ToxSci Advance Access originally published online on May 4, 2007
Toxicological Sciences 2007 99(2):395-402; doi:10.1093/toxsci/kfm100
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Characterizing Uncertainty and Variability in Physiologically Based Pharmacokinetic Models: State of the Science and Needs for Research and Implementation
* US EPA, ORD, National Center for Computational Toxicology, RTP, North Carolina 27711
US EPA, ORD, National Center for Environmental Assessment, Washington, DC 20460
US EPA, ORD, National Center for Computational Toxicology, RTP, North Carolina 27711
The Hamner Institutes For Health Sciences, Research Triangle Park, North Carolina 27709
¶ Miami University, Oxford, Ohio 45056
|| Institut National de L'Environnement Industriel et des Risques, Unité de Toxicologie Expérimentale, 60550 Vernuil-En-Halatte, France
||| US EPA, ORD, National Center for Environmental Assessment, RTP, North Carolina 27711
|||| Summit Toxicology, Lyons, Colorado 80540
# Karolinska Institute, Stockholm, Sweden
** EC/R Incorporated, 6330 Quadrangle Drive, Suite 325, Chapel Hill, North Carolina 27517
Health and Safety Laboratory, Buxton S17 9JN, UK
a US EPA, ORD, National Health and Environmental Effects Laboratory, RTP, North Carolina 27711
b National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709
c Health and Safety Laboratory, Buxton S17 9JN, UK
d The Hamner Institutes For Health Sciences, Research Triangle Park, North Carolina 27709
1 To whom correspondence should be addressed at ORD National Center for Computational Toxicology, U.S. EPA B205-01, 109 TW Alexander Drive, Research Triangle Park, NC 27711. Fax: (919) 541-1194. E-mail: barton.hugh{at}epa.gov.
Received March 1, 2007; accepted April 24, 2007
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Abstract |
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Physiologically based pharmacokinetic (PBPK) models are used in mode-of-action based risk and safety assessments to estimate internal dosimetry in animals and humans. When used in risk assessment, these models can provide a basis for extrapolating between species, doses, and exposure routes or for justifying nondefault values for uncertainty factors. Characterization of uncertainty and variability is increasingly recognized as important for risk assessment; this represents a continuing challenge for both PBPK modelers and users. Current practices show significant progress in specifying deterministic biological models and nondeterministic (often statistical) models, estimating parameters using diverse data sets from multiple sources, using them to make predictions, and characterizing uncertainty and variability of model parameters and predictions. The International Workshop on Uncertainty and Variability in PBPK Models, held 31 Oct–2 Nov 2006, identified the state-of-the-science, needed changes in practice and implementation, and research priorities. For the short term, these include (1) multidisciplinary teams to integrate deterministic and nondeterministic/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 improved documentation of model structure(s), parameter values, sensitivity and other analyses, and supporting, discrepant, or excluded data. Longer-term needs include (1) theoretical and practical methodological improvements for nondeterministic/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 reference materials, such as cases studies, bibliographies/glossaries, model repositories, and enhanced software. The multidisciplinary dialogue initiated by this Workshop will foster the collaboration, research, data collection, and training necessary to make characterizing uncertainty and variability a standard practice in PBPK modeling and risk assessment.
Key Words: physiologically based pharmacokinetic modeling; uncertainty; population variability; nonlinear modeling; risk assessment; Bayesian models.
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