Evaluation of pesticide toxicity: a hierarchical QSAR approach to model the acute aquatic toxicity and avian oral toxicity of pesticides

The thesis aimed to extract information relevant to the hazard and risk assessment of pesticides. In particular, quantitative structure-activity relationship (QSAR) approaches have been used to build up a mathematical model able to predict the aquatic acute toxicity, LC50, and the avian oral toxicity, LD50, for pesticides. Ecotoxicological values were collected from several databases, and screened according to quality criteria. A hierarchical QSAR approach was applied for the prediction of acute aquatic toxicity. Chemical structures were encoded into molecular descriptors by an automated, seamless procedure available within the OpenMolGRID system. Different linear and non-linear regression techniques were used to obtain reliable and thoroughly validated QSARs. The final model was developed by a counter-propagation neural network coupled with genetic algorithms for variable selection. The proposed QSAR is consistent with McFarland's principle for biological activity and makes use of seven molecular descriptors. The model was assessed thoroughly in test (R2 = 0.8) and validation sets (R2 = 0.72), the y-scrambling test and a sensitivity/stability test. The second endpoint considered in this thesis was avian oral toxicity. As previously, the chemical description of chemicals was generated automatically by the OpenMolGRID system. The best classification model was chosen on the basis of the performances on a validation set of 19 data points, and was obtained from a support vector machine using 94 data points and nine variables selected by genetic algorithms (Error Ratetraining = 0.021, Error Ratevalidation = 0.158). The model allowed for a mechanistic estimation of the toxicological action. In fact, several descriptors selected for the final classification model encode for the interaction of the pesticides with other molecules. The presence of hetero-atoms, e.g. sulphur atoms, is correlated with the toxicity, and the pool of descriptor selected is generally dependent from the 3D conformation of the structures. These suggest that, in the case of avian oral toxicity, pesticides probably exert their toxic action through the interaction with some macromolecule and/or protein of the biological system

Additive SMILES-Based Carcinogenicity Models: Probabilistic Principles in the Search for Robust Predictions

Optimal descriptors calculated with the simplified molecular input line entry system (SMILES) have been utilized in modeling of carcinogenicity as continuous values (logTD50). These descriptors can be calculated using correlation weights of SMILES attributes calculated by the Monte Carlo method. A considerable subset of these attributes includes rare attributes. The use of these rare attributes can lead to overtraining. One can avoid the influence of the rare attributes if their correlation weights are fixed to zero. A function, limS, has been defined to identify rare attributes. The limS defines the minimum number of occurrences in the set of structures of the training (subtraining) set, to accept attributes as usable. If an attribute is present less than limS, it is considered “rare”, and thus not used. Two systems of building up models were examined: 1. classic training-test system; 2. balance of correlations for the subtraining and calibration sets (together, they are the original training set: the function of the calibration set is imitation of a preliminary test set). Three random splits into subtraining, calibration, and test sets were analysed. Comparison of abovementioned systems has shown that balance of correlations gives more robust prediction of the carcinogenicity for all three splits (split 1: rtest2=0.7514, stest=0.684; split 2: rtest2=0.7998, stest=0.600; split 3: rtest2=0.7192, stest=0.728)

Servio Tullio e Mastarna nella «Tabula Lugdunensis»

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Tuning Neural and Fuzzy-Neural Networks for Toxicity Modeling

Service (CAS). In view of their abundance and wide use in all spheres of production we need a better understanding of their ecotoxicological impact on plant life, wildlife, and the environment in general. Apart from the ethical considerations associated with the use of animals, experimental determination of ecotoxicity and toxicity would require huge financial resources and much time to be done methodically on all the compounds of interest. Thus, new alternatives are needed

Anticorruzione e performance nelle Università: modalità e condizioni abilitanti per l’integrazione

Il presente contributo, partendo dall’analisi delle recenti innovazioni normative e della letteratura che considera la gestione del rischio corruttivo e la gestione della performance come interdipendenti, cerca di collocare la prospettiva di raccordo tra il ciclo dell’anticorruzione ed il ciclo della performance all’interno delle esigenze presenti del settore pubblico italiano, ed in modo particolare nel contesto delle Università. Tale obiettivo è perseguito tramite lo svolgimento di un caso studio longitudinale seguendo una metodologia dell’action research. I risultati preliminari permettono di evidenziare i fattori abilitanti in grado di supportare le aziende nei processi di integrazione degli strumenti di gestione e per superare eventuali ostacoli derivanti dal cambiamento organizzativo indotto