Risk assessment default assumptions

Precaution and Environmental Science. When the precautionary principle is discussed in its relationship to science, it is often portrayed as an antiscience or a risk-management principle that is only used after undergoing conventional scientific processes. As discussed earlier, in practice the limitations of science to characterize complex risks show that precaution is not at odds (Kriebel et al., 2001). Further, precaution is not just about additional safety factors or changing risk assessment default assumptions. Research by U.S. EPA scientists has demonstrated that many of the EPA s Reference Doses - or conservative safe exposures - may correspond to risks of greater than 1 in 1000, meaning that safety factors alone may not protect health (Castorina and Woodruff, 2003). 

The science policy components of risk assessment have led to what have come to be called default assumptions. A default is a specific, automatically applied choice, from among several that are available (in this case it might be, for example, a model for extrapolating animal dose-response data to humans), when such a choice is needed to complete some undertaking (e.g., a risk assessment). We turn in the next chapter to the conduct of risk assessment and the ways in which default assumptions are used under current regulatory guidelines. We might say we have arrived at the central subject of this book. 

At every step of the way an attempt is made to present a typical approach, and the usual default assumptions it must be recognized that individual assessments often contain (usually minor) deviations from what is presented here, but what is presented should capture the most important aspects of current chemical risk assessment practice. 

There are several large impediments to achieving the goal of more accurate risk assessments. First, it often requires a considerable investment in the research necessary to uncover the types of information needed to replace default assumptions in specific cases. If one hypothesizes that di-(2-ethylhexyl)phthalate (DEHP, a real and important chemical) produces liver tumors in rodents by mechanisms that either do not apply to humans at all, or that do not operate at low (human) doses, or both, then there arises the question of what type of research information is necessary to test the validity of such hypotheses If such research is actually carried out, then what type of results from that research would allow conclusions to be drawn about the validity of the hypotheses In many specific cases creative and knowledgeable scientists can hypothesize alternatives to the usual defaults and ways to test their validity. But it often turns out to be difficult to arrive at... 

Data from studies in experimental animals are the typical starting points for hazard and risk assessments of chemical substances and thus differences in sensitivity between experimental animals and humans need to be addressed, with the default assumption that humans are more sensitive than experimental animals. The rationale for extrapolation of toxicity data across species is founded in the commonality of anatomic characteristics and the universality of physiological functions and biochemical reactions, despite the great diversity of sizes, shapes, and forms of mammalian species. 

It may often be useful to initially conduct an exposure assessment based on worst-case assumptions, and to use default values when model calculations are applied. Such an approach can also be used in the absence of sufficiently detailed data. If the outcome of the risk characterization based on worst-case exposure assumptions is that the substance is not of concern, the risk assessment for that substance can be stopped with regard to the effect/population considered. If, in contrast, the outcome is that a substance is of concern, the assessment must, if possible, be refined using a more realistic exposure prediction in order to come to a definitive conclusion. 

The Risk Characterization Handbook (US-EPA 2000) is thus a practical guide in how to perform the risk characterization. However, the Handbook does not include any detailed information on the practices employed in the risk assessment itself, including use of uncertainty factors and use of default and extrapolation assumptions in the risk characterization step. This information and practices are provided in the US-EPA staff paper from 2004 titled An Examination of EPA Risk Assessment Principles and Practices (US-EPA 2004). 

In section 2.3 of this chapter the present approach to characterisation of dose-response relationships was described. In most cases it is necessary to extrapolate from animal species that are used in testing to humans. It may also be necessary to extrapolate from experimental conditions to real human exposures. At the present time default assumptions (which are assumed to be conservative) are applied to convert experimental data into predictive human risk assessments. However, the rates at which a particular substance is adsorbed, distributed, metabolised and excreted can vary considerably between animal species and this can introduce considerable uncertainties into the risk assessment process. The aim of PB-PK models is to quantify these differences as far as possible and so to be able to make more reliable extrapolations. 

Human health risk assessment has often been dominated by the use of default assumptions and worst case analyses, based on the use of upper bounds on the dose from exposure instead of distributional characterizations of that dose. There are severe limitations associated with the use of default assumptions and upper bounds instead of distributions when detailed exposure and/or dose-response data are available. The US National Academy of Sciences, the USEPA, and many others have recognized the need for new risk assessment methodology (NRC, 1983, 1993, 1994 USEPA, 1992 CRARM, 1997). This need has promoted the development of new quantitative risk assessment methods that use probabilistic techniques, especially Monte Carlo simulation and distributional characterizations of dose-response, exposure, and risk. For these reasons, this paper uses a probabilistic approach. An indication of some of these new methods and the type of results they produce are given below. 

Furthermore, default constants and assumptions do not explicitly address the uncertainty and variability that are an inherent part of human risk assessments however, probability distributions can explicitly include both uncertainty and variability. The uncertainty here refers to lack of knowledge or the limitations in the current state of knowledge. Variability, on the other hand, refers to the parameter value differing from one individual to another individual in a population, or from one instance to another. Additional research may reduce uncertainty, but not variability. 

Default is a choice used in the absence of any sufficiently defensible alternative. In risk assessment, a default is usually a policy-driven assumption, choice, or value intended to make it reasonably certain that exposure or risk is not underestimated and to err on the side of overestimating. 

The relevance of the observed rodent liver effect is evaluated using the common default assumption in risk assessment that an effect seen in experimental animals is considered relevant to human health risks in the absence of evidence to the contrary... 

European Commission 2003a). However, the risk assessors seem to be more reluctant to use this default assumption to assess the relevance of the observed indications of developmental neurotoxicity in rodents. For this endpoint additional data are requested. 

Default Assumptions To Be Considered in Assessing Reproductive and Developmental Toxicity Risk... 

An overview of a tiered approach for the use of dermal absorption data in dermal risk assessments is provided. Initial tiers utilize default assumptions, while higher tiers require results from in vivo and in vitro dermal absorption studies. For dermal absorption studies, challenges in data analysis, as well as in application of the data to risk assessment, are identified. 

In the absence of human data (the most preferred data for risk assessment), the dose-response assessment for either cancer or noncancer toxicity is determined from animal toxicity studies using an animal model that is relevant to humans or using a critical study and species that show an adverse effect at the lowest administered dose. The default assumption is that humans may be as sensitive as the most sensitive experimental species. 

There are many parameters and factors that are components of an exposure assessment. Where there is lack of adequate information on any of these parameters and factors, default assumptions are used. However, some of these parameters and factors (e.g., body weight, exposure frequency, and duration) can be represented by a range of values. If these uncertainties are not reduced, a highly conservative risk may result. 

The traditional scientific and political response to these data gaps has been to collect more information and use a technique called quantitative risk assessment to calculate the probability of harm given particular exposures, applying numerous assumptions in the process. While this process has been termed the sound science approach, it is often far from that. Quantitative risk assessments often narrow the types of information that go into decision-making and hide uncertainties. They are time-consuming and costly to complete and while debates over details of these assessments occur, the default policy option is that no policy action is necessary. 

Agencies like the EPA commonly employ default assumptions in exposure, hazard assessment, and risk assessment assumptions. In most of these matters, the amount of available evidence is far less than that available for hormesis. Furthermore, our collective information confirms that among the available toxicological models, the hormetic one is the most predominant. 

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