , biology
There has been an increasing interest in physiologically based pharmacokinetic (PBPK)models in the area of risk assessment. The use of these models raises two important issues: (1)How good are PBPK models for predicting experimental kinetic data? (2)How is the variability in the model output affected by the number of parameters and the structure of the model? To examine these issues, we compared a five‐compartment PBPK model, a three‐compartment PBPK model, and nonphysiological compartmental models of benzene pharmacokinetics. Monte Carlo simulations were used to take into account the variability of the parameters. The models were fitted to three sets of experimental data and a hypothetical experiment was simulated with each model to provide a uniform basis for comparison. Two main results are presented: (1)the difference is larger between the predictions of the same model fitted to different data se1ts than between the predictions of different models fitted to the dame data; and (2)the type of data used to fit the model has a larger effect on the variability of the predictions than the type of model and the number of parameters.