Accelerating QA review of PBPK models: A template approach to model implementation
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Physiologically based pharmacokinetic (PBPK) models describe the disposition of a chemical throughout an animal’s body following exposure by accounting for absorption, distribution, metabolism, and elimination of the chemical. Parameters used in these models are based on the anatomy and physiology of the animal as well as biochemical interactions and processes. Chemical risk assessors use PBPK models for dosimetric calculations in support of risk assessment, but the models should first undergo quality assurance (QA) review before use to ensure biological plausibility and correct implementation. We developed a PBPK model template that allows for faster and more efficient QA review. The model template consists of a single model “superstructure” with equations and logic commonly found in PBPK models. Model template users can implement a wide variety of chemical-specific PBPK models by selecting and omitting specific features. QA review of models implemented using the template can be completed more quickly than reviews of conventionally-implemented (“stand-alone”) models because the general model equations have already been evaluated and verified and only the parameters describing chemical-specific model and exposure scenarios need to be reviewed. The model template includes features to model a variable number of tissue compartments, background rates of exposure, multiple excretion pathways, and oral, intravenous, or inhalation exposure routes. It also offers multiple ways to represent amounts and concentrations of chemical in blood and gas exchange regions. Using the model template, we have implemented and precisely replicated results from published PBPK models for five per- and polyfluoroalkyl substances and three volatile organic compounds. Thus, the model template approach can be applied to broad classes of chemical-specific PBPK models and continues to bolster efficiency of QA processes that should be conducted prior to using models for risk assessment applications.