Dataset for: A Petri Net Approach to Physiologically Based Toxicokinetic (PBTK) Modeling

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.基于生理学的毒代动力学（Physiologically based toxicokinetic, PBTK）建模技术可让研究人员无需针对每种暴露场景开展体内实验，就能预测不同物种经不同暴露途径、以不同浓度接触外源性化合物时的体内组织浓度。该类模型的参数可通过体内、体外实验测定、跨物种或跨化学物外推、文献调研或其他模型获取。基于常微分方程（ordinary differential equations, ODE）构建的PBTK模型，可借助上述参数针对特定化合物、暴露场景与物种组合开展模拟。尽管该技术在监管毒理学领域具备应用潜力，但ODE编程与模拟的复杂性仍阻碍了诸多潜在研究者的使用。佩特里网（Petri Nets, PN）作为一种图形化建模框架，为PBTK建模提供了更为直观的实现路径。为验证该方法的实用性与易用性，我们基于图形化PN开发与模拟软件包Snoopy，构建并模拟了虹鳟（Oncorhynus mykiss）经水体接触荧蒽的PBTK模型。我们将已有的ODE-PBTK模型转换为PN模型，并以原ODE模型的模拟结果作为基准对PN模型进行了验证。PN模型模拟得到的组织浓度与ODE模型的模拟结果高度吻合。为实现ODE模型到PN模型的转换，我们新增了‘血液体积（VBLOOD）’这一参数。敏感性分析结果显示，VBLOOD在一个数量级范围内变动时，模型结果仍具备极强的鲁棒性。最终得到的PN-PBTK模型在保留与ODE模型相当的预测性能的同时，具备多项ODE模型所不具备的优势。