Quantitative risk assessment extrapolation

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. 

The quantitative risk assessment can be divided into at least three types of extrapolation ... 

The most widely used of the many mathematical models proposed for extrapolation of carcinogenicity data from animal studies to low-dose human exposures (i.e., low-dose extrapolation) is the LMS model. This has, in effect, become the default approach for quantitative risk assessment and has been used by, e.g., the US-EPA for many years as well as by the WHO in relation to derivation of drinking-water guideline values for potential carcinogens (WHO 1996) (see Section 9.2.1.2 for drinking-water guideline values). 

The major change from the previous guidelines in terms of the quantitative risk assessment is that the LMS model no longer is the recommended default approach for low-dose extrapolation. Instead, an MOE approach is recommended based on curve fitting within the range of observation with extrapolation from a UED (the 95% lower confidence limit on a dose associated with an extra tumor risk) chosen to be representative of the lower end of the observed range. 

For food allergens, validated animal models for dose-response assessment are not available and human studies (double-blind placebo-controlled food challenges [DBPCFCs]) are the standard way to establish thresholds. It is practically impossible to establish the real population thresholds this way. Such population threshold can be estimated, but this is associated with major statistical and other uncertainties of low dose-extrapolation and patient recruitment and selection. As a matter of fact, uncertainties are of such order of magnitude that a reliable estimate of population thresholds is currently not possible. The result of the dose-response assessment can also be described as a threshold distribution rather than a single population threshold. Such distribution can effectively be used in probabilistic modeling as a tool in quantitative risk assessment (see Section 15.2.5)... 

Because the literature describes several limitations in the use of NOAELs (Gaylor 1983 Crump 1984 Kimmel and Gaylor 1988), the evaluative process considers other methods for expressing quantitative dose-response evaluations. In particular, the BMD approach originally proposed by Crump (1984) is used to model data in the observed range. That approach was recently endorsed for use in quantitative risk assessment for developmental toxicity and other noncancer health effects (Barnes et al. 1995). The BMD can be useful for interpreting dose-response relationships because it accounts for all the data and, unlike the determination of the NOAEL or LOAEL, is not limited to the doses used in the experiment. The BMD approach is especially helpful when a NOAEL is not available because it makes the use of a default uncertainty factor for LOAEL to NOAEL extrapolation unnecessary. 

A different approach, called a quantitative risk assessment, is used for nonthreshold effects, such as cancer. Sophisticated statistical models are used to extrapolate the experimental animal data obtained at high doses to the low exposures predicted in humans. The linearized multistage (LMS) model is frequently... 

In the United States, some state and federal regulatory agencies conduct quantitative risk assessments on known or suspect carcinogens for continuous or long-term human exposure by extrapolating downward in linear fashion from an npper confidence limit on theoretical excess risk (FDA 1985 EPA 1986). The values derived for a specified acceptable theoretical excess risk to the U.S. human population, based on a lifetime of exposure to a carcinogenic substance, have been used extensively for regulatory purposes. 

Quantitative risk assessment depends on data that are reliable, sensitive and quantitative. It may well be that the numerical extrapolation from the current small scale (but manageable) laboratory tests can be substantially improved and moved downward to the effects of lower dose levels through the shrewd use of these isolated cell and biochemical test systems where the interplay of inactivation, activation and target molecule injury can be studied at concentrations well below those possible where one is looking at endpoints in relatively small groups of whole animals. 

Quantitative risk assessment requires extrapolation from results of experimental assays conducted at high dose levels to predicted effects at lower dose levels which correspond to human exposures. The meaning of this high to low dose extrapolation within an animal species will be discussed, along with its inherent limitations. A number of commonly used mathematical models of dose-response necessary for this extrapolation, will be discussed. Other limitations in their ability to provide precise quantitative low dose risk estimates will also be discussed. These include the existence of thresholds incorporation of background, or spontaneous responses modification of the dose-response by pharmacokinetic processes. 

