(286b) Qsar Approach for Mixture Toxicity Prediction Using Independent Latent Descriptors and Fuzzy Membership Functions

Current methods for the prediction of mixture toxicity have shown to be valid for mixtures that conform to some assumptions that were ideally formulated for mixtures comprising constituents exhibiting either completely similar or dissimilar mechanisms of action. Approaches are needed that predict the toxicity of mixtures representative of real environmental occurrences i.e. those comprising constituents of mixed similar and dissimilar compounds and therefore are more complex.

In this paper such a methodology is proposed which uses molecular descriptors and fuzzy set theory to characterise the degree of similarity and dissimilarity of mixture constituents, integrates the concentration addition and independent action models, and therefore is called INFCIM (INtegrated Fuzzy Concentration addition - Independent action Model). In this method, a feature extraction technique, independent component analysis, is used to remove the correlations and dependencies between descriptors and reduce the dimension prior to similarity and dissimilarity calculations. In addition, a goal attainment multi-objective optimisation technique is used for the determination of the fuzzy membership function parameters. Unlike QSARs for pure compounds that require large collections of data, the new approach for mixtures only requires one mixture at a particular composition to determine the necessary fuzzy membership function parameter values. These values can then be used to predict the toxicity of the mixture at any other compositions. This could potentially lead to a reduction in the frequency of bioassay tests.

INFCIM is tested in three case studies and compared against those of both concentration addition and independent action models. For three mixtures of s-triazines, dissimilar organics and herbicides (Chloroacetanilides), the predictions of EC50 and EC1 values, tt was shown that INFCIM performed comparably or better than the best performing existing model in the original studies for all the mixtures tested.