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A Quality Assurance Project Plan to Evaluate Physiologically-Based Pharmacokinetic (PBPK) Models for Use in Risk Assessment-poster
Citation:
Schlosser, P., D. Kapraun, C. Itkin, Yu-Sheng Lin, A. Sasso, AND V. Morozov. A Quality Assurance Project Plan to Evaluate Physiologically-Based Pharmacokinetic (PBPK) Models for Use in Risk Assessment-poster. SRA Annual Meeting, New Orleans, LA, December 02 - 06, 2018.
Impact/Purpose:
To present and describe the process for QA evaluation of PBPK models prior to use in NCEA products. To also provide comprehensive an understanding of the data landscape for the ADME model.
Description:
The U.S. Environmental Protection Agency (EPA) requires development of a Quality Assurance (QA) Project Plan (QAPP) to document the type and quality of data and model information used for making environmental decisions. This applies to PBPK models, which are mathematical descriptions of the disposition of chemicals in the bodies of living organisms. PBPK models quantitatively represent sets of hypotheses regarding the major determinants of absorption, distribution, metabolism, and excretion (ADME). A key advantage of these models is that they can be used for dose extrapolation across species, route-to-route (e.g. inhalation to oral), and among exposure scenarios, which facilitates human health risk estimation and the setting of regulatory exposure levels. Classical pharmacokinetic (PK) models which are more empirical can also be used. A QAPP has been developed that covers the basic data collection and modeling methodologies for PBPK and PK models. To adequately evaluate the quality of a PBPK model, a comprehensive understanding of a chemical’s ADME processes (to the extent possible) is needed, hence the first step is a QA review of existing PK data. After this PK data review, a set of key data files becomes available for model evaluation (including but not limited to any data sets used in model development). A model should reasonably match these data using a single set of parameters or ones that vary in a predictable way. For example, while animal respiration rate can vary depending on the experimental exposure system, variation in that parameter should be consistent with the system used in each experiment. Two other factors required for confidence in a PBPK model are: 1) proper representation of the underlying biology given the model’s assumptions (i.e., the model equations are correct) and 2) accurate transcription and appropriate application of model parameters taken from the scientific literature. Components of this QAPP are illustrated with specific examples.