Tumor dose-response curve shape

All of these considerations indicate that the biology behind the shape of the tumor dose-response curve is much more complex than a simple conclusion that mutagenic activity = linear dose-response. Ultimately, biologically based dose-response models and use of biomarker data may make it possible to extend the tumor dose-response curve to low doses based on biological data, rather than presumptions about the shape of the dose-response curve. In the shorter term, it is important to recognize that the biology is complex, and linear extrapolation from tumor data is a health-protective science policy decision. 

Eor most of the toxic effects that might be exerted by a chemical substance, including at least certain types of genotoxic carcinogenicity, the dose-response curve is S-shaped as illustrated in Eigure 4.1 where the carcinogenic response is the mmor incidence. This means that no or only a few tumors occur at the lower dose levels, but the mmor incidence increases as the dose level increases, in many cases until a plateau is reached. 

The concept of categorizing carcinogens into threshold carcinogens and non-threshold carcinogens is a pragmatic approach that simplifies the reality of dose-response relationships. The observed dose-response curve for tumor formation in some cases represents a single rate-determining step however, in many cases it may be more complex and represent a superposition of a number of dose-response curves for the various steps involved in the mmor formation. It is therefore more realistic to assume that there is a continuum of shapes of dose-response relationships which cannot be easily differentiated by data and information usually available. 

The second step of the dose-response assessment is an extrapolation to lower dose levels, i.e., below the observable range. The purpose of low-dose extrapolation is to provide as much information as possible about risk in the range of doses below the observed data. The most versatile forms of low-dose extrapolation are dose-response models that characterize risk as a probability over a range of environmental exposure levels. Otherwise, default approaches for extrapolation below the observed data range should take into account considerations about the agent s mode of action at each tumor site. Mode-of-action information can suggest the likely shape of the dose-response curve at these lower doses. Both linear and nonlinear approaches are available. 

Considering all the above data, the U.S. EPA (1991) selected the unit risk of 8.5 x 10 per pg/m, derived from the Weibull time-to-tumor model, as the recommended upper bound estimate of the carcinogenic potency of sulfur mustard for a lifetime exposure to HD vapors. However, U.S. EPA (1991) stated that "depending on the unknown true shape of the dose-response curve at low doses, actual risks may be anywhere from this upper bound down to zero". The Weibull model was considered to be the most suitable because the exposures used were long-term, the effect of killing the test animals before a full lifetime was adjusted for, and the sample size was the largest obtainable from the McNamara et al. (1975) data. 

In considering the shape of the tumor dose-4 esponse curve, it is important to recognize that the mere observation of a tumor response that appears different from linearity is not in itself an adequate justification for the determination that nonlinear low-dose extrapolation should be used for that chemical. For example. Lutz et al. 

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