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Characterizing Uncertainty and Variability in PBPK Models: State of the Science and Needs for Research and Implementation
Citation:
BARTON, H. A., W. CHIU, R. W. SETZER, M. E. Andersen, A. J. Bailer, F. Y. Bois, R. S. DeWoskin, S. Hays, G. Johanson, N. Jones, G. Loizou, C. Portier, M. Spendiff, AND Y. Tan. Characterizing Uncertainty and Variability in PBPK Models: State of the Science and Needs for Research and Implementation. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 99(2):395-402, (2007).
Impact/Purpose:
The International Workshop on Uncertainty and Variability in PBPK Models, held Oct 31-Nov 2, 2006, sought to identify the state-of-the-science in this area and recommend priorities for research and changes in practice and implementation. For the short term, these include: (1) multidisciplinary teams to integrate deterministic and non-deterministic/statistical models; (2) broader use of sensitivity analyses, including for structural and global (rather than local) parameter changes; and (3) enhanced transparency and reproducibility through more complete documentation of the model structure(s) and parameter values, the results of sensitivity and other analyses, and supporting, discrepant, or excluded data. Longer-term needs include: (1) theoretic and practical methodological improvements for non-deterministic/statistical modeling; (2) better methods for evaluating alternative model structures; (3) peer-reviewed databases of parameters and covariates, and their distributions; (4) expanded coverage of PBPK models across chemicals with different properties; and (5) training and references materials, such as cases studies, tutorials, bibliographies/glossaries, model repositories, and enhanced software.
Description:
Mode-of-action based risk and safety assessments can rely upon tissue dosimetry estimates in animals and humans obtained from physiologically-based pharmacokinetic (PBPK) modeling. However, risk assessment also increasingly requires characterization of uncertainty and variability; such characterization for PBPK model predictions represents a continuing challenge to both modelers and users. Current practices show significant progress in specifying deterministic biological models and the non-deterministic (often statistical) models, estimating their parameters using diverse data sets from multiple sources, and using them to make predictions and characterize uncertainty and variability.
