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Monday, September 06, 2010

BENCHMARK DOSES

 

A benchmark dose is a characterization of the dose corresponding to a specified increase in the probability of a specified response.  For example, an ED10 is the estimated dose corresponding to an increase of 0.10 in the probability of the specified response relative to the probability of that same response at dose zero.  Similarly, an ED05 is the estimated dose corresponding to an increase of 0.05 in the probability of the specified response relative to the probability of that same response at dose zero.  A graphical illustration of the definitions of ED10 and ED05 is given in Figure 13.

 

If

 

P(d) = probability of the specified response at dose d

 

then

 

P(ED10) - P(0) = 0.10

 

and

 

P(ED05) - P(0) = 0.05.

 

The ED10 or ED05 depend upon the specified response of concern.  For example, the specified response might be malignant lung carcinomas.  The ED10 also depends on the population in which the probability of the specified response is being observed.  For example, if an animal bioassay has been conducted using male B6C3F1 mice, then the ED10 for this population is the dose that causes the probability of a malignant lung carcinoma in male B6C3F1 mice to increase from its background value to its background value plus 0.10.  For a different example, if human workers were being observed for malignant lung carcinomas in a human epidemiology study, then the ED10 for this population is the dose that causes the probability of a malignant lung carcinoma in human workers to increase from its background value to its background value plus 0.10. 

 

Both ED10 and ED05 are estimated doses calculated, for example, using the maximum likelihood estimates of the probability of a specified response in a dose-response model (like the multistage dose-response model).  The benchmark dose could be defined to be either ED10 or ED05.  Although both ED10 and ED05 have been proposed as candidates for the benchmark dose, the ED10 is usually a dose level closer to the dose levels actually observed in the experimental data and hence is probably the leading candidate to be defined as the benchmark dose.

 

It has sometimes been proposed that the benchmark dose be defined in terms of a lower bound (rather than a maximum likelihood estimate) for the dose corresponding to a specified increase in the probability of a specified response.  For example, an LED10 is the lower bound on the dose corresponding to an increase of 0.10 in the probability of the specified response relative to the probability of that same response at dose zero.  The definition of the LED10 and its relation to the ED10 is illustrated in Figure 14.  The LED10 in Figure 14 is a lower bound on the dose corresponding to an increase of 0.10 in the probability of a specified response and is calculated using the upper bounds on risk in the linearized multistage model.  The ED10 in Figure 14 is a maximum likelihood estimate of the dose corresponding to an increase of 0.10 in the probability of a specified response and is calculated using the maximum likelihood estimates of risk in the multistage model. 

 

Because the ED10 is more directly related to the observed dose-response relationship than the LED10 and the ED10 does not require any complex statistical confidence limit procedures to calculate, the recommendation is frequently made to use the ED10 instead of the LED10 as the benchmark dose.

 

 

>> Figure 14

3. Human Health Risk Assessment
3.1     
Quantitative Risk Assessment and Statistical Analysis
3.2      Importance of Dose and Dose-Response Relationships
3.3      Misuse of Regulatory Upper-Bound Risk Characterizations
3.4      Risk Characterization Choices and Risk Exaggeration
3.5      A Better Approach to Cancer Risk Characterization
3.6      Overview of Background, Motivation, and Statistical Methods for Margin-of-Exposure Characterizations of Cancer Risks
           3.6.1    Importance of Dose
           3.6.2    Dose-Response Modeling
           3.6.3    Dose-Response Models
           3.6.4    Maximum Likelihood Estimation
           3.6.5    
Multistage Model
           3.6.6    Example of Fitted Multistage Model
           3.6.7    Potency
           3.6.8    Linearized Multistage Model
           3.6.9    Overstatement of Risks by the Linearized Multistage Model
           3.6.10  Adverse Impacts of the Variability in the Magnitude of the Bias in the Linearized Multistage Model's Overstatement of Risks
           3.6.11  Non-Responsiveness of the Linearized Multistage Model to Data
           3.6.12  Ranking Relative Risks
           3.6.13  Added Risk versus Extra Risk
           3.6.14  Need for a Better Dose-Response Characterization
           3.6.15  Better Dose-Response Characterization
           3.6.16  Benchmark Doses
           3.6.17  Responsiveness of Benchmark Doses Data Versus the Relative Non-Responsiveness of the Regulatory Upper-Bound Potency Q1* based on the Linearized Multistage Model
           3.6.18  Recommended Dose-Response Characterization
           3.6.19  Margin-of-Exposure Characterizations
           3.6.20  Conclusion
           3.6.21  Figures 1 to 16
3.7      Innovative Risk Assessment
3.8      Components of High-to-Low-Dose Extrapolation and Dose-Response Modeling
3.9      Probabilistic Exposure Assessment
3.10    Aggregate Risk Assessment
3.11    Cumulative Risk Assessment
3.12    Example Activities