Extrapolation factors, risk assessment

Second, there is a procedure for scaling doses between animals and humans, to take account of differences in body size and rates of various physiological processes. Interestingly, as the EPA and other regulators practice risk assessment, animal-to-human extrapolation for carcinogens is based on the use of such scaling factors, rather than... 

As mentioned previously, the assessment of hazard and risk to humans from exposure to chemical substances is generally based on the extrapolation from data obtained in smdies with experimental animals. In the absence of comparative data in humans, a basic assumption for toxicological risk assessment is that effects observed in laboratory animals are relevant for humans, i.e., would also be expressed in humans. In assessing the risk to humans, an assessment factor is applied to take account of uncertainties in the differences in sensitivity to the test substance between the species, i.e., to account for interspecies variability (Section 5.3). If data are available from more than one species or strain, the hazard and risk assessment is generally based on the most susceptible of these except where data strongly indicate that a particular species is more similar to man than the others with respect to toxicokinetics and/or toxicodynamics. Two main aspects of toxicity, toxicokinetics and toxicodynamics, account for the namre and extent of differences between species in their sensitivity to xenobiotics this is addressed in detail in Chapter 5. 

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. 

The assessment factors generally apphed in the estabhshment of a tolerable intake from the NOAEL, or LOAEL, for the critical effect(s) are apphed in order to compensate for rmcertainties inherent to extrapolation of experimental animals data to a given human situation, and for rmcertainties in the toxicological database, i.e., in cases where the substance-specific knowledge required for risk assessment is not available. As a consequence of the variabihty in the extent and nature of different databases for chemical substances, the range of assessment factors apphed in the establishment of a tolerable intake has been wide (1-10,000), although a value of 100 has been used most often. An overview of different approaches in using assessment factors, historically and currently, is provided in Section 5.2. 

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 EU TGD (EC 2003) recognized that the NOAEL is not very accurate with respect to the degree to which it corresponds with the (unknown) true NAEL. In the case of a steep curve the derived NOAEL can be considered as more reliable (the greater the slope, the greater the reduction in response to reduced doses) in the case of a shallow curve, the uncertainty in the derived NOAEL may be higher and this has to be taken into account in the assessment. If a LOAEL has to be used in the assessment, then this value can only be considered reliable in the case of a very steep curve. According to KEMI (2003), extrapolation factors of between 3-5 are used for LOAEL-to-NOAEL extrapolation without any scientific basis in risk assessment reports of existing substances within the European Union. 

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). 

There are of course many mathematically complex ways to perform a risk assessment, but first key questions about the biological data must be resolved. The most sensitive endpoint must be defined along with relevant toxicity and dose-response data. A standard risk assessment approach that is often used is the so-called divide by 10 rule . Dividing the dose by 10 applies a safety factor to ensure that even the most sensitive individuals are protected. Animal studies are typically used to establish a dose-response curve and the most sensitive endpoint. From the dose-response curve a NOAEL dose or no observed adverse effect level is derived. This is the dose at which there appears to be no adverse effects in the animal studies at a particular endpoint, which could be cancer, liver damage, or a neuro-behavioral effect. This dose is then divided by 10 if the animal data are in any way thought to be inadequate. For example, there may be a great deal of variability, or there were adverse effects at the lowest dose, or there were only tests of short-term exposure to the chemical. An additional factor of 10 is used when extrapolating from animals to humans. Last, a factor of 10 is used to account for variability in the human population or to account for sensitive individuals such as children or the elderly. The final number is the reference dose (RfD) or acceptable daily intake (ADI). This process is summarized below. 

Because of the first of these uncertainties (the extrapolation across species), assessments of risks to human health apply an uncertainty or safety factor of 100 to the experimentally derived no observed adverse effect concentration (NOAEC), in other words the NOAEC is divided by 100 to derive a no-effect level for human toxicity. This factor has been used since 1961, when it was chosen on an essentially arbitrary basis (RCEP, 2003, p22). In the assessment of risks to the environment, application factors of 10, 50, 100 or 1000 are applied to the results of tests carried out on specific species,2 depending on the species used and whether the tests were long term or short term. Evidence to the Royal Commission on Environmental Pollution (RCEP) for their report Chemicals in products indicated that these are merely extrapolation factors — they express the statistical variability of test results but do not effectively take into account inter-species variability, the vulnerability of threatened species, lifetime exposures or the complexity of biological systems... 

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. 

Most extrapolations from animal experimental data in the risk assessments require the utilization of uncertainty factors. This is because we are not certain how to extrapolate across species, with species for the most sensitive population, and across duration. To account for variations in the general population and to protect sensitive subpopulations, an uncertainty factor of 10 is used by EPA and ATSDR. The value of 10 is derived from a threefold factor for differences in toxicokinetics and for threefold factor for toxicodynamics. To extrapolate from animals to humans and account for interspecies variability between humans and other mammals, an uncertainty factor of 10 is used by EPA and ATSDR, and as with intraspecies extrapolations, this 10-fold factor is assumed to be associated with in toxicodynamics and toxicokinetics. An uncertainty... 

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 risk interpretation of biomonitoring results will tend to have additional uncertainties. That is because, in addition to the standard uncertainties encountered in risk assessment, there is the uncertainty of extrapolating from a blood or urinary concentration to an external dose. There will be variability both in the timing between sample draw and most recent exposure and in the relationship between blood concentration and dose. Those kinds of variability are compounded by uncertainty in the ability of a PK calculation or model to convert biomarker to dose accurately. For example, reliance on urinary biomarker results expressed per gram of urinary creatinine leads to an uncertain calculation of total chemical excretion per day because of the considerable variability in creatinine clearance per day. That complicates an otherwise simple approach to estimating dose. Furthermore, the conversion requires knowledge of fractional excretion via various pathways, which may not be present for a large sample of humans. The uncertainties created by these factors can be bounded via sensitivity and Monte... 

The use of MLEs of probability coefficients for radionuclides but UCLs for chemicals that induce stochastic responses is the most important issue that would need to be resolved to achieve a consistent approach to estimating risks for the purpose of waste classification. For some chemicals, the difference between MLE and UCL can be a factor of 100 or more. The difference between using fatalities or incidence as the measure of response is unlikely to be important. Use of the linearized, multistage model to extrapolate the dose-response relationship for chemicals that induce stochastic effects, as recommended by NCRP, should be reasonably consistent with estimates of the dose-response relationship for radionuclides, and this model has been used widely in estimating probability coefficients in chemical risk assessments. The difference in the number of organs or tissues that are taken into account, although it cannot be reconciled at the present time, should be unimportant. 

Current procedures of higher tier risk assessment are often based on the extrapolation of responses observed in relatively simple and short-term (weeks) cosm tests to structurally more complex ecosystems in the field. The predictive value of studies in small cosms (microcosms), however, depends on factors such as fate and exposure of the stressor and the sensitivity and recovery potential of the populations present. The role of cosm studies in extrapolation is discussed in more detail in Chapter 4. 

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