Juan A. Castillo-Garit, Concepción Abad, Gerardo M. Casañola-Martin, Stephen Jones Barigye, Francisco Torrens and Amparo Torreblanca Pages 5085 - 5094 ( 10 )
Background: Many QSAR studies have been developed to predict acute toxicity over several biomarkers like Pimephales promelas, Daphnia magna and Tetrahymena pyriformis. Regardless of the progress made in this field there are still some gaps to be resolved such as the prediction of aquatic toxicity over the protozoan T. pyriformis still lack a QSAR study focused in accomplish the OECD principles.
Methods: Atom-based quadratic indices are used to obtain quantitative structure-activity relationship (QSAR) models for the prediction of aquatic toxicity. Our models agree with the principles required by the OECD for QSAR models to regulatory purposes. The database employed consists of 392 substituted benzenes with toxicity values measured in T. pyriformis (defined endpoint), was divided using cluster analysis in two series (training and test sets).
Results: We obtain (with an unambiguous algorithm) two good multiple linear regression models for non-stochastic (R2=0.807 and s=0.334) and stochastic (R2=0.817 and s=0.321), quadratic indices. The models were internally validated using leave-one-out, bootstrapping as well as Y-scrambling experiments. We also perform an external validation using the test set, achieving values of R2 pred values of 0.754 and 0.760, showing that our models have appropriate measures of goodness- of-fit, robustness and predictivity. Moreover, we define a domain of applicability for our best models.
Conclusion: The achieved results demonstrated that, the atom-based quadratic indices could provide an attractive alternative to the experiments currently used for determining toxicity, which are costly and time-consuming.
Atom-based non-stochastic and stochastic linear indices, aquatic toxicology, ecotoxicity, QSAR, Tetrahymena pyriformis.
Institut Universitari de Ciència Molecular, Universitat de València, Edifici d'Instituts de Paterna, P. O. Box 22085, 46071 Valencia, Spain.