Toxicity models of metal mixtures established on the basis of “additivity” and “interactions”

Abstract

· No preference is set between CA and IA models to assess toxicity of metal mixtures.

· Increased model complexity does not, by itself, lead to improved performance.

· Not all significant deviations have biological meaning due to poor reproducibility.

· Interactions are suggested to incorporate when they are significant and repeated.

Observed effects of metal mixtures on animals and plants often differ from the estimates, which are commonly calculated by adding up the biological responses of individual metals. This difference from additivity is commonly referred to as being a consequence of specific interactions between metals. The science of how to quantify metal interactions and whether to include them in risk assessment models is in its infancy. This review summarizes the existing predictive tools for evaluating the combined toxicity of metals present in mixtures and indicates the advantages and disadvantages of each method. We intend to provide eco-toxicologists with background information on how to make good use of the tools and how to advance the methods for assessing toxicity of metal mixtures. It is concluded that statistically significant deviations from additivity are not necessarily biologically relevant. Incorporation of interactions between metals in a model does not on forehand mean that the model is more accurate than a model developed based on additivity only. It is recommended to first use a relatively simple method for effect prediction of uninvestigated metal mixtures. To improve the reliability of toxicity modeling for metal mixtures, further efforts should focus on balancing the relationship between the significance of statistics and the biological meaning, and unraveling the toxicity mechanisms of metals and their mixtures.

Graphical abstract

Keywords

Metal / Mixtures / Toxicity / Additivity / Modeling / Interactions

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Acknowledgements

Support provided to Yang Liu by a talent cultivation project at Kunming University of Science and Technology (KKSY201622012) is gratefully acknowledged.

Conflicts of interest

The authors declare no competing financial interest.

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