Risk assessment default factors

Chapter 5 of the document reviews the UFs used by UK Government departments, agencies, and their advisory committees in human health risk assessment. Default values for UFs are provided in Table 3 in the UK document with the factors separated into four classes (1) animal-to-human factor, (2) human variability factor, (3) quality or quantity of data factor, and (4) severity of effect factor. The following chemical sectors are addressed food additives and contaminants, pesticides and biocides, air pollutants, drinking water contaminants, soil contaminants, consumer products and cosmetics, veterinary products, human medicines, medical devices, and industrial chemicals. 

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). 

Felter, S.P., et al., An evaluation of the scientific basis for default uncertainty factors for use in quantitative risk assessment of the induction of allergic contact dermatitis, Contact Derm. Al, 257-266, 2002. 

Data on the toxicokinetics of a substance can be very useful in the interpretation of toxicological findings, and may replace the use of some default extrapolation factors used in route-to-route (Section 5.5) or interspecies extrapolations (Section 5.3). In addition, interindividual differences in sensitivity to toxicants may be identified on the basis of toxicokinetic data, thereby making it possible to make the risk assessment more comprehensive by including sensitive subpopulations (Section 5.4). In conjunction with information on the relationship between concentration-dose at the target site and the toxic effect, toxicokinetic information may be an important tool for extrapolation from high to low dose effects. 

Vermeire et al. (1999) have published a discussion paper with focus on assessment factors for human health risk assessment. The status quo with regard to assessment factors is reviewed and the paper discusses the development of a formal, harmonized set of assessment factors. Options are presented for a set of default values and probabilistic distributions for assessment factors based on the state of the art. Methods of combining default values or probabUistic distributions of assessment factors (Section 5.11) are also described. In relation to assessment factors, the authors recommended ... 

A more recent Dutch report (Vermeire et al. 2001) provides a practical guide for the application of probabilistic distributions of default assessment factors in human health risk assessments, and it is stated that the proposed distributions will be applied in risk assessments of new and existing substances and biocides prepared at RIVM (the National Institute of Public Health and the Environment) and TNO. The report concentrated on the quantification of default distributions of the assessment factors related to interspecies extrapolation (animal-to-human), intraspecies extrapolation (human-to-human), and exposure duration extrapolation. 

The use of default assessment factors is recommended in risk assessments, when justifiable, although the scientific background for such factors in general was considered unsatisfactory. The default assessment factors suggested are summarized in Table 5.2. It is recommended to use assessment factors derived from probabilistic distributions in favor of deterministic assessment factors, see Table 5.2. 

A WHO/IPCS (2005) Harmonization Project Document has proposed using chemical-specific toxicological data instead of default assessment factors, when possible. The concept of Chemical-Specific Adjustment Factors (CS AFs) has been introduced to provide a method for the incorporation of quantitative data on interspecies differences or human variability in either toxicokinetics or toxicodynamics into the risk assessment procedure, by modifying the relevant default UF of 10. Incorporation of toxicokinetic or toxicodynamic data becomes possible if each factor of 10 is divided into appropriately weighted sub-factors as suggested by Renwick (1991, 1993) and adopted by WHO/IPCS (1994), see Section 5.2.1.3. 

Renwick, A.G. and N.R. Lazarus. 1998. Human variability and noncancer risk assessment—An analysis of the default uncertainty factor. Regul. Toxicol. Pharmacol. 27 3-20. 

Walton, K., J.L. Dome, and A.G. Renwick. 2001c. Categorical default factors for interspecies differences in the major routes of xenobiotic elimination. Hum. Ecol. Risk Assess. 7 181-201. 

The 95% confidence limits of the estimate of the linear component of the LMS model, /, can also be calculated. The 95% upper confidence limit is termed qi and is central to the US-EPA s use of the LMS model in quantitative risk assessment, as qi represents an upper bound or worst-case estimate of the dose-response relationship at low doses. It is considered a plausible upper bound, because it is unlikely that the tme dose-response relationship will have a slope higher than qi, and it is probably considerably lower and may even be zero (as would be the case if there was a threshold). Lfse of the qj as the default, therefore, may have considerable conservatism incorporated into it. The values of qi have been considered as estimates of carcinogenic potency and have been called the unit carcinogenic risk or the Carcinogen Potency Factor (CPF). 

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). 

