Computational approaches in assessments of mixture toxicity

There are various paths of interactions of combination of two or more chemicals with biological systems. The response of chemicals in a mixture can be predicted employing the perceptions of concentration or dose addition for chemicals with identical mode of action (MOA) and/or common target of effect. While response addition can be considered for chemicals acting on diverse biological targets. Both hypotheses are feasible only when there is no interaction between chemicals. On the contrary, if interaction occurs between chemicals in a mixture results in synergism or potentiation if induction of activating enzyme/inhibition of detoxifying enzyme happens. In contrast, competition of individual chemicals at biological target site show antagonism. Experimental models are time-consuming and costly. Diversity of mixtures and the necessity to test multiple organisms covering different ecosystems to avail the toxicity data make the experimentalist job more challenging. There comes the importance of computational approaches, proven and efficient alternatives to fill the toxicity data gaps, prioritization of chemicals, identification of the toxicity mechanism, and risk assessment and management.

Automated summary

There are various ways in which chemicals interact with biological systems, and the response of chemical mixtures can be predicted using concentration or dose addition. However, this only holds true when there is no interaction between the chemicals. Experimental models are time-consuming and costly, which highlights the importance of computational approaches in filling data gaps and assessing risks.