Cancer dose-response extrapolation

Three additional points need to be mentioned. First, if the observed cancer dose-response relationship derives from epidemiology data, the observed risks are relative, not absolute (the latter are usually reserved for data from animal experiments). Thus, for human carcinogens with reliable dose-response information (e.g., as exists for benzene, arsenic, chromium (+6), asbestos, and several other carcinogens), it is necessary to convert relative risks to absolute risks before extrapolating to low dose. 

By facilitating the simulation of the dose metrics for use in cancer dose-response analysis, the PBPK models address the uncertainty associated with interspecies, route-to-route, and high-dose to low-dose extrapolations (Andersen et al. 1993 Andersen and Krishnan 1994 Clewell et al. 2002a Clewell and Andersen 1987 Melnick and Kohn 2000). Since the first demonstration of the application of PBPK models in cancer risk assessment by Andersen and co-workers in 1987, there have been substantial efforts to evaluate the appropriate dose metrics and cancer risk associated with a number of other volatile organic chemicals using the PBPK modeling approach (Table 21.3). These risk assessments have been based on the PBPK model simulations of a variety of dose metrics that reflect the current state... 

The next question to be addressed was that of the mathematical model to be used for the extrapolation. Most particularly, would one model do for all effects or was more than one required This is obviously particularly a problem with cancer. Various models have been proposed for cancer, but there has been little consideration of the use of dose/response extrapolation for effects other than cancer the safety factor approach is assumed adequate. For reasons given above, the Committee did not agree. 

The following example is based on a risk assessment of di(2-ethylhexyl) phthalate (DEHP) performed by Arthur D. Little. The experimental dose-response data upon which the extrapolation is based are presented in Table II. DEHP was shown to produce a statistically significant increase in hepatocellular carcinoma when added to the diet of laboratory mice (14). Equivalent human doses were calculated using the methods described earlier, and the response was then extrapolated downward using each of the three models selected. The results of this extrapolation are shown in Table III for a range of human exposure levels from ten micrograms to one hundred milligrams per day. The risk is expressed as the number of excess lifetime cancers expected per million exposed population. 

In animal experiments exposures can be carefully controlled, and dose-response curves can be formally estimated. Extrapolating such information to the human situation is often done for regulatory purposes. There are several models for estimating a lifetime cancer risk in humans based on extrapolation from animal data. These models, however, are premised on empirically unverified assumptions that limit their usefulness for quantitative purposes. While quantitative cancer risk assessment is widely used, it is by no means universally accepted. Using different models, one can arrive at estimates of potential cancer incidence in humans that vary by several orders of magnitude for a given level of exposure. Such variations make it rather difficult to place confidence intervals around benefits estimations for regulatory purposes. Furthermore, low dose risk estimation methods have not been developed for chronic health effects other than cancer. The... 

There are of course many mathematically complex ways to perform a risk assessment, but first key questions about the biological data must be resolved. The most sensitive endpoint must be defined along with relevant toxicity and dose-response data. A standard risk assessment approach that is often used is the so-called divide by 10 rule . Dividing the dose by 10 applies a safety factor to ensure that even the most sensitive individuals are protected. Animal studies are typically used to establish a dose-response curve and the most sensitive endpoint. From the dose-response curve a NOAEL dose or no observed adverse effect level is derived. This is the dose at which there appears to be no adverse effects in the animal studies at a particular endpoint, which could be cancer, liver damage, or a neuro-behavioral effect. This dose is then divided by 10 if the animal data are in any way thought to be inadequate. For example, there may be a great deal of variability, or there were adverse effects at the lowest dose, or there were only tests of short-term exposure to the chemical. An additional factor of 10 is used when extrapolating from animals to humans. Last, a factor of 10 is used to account for variability in the human population or to account for sensitive individuals such as children or the elderly. The final number is the reference dose (RfD) or acceptable daily intake (ADI). This process is summarized below. 

BROWN, C.C. (1978). Statistical aspects of extrapolation of dichotomous dose-response data, J. Natl. Cancer Inst. 60, 101-108. 

Additional bioassays in animals do not seem necessary. Further research on dose-response relationships for the many biochemical effects of peroxisome proliferators leading to liver cancer in rodents, identification of specific thresholds, and potential reversibility, would be informative only if an extrapolation model for cancer was deemed appropriate in spite of profound differences between human and rodent responses. 

---