Extrapolating Rodent Cancer Test Results to Humans. It is prudent to assume that if a chemical is a carcinogen in rats and mice at the maximum tolerated dose (MTD), it is also likely to be a carcinogen in humans the MTD. However, until we understand more about mechanisms, we cannot reliably predict risk to humans at low doses, often hundreds of thousands of times below the dose where an effect is observed in rodents. Thus, quantitative risk assessment is currently not scientifically possible (1.17,20). 

There are, of course, two extreme views of the validity of such quantitative risk assessment. On the one hand, it is held that there is no valid method for extrapolating cancer data in animals to arrive at risk assessments for humans. 

The decision to use an amortized exposure value or a peak exposure value has a profound Impact on the outcome of the quantitative risk assessment. To Illustrate this point, data from an actual field exposure study were used. The average dally dermal exposure level as measured by the patch technique was used to calculate the amortized exposure level and the peak exposure level (Table VIII). Estimates of risk at low doses were obtained using linear extrapolation from the 11 excess risk point based on a fitted Welbull model (32) and the Armltage-Doll multi-stage model (33). While both models gave similar results, the effect of the exposure estimates had a dramatic effect on the risk estloiates. The amortized exposure estloiates lowered the estloiates of risk substantially. 

Methods for toxicological safety assessments are multiple and varied - some are more reliable than others, some more radical than others, but all are important. Their nature greatly depends on their endpoints, namely, the degree of practical safety they attempt to attain. Unfortunately, the true validity of these methods can only be assessed retrospectively, that is to say, by the record of cases of health impairment they were able to prevent over a reasonable period of time. Because of this and the uncertainty inherent in any extrapolation technique, the final products expressed in numerical form can only be considered as opinions. Some important toxicological opinions presented in numerical form are the LD50 the quantitative risk assessments, the threshold limit values (TLV), and the acceptable daily intakes (ADIs). 

As probabilistic exposure and risk assessment methods are developed and become more frequently used for environmental fate and effects assessment, OPP increasingly needs distributions of environmental fate values rather than single point estimates, and quantitation of error and uncertainty in measurements. Probabilistic models currently being developed by the OPP require distributions of environmental fate and effects parameters either by measurement, extrapolation or a combination of the two. The models predictions will allow regulators to base decisions on the likelihood and magnitude of exposure and effects for a range of conditions which vary both spatially and temporally, rather than in a specific environment under static conditions. This increased need for basic data on environmental fate may increase data collection and drive development of less costly and more precise analytical methods. 

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 first step, extrapolation of data from experimental animals to the human simation, is similar to the interspecies extrapolation described in detail for threshold effects (Section 5.3). The second step, evaluation of a carcinogen s mechanism(s) or mode of action(s), is very important for the choice of model for the risk assessment, i.e., non-threshold or threshold this issue is addressed in Section 4.9. The third step, quantitative dose-response assessment, is the main focus of this chapter and is addressed in more detail in the following text. 

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. 

In the case of biological contamination, the identification of risk became obvious by experience, the risk assessment was made unambiguous by epidemiology, and the immediate and obvious effectiveness of the risk management decisions demonstrated their wisdom in the absence of elegant quantitative risk extrapolation models and projections of costs per case averted. Costs of water treatment and distribution became trivial relative to almost all other essential commodities, and in the public expectation the biological safety of drinking water became axiomatic. 

In summary, in studies of chemical toxicity, pathways and rates of metabolism as well as effects resulting from toxicokinetic factors and receptor affinities are critical in the choice of the animal species and experimental design. Therefore it is important that the animal species chosen as a model for humans in safety evaluations metabolize the test chemical by the same routes as humans and, furthermore, that quantitative differences are considered in the interpretation of animal toxicity data. Risk assessment methods involving the extrapolation of toxic or carcinogenic potential of a chemical from one species to another must consider the metabolic and toxicokinetic characteristics of both species. 

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