Computational approaches in assessments of mixture toxicity

Abstract

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.