Refinements of the RfC have utilized mechanistic data to modify the interspecies uncertainty factor of 10 (Jarabek, 1995). The reader should appreciate that with the inhalation route of exposure, dosimetric adjustments are necessary and can affect the extrapolations of toxicity data of inhaled agents for human health risk assessment. The EPA has included dosimetry modeling in RfC calculations, and the resulting dosimetric adjustment factor (DAF) used in determining the RfC is dependent on physiochemical properties of the inhaled toxicant as well as type of dosimetry model ranging from rudimentary to optimal model structures. In essence, the use of the DAF can reduce the default uncertainty factor for interspecies extrapolation from 10 to 3.16. 

The 1996 Food Quality Protection Act (FQPA) now requires that an additional safety factor of 10 be used in the risk assessment of pesticides to ensure the safety of infants and children, unless the EPA can show that an adequate margin of safety is assured with out it (Scheuplein, 2000). The rational behind this additional safety factor is that infants and children have different dietary consumption patterns than adults and infants, and children are more susceptible to toxicants than adults. We do know from pharmacokinetics studies with various human pharmaceuticals that drug elimination is slower in infants up to 6 months of age than in adults, and therefore the potential exists for greater tissue concentrations and vulnerability for neonatal and postnatal effects. Based on these observations, the US EPA supports a default safety factor greater or less than 10, which may be used on the basis of reliable data. However, there are few scientific data from humans or animals that permit comparisons of sensitivities of children and adults, but there are some examples, such as lead, where children are the more sensitive population. It some cases qualitative differences in age-related susceptibility are small beyond 6 months of age, and quantitative differences in toxicity between children and adults can sometimes be less than a factor of 2 or 3. 

Much of the research efforts in risk assessment are therefore aimed at reducing the need to use these default uncertainty factors, although the risk assessor is limited by data quality of the chemical of interest. With sufficient data and the advent of sophisticated and validated physiologically based pharmacokinetic models and biologically based dose-response models (Conolly and Butterworth, 1995), these default values can be replaced with science-based factors. In some instances there may be sufficient data to be able to obtain distributions rather than point estimates. 

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. 

Renwick, A.G., and N.R. Lazarus. 1998. Human variability and noncancer risk assessment An analysis of the default uncertainty factor. Regul. Toxicol. Pharmacol. 27(1 Part l) 3-20. Reidy, C.A., J.T. Johnson, and M.J. Olson. 1990. Metabolism in vitro of fluorocarbon R-134a. 

Residential exposure should be estimated by taking into account distributions of exposure factors. Methods to assess distributions are through the deterministic or probabilistic approach (Figure 6.6). The former is often taken in preventive risk assessment in which each default value is determined from each distribution as a reasonable worst-case . The estimated exposures for the deterministic approach are expected to occur in the upper range. For actual risk assessments, the probabilistic approach directly uses the parameter distributions instead of single values to calculate distributions of exposure. To characterize exposure, an... 

There is an emerging body of evidence that suggests person-to-person differences in exposure play an important role in the variability and uncertainty associated with risk assessments for chemicals (and other agents). The traditional or standard default approaches used in human health risk assessment often do not effectively evaluate interindividual variation and may underestimate the impact of chemical exposures on particular groups of individuals. Traditional approaches must be refined to adequately account for temporal variation in factors that contribute to complex aggregate exposure patterns (e.g., chemical-specific exposure media concentrations and time-activity interactions by humans) involving multiple, intermittent exposures. 

Meek ME (2001) Categorical default uncertainty factors -interspecies variation and adequacy of database. Human and Ecological Risk Assessment 7 157-163. 

The initial process in the application of toxicity (dose-response) data in risk assessment is the extrapolation of findings to establish acceptable levels (AL) of human exposure. These levels may be reference values (inhalation reference concentrations, RfC or oral reference doses, RfD), minimal risk levels (MRL) values, occupational exposure limits, and so on. When the toxicity data are derived from animals, the lowest dose representing the NOAEL (preferably) or the LOAEL defines the point of departure (POD). In setting human RfD, RfC, or MRL values, the POD requires several extrapolations (see [13] and revisions). Extrapolations are often made for interspecies differences, intraspecies variability, duration of exposure, and effect level. Each area is generally addressed by applying a respective uncertainty factor having a default value of 10 their multiplicative value is called the composite uncertainty factor (UF). The UF is mathematically combined with the dose at the POD to determine the reference value ... 

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