Extrapolation of dose response

Thresholds may also be determined by extrapolation of dose-response plots. In this approach, the perceived odor intensity is measured at several... 

PBPK models are particularly useful for interspecies extrapolations of dose-response data. In using a PBPK model of uptake, distribution, and elimination, an exponential power (e.g., 0.75) of the body weight is used to scale the cardiac output and ventilation rate between the laboratory species (typically rat) and humans. A PBPK model will therefore contain adequate logic to account for routes of administration, storage tissues and residence time therein, elimination rates, and sufficient mathematical detail to mimic the integration of these processes. It is important that the model parameters (e.g., elimination rates) be validated as much as possible by separate kinetic studies in the relevant species. The ultimate test of the model is how the model predictions are for parameters such as blood levels, rate of metabolism, and tissue concentrations relative to real-life animal data for the chemical. 

Figure 11. Extrapolation of Dose-Response Models to Low Doses of TCE. Adapted with permission from Ref. 21. Copyright 1984, Regul. Toxicol. Pharmacol.

Based on these principles, quantitative and qualitative toxicology data on pesticides are generated from animal studies and extrapolated to sian. The extrapolation, however, is usually not direct and stay include several assumptions. Species susceptibility, species metabolism differences, and extrapolations of dose response relationships below the experimental range should be considered (7 ). In a work situation, the husian body burden is determined by the exposure, absorption, and excretion rates. The same is true in animal studies, although continuous exposure is usually incorporated in the study design. Absorption is usually considered relatively complete. Excretion rates are usually specific to the physico-chemical properties of the chemical and the species however differences in excretion rates are not usually incorporated into extrapolations to man ( ). 

Assumptions in Risk Extrapolation. Risk extrapolation cannot be performed as a mechanical exercise, due to the need for judgment in the selection of data and application of dose-response models. In particular, there are a number of implicit assumptions inherent in risk extrapolation. They may be summarized as follows ... 

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 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. 

Risk is defined as the expected frequency of the occurrence of an undesirable effect arising from exposure to a chemical or physical agent. Estimation of risk makes use of dose-response data and extrapolation from the observed relationships to the expected responses at doses occurring in actual exposure situations. The quality and suitability of the biologic data used in such estimates are major limiting factors. 

The safety factor is a number that reflects the degree or amount of uncertainty that must be considered when experimental data are extrapolated to the human population. When the quality and quantity of dose-response data are high, the uncertainty factor is low when the data are inadequate or equivocal, the uncertainty factor must be larger... 

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. 

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. 

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. 

The problem of interspecies extrapolation most dose—response data are available from animals, not people. 

The most puzzling issue of all concerns the human being to whom we intend to extrapolate. If the issue of interspecies extrapolation concerns qualitative inferences only, then it is safe to talk generically about human beings , because it is likely that most people will respond, at some dose, to the toxic effects of a substance. But much experience tells us that the dose at which people respond varies among them some people are much more sensitive than others and will exhibit responses to the same chemical at lower doses. To make matters worse, people who are most sensitive to the effects of one chemical may not be among the most sensitive responders to another chemical that exerts its effects by a different mechanism. Discussions of dose-response extrapolation to humans need to take into account the variability in sensitivity among members of the human population. 

The extrapolations from mouse-to-man described in earlier chapters are primarily qualitative in nature, not quantitative. In the context of dose—response evaluations, quantitative extrapolation might concern, for example, estimation of the size of the minimum toxic dose in humans based on observations of the size of that dose in rodents or monkeys. This is trickier, by far, than the type of qualitative extrapolation involved in limited statements such as observations of nervous system toxicity in Fischer strain rats are applicable to human beings. The twin problems of Interspecies and High-to-Low Dose Extrapolations each has several sets of associated issues, so we shall deal with them one at a time, in as simple a way as possible. 

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