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Computational Toxicology Research Program

International Workshop on Uncertainty and Variability in Physiologically Based Pharmacokinetic (PBPK) Models

Research Triangle Park, North Carolina, U.S.
October 31 - November 2, 2006
Location: Auditorium (C111), US. Environmental Protection Agency

View the UVPKM Agenda and Presentations

Sponsors

  • U.S. Environmental Protection Agency (EPA), Office of Research and Development
  • National Center for Environmental Assessment (NCEA)
  • Computational Toxicology Research Program
  • National Health Effects and Environmental Research Laboratory (NHEERL)
  • National Institute of Environmental Health Sciences (NIEHS)

Additional Support

  • CIIT Centers for Health Research (CIIT)
  • L'Institut National de l'environnement industriel et des risques (INERIS)
  • Miami University
  • Summit Toxicology
  • U.K. Health and Safety Executive, Health and Safety Laboratory (HSL)

Organizing Committee

  • Weihsueh Chiu (chair), EPA/NCEA
  • Woodrow Setzer (co-chair), EPA/NCCT
  • John Bailer, Miami University
  • Hugh Barton, EPA/NCCT
  • Frédéric Bois, INERIS
  • Harvey Clewell, CIIT
  • Hisham El-Masri, EPA/NHEERL
  • George Loizou, HSL
  • Christopher Portier, NIEHS
  • Martin Spendiff, HSL
  • Sean Hays, Summit Toxicology

Summary

Risk assessments for chemicals increasingly utilize pharmacokinetic models, particularly physiologically based pharmacokinetic (PBPK) models. Characterizing the uncertainty in PBPK model parameterization and estimation of dose metrics for use in risk assessment is essential. In addition, human variability is an important factor to be characterized to appropriately describe human populations. The purpose of this workshop is to review and promote the appropriate application of methods for statistical characterization of PBPK models and the characterization of uncertainty and human variability through a cross-disciplinary exchange of experience and ideas among laboratory scientists, PBPK modelers, statisticians, and others with expertise in applied mathematics, biology, and pharmacokinetic analyses.

Introduction

As physiologically based pharmacokinetic (PBPK) models have become increasingly available for environmental chemicals and pharmaceuticals, there is an ongoing shift from the early model development efforts focused on demonstrating their feasibility and utility, to a recognition of the need for more formal and rigorous characterization of model inputs and outputs critical in their applications in risk or safety assessments. Areas for better characterization include:

  • Estimation of parameter values from in vivo and/or from in vitro data
  • Evaluation of alternative model structures
  • Assessment of uncertainty in model outputs
  • Characterization of human variability and its uncertainty

Recent efforts have used of a number of quantitative and statistical tools to better characterize the uncertainty and variability in pharmacokinetic analyses, including:

  • Sensitivity analyses
  • Monte Carlo simulation
  • Non-linear mixed effects modeling
  • Markov chain Monte Carlo (for Bayesian analyses)

However, to date, there has been no broad review or discussion of the many methodological and implementation issues uniquely posed by the application of statistical methods to PBPK model-based pharmacokinetic analyses.

Purpose and Goals

The purpose of this international workshop is to promote the appropriate application of methods for statistical characterization of PBPK models and the characterization of uncertainty and human variability through a cross-disciplinary exchange of experience and ideas among laboratory scientists, PBPK modelers, statisticians, and others with expertise in applied mathematics, biology, and pharmacokinetic analyses. To achieve its goal, this workshop will:

  1. Review and assess the status of existing methodologies for characterizing uncertainty and variability in PBPK models.
  2. Identify applications for which appropriate methodologies can be readily implemented given currently available data, information, and resources.
  3. Propose ways to improve/expand the implementation of these methods (including software and training).
  4. Identify key research priorities and/or data needs
  5. Make available the findings of the workshop through workshop proceeding and a series of publications in the peer-reviewed literature.

Premeeting Draft White Papers

The following premeeting draft white papers are available:

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