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The traditional approach to regulatory risk assessment is mostly focused on single chemicals rather than on (unintentional) mixtures toxicity. Human beings and ecosystems are exposed to mixtures of chemicals whose combination is rarely addressed and properly characterized. To date, data and predictive models are available to estimate the toxicity of discrete chemicals on most used chemicals classes. However, we vastly lack data and models to explain the potential combination of the toxicological effects of mixtures without extensive experimental testing. Moreover, although rare, the matter is complicated by synergistic and antagonistic effects of components within the mixture. For safe usage of a mixture of chemicals, having experimental toxicity data would broaden our understanding of mixture toxicity. Modeling would aid to extend the toxicological properties from one mixture to other similar mixtures would lessen in-vivo and in-vitro testing of mixtures, generally by approaches known as a read-across or a category approach. Therefore, the grouping of mixtures, analyzing data gaps, and modeling mixture toxicity is of utmost importance in today’s age.

Efforts are ongoing to further exploit this topic in toxicology, and computational methods, including Quantitative Structure-Activity Relationships (QSAR), may give an important contribution due to the complexity derived by the exponential number of compound combinations. The modeling should rely on curated data, however, data matrix for mixtures are rare to find. Therefore, efforts must be put in to gather and curate data, and to generate reliable data collection on a chosen set of mixtures. Modeling mixtures toxicity based on experimental data and compositional similarity between mixtures is a truly multidimensional problem. To that end, we must computationally define the term mixture similarity for our purpose. Then, using this definition, we can group or cluster mixtures. Lastly, a huge step will be to utilize the data matrix and groups of mixtures for model building for a specific toxicological endpoint or a combination of (adverse outcome pathways or) adverse outcomes. To be useful in practice, the model should be well validated with experimental data generated for real mixtures at realistic (environmental) concentrations. In addition, it is of paramount importance that the applicability domain is clearly defined.

This Research Topic welcomes manuscripts dealing with computational efforts transitioning from single substance prediction to the prediction of possible combined effects of mixtures. Tentative subtopics could includes Handling chemical structure representation of complex substances in machine-readable formats. Approaching the concept of similarity in mixtures. Modeling of aggregate exposures of substances or classes of substances.

Media Contact:

Larry Tyler

Managing Editor

Journal of Clinical Toxicology

Mail ID: jct@peerjournal.org

Whatsapp: +1-504-608-2390