Default dose-response models

Galabrese, E.J. 2004. Hormesis from marginalization to mainstream. A case for hormesis as the default dose-response model in risk assessment. Toxicology and Applied Pharmacology 197 125-136. 

But subjectivism raises the paradox Poorly understood causation requires more, not less, knowledge. Thus, when default dose-response models become the benchmark for comparing results with other models, how can the decision be... 

Should hormetic dose responses have to be demonstrated in each case or can it be assumed to occur if it is in fact far more common than the threshold response Should there be a default dose response model Why Why not ... 

Calabrese, E.J. (2004) Hormesis from marginalization to mainstream. A case for hormesis as the default dose-response model in risk assessment. Toxicol Appl Pharmacoly 197, 125-136. Calabrese, E.J. and Baldwin, L.A. (1998) Hormesis as abiological hypothesis. Eviron. Health Perspect.y 106 (Suppl. 1), 357-362. 

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. 

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. 

Jarabek, A. M., The application of dosimetry models to identify key processes and parameters for default dose-response assessment approaches. Toxicol. Lett. 79(1-3) 171-184, 1995. 

Physiologically based pharmacokinetic (PBPK) modelling sometimes constitutes a basis for replacement of default components of uncertainty for toxicokinetics and a portion of toxicodynamics. Where data are sufficient, a full biologically based dose-response model addresses additional uncertainties with respect to both interspecies differences and interindividual variability in both kinetics and dynamics. 

Overall, cancer risk assessment involves the four steps of hazard identification, dose-response, exposure assessment, and risk characterization. The dose-response curve established for cancer potency derivation for a chemical is based on evaluation of data on the carcinogenicity and dose-response characteristics of the chemical. The pharmacokinetics and mechanistic data evaluation (e.g., genotoxic or nongenotoxic) and a dose-response review of all adequate bioassays are conducted to determine, if target dose estimates or a dose-response model different from the default may be suggested. 

The totality of the scientific evidence for a causal default—a fundamental dose-response model, given the state-of-science—now discounts conjectural arguments (the linear, at low-dose, nonthreshold model) or arbitrary ones, such as those based on extrapolation (the threshold model) because both of them eliminate a very large number of experimentally observed health benefits. According to the EPA, the use of defaults is a subjective choice (EPA 2005). As the EPA states ... 

Corollary Question Since the J-shaped hormetic (or biphasic) cancer dose-response model yields empirically demonstrated protective (stimulatory) effects at low doses in one or more species, is biologically plausible, and describes a damaging relationship at higher dose that is consistent with the LNT, which of the two is the logical and prudential default model ... 

The first conclusion is that the factual and theoretical evidence points to replacing the classical causal regulatory defaults used to deal with low dose-response, the linear no-threshold, and the linear at low-dose-response models, or monotonic functions, with the J- and inverse J-shaped models—or relations. These models have been demonstrated to apply to toxicological and cancer outcomes for a very wide range of substances and diseases. The classical defaults may stiU be applicable on a case-by-case basis. The reasons for changing the defaults include the fact that the J-shaped class of models quantities a wide set of health benefits that are completely excluded from estimations that use monotonic models. We conclude that replacing both a conjecture and an arbitrary model with two theoretically and empirically sound ones leads to rational decision and does not exclude actually demonstrable benefits. Overall, the sum is positive for society. 

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. 

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 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 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. 

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. 

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. 

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. 

The U.S. EPA applies an alternative dose-response evaluation of carcinogens using a low-dose, linear model (EPA 2005). The linear extrapolation is applied under two circumstances (1) when there are data to indicate that the dose-response curve has a linear component below the point of departure or (2) as a default for a tumor site where the mode of action is not established. For a linear extrapolation, a straight line is drawn from the point of departure to the origin. The slope of the line, known as the slope factor, is an upper-bound estimate of risk per increment of dose that can be used to estimate risk probabilities for different exposure levels. The slope factor is equal to 0.01/LEDoi, for example, if the LEDqi is used as the point of departure. The lower hmit on effective doscoi (LEDoi) is the 95% lower confidence hmit of the dose of a chemical needed to produce an adverse effect in 1% of those exposed to the chemical, relahve to control. If, however, there are sufficient data to ascertain that a chemicaTs mode of action supports modeling at low doses, a reference dose or concentrahon may be developed in lieu of a cancer slope factor. 

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