High-to-Low Dose Extrapolation: Issues and Approaches by WU70cbQ

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									High-to-Low Dose Extrapolation:
    Issues and Approaches

  BOSC Workshop on EPA Chemical Risk Assessment
                  Principles and Practices
                      February 2, 2005
                   Weihsueh Chiu, Ph.D.
 National Center for Environmental Assessment, U.S. EPA
             Summary
• EPA uses a variety of approaches for
  low-dose extrapolation.
• A number of important issues need to
  be considered in the choice and
  implementation of these approaches.
• EPA is actively working to
   Advance the science and methods for low-
    dose extrapolation.
   Make use of the best science that is
    available for its current risk assessments.


                                                  2
             EPA often needs to estimate risks at
              exposures below the range of data

              Exposures/Doses of
               Regulatory Interest




                                                                                                    “High” Dose
“Low” Dose




                            Environmental Epidemiology
                            Exposure Biomarker Studies


                                                Occupational Epidemiology
                                              Human Pharmacokinetic Studies


                                                                          Animal Bioassays
                                                                   Animal Pharmacokinetic Studies


                                                                 In Vitro studies
                                     ??                       Emerging Data (-omics)




                                                                                                         3
  Low-dose extrapolation has both
biological and statistical components
• Population dose-response (thick blue lines)
  combines individual/biological dose-response (thin
  lines) and inter-individual variability (different thin
  lines).


                     Same Population Dose Response from
                 Different Underlying Individual Dose-Response




                                                                 4
   Approaches to extrapolation
  below the range of observation
• Model-independent
   Linear from Point-of-Departure (POD)
   RfD/RfC from POD using uncertainty factors (UFs)
• Model-dependent
   Empirical models (e.g., multistage model)
   Biologically-based models
• Combination of approaches
   Linear from POD for target dose metric,
    pharmacokinetic model for exposure dose metric
   RfD/RfC from POD with data-/ model-based UFs

                                                       5
       Characteristics of model-
       independent approaches
• Separation of “observed range” and “extrapolation
  range”
   Explicit avoidance of quantifying relationships below
    range of observation
• Choice of “linear” and “non-linear” depends on
  knowledge of mode-of-action
   Linear extrapolation from POD generally interpreted as
    a “plausible upper bound” on potency
   RfD/RfC extrapolation from POD defined as “likely to be
    without an appreciable risk”
• Need for consistency in procedures and results

                                                              6
     Where “observation” ends and
        “extrapolation” begins
• Delineation of Point-of-Departure
   Evolution from LOAEL/NOAEL to modeling based on
    observed dose-response (Benchmark Dose)
   Benchmark Dose Software (BMDS) facilitates
    consistency and reproducibility
• Current internal efforts aimed at improving
  consistency in choice of benchmark response
• BMDS currently being expanded to include time-
  to-tumor modeling

                                                      7
Model-independent approach depends on
     knowledge of mode-of-action

“Linear”                   “Non-linear”
  Response                  Response




                    Dose                      Dose

     LED10                      LED10
  CSF = 0.1/LED10          RfD = LED10/(UFAH x UFH x …)


                                                          8
   Linear extrapolation consistent
     with previous LMS method
• Analysis by Subramaniam et al
  2005 of LMS and linear                                            Histogram of Ratio of Slope of Linear
                                                                    Extrapolation from LED10 (s*) and q1*
  extrapolation procedures
                                                                                    (s*:q1*)
• Of 102 data sets from IRIS                                   80

  database                                                     70




                                     Number of IRIS Datasets
    84% had differences of < 10%                              60

     in potency estimates from the                             50
     two approaches                                            40
    95% had < 2-fold differences.                             30

• Linear extrapolation based on                                20

  upper bound (LED) and MLE                                    10

  (ED) were also compared –                                    0
  75% of datasets had < 2-fold                                        1.0   1.5   2.0    2.5       3.0   3.5 7.0   8.0

                                                                                        s* / q1*
  differences.

                                                                                                                         9
           Re-examination of RfD
               methodology
• Currently completing review of the scientific
  foundations of RfD low-dose extrapolation process
   Data that has been cited as basis for UFs and/or
    developing UF distributions
   Probabilistic methods that have been proposed for
    combining UFs
• See need for a common conceptual framework
   For instance, SAB Panel on TCE (2002) wrote
    Ultimately, the whole system of uncertainty factors could be usefully
    revisited and defined in terms of an object of achieving x level of risk for the
    yth percentile of the variable human population with z degree of
    confidence.


