Incorporating Monte Carlo simulation into physiologically based pharmacokinetic models using advanced continuous simulation language (ACSL): a computational method.
Biologically based models with physiological parameters are becoming more popular as a tool to estimate target tissue doses from chemical exposures. However, the majority of current physiologically based pharmacokinetic (PBPK) models do not take into account the uncertainty and/or variability within the various model parameters. Consideration of uncertainty is important to evaluate the predictive ability and complexity of a model as well as identification of parameters which contribute disproportionately to variability in model output. In order to estimate the uncertainty in PBPK model output, a versatile and simple computational method is presented which can be readily incorporated into the majority of PBPK models without extensive additions to model computer code. In this paper, a separate computer program for Monte Carlo simulation is furnished that randomly samples values for model parameters and writes them into a run-time language (command file) format which can then be utilized to execute individual PBPK models. Modifications to the PBPK model allow the desired output to be written to a data file for statistical analysis. The method presented in this paper is applied to a simple PBPK model for benzene disposition.
Thomas
R S
RS
Center for Environmental Toxicology and Technology, Department of Environmental Health, Colorado State University, Fort Collins 80523-1680, USA.
Lytle
W E
WE
Keefe
T J
TJ
Constan
A A
AA
Yang
R S
RS
D000465:Algorithms; D001554:Benzene / Q000493:pharmacokinetics / Q000633:toxicity; D002273:Carcinogens / Q000493:pharmacokinetics / Q000633:toxicity; D003198:Computer Simulation; D008954:Models, Biological; D009010:Monte Carlo Method; D010599:Pharmacokinetics; D011237:Predictive Value of Tests; D011381:Programming Languages
D002273:Carcinogens; D001554:Benzene