Dose-response assessment extrapolation models

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. 

Dose-response assessment today is generally performed in two steps (1) assessment of observed data to derive a dose descriptor as a point of departure and (2) extrapolation to lower dose levels for the mmor type under consideration. The extrapolation is based on extension of a biologically based model (see Section 6.2.1) if supported by substantial data. Otherwise, default approaches that are consistent with current understanding of mode of action of the agent can be applied, including approaches that assume linearity or nonlinearity of the dose-response relationship, or both. The default approach is to extend a straight line to the human exposure doses. 

The first step of the dose-response assessment is the evaluation of the data within the range of observation. If there are sufficient quantitative data and adequate understanding of the carcinogenic process, a biologically based model may be developed to relate dose and response data. Otherwise, as a default procedure, a standard model can be used to curve-fit the data. For each mmor response, a POD from the observed data is estimated to mark the beginning of extrapolation to lower doses. The POD is an estimated dose (expressed in human-equivalent terms) near the lower end of the observed range, without significant extrapolation to lower doses. 

The second step of the dose-response assessment is an extrapolation to lower dose levels, i.e., below the observable range. The purpose of low-dose extrapolation is to provide as much information as possible about risk in the range of doses below the observed data. The most versatile forms of low-dose extrapolation are dose-response models that characterize risk as a probability over a range of environmental exposure levels. Otherwise, default approaches for extrapolation below the observed data range should take into account considerations about the agent s mode of action at each tumor site. Mode-of-action information can suggest the likely shape of the dose-response curve at these lower doses. Both linear and nonlinear approaches are available. 

In the dose-response assessment to determine a dosage that is risk-free for human health, the JFCFA has never used mathematical models to extrapolate risks at low dose and determine a virtually safe dose, on the grounds that the lack of validation would produce very different results. However, the IFCFA could usefully address this matter in its deliberations. When progress in this area permits selection from various validated models, this exercise should no longer be solely associated with risk assessment but will also incorporate an element of risk management. 

There are two possible approaches to estimating the human safe dose for chemicals that cause deterministic effects the use of safety and uncertainty factors and mathematical modeling. The former constitutes the traditional approach to dose-response assessment for chemicals that induce deterministic effects. Biologically-based mathematical modeling approaches that more realistically predict the responses to such chemicals, while newer and not used as widely, hold promise to provide better extrapolations of the dose-response relationship below the lowest dose tested. 

Low-dose extrapolation models are the backbone of dose-response assessments. Because they can play such a dominant role in the regulatory process, it is important to understand some of their characteristics. As shown in Figure 3.10, different extrapolation models usually fit the data in the observable dose region in animal tests about equally well (Krewski et al., 1989), but they often give quite different results in the unobserved low-dose region of interest in assessments of risk to human health. The results obtained by extrapolation of the most commonly used low-dose models usually vary in a predictable manner, because the models use different mathematical equations to describe the chemical s likely behavior in the low-dose region. 

Failure to adjust dose-response estimates by considering biological information. In many dose-response assessments, potentially important biological information is not taken into account in selecting an extrapolation model. Examples of information often not included when a model is selected are the types of tumors, time to onset, and whether the chemical is genotoxic. Some... 

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)... 

Qf fundamental importance in conducting a risk assessment for a substance such as a nanomaterial is an understanding of the dose-response for adverse effects associated with exposure. A dose-response assessment is an evaluation of the relationship between dose and incidence (or severity) of an adverse effect. In many cases, a dose-response assessment must be conducted to extrapolate the doses associated with adverse effects observed in toxicological studies (typically in an animal model that has been treated with... 

The following example is based on a risk assessment of di(2-ethylhexyl) phthalate (DEHP) performed by Arthur D. Little. The experimental dose-response data upon which the extrapolation is based are presented in Table II. DEHP was shown to produce a statistically significant increase in hepatocellular carcinoma when added to the diet of laboratory mice (14). Equivalent human doses were calculated using the methods described earlier, and the response was then extrapolated downward using each of the three models selected. The results of this extrapolation are shown in Table III for a range of human exposure levels from ten micrograms to one hundred milligrams per day. The risk is expressed as the number of excess lifetime cancers expected per million exposed population. 

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 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. 

The explanation of the pharmacokinetics or toxicokinetics involved in absorption, distribution, and elimination processes is a highly specialized branch of toxicology, and is beyond the scope of this chapter. However, here we introduce a few basic concepts that are related to the several transport rate processes that we described earlier in this chapter. Toxicokinetics is an extension of pharmacokinetics in that these studies are conducted at higher doses than pharmacokinetic studies and the principles of pharmacokinetics are applied to xenobiotics. In addition these studies are essential to provide information on the fate of the xenobiotic following exposure by a define route. This information is essential if one is to adequately interpret the dose-response relationship in the risk assessment process. In recent years these toxicokinetic data from laboratory animals have started to be utilized in physiologically based pharmacokinetic (PBPK) models to help extrapolations to low-dose exposures in humans. The ultimate aim in all of these analyses is to provide an estimate of tissue concentrations at the target site associated with the toxicity. 

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. 

PB-PK modelling allows further refinement of the dose-response evaluation by partitioning the relationship into pharmacokinetic (exposure vs. tissues dose) and pharmacodynamic (tissue dose vs. toxic response) components. This allows the uncertainties associated with each component to be assessed separately and adds accuracy to the overall animal to man extrapolation. Future developments of PB-PK modelling may allow specific sub-populations such as the newborn or individuals with metabolic variations to be taken into account. However, before this can be done there will need to be considerable growth in the amounts of physiological, pharmacokinetic and pharmacodynamic information available. 

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. 

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