Development of a new approach using mathematical modeling to predict cocktail effects of micropollutants of diverse origins.

ABSTRACT

A wide variety of micropollutants (MP) of diverse origins is present in waste and surface waters without knowing the effect of their combination on ecosystems and human. The impact of chemical mixtures is poorly documented and often limited to binary mixtures using MP of the same category. Knowing that it is not realistic to test every possible combination found in mixtures, we aimed to develop a new method helping to predict cocktail effects. Six chemicals of agriculture, industry or pharmaceutical origin were selected cyproconazole, diuron, terbutryn, bisphenol A, diclofenac and tramadol. Individual MP were first used in vitro to determine the concentration at which 10% (Effective Concentration EC10) or 25% (EC25) of their maximal effect on human cytotoxicity was observed. Using an Orthogonal Array Composite Design (OACD), relevant complex mixtures were then tested. Multiple linear regression was applied for response surface modeling in order to evaluate and visualize the influence of the different MP in mixtures and their potential interactions. The comparison of the predicted values obtained using the response surface model with those obtained with the model of independent effects, evidenced that the hypothesis of independence was unjustified. The cocktail effect was further investigated by considering micropollutant response surfaces pairwise. It was deduced that there was a neutralizing effect between bisphenol A and tramadol. In conclusion, we propose a new method to predict within a complex mixture of MP the combinations likely involved in cocktail effects. The proposed methodology coupling experimental data acquisition and mathematical modeling can be applied to all kind of relevant bioassays using lower concentrations of MP. Situations at high ecological risk and potentially hazardous for humans will then be identified, which will allow to improve legislation and policies.