Cancer risk assessment extrapolation

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... 

The EPA uses the linearized multistage model (LMS)—illustrated in Figure 9.34—to conduct its cancer risk assessments. It yields a cancer slope factor, known as the ql (pronounced Ql-star), which can be used to predict cancer risk at a specific dose. The LMS assumes a linear extrapolation with a zero dose threshold from the upper confidence level of the lowest dose that produced cancer in an animal test or in a human epidemiology study. 

On the basis of the discussions and considerations presented in the earlier sections of this chapter on cancer risk assessment, the NAC/AEGL Committee has developed no AEGL values based on carcinogenicity. In view of the great uncertainty of the assumptions used in extrapolating from lifetime exposures to 8 h or less, the paucity of single-exposure inhalation data, the relatively... 

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 PBPK-based route-to-route extrapolation in cancer risk assessment frequently begins with the determination of a slope factor, associated with the response data for one exposure route, on the basis of the appropriate dose metrics (Dt), e.g., 2 X 10 pCT milligram metabofized per day per g of tissue. Then, the PBPK model, parametrized for other exposure route(s) of interest, is used to deter-nune the exposure dose that genrates the same Dt—that is, that corresponding to a predetermined risk level (e.g., 1 x 10 ) (Clewell et al. 2001). When linear extrapolation is appropriate, the following equation is used ... 

Linear low-dose extrapolation has been chosen by many regulatory agencies as a health-protective approach to cancer risk assessment. While this is a conservative approach, research in recent years has found many exceptions to this approach. 

The similarities in VX sensitivity between sheep and humans may, in part, be accounted for by similarities in rates of metabolic detoxification of the agent. The latter can be estimated from a comparison of body surface areas (based on body weight) as is done for animal-to-human extrapolations used in EPA cancer risk assessments (USEPA 1996c). Using this approach, the human equivalent dose for the 0.06 ig/kg/d sheep LOAEL in the Rice et al. study can be calculated as ... 

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 this paper I have tried to show that measurement of health benefits attributable to TSCA is not feasible. I hope that in doing so I have not belabored the obvious. For new chemicals and for most existing chemicals, prospective evaluation of health benefits to be achieved by various exposure controls will have to be based on extrapolation from microbial and animal data. However, while such extrapolation may be useful in a qualitative sense, quantitative risk assessment techniques involve considerable uncertainty, and in any case have not been developed for chronic effects other than cancer. 

In 1958, in response to the increased awareness that chemicals can cause cancer, the US Congress passed the Delaney clause, which prohibited the addition to the food supply of any substance known to cause cancer in animals or humans. Compared with today s standards, the analytical methods to detect a potentially harmful substance were very poor. As the analytical methods improved, it became apparent that the food supply had low levels of substances that were known to cause cancer in either animals or humans. The obvious question was Is a small amount of a substance safe to consume. This question in turn raised many others about how to interpret data or extrapolate data to very low doses. The 1970s saw a flourishing of activity to develop and refine risk assessment methodologies. 

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. 

The Monographs represent the first step in carcinogenic risk assessment, which involves examination of all relevant information in order to assess the strength of the available evidence that certain exposures could alter the incidence of cancer in humans. The second step is quantitative risk estimation. Detailed, quantitative evaluations of epidemiological data may be made in the Monographs, but without extrapolation beyond the range of the data available. Quantitative extrapolation from experimental data to the human situation is not undertaken. 

In the absence of definitive human data, risk assessment may have to depend on the results of cancer bioassays in laboratory animals, short-term tests, or other experimental methods. Hence the following issues must be addressed under such circumstances the ability of the test system to predict risks for man (quantitatively as well as qualitatively) the reproducibility of test results the influence of species differences in pharmacokinetics, metabolism, homeostasis, repair rates, life span, organ sensitivity, and baseline cancer rates extrapolation across dose and dose rates, and routes of exposure the significance of benign tumors fitting models to the data in order to characterize dose-incidence relationships and the significance of negative results. 

---