Dataset for: A Petri Net Approach to Physiologically Based Toxicokinetic (PBTK) Modeling
Description
Physiologically based toxicokinetic (PBTK) modeling enables researchers to predict internal tissue concentrations for various species exposed to exogenous compounds through different routes at varying concentrations without having to run in vivo experiments for each scenario. Parameters for the models may be gathered from in vivo or in vitro measurements, cross species or cross chemical extrapolations, literature reviews, or other models. PBTK models, described using ordinary differential equations (ODEs), are then simulated using these parameters for a given compound / exposure / species scenario. Though potentially useful for regulatory toxicology, the complexity of ODE programming and simulation remains a barrier for many would be researchers. Petri Nets (PN), a graphical modeling framework, offers a more intuitive approach to PBTK modeling. To demonstrate the utility and ease of use, we present a model of waterborne fluoranthene exposure to rainbow trout (Oncorhynus mykiss) written and simulated in Snoopy, a graphical PN development and simulation software package. We converted an existing ODE PBTK model and evaluated the PN model against the ODE model results. The simulated tissue concentrations of the PN model closely mirrored the simulated concentrations of the ODE model. In order to convert the ODE model to a PN model, we introduced a new parameter ‘Blood Volume (VBLOOD)'. Sensitivity analysis found VBLOOD to be very robust when varied over an order of magnitude. The resulting PN PBTK model has a number of advantages over ODE models, while maintaining equivalent predictive functionality.
Publication Date
1-1-2022
Publisher
figshare Academic Research System
DOI
10.6084/m9.figshare.c.5801579.v1
Document Type
Data Set
Recommended Citation
Edhlund, Ian; Lee, Cindy (2022), "Dataset for: A Petri Net Approach to Physiologically Based Toxicokinetic (PBTK) Modeling", figshare Academic Research System, doi: 10.6084/m9.figshare.c.5801579.v1
https://doi.org/10.6084/m9.figshare.c.5801579.v1
Identifier
10.6084/m9.figshare.c.5801579.v1
Embargo Date
1-1-2022