Login
Monday, September 06, 2010

POTENCY

 

Potencies are frequently used to loosely characterize dose-response models. For example, when the time of the occurrence of a specified response is being ignored, then a dose-response model evaluated at a dose d is the probability, say P(d), that the specified response will occur at dose d.  The added risk from being at dose d instead of dose 0 is P(d)-P(0) -- the increased probability above the background probability, P(0).  This added risk can be calculated directly from the dose-response model.  However, such added risks are frequently approximated (overstated) by straight-line representations of the dose-response curve or upper bounds on the dose-response curve.  The slopes of these straight lines are called potencies.

 

For example, in Figure 3 a straight line has been drawn between the fitted dose-response models estimated probability of the specified response at dose d=MTD/2 (one half of the maximum tolerated dose (MTD)) and the corresponding probability at dose d=0.  The slope of such a straight line is frequently called the potency or even more loosely called the low-dose potency even though it may have been determined from the fitted model at relatively high doses (or determined from upper bounds on the response probabilities at relatively high doses).  Such a slope is often used to approximate (mischaracterize, overstate) the added risks at lower doses.  For example, even at a dose like MTD/4 (which would usually be quite high relative to environmental exposures), the added risk according to the estimated dose-response model is significantly less than the added risk inferred from the potency and the corresponding straight-line representation of the dose-response curve.   The overstatement of the added risk at MTD/4 inferred from the potency instead of the maximum likelihood estimate from the fitted multistage dose-response model is indicated in Figure 3.

 

>> Figure 3

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