Linear regression model for predicting interactive mixture toxicity of pesticide and ionic liquid

The nature of most environmental contaminants comes from chemical mixtures rather than from individual chemicals. Most of the existed mixture models are only valid for non-interactive mixture toxicity. Therefore, we built two simple linear regression-based concentration addition (LCA) and independent action (LIA) models that aim to predict the combined toxicities of the interactive mixture. The LCA model was built between the negative log-transformation of experimental and expected effect concentrations of concentration addition (CA), while the LIA model was developed between the negative log-transformation of experimental and expected effect concentrations of independent action (IA). Twenty-four mixtures of pesticide and ionic liquid were used to evaluate the predictive abilities of LCA and LIA models. The models correlated well with the observed responses of the 24 binary mixtures. The values of the coefficient of determination (R (2)) and leave-one-out (LOO) cross-validated correlation coefficient (Q(2)) for LCA and LIA models are larger than 0.99, which indicates high predictive powers of the models. The results showed that the developed LCA and LIA models allow for accurately predicting the mixture toxicities of synergism, additive effect, and antagonism. The proposed LCA and LIA models may serve as a useful tool in ecotoxicological assessment.