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Evaluation of physiologically based pharmacokinetic models for use in risk assessment
Author(s) -
Chiu Weihsueh A.,
Barton Hugh A.,
DeWoskin Robert S.,
Schlosser Paul,
Thompson Chad M.,
Sonawane Babasaheb,
Lipscomb John C.,
Krishnan Kannan
Publication year - 2007
Publication title -
journal of applied toxicology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.784
H-Index - 87
eISSN - 1099-1263
pISSN - 0260-437X
DOI - 10.1002/jat.1225
Subject(s) - physiologically based pharmacokinetic modelling , computer science , flexibility (engineering) , risk assessment , mathematical model , representation (politics) , model selection , risk analysis (engineering) , relevance (law) , model validation , biochemical engineering , machine learning , data science , engineering , bioinformatics , statistics , biology , medicine , mathematics , pharmacokinetics , computer security , politics , political science , law
Abstract Physiologically based pharmacokinetic (PBPK) models are sophisticated dosimetry models that offer great flexibility in modeling exposure scenarios for which there are limited data. This is particularly of relevance to assessing human exposure to environmental toxicants, which often requires a number of extrapolations across species, route, or dose levels. The continued development of PBPK models ensures that regulatory agencies will increasingly experience the need to evaluate available models for their application in risk assessment. To date, there are few published criteria or well‐defined standards for evaluating these models. Herein, important considerations for evaluating such models are described. The evaluation of PBPK models intended for risk assessment applications should include a consideration of: model purpose, model structure, mathematical representation, parameter estimation, computer implementation, predictive capacity and statistical analyses. Model purpose and structure require qualitative checks on the biological plausibility of a model. Mathematical representation, parameter estimation, computer implementation involve an assessment of the coding of the model, as well as the selection and justification of the physical, physicochemical and biochemical parameters chosen to represent a biological organism. Finally, the predictive capacity and sensitivity, variability and uncertainty of the model are analysed so that the applicability of a model for risk assessment can be determined. Published in 2007 by John Wiley & Sons, Ltd.