Critical effect factors

There are several limitations to tliis approach that must be acknowledged. As mentioned earlier, tlie level of concern does not increase linearly as the reference dose is approached or exceeded because the RfDs do not luive equal accuracy or precision and are not based on the same severity of effects. Moreover, luizm-d quotients are combined for substances with RfDs based on critical effects of vaiy ing toxicological significance. Also, it will often be the case that RfDs of varying levels of confidence Uiat include different uncertainty adjustments and modifying factors will be combined (c.g., extrapolation from animals to hmnans, from LOAELs to NOAELs, or from one exposure duration to anoUier). 

It will be shown, however, that the effectiveness factor does not critically depend on the shape of the particles, provided that their characteristic length is defined in an appropriate way. Some comparison is made be made between calculated results and experimental measurements with particles of frequently ill-defined shapes. 

The solution of this equation is in the form of a Bessel function 32. Again, the characteristic length of the cylinder may be defined as the ratio of its volume to its surface area in this case, L = rcJ2. It may be seen in Figure 10.13 that, when the effectiveness factor rj is plotted against the normalised Thiele modulus, the curve for the cylinder lies between the curves for the slab and the sphere. Furthermore, for these three particles, the effectiveness factor is not critically dependent on shape. 

Some deactivation processes are reversible. Deactivation by physical adsorption occurs whenever there is a gas-phase impurity that is below its critical point. It can be reversed by eliminating the impurity from the feed stream. This form of deactivation is better modeled using a site-competition model that includes the impurities—e.g., any of Equations (10.18)-(10.21)— rather than using the effectiveness factor. Water may be included in the reaction mixture so that the water-gas shift reaction will minimize the formation of coke. Off-line decoking can be... 

Stages in hazard characterization according to the European Commission s Scientific Steering Committee are (1) establishment of the dose-response relationship for each critical effect (2) identification of the most sensitive species and strain (3) characterization of the mode of action and mechanisms of critical effects (including the possible roles of active metabolites) (4) high to low dose (exposure) extrapolation and interspecies extrapolation and (5) evaluation of factors that can influence severity and duration of adverse health effects. 

The error estimate based on the variance of the design experiments themselves leads to similar critical effects as the algorithm of Dong when no or small significant effects occur. However, when a large effect is present, e.g., that of factor B on Rs in Table 10 or that of factor D on response MTs t in Table 11, the error is overestimated, compared to the algorithm of Dong. 

For example, the effect of factor A on response CR t at ot = 0.05 was found significant when using the variance from duplicated design experiments to estimate the critical effect (see Table 11). However, since this factor represents different CE equipments, i.e., is discrete, calculating a non-significance interval is irrelevant. 

Suppose a factor X has 45, 50, and 55 as extreme low, nominal, and extreme high levels, respectively, and an effect of 100 on response Y, with the critical effect equal to 80. Then the non-significance interval limits for this factor are [46.0,54.0], which means that when restricting the levels of X to this interval, the quantitative aspect of the method is considered robust. It can be noticed that the interval is symmetrically around the nominal level and meant for factors thus examined, i.e., with extreme levels symmetrically around the nominal. 

In references 71 and 72, SST limits are defined based on experience, and the examined responses should fall within these limits. The two papers do not provide much information concerning the robustness test performed. Therefore, it is not evident to comment on the analysis applied, or to suggest alternatives. In reference 73, a graphical analysis of the estimated effects by means of bar plots was performed. In reference 74, a statistical analysis was made in which an estimation of error based on negligible two-factor interaction effects was used to obtain the critical effects between levels [—1,0] and [0,4-1]. 

In references 82-86, the results were treated statistically. Main effects and standard errors were calculated. In references 83, 85, and 86 also a graphical interpretation by means of bar plots was performed. Both positive and negative effects were seen on these plots, but all effects between levels [—1,0] are negative, while all those between [0,4-1] are positive. Possibly, the length of the bars represents the absolute value of the factor effects, and all effects for the interval [—1,0] seem to be given a negative sign, while all those for [0,4-1] a positive. However, the above are assumptions since no details were provided. In references 83 and 86, critical effects are drawn on the bar plots. 

The distinction between non-adverse effects and adverse effects can seem academic, but is essential in the hazard assessment in relation to, e.g., evaluation of no-effect levels and lowest-effect levels (Section 4.2.4), identification of the critical effect(s) (Section 4.2.7), and to the magnitude of the assessment factor to be used for taking into account the uncertainty due to the nature and severity of effects (Section 5.8). 

