The “Straw Man” System for Defining the RfD as a Risk-Specific Dose Making Use of Empirical Dist...
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The “Straw Man” System for Defining the RfD as a Risk-Specific Dose Making Use of Empirical Distributions. Dale Hattis, Meghan Lynch, Sue Greco Clark University, Abt Associates Inc. Alliance for Risk Assessment Workshop III Beyond Science and Decisions Falls Church, VA May 4, 2011.

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Dale Hattis, Meghan Lynch, Sue Greco Clark University, Abt Associates Inc.

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Dale hattis meghan lynch sue greco clark university abt associates inc

The “Straw Man” System for Defining the RfD as a Risk-Specific Dose Making Use of Empirical Distributions

Dale Hattis, Meghan Lynch, Sue Greco

Clark University, Abt Associates Inc.

Alliance for Risk Assessment Workshop III

Beyond Science and Decisions

Falls Church, VA

May 4, 2011


Basic ideas behind straw man system

Basic Ideas Behind “Straw Man” System

  • Characterize uncertainty in the RfD inputs using information from analogous data, e.g.,

    • POD distribution based on fits to underlying dose-response data

    • UFS distribution based on studies having both subchronic and chronic information)

  • Using empirical data as the basis for these uncertainties is an improvement over the use of traditional uncertainty factors (e.g., UFs = 10)

  • Defining the RfD as a risk-specific dose (less than x incidence of harm with z confidence) has been recommended by the NRC in Science and Decisions


Steps in the straw man approach

Steps in the Straw Man Approach

  • Estimate an animal ED50 by fitting dose-response models to the original toxicological data.

  • Apply a subchronic-to-chronic uncertainty factor distribution (UFs) to transform the subchronic dose that affects 50% of the lab animal populations to a chronic dose that would be expected affect 50% of the animals.

  • Apply the animal-to-human uncertainty factor distribution (UFA) to convert the estimated animal ED50’s to human ED50’s.

  • Apply a database uncertainty factor distribution (D) to represent deficiencies in the toxicological datasets (different depending on whether repro or chronic toxicity studies are missing).

  • Apply an interindividual uncertainty factor distribution to account for uncertainties in the extent of susceptibility differences across humans, derived from human studies of analogous chemicals to produce separate distributions of human pharmacokinetic (GSDPK) and pharmacodynamic (GSDPD) variability. Use the combined GSDPK and GSDPD to assess risks as a function of dose, using the probit model.

  • Combine input from Steps 1 though 5 into a Monte Carlo simulation to evaluate a distribution of Doses corresponding to a given level of risk (Presponse).


Straw man approach in functional form

Straw Man Approach in Functional Form

Distribution of doses at which a probability of response is expected.

RfD defined as 5th pct dose corresponding to 1 in 100,000 increase in risk.

Distribution of risks corresponding to a given dose.

Can summarize mean/95th pct risk levels corresponding to a particular dose.

F = Standard normal cumulative distribution. Hats represent distributions


How does straw man compare to bmds case study summary results

How does Straw Man compare to BMDS? Case Study Summary Results

  • Carbonyl Sulfide (COS)

    • Log-probit model provided a good fit to the D-R data

    • RfC estimated using Traditional and BMDS approaches similar, due to similar estimated POD

    • Straw Man RfC 3-fold lower

    • Risks at low doses estimated by Straw Man model higher than those estimated by BMDS, which were 0 at dose range of interest

  • 1,2,4,5-Tetrachlorobenzene (TCB)

    • No BMDS models provided a good fit to the D-R data due to high incidence of kidney lesions in controls

    • POD estimated by Traditional/IRIS and BMDS different

      • IRIS considered the severity of the lesions, resulting in a higher POD

    • BMDS RfD < Straw Man RfD < Traditional RfD

    • Straw Man model estimated higher risks at low doses, and over a wider range of dose values, compared to BMDS


Getting the straw man framework solidified

Getting the “Straw Man” framework solidified

  • Updating and improving the distributions

    • Ensuring sensitive subpopulations are included in the interindividual variability terms (GSDPK, GSDPD)

    • Incorporating measurement error in the distributions

  • Expanding the model

    • Accounting for background interactions

    • Explore use of susceptibility distributions other than log-normal, and assumption of independence for distributions

  • User-friendliness and transparency

    • Updated methods paper

    • Updated “Straw Man” modeling platform


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