                                                                                   10
      Characteristics of model-
       dependent approaches
• Presumption that model (e.g., Mode-of-action,
  structural assumptions) is valid below range of
  observation.
• Often involve unobserved parameters estimated
  by fitting models to dose-response data.
• Can be implemented so as to be interpreted as a
  “central estimate” (with uncertainty bounds),
  assuming model is “true.”
• Can be used in combination with model-
  independent approaches
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            Historical Example:
  Empirical cancer dose-response models

• Well known that different       P(Dose)


  empirical models may fit                         One-hit
  experimental data equally                                      Weibull

  while differing by orders of
  magnitude at low doses                                                               Dose
    Food Safety Council (1980)   P(Dose)-P(0)

    Crump and Howe (1985)
                                                       One-hit             Weibull
    Many others…                           Armitrage-Doll
                                                                      Gamma Multi-hit

• Default approach at the
  time was to use the
  linearized multistage                                                              Dose
  (LMS) model (q1*)

                                                                                              12
Example: PBPK model uncertainty
                                        Inhalation/
• Most PBPK models provide              exhalation
                                                        Gas
  “central tendency” estimates,                       Exchange
  but little or no characterization
  of uncertainty.
                                                       Slowly
• Tetrachloroethylene (PERC)                          Perfused
     Three human PBPK models
      with similar model structures,     Ingestion     Rapidly
      but different parameters                        Perfused
• Trichloroethylene (TCE)
     Two different model structures,                   Fat
      each with two alternative          Stomach
      parameterizations
                                         Intestine
                                                        Liver

                                                                 Metabolism

                                                                              13
                           PERC: Three human PBPK models
                           with different low-dose predictions
                                                           Bois model
                                                           Rao & Brown model
                      10                                   Reitz model
Alveolar Conc (ppm)




                                                           Stewart Expt




                       1

                      • Human in vivo data weakly constrain metabolism
                              Model predictions and observation of parent compound blood
                           0   concentrations within factor of 250
                                 50     100       150      200       ~2. 300 350
                              Up to 10-fold range of predicted total metabolism in observed range.
                                            Post-Exposure Time (hrs)
                              Ten-fold range of extrapolations of metabolism to low dose (< 1 ppm).


                                                                                                       14
   Example: PBPK-based UF for
        Human Variation
• Ongoing efforts evaluating use of PBPK
  models to help inform choice of toxicokinetic
  portion of UFH
   Chloroform
   MTBE
   TCE
• One implementation issue is accounting for
  residual uncertainty (model or parameter)
                                                  15
    Chloroform: Data on human
   variability input to PBPK model
• Impact on level of
                                                            Inter-individual Variability Data
  metabolites of inter-                                         on Hepatic Blood Flow
  individual variability in:
    Blood Solubility                               18
                                                    16
    Hepatic Blood Flow




                                 Frequency (n=59)
                                                    14

    Enzyme content                                 12
                                                    10
• In addition, for children                         8
                                                    6
    Use of age-specific organ                      4
     sizes and blood flow                           2

    Development of PBPK                            0

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                                                                              2



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                                                                                                            4



                                                                                                                    5
                                                       05



                                                                      15



                                                                                     25



                                                                                                    35



                                                                                                                   45



                                                                                                                   55
     model for neonates (1 yr)                              0.



                                                                           0.



                                                                                          0.



                                                                                                         0.



                                                                                                                 0.
                                                    0.



                                                                   0.



                                                                                  0.



                                                                                                 0.



                                                                                                                0.



                                                                                                                0.
     and juveniles (9 yr)                                          Hepatic Blood Flow / Cardiac Output




                                                                                                                        16
      Example: Two-stage (MVK)
        carcinogenesis model
• Formaldehyde model
                                         Normal
  (Conolly et al 2003, 2004)              Cells
    CFD dosimetry model
    Hybrid PBPK-CFD model               n
     (and data) for DPX                 Initiated
                                                        b
    Two-stage clonal growth              Cells     a           Death/
                                                            Differentiation
     model for nasal cancer
    Parameter sources:                  m
      • in vitro measurements (e.g.,    Malignant
        cell labeling)
                                         Cells
      • Fitting to time-to-tumor data



                                                                          17
    Clonal growth models exhibit
    strong parameter sensitivity
• Crump (1994) showed examples where low-dose
  extrapolation of MVK model could change by >105 with 1%
  change in initiated cell birth/death rates (a or b)
• More generally, low-dose extrapolation thus needs:
    Reliable information on biological parameters and/or their
     relationships at (low) dose.
    Understanding of the sensitivity of low-dose extrapolation to
     parameter uncertainty and variability
    Characterization of the range of risk estimates from different
     plausible model structures.
• In the case of formaldehyde, EPA is working to better
  characterize these and other uncertainties and so as to
  evaluate plausible bounds on risk.

                                                                      18
   Issues for Implementation of
   Model-Dependent Approaches
• Characterization of both qualitative and
  quantitative uncertainty / variability.
   Model structure uncertainty, including dose-response of
    model parameters
   Parameter uncertainty, and variability
   Data reliability / relevance
   Ultimate impact on quantitative risk estimate
• Given such a characterization, what level of
  confidence is necessary to replace estimates
  based on model-independent approaches?

                                                              19
  Summary of EPA’s approach to
  high-to-low dose extrapolation
• Both model-independent and model-dependent
  approaches used in EPA’s current and future risk
  assessments.
• Major issues with choosing and implementing different
  approaches include:
    Knowledge of mode-of-action, biological relationships at low dose
    Characterization of uncertainty and variability
    Degree of confidence and consistency in results
• EPA is working both to advance the science and methods
  in this area as well as to make use of the best science that
  is available for its current risk assessments.


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          Acknowledgements

NCEA Leadership:          NCEA Staff:
Peter Preuss, Director    Chao Chen
  NCEA                    Karen Hogan
David Bussard, Director   Jennifer Jinot
  NCEA-W                  John Lipscomb
Paul White, Chief,        Cheryl Siegel Scott
  QRMG
                          Ravi Subramaniam


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