Factors influencing predictive models for toxicology

Comparisons of different models to predict toxicity and evaluation of the predictive power of a model are affected by the variability of the data. We assessed this problem by considering experimental toxicity data and chemical descriptors. We evaluated several toxicological end-points (Oncorhynchus mykiss, Daphnia magna, Acceptable Daily Intake, Anas Platyrhynchos, Colinus virginianus and Muridae) in the case of pesticides and also considered the availability of toxicological data. We calculated hundreds of molecular descriptors (divided into constitutional, electrostatic, geometrical, quantum-chemical and topological ones) for the selected compounds using CODESSA, HyperChem. and Pallas. Molecular descriptors may vary depending on the conformation of the molecules and on the software used. We evaluated the extent of this variability, and compared it with the variability of the experimental toxicological values.