the evaluation process (5). Such negative studies may be used to contradict poorly conducted studies that have reported a positive finding.
It is now recognized that certain chemical-induced neoplastic effects in animals within certain target organs may not be predictors of risks for humans, especially at human exposure levels. Such mechanistic evaluations formed the basis for a monograph published by the International Expert Panel on Carci-nogen Risk Assessment (6). In these series of evaluations, evidence whether a chemical produced cancer in animals by a DNA-reactive mechanisms was found to be of primary importance in the assessment of human cancer risk.
The designation of a chemical as producing tumors in animals by a non-DNA-reactive mechanism raises the possibility that these chemicals would not produce cancer in humans. Several chemicals have been evaluated and found to be unlikely to cause cancer in humans. These include the food addi-tives d-limonene (7), butylated hydroxyanisole (8), and saccharin (9).
In those cases where a chemical has been shown to produce neoplasms in animals by a mode of action that could not be operative in humans, risk assessment is not performed based upon such neoplasms. In the case of the EPA’s proposed descriptors (5), such agents would be designated as ‘‘not likely to be carcinogenic in humans.’’ This is the same designation as for chemical that have been shown to be negative in adequate well-conducted rodent testing. The IARC has also begun using cancer mechanism data for risk assessment, and several chemicals including melamine, d-limonene, saccharin, and atrazine were placed in Group 3 (insufficient evidence) based on such considerations (10).
in deriving point estimates of risk. An MLE approach is better to use with large numbers of data points. For most chemicals, the only reliable dose–
response information comes from studies in rats or mice. In these cases the upper bound estimate is used, and various mathematical models can be used to fit the data (Fig. 1A).
3.1. Extrapolation to Doses Below the Observed Effects
The relationship between dose and toxicological response of any particular chemical is usually a complex one and may involve sublinear, linear, and supralinear components (Fig. 1). This is also true of neoplastic responses and depends upon the mode of action. When cancer risk assessment was first developed, all carcinogens were believed to act as mutagens producing irre-versible changes and acting at one or more steps in a sequence of events leading to neoplasia. It is now known that chemicals may be involved in many steps of a neoplastic process in which they may directly or indirectly produce mutagenic effects, alternatively they may produce other changes that enhance neoplastic conversion or development.
The underlying default assumption for dose–response has been that the low-dose portion of the curve is linear unless proven otherwise. The ori-ginal justification for this assumption was mathematical and derived from the multistage model of carcinogenesis. Animal tumor data is analyzed by the linearized multistage (LMS) procedure, which provides a first-order cancer potency factor at low dosage levels. The cancer slope factor (q1) is
Figure 1 Dose–response extrapolation for carcinogens. Data points represent hypothetical data for tumor response versus dose. (A) Dose–response extrapolation using a linear-at-low-dose approach for estimates of human risk. (B) Dose response extrapolation using the margin of exposure (MOE) approach. Abbreviations are defined in the text. Note that the ‘‘human exposure of interest’’ is usually much nearer to the zero dose than shown.
the linear extrapolation line of the dose–response data and is expressed in units of risk per dosage (mg=kg b.w.=day)1. The q1 represents the 95%
upper confidence limit on that slope.
Since the early 1980s, the FDA has used a somewhat different low-dose extrapolation where linear extrapolation to zero proceeds from the upper confidence limit on the lowest experimental dose (11). In this procedure, a point on the dose–response curve (tumor incidence vs. dose) for a chemical is chosen below which the data no longer appear to be reliable and a straight line is drawn form the upper confidence limit to the origin. The EPA has recently developed a similar method for deriving the relationship between dose and response for low doses (4,5), which uses a straight line extrapolation to the origin from the low-end dose of the observed tumor data, usually the 10% tumor response, which is termed the LED10(Fig. 1A).
Several other procedures have been used for dose–response extrapola-tion, which lead to widely differing estimates of potency. The model with the most significant departure from the LMS model is the threshold model, which assumes that no significant risk is present below an identified expo-sure. In this model, a no-observed-adverse effect level (NOAEL) is deter-mined, which serves a point of departure for the development of an acceptable dose. The NOAEL approach has been used extensively along with safety or uncertainty factors for the determination of acceptable doses for toxic effects other than cancer. However, this procedure has also been used by some European nations and on a limited basis in the United States where the chemical is believed to produce neoplasia by a process that involves a threshold (12). The major determinant for the use of a threshold model for a chemical is the lack of DNA reactivity coupled with a plausible explanation, such as chronic toxicity of the target organ, as the basis for the tumorigenic response.
The EPA has recently developed a similar procedure for carcinogens that exhibit a dose–response that either has a lack of demonstrated effect at low doses (threshold dose) or a much lower than expected effect at low doses (sublinear dose–response) (5). In this case a margin of exposure (MOE) is determined, which is the difference between the LED10 and the estimated human exposure level (Fig. 1B). This procedure is similar to the use of uncertainty factors with a NOAEL; however, the use of the MOE method does not require the experimental determination of a threshold dose for the neoplastic effect.
3.2. Rodent-to-Human Extrapolation
Although humans have been exposed to many chemicals classified as carci-nogens, usually adequate exposure information is lacking from epidemiology studies for use in dose–response development. Also, for most chemicals that have been found to produce tumors in experimental animals, human studies
are lacking or have not found increases in cancer that can be quantified. Con-sequently, the estimation of cancer risks for humans are usually based upon extrapolation from rodents. In addition, animal studies have two primary advantages over epidemiological studies: 1) dose, environmental, and extra-neous exposures are strictly controlled, and 2) adverse affects are directly measured through pathological examination and necropsy. The obvious dis-advantage is that humans may not respond to chemicals in the same manner as rodents either qualitatively or quantitatively.
For carcinogens, the EPA’s default method of extrapolation from animals to humans has been traditionally based upon comparative surface areas, which is related to metabolic rate. The surface area is approximately proportional to the two-third power of body weight. However, based upon empirical data for chemotherapeutic drugs in rodents and humans, the ratio of the three-quarter power of body weight or (BW1)3=4=(BW2)3=4 is now used both by the EPA and FDA (13). In practice, this means that the cancer slope factor in mg=kg=day for the rat or mouse would be multiplied by a factor of between 5 and 10 for humans.
If data regarding the chemical-specific relative metabolic rates, tissue distributions, or other factors are available for rodents and humans, phar-macologically based pharmacokinetic (PBPK) modeling may be used to extrapolate between species. PBPK modeling is a mathematical method for extrapolating between species that accounts for differences in target organ concentrations of the reactive metabolite(s) due to absorption, bio-transformation, distribution and elimination. Difficulty in applying this level of sophistication to the species-to-species extrapolation is usually due to the lack of information in human parameters for many chemicals.
Furthermore, individual differences among humans for many of these parameters requires that PBPK modeling use statistical distributions of parameters, and the combinations of distributions may give a result with a large range of values.
Ideally, the route of administration of animal studies used for dose–
response data should be the same as the human route of exposure (i.e., inhalation, dermal contact, ingestion). If it is not, an extrapolation from the animal route of administration to the human route of exposure may be possible. The target organ(s) and mechanism(s) of action determine whether route-to-route extrapolation is appropriate. For an agent causing adverse effects at the point of contact (e.g., skin, lung) extrapolation from one route of administration is usually not valid. But for carcinogens with a systemic mode of action, route-to-route extrapolation may be biologically plausible. In order to perform route-to-route extrapolation, pharmacoki-netic data for the substance being evaluated are desirable, but not always available, and estimates can be made in the absence of such data. Pharma-cokinetic data can also be used in PBPK models to convert the dose to a different route.