RISK DESCRIPTORS

Variability arises from true heterogeneity in characteristics such as dose-response differences within a population, or differences in contaminant levels in tlie enviromiient The values of some variables used in an assessment change witli time and space, or across tlie population whose exposure is being estimated. Assessments should address tlie resulting variability in doses received by members of the target population. Individual exposure, dose, and risk can vary widely in a large population. The central tendency and high end individual risk descriptors are intended to capture tlie variability in exposure, lifestyles, and other factors tliat lead to a distribution of risk across a population. 

Reproductive risk descriptors are intended to address variability of risk within the population and the overall adverse impact on the population. In particular, differences between high-end and central tendency estimates reflect variability in the population but not the scientific uncertainty inherent in the risk estimates. There is uncertainty in all estimates of risk, including reproductive risk. These uncertainties can result from measurement uncertainties, modelling uncertainties and assumptions made due to incomplete data. Risk assessments should address the impact of each of these uncertainties on confidence in the estimated reproductive risk values. 

It is not possible to claim one of these approaches as best because they are complementary to each other and a combination of them is ideal if possible. The method takes concentration addition (CA) as the starting point for mixture toxicity, so that the same risk descriptors can indicate toxic actions that add to each other between different substances (Thomsen et al. 2006). 

As a first step to the risk reduction process, the results of a risk assessment report (i.e., a registration or evaluation dossier) must be organised according to seven risk descriptors shown in Box 5.4. 

As part of this first step, one of three possible conclusions used in current EU risk assessment reports would be applied for risk descriptors I to VI ... 

Risk descriptor VII, national dimensions , would be defined as whether or not target-setting or national permitting schemes should be applied for the scenarios that were described in Box 5.3 on. Examples of four types of uncertainty that can lead to conclusion (i) are provided in Table 5.5. 

As a second step, prior to entering the risk descriptors into the decisionmaking matrix, a potential risk that requires particular regulatory attention needs to be excluded from further decision-making (Figure 5.10). Reasons for excluding potential risks include ... 

Table 5.5 Examples of uncertainty that could lead to conclusion (i) under the risk descriptors...

Risk descriptors (according to risk assessment conclusions) VII Regulatory options (guidance)... 

Probabilistic analysis Calculations and expression of health risks using multiple-risk descriptors to provide the likelihood of various risk levels. Probabilistic risk results approximate a full range of possible ontcomes and the likelihood of each, which is often presented as a freqnency distribntion graph, thns allowing uncertainty or variability to be expressed quantitatively (USEPA, 1999). 

Their advantages are that they are simple to use and are transparent that is, the descriptors that best model the biological activity can be seen and— hopefully—understood. Their disadvantages are that they work best when restricted to congeneric series of compounds, they assume that the biological activity is a rectilinear function of each descriptor, and they can suffer from a high risk of chance correlations, especially when a large pool of descriptors is used. 

Concerning the last point, Topliss and Costello [42] proposed that, to minimize the risk of chance correlations, a QSAR developed with MLR should utilize at least five data points (compounds) for each descriptor included in the equation. Later work [17] showed that it was necessary to take into account not only the number of descriptors in the QSAR (usually several) but also the whole of the descriptor pool (often several hundred) from which the best descriptors were selected. 

Obtaining a good quality QSAR model depends on many factors, such as the quality of biological data and the choice of descriptors and statistical methods. As a consequence, the uncertainty of the QSAR predictions is a combination of experimental uncertainties and model uncertainties. QSAR methods have to be applied to individual chemicals, not on mixtures. If the QSAR demands it, the components of the mixture have to be addressed separately and individually - in case of unknown compounds, QSAR cannot identify the toxicity risk and is therefore not useful. 

The US-EPA has in its 1996 Proposed Guidelines for Carcinogen Risk Assessment (US-EPA 1996) adopted the dose descriptor LEDio (the 95% lower confidence limit on a dose associated with a 10% extra tumor risk) whereas in its 2005 Guidelines for Carcinogen Risk Assessment (US-EPA 2005), no defined incidence has been recommended (see Section 6.3.2). Within the EU chemical s regulation, the dose descriptor T25 has been proposed (see Section 6.3.3). In the newly proposed MOE approach, the JECFA and the EFSA have recommended the dose descriptor BMDLio (see Section 6.4). 

Within the EU chemical s regulation, a more simple approach based on the dose descriptor T25 has been proposed as a basis for quantitative risk characterization of non-threshold carcinogens. 

Sanner, T., E. Dybing, M.I. Willems, and E.D. Kroese. 2001. A simple method for quantitative risk assessment of non-threshold carcinogens based on the dose descriptor T25. Pharmacol Toxicol. 88 331-341. 

The third descriptor that is sometimes used to characterize risk is the margin of exposure ( ). The is the ratio of the NOAEL (or BMD) from the most appropriate or sensitive species to the estimated level of human exposure from all potential sources. 

At the other extreme, a three-dimensional (3D) model of the molecule itself can be considered as a descriptor. In order to compare them, the molecules have to be aligned in 3D space, which is a difficult task, mostly owing to the conformational flexibility of most compounds of interest. Such 3D alignment-based comparisons of molecules are therefore time intensive and bear the risk of missing the right alignment. 

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