Deviations, risk assessment

Development stage, life cycle issues, 20-23 Deviations, risk assessment, 94-95 Differential scanning calorimetry, chemical reactivity tests, 87... 

Assumption deviation risk assessment Evaluations to reveal potential unknown knowns (black swans type ii)), i.e. knowledge about the topic not possessed by the group, but which others may possess Evaluations of events judged to not occur due to negligible probability (black swans type iii)) Evaluations to reveal potential weaknesses and gaps in the knowledge underpinning the risk analysis Scenarios/con sequences Probabilities... 

Table 1. Assumption deviation risk assessment of gas detection time.

In setting VI the background knowledge on which to base a quantitative uncertainty representation of X is moderate or weak. It may still be possible to establish a probability distribution F(x z , K) however, such a distribution will be based on more or less reasonable assumptions, and so the risk index [F(V) z , K resulting from an integration according to Equation (1) needs to be supplemented by an assumption deviation risk assessment of the assumption Z = z. This could be achieved by reporting of (interval) probabilities... 

Low Moderate/ high Moderate/ high Low Report R(x ) Report R(x ) Highlight assumption Highlight assumption deviation risk deviation risk assessment assessment for assumption X = x , for assumption X=x based on probability or interval/ based on probabUity imprecise probability OR OR See Table 4 See Table 4 ... 

At every step of the way an attempt is made to present a typical approach, and the usual default assumptions it must be recognized that individual assessments often contain (usually minor) deviations from what is presented here, but what is presented should capture the most important aspects of current chemical risk assessment practice. 

The major objective in the risk assessment of exposure to mixtures of chemicals is to establish or predict how the resulting toxicological effect might turn out. Will the toxic effect be determined by simple additivity of dose or effect, or will it deviate from additivity, either by an effect stronger or less than expected on the basis of additivity ... 

Although testing of the whole mixture as such seems to be the proper way to approach the risk assessment of exposure to that mixture, it will not provide data on combined actions and/or interactions between the individual components of the mixture. Even if the effect of the mixture is compared with the effects of each individual component at comparable concentrations, this will not allow a description of potential synergism, potentiation, or antagonism, and it is even doubtful that deviations from additivity can be concluded. This can only be achieved if dose-response curves are obtained for each of the single compounds. 

Despite its popularity in risk assessment practice, limitations of EP have been observed. The most striking deviations are discussed below ... 

Like many data, emission and exposure data are presented as constant values, often a mean with standard deviation. In environmental risk assessment, however, awareness is growing that a stochastic or probabilistic approach is more suitable to obtain insight in the possible risk of chemicals. This also requires expressing exposure data as statistical, probabilistic distributions. Also in this case, the focus should be extended to mixtures. 

If the toxic effect of a chemical combination is tested and compared with the effect of the individual chemicals, it may happen that the effect of the tested mixture deviates from the effect predicted by CA or IA. This mixture can be considered as 1 combination of the endless number of other possible combinations in which these chemicals can be mixed. If more combinations of this specific set of chemicals are tested, it can happen that effects of a number of different combinations at low concentrations differ from CA or IA, but that the effects of high-concentration combinations are well predicted. Such a systematic deviation pattern may be relevant for risk assessment, or may provide insight into the modes of action. Three types of systematic deviations from CA or IA can be defined as biologically relevant, based on studies published in the literature ... 

Flowever, it must be recognized that due to the complex interactions of components in the mixtures, quantitative applications of QSARs to mixtures in environmental risk assessment are limited. For exploratory analysis and interpretation of environmental observations and test results, QSARs can be useful for developing a basic understanding of the mixture and its deviation from the theory for ideal mixtures. 

Another typical source of uncertainty in mixture assessment is the potential interaction between substances. Interactions may occur in the environment (e.g., precipitation after emission in water), during absorption, transportation, and transformation in the organism, or at the site of toxic action. Interactions can be either direct, for example, a chemical reaction between 2 or more mixture components, or indirect, for example, if 1 mixture component blocks an enzyme that metabolizes another mixture component (see Chapters 1 and 2). Direct interactions between mixture components are relatively easy to predict based on physical-chemical data, but prediction of indirect interactions is much more difficult because it requires detailed information about the processes involved in the toxic mechanisms of action. One of the main challenges in mixture risk assessment is the development of a method to predict mixture interactions. A first step toward such a method could be the setup of a database, which contains the results of mixture toxicity tests. Provided such a database would contain sufficient data, it could be used to predict the likelihood and magnitude of potential interaction effects, that is, deviations for CA and RA. This information could subsequently be used to decide whether application of an extra safety factor for potential interaction effects is warranted, and to determine the size of such a factor. The mixture toxicity database could also support the search for predictive parameters of interaction effects, for example, determine which modes of action are involved in typical interactions. 

Processes used to determine functional criticality, such as FMEA, have already been discussed within this chapter. Similarly, FMEA and other risk assessment tools can be used to determine the scope of validation. The risk of failure increases as information systems supporting the EMS strategy deviates from a standardized solution that is, the level of tailored development increases. In addition, the extent to which a product is utilized within industry, in particular pharmaceuticals, must be taken into consideration when determining the scope of validation. 

The EFSA has been appointed to advise the European Commission on the safety of substances to come into contact with foods. Opinions of EFSA are based on a risk assessment. In general the conclusions given in an opinion will be adopted by the Commission, although occasionally the Commission may decide to deviate from the EFSA opinion as a risk management issue. In Fig. 17.1 a flow scheme related to the authorisation procedure is depicted. 

EPA recommends three approaches (1) if the toxicity data on mixture of concern are available, the quantitative risk assessment is done directly form these preferred data (2) when toxicity data are not available for the mixture of concern, data of a sufficiently similar mixture can be used to derive quantitative risk assessment for mixture of concern and (3) if the data are not available for both mixture of concern and the similar mixture, mixture effects can be evaluated from the toxicity data of components. According to EPA, the dose-additive models reasonably predict the systemic toxicity of mixtures composed of similar (dose addition) and dissimilar (response addition) compounds. Therefore, the potential health risk of a mixture can be estimated using a hazard index (HI) derived by summation of the ratios of the actual human exposure level to estimated maximum acceptable level of each toxicant. A HI near to unity is suggestive of concern for public health. This approach will hold true for the mixtures that do not deviate from additivity and do not consider the mode of action of chemicals. Modifications of the standard HI approach are being developed to take account of the data on interactions. 

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