The Bayesian population approach to physiological toxicokinetic-toxicodynamic models--an example using the MCSim software.
The calibration of physiologically based toxicokinetic models against experimental data encompasses the merging of prior knowledge with information present in the data. This prior knowledge is manifested in the scientific literature and associated with various degrees of uncertainty. The most convenient way to combine these sources of information is via the use of Bayesian statistical methods. Furthermore, toxicokinetic models are subject to both inter- and intra-individual variability. This variability may be handled statistically by the use of a population model. The MCSim software, which is available for free download on the Internet, permits the use of a population model in combination with a Bayesian statistical approach. An example of the use of MCSim in a recent model-based risk assessment of dichloromethane (DCM) is given and discussed.
Jonsson
Fredrik
F
Division of Pharmacokinetics and Drug Therapy, Department of Pharmaceutical Biosciences, Faculty of Pharmacy, Uppsala University, Box 591, SE-751 24 Uppsala, Sweden.
Johanson
Gunnar
G
D000818:Animals; D001499:Bayes Theorem; D004305:Dose-Response Relationship, Drug; D006801:Humans; D019570:Inhalation Exposure; D008752:Methylene Chloride / Q000493:pharmacokinetics / Q000633:toxicity; D008954:Models, Biological; D009010:Monte Carlo Method; D010599:Pharmacokinetics; D018570:Risk Assessment / Q000379:methods; D012984:Software / Q000706:statistics & numerical data*; D014116:Toxicology / Q000706:statistics & numerical data*
D008752:Methylene Chloride