The approach of deriving a tolerable intake by dividing the N/LOAEL, or alternatively a BMD for the critical effect(s) by an assessment factor has been described and discussed extensively in the scientihc literature. It is beyond the scope of this book to review all these references. This chapter presents an overview of pubhshed extrapolation methods for the derivation of a tolerable intake based on the assessment factor approach, i.e., limited to address effects with threshold characteristics, and is not meant to be exhaustive. The main focus is on the rationale for and the use of the assessment factors. Pertinent guidance documents and reviews for the issues addressed in this chapter include WHO/IPCS (1994, 1996, 1999), US-EPA (2002, 2004), IGHRC (2003), ECETOC (2003), KEMI (2003), Kalberlah and Schneider (1998), Vermeire et al. (1999), and Nielsen et al. (2005). 

The assessment factors generally apphed in the estabhshment of a tolerable intake from the NOAEL, or LOAEL, for the critical effect(s) are apphed in order to compensate for rmcertainties inherent to extrapolation of experimental animals data to a given human situation, and for rmcertainties in the toxicological database, i.e., in cases where the substance-specific knowledge required for risk assessment is not available. As a consequence of the variabihty in the extent and nature of different databases for chemical substances, the range of assessment factors apphed in the establishment of a tolerable intake has been wide (1-10,000), although a value of 100 has been used most often. An overview of different approaches in using assessment factors, historically and currently, is provided in Section 5.2. 

Application of a scientifically derived adjustment factor to the NOAEL, or LOAEL, of the critical effect established in the pivotal study. It is stated that if the database is inadequate, then human PNAELs cannot be derived scientifically. 

Vermeire et al. (1999) considered that the type of critical effect should be taken into account. Assessment factors may be applied by expert judgment depending on each individual case by default, it can be assumed that no extra correction is necessary. 

An additional assessment factor, of up to 10, has been apphed in some cases where the NOAEL has been derived for a critical effect, which is considered as a severe and irreversible effect, such as teratogenicity or non-genotoxic carcinogenicity, especially if associated with a shallow dose-response relationship. The principal rationale for an additional factor for nature of toxicity has been to provide a greater margin between the exposure of any particularly susceptible humans and the dose-response curve for such toxicity in experimental animals. 

The apphcation of an extra factor for nature of toxicity is difficult to justify scientifically. Renwick (1995) concluded that if a factor for nature of toxicity is to be used then it should be apphed to the NOAEL for the toxicity which resulted in its use. Vermeire et al. (1999) considered that the type of critical effect should be taken into account but, by default, no extra correction is necessary. WHO/IPCS (1994, 1996, 1999) stated that the nature of toxicity, i.e., whether the effect is adverse or not, is considered in the determination of the NOAEL and LOAEL however, an additional factor of up to 10 could be incorporated in cases where the NOAEL is derived for a critical effect which is severe and irreversible. The EU TGD (EC 2003) pointed out that the nature and severity of the effect need to be considered in the evaluation of the MOS. TNO (Hakkert et al. 1996) considered that the biological significance of the critical adverse effect in terms of its presumable health consequence should be considered in the selection of assessment factor the default value is 1. KEMI (2003) recommended that the nature of effect should be taken into account case-by-case on expert judgment and, in comparison with other organizations, a factor of I to 10 was suggested. 

High degree of confidence The database contains high quality human or animal studies, i.e., two or more studies with the same endpoint. The database should be sufficiently extensive to give confidence that the correct critical effect has been selected, and that there are no major uncertainties in this respect. No additional numerical UF required, i.e., the default factor is 1. 

They suggested the effect parameter the Critical Effect Dose (CED, a benchmark dose. Section 4.2.5) derived from the dose-response data by regression analysis. This CED was defined as the dose at which the average animal shows the Critical Effect Size (CES) for a particular toxicological endpoint, below which there is no reason for concern. The distribution of the CED can probabilistically be combined with probabilistic distributions of assessment factors for deriving standards... 

The tolerable intake (TI) is calculated by dividing the NOAEL (or LOAEL) for the critical effect(s) by the derived overall assessment factor (AF) ... 

Guidance values are developed from a standard such as, e.g., an Acceptable/Tolerable Daily Intake (ADI/TDI), and Reference Dose/Concentration (RfD/RfC). For threshold effects, the standard is derived by dividing the No-Observed-Adverse-Effect Level (NOAEL) or Lowest-Observed-Adverse-Effect Level (LOAEL), or alternatively a Benchmark Dose (BMD) for the critical effect (s) by an overall assessment factor, described in detail in Chapter 5. For non-threshold effects, the standard is derived by a quantitative assessment, described in detail in Chapter 6. 

Multiple-factor interaction effects can be used to determine a critical effect value for the interpretation of full and fractional factorial designs [29] ... 

Critical position/factor Effect on carcinogenic potential Rationale... 

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