# Presentation: 332988

## 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.

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

- “High” Dose

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

- Animal Bioassays
- Animal Pharmacokinetic Studies

- Occupational Epidemiology
- Human Pharmacokinetic Studies

- Environmental Epidemiology
- Exposure Biomarker Studies

- Exposures/Doses of
- Regulatory Interest

- ??

- In Vitro studies
- Emerging Data (-omics)

**Fewer studies**

## 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).

## 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

## 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

## 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

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

**“****Linear”**

**“****Non-linear”**

**Notes:**
See cancer guidelines

## Linear extrapolation consistent with previous LMS method

- Analysis by Subramaniam et al 2005 of LMS and linear extrapolation procedures
- Of 102 data sets from IRIS database
- 84% had differences of < 10% in potency estimates from the two approaches
- 95% had < 2-fold differences.
- Linear extrapolation based on upper bound (LED) and MLE (ED) were also compared – 75% of datasets had < 2-fold differences.

## 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.

## 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

## Historical Example: Empirical cancer dose-response models

- Well known that different empirical models may fit experimental data equally while differing by orders of magnitude at low doses
- Food Safety Council (1980)
- Crump and Howe (1985)
- Many others...
- Default approach ***at the time ***was to use the linearized multistage (LMS) model (q1*)

## Example: PBPK model uncertainty

- Most PBPK models provide “central tendency” estimates, but little or no characterization of uncertainty.
- Tetrachloroethylene (PERC)
- Three human PBPK models with similar model structures, but different parameters
- Trichloroethylene (TCE)
- Two different model structures, each with two alternative parameterizations

## PERC: Three human PBPK models with different low-dose predictions

***PERC: Three human PBPK models with different low-dose predictions***

- Human in vivo data weakly constrain metabolism
- Model predictions and observation of parent compound blood concentrations within factor of ~2.
- Up to 10-fold range of predicted total metabolism in observed range.
- Ten-fold range of extrapolations of metabolism to low dose (< 1 ppm).

**Notes:**
Metabolism parameters had different sources:
Literature (e.g., in vitro)
Cross-species extrapolation
Fitting to time course data
Combination

## 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)

## Chloroform: Data on human variability input to PBPK model

- Impact on level of metabolites of inter-individual variability in:
- Blood Solubility
- Hepatic Blood Flow
- Enzyme content
- In addition, for children
- Use of age-specific organ sizes and blood flow
- Development of PBPK model for neonates (1 yr) and juveniles (9 yr)

**Notes:**
Blood partitioning determined in
laboratory studies with adult and
child blood samples left-over from
clinical evaluation (Not a Human Study)

Variability of blood flow to liver
in adults determined using results
reported in the literature for 59
individual adults (Not a Human Study)

Variability of enzyme content
determined using tissues from
adult and child organ donors;
enzyme activity determined in
laboratory studies

## Example: Two-stage (MVK) carcinogenesis model

- Formaldehyde model (Conolly et al 2003, 2004)
- CFD dosimetry model
- Hybrid PBPK-CFD model (and data) for DPX
- Two-stage clonal growth model for nasal cancer
- Parameter sources:
- in vitro measurements (e.g., cell labeling)
- Fitting to time-to-tumor data

- Normal
- Cells

- Initiated
- Cells

- Malignant
- Cells

- 

- 

- 

- 

- Death/
- Differentiation

## 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 ( or 
- 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.

## 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?

## 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.

## Acknowledgements

- _NCEA Leadership:_
- Peter Preuss, Director NCEA
- David Bussard, Director NCEA-W
- Paul White, Chief, QRMG

- _NCEA Staff:_
- Chao Chen
- Karen Hogan
- Jennifer Jinot
- John Lipscomb
- Cheryl Siegel Scott
- Ravi Subramaniam