Support Vector Machine (SVM) as Alternative Tool to Assign Acute Aquatic Toxicity Warning Labels to Chemicals.

Abstract

Quantitative structure-activity relationship (QSAR) analysis has been frequently utilized as a computational tool for the prediction of several eco-toxicological parameters including the acute aquatic toxicity. In the present study, we describe a novel integrated strategy to describe the acute aquatic toxicity through the combination of both toxicokinetic and toxicodynamic behaviors of chemicals. In particular, a robust classification model (TOXclass) has been derived by combining Support Vector Machine (SVM) analysis with three classes of toxicokinetic-like molecular descriptors: the autocorrelation molecular electrostatic potential (autoMEP) vectors, Sterimol topological descriptors and logP(o/w) property values. TOXclass model is able to assign chemicals to different levels of acute aquatic toxicity, providing an appropriate answer to the new regulatory requirements. Moreover, we have extended the above mentioned toxicokinetic-like descriptor set with a more toxicodynamic-like descriptors, as for example HOMO and LUMO energies, to generate a valuable SVM classifier (MOAclass) for the prediction of the mode of action (MOA) of toxic chemicals. As preliminary validation of our approach, the toxicokinetic (TOXclass) and the toxicodynamic (MOAclass) models have been applied in series to inspect both aquatic toxicity hazard and mode of action of 296 chemical substances with unknown or uncertain toxicodynamic information to assess the potential ecological risk and the toxic mechanism.