Mixture extrapolation approaches compounds

In our opinion, the data are sufficiently clear to suggest that when it is not feasible to test the mixture in question, mixture extrapolation is the preferred option compared to no extrapolation. Indeed, all literature observations suggest that applying mixture extrapolation is to be preferred over not applying mixture extrapolation. Technical options for extrapolation are concentration addition, response addition, and the mixed-model approach, of which concentration addition is most often applied. Exceptions may apply in cases that are more specific. For example, when it is clear that 2 compounds precipitate (a situation of no exposure due to chemical interactions in the environment), one should acknowledge this prior to assessing mixture risks by mixture extrapolation approaches. When the data of a study allow, refined conclusions are possible. For example, when the study design is appropriate and the mathematical models are appropriate, researchers are able to discriminate between concentration addition and response addition, and (with sufficient experiment efforts) between these models and the mixed-model approach. 

In single-species assessments, the interpretations of mixture assessments tend to be mostly absolute. Hence, risk assessors often focus on particular species and particular compound groups (e.g., risks of PCB mixtures for birds), allowing them to interpret and explain their experimental data to the best of their abilities. On the other hand, many risk assessors apply mixture extrapolation methods to address risks for communities. The applications of SSD-based methods for this evolved fast and now cover a wide set of approaches, ranging from ecological multiple-stress analyses to overall approaches such as life-cycle assessment. Especially in the latter set of approaches, the risk assessor can often allow the method to only yield relative... 

Current efforts to extrapolate mixture effects are dominated by TU-based approaches, which result in prediction error when the models are used for situations where the concentrations deviate from the original effect level that is used to define TU. Provided that the data are available, mixture extrapolation at the species level may improve by using the proposed higher tier protocols. It should be acknowledged, however, that the data needed for such an enterprise at the species level are not systematically stored in databases, as is the case for the databases available to construct SSDs (see Section 5.6.1). For a significant advancement, researchers therefore should strive for full-curve modeling over point-estimate models (i.e., to model at Tier-2 and Tier-3). The major requirement would therefore be to not only produce but also report systematically on concentration response functions for individual compounds, as this would allow prediction of any yet untested mixture for the same biological response. 

A tiered system for mixture extrapolation is proposed. The lowest tier is based on extrapolation using toxicological point-estimate information such as EC50 values. This translates into the use of toxic units, toxic equivalencies, and similar techniques. The use of the entire concentration-response relationships of the separate compounds is recommended for Tier-2, in conjunction with the use of either concentration or response addition as a modeling approach. In Tier-3, a mixed-model approach can be considered, to more specifically address considerations on toxic modes of action. In the latter case, the approach may be extended to allow incorporation of the responses of different ecological receptors (Tier-4). Research needs have been clearly identified in community-level mixture assessments. 

When the compound is not soluble in water, a mixed-solvent approach is the way to go. Mixed-solvent solutions of various co-solvent-water ratios are prepared and the psKa (apparent pKa) is determined in each mixture. The aqueous pKa is determined by extrapolation of the psKa values to zero... 

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