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Grisoni F.,University of Milan Bicocca | Grisoni F.,Milan Chemometrics and QSAR Research Group | Consonni V.,University of Milan Bicocca | Consonni V.,Milan Chemometrics and QSAR Research Group | And 5 more authors.
Environmental Research | Year: 2016

Expert systems are a rational integration of several models that generally aim to exploit their advantages and overcome their drawbacks. This work is founded on our previously published Quantitative Structure-Activity Relationship (QSAR) classification scheme, which detects compounds whose Bioconcentration Factor (BCF) is (1) well predicted by the octanol-water partition coefficient (KOW), (2) underestimated by KOW or (3) overestimated by KOW. The classification scheme served as the starting point to identify and combine the best BCF model for each class among three VEGA models and one KOW-based equation. The rationalized model integration showed stability and surprising performance on unknown data when compared with benchmark BCF models. Model simplicity, transparency and mechanistic interpretation were fostered in order to allow for its application and acceptance within the REACH framework. © 2016 Elsevier Inc. Source


Grisoni F.,University of Milan Bicocca | Grisoni F.,Milan Chemometrics and QSAR Research Group | Consonni V.,University of Milan Bicocca | Consonni V.,Milan Chemometrics and QSAR Research Group | And 4 more authors.
Environment International | Year: 2016

This paper proposes a scheme to predict whether a compound (1) is mainly stored within lipid tissues, (2) has additional storage sites (e.g., proteins), or (3) is metabolized/eliminated with a reduced bioconcentration. The approach is based on two validated QSAR (Quantitative Structure-Activity Relationship) trees, whose salient features are: (a) descriptor interpretability and (b) simplicity. Trees were developed for 779 organic compounds, the TGD approach was used to quantify the lipid-driven bioconcentration, and a refined machine-learning optimization procedure was applied. We focused on molecular descriptor interpretation, which allowed us to gather new mechanistic insights into the bioconcentration mechanisms. © 2015 Elsevier Ltd. Source


Grisoni F.,Milan Chemometrics and QSAR Research Group | Grisoni F.,University of Milan Bicocca | Consonni V.,Milan Chemometrics and QSAR Research Group | Consonni V.,University of Milan Bicocca | And 4 more authors.
Chemosphere | Year: 2015

This study compares nine QSAR models for the prediction of BCF on fish: four KOW based models (Veith, Mackay, Bintein and TGD equations) and five complex models (EPI Suite BCFBAF, VEGA CAESAR, VEGA Meylan, VEGA Read-across and VEGA consensus). The aim is to test if increasing complexity leads to predictions that are more accurate than those based only on KOW are. To this end, experimental BCF data for 1056 compounds, along with experimental and predicted KOW values, were collected and used for the comparison. A particular focus has been placed on compounds for which metabolism, elimination and specific interactions with tissues can be hypothesized. VEGA Read-across improved global predictions with respect to the KOW based models and resulted to be a good approach to take into account metabolism and interactions with tissues. For the other complex models, several drawbacks were highlighted. Finally, for different classes of compounds (i.e. Perfluorinated Compounds, Organophosphorous Compounds, Synthetic Pyrethroids and Polychlorinated Biphenyls) results confirmed the mechanistic interpretation of the processes involved in their bioconcentration. © 2015 Elsevier Ltd. Source

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