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We compared the observed mixture effects against component-based mixture effect predictions derived from additivity expectations (assumption of non-interaction).
Information on common toxicity pathways may also provide input for the assessment of mixture effects.
Apparently, the grid-based inventory shows various mixture effects but no over-yielding.
Pesticides followed by pharmaceuticals and personal care products dominated the observed mixture effects.
To analyze the mixture effect, the observed data were compared with the expected mixture effects predicted by the concentration addition (CA) model and by deviations for synergistic/antagonistic interactions and dose-level and dose-ratio dependencies.
Hence a specific mixture assessment factor (MAF) has been discussed in order to safeguard against unwanted mixture effects from multi-component mixtures of partly unidentified composition[5].
Conventional target analysis of biological samples such as blood limits our ability to understand mixture effects of chemicals.
Based on these concentration-response data, mixture effects were predicted by applying the model of concentration addition.
Furthermore, these mixture effects occurred in a quite predictable manner.
These, and similar, mixture effects are thought to derive from mixture interactions in odor coding.
Thus, observed and predicted mixture effects can be compared only for the tested mixture concentrations.
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