Risk extrapolation, estimating potential risks

A number of methods and models have been used at sites to estimate potential risks from exposure to lead. One method is the use of prevalence data for estimating PbB levels. In this case, PbB measurements can be made at a site and extrapolated to other sites with similar environmental and demographic data. Limitations of this method include site-to-site variability with respect to, among other things, children s behavioral patterns, age, and bioavailability issues. Estimation of past exposures can be problematic because of redistribution of Pb out of the blood compartment since PbB is only an indicator of recent exposure (<90 days). 

For most chemicals, actual human toxicity data are not available or critical information on exposure is lacking, so toxicity data from studies conducted in laboratory animals are extrapolated to estimate the potential toxicity in humans. Such extrapolation requires experienced scientific judgment. The toxicity data from animal species most representative of humans in terms of pharmacodynamic and pharmacokinetic properties are used for determining AEGLs. If data are not available on the species that best represents humans, the data from the most sensitive animal species are used to set AEGLs. Uncertainty factors are commonly used when animal data are used to estimate minimal risk levels for humans. The magnitude of uncertainty factors depends on the quality of the animal data used to determine the no-observed-adverse-effect level (NOAEL) and the mode of action of the substance in question. When available, pharmocokinetic data on tissue doses are considered for interspecies extrapolation. 

Recently, metapopulation models have been successfully applied to assess the risks of contaminants to aquatic populations. A metapopulation model to extrapolate responses of the aquatic isopod Asellus aquaticus as observed in insecticide-stressed mesocosms to assess its recovery potential in drainage ditches, streams, and ponds is provided by van den Brink et al. (2007). They estimated realistic pyrethroid concentrations in these different types of aquatic ecosystems by means of exposure models used in the European legislation procedure for pesticides. It appeared that the rate of recovery of Asellus in pyrethroid-stressed drainage ditches was faster in the field than in the isolated mesocosms. However, the rate of recovery in drainage ditches was calculated to be lower than that in streams and ponds (van den Brink et al. 2007). In another study, the effects of flounder foraging behavior and habitat preferences on exposure to polychlorinated biphenyls in sediments were assessed by Linkov et al. (2002) using a tractable individual-based metapopulation model. In this study, the use of a spatially and temporally explicit model reduced the estimate of risk by an order of magnitude as compared with a nonspatial model (Linkov et al. 2002). 

The use of the terms upper bound and worst-case refer to the expectations that this approach is likely to be highly conservative and will not underestimate potential risk. These terms are not meant to connote that statistical analysis to estimate error bounds would be performed, or that additional safety factors (traditional for extrapolation to acceptable daily intake values for non-carcinogens) would be incorporated into the extrapolation. 

Each of these steps combines numerous assumptions that allow the risk assessor to extrapolate from the available data to potential real-world scenarios. A detailed description of the art and science of risk characterization is beyond the scope of this book the reader should simply be aware that the process can reflect both scientific thinking and subjective judgment [78], and that in consequence risk assessments can produce a variety of estimates of risk from the same data. 

Oncogenic Risk Calculations. On the basis of the expos ire analysis and potential oncogenic risk (oncogenic potency might be more descriptive), a risk analysis will be performed according to statistical methods like linear extrapolation (one-hit model) or multistage estimation (9.). 

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... 

An important outcome of the JECFA evaluation is the establishment of an ADI for a food additive. The ADI is based on the available toxicological data and the no adverse effect level in the relevant species. JECFA defines the ADI as an estimate of the amount of a food additive, expressed on a body weight basis, that can be ingested daily over a lifetime without appreciable health risk (8). JECFA utilizes animal data to determine the ADI based on the highest no-observed-adverse-effect level (NOAEL), and a safety factor is applied to the NOAEL to provide a margin of safety when extrapolating animal data to humans. JECFA typically uses safety factors of 50, 100, or 200 in the determination of an ADI. The NOAEL is divided by the safety factor to calculate the ADI. The food additive is considered safe for its intended use if the human exposure does not exceed the ADI on a chronic basis. This type of information may potentially be used to help assess the safety of a pharmaceutical excipient that is also used as a food additive, based on a comparison of the ADI to the estimated daily intake of the excipient. 

Suter et al. 1993 Society of Environmental Toxicology and Chemistry [SETAC] 1994 European Union 1997 Ecological Committee on FIFRA Risk Assessment Methods [ECOFRAM] 1999 Campbell et al. 1999). The initial use of conservative assessment criteria (i.e., err on the side of caution) allows substances that do not present a risk to be eliminated from the risk assessment process early, thus allowing the focus of resources and expertise to be shifted to potentially more problematic substances or situations. As one ascends through the tiers, the estimates of exposure and effects become more realistic with the acquisition of more accurate and/or representative data, and uncertainty in the extrapolation of effects is thus reduced or at least better characterized. Likewise, the methods of extrapolation may become more sophisticated as one ascends through the tiers (Figure 1.2). 

Ecological risk assessment differs importantly from human health risk assessment in that the latter attempts to extrapolate effects of chemicals on a handful of model species to 1 target species (humans), whereas the former attempts to extrapolate effects on a handful of model species to all species in potentially exposed ecosystems. Even if it were possible to test all species in all ecosystems (which it is not), there would be both moral and economic reasons for not wishing to do so. Ecological models are the only practical way to cover the range of situations for which we wish to estimate risk. 

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