Polybrominated diphenyl ethers (PBDEs) are persistent chemicals that have been among the most marketed flame retardants used all over the world in the last decades. PBDEs have been detected in all environmental compartments, as well as in humans and wildlife, where they are able to accumulate and exert their toxic effects. At present only a limited amount of experimental data is available to characterize the physico-chemical and toxicological behavior of PBDEs and similar brominated flame retardants. QSA(P)R approaches are very useful tools to predict missing data starting from the chemical structure of compounds. In this study several local QSPR models, developed specifically for the prediction of logKoa, logKow and melting point of PBDEs, were compared with predictions by global QSPR models, such as KoaWIN, KowWIN and MPBPWIN from the EPI Suite package, and AlogP and MlogP from DRAGON software, which were trained on heterogeneous and large datasets. The analysis addressed in the paper supported the identification of points of strength and weaknesses of both local models, and global models. The results are relevant to support decisions made by general QSAR users and regulators, when they have to select and apply one of the analyzed models to predict properties for PBDEs.
On the use of local and global QSARs for the prediction of Physico-Chemical Properties of Polybrominated Diphenyl Ethers
PAPA, ESTER;GRAMATICA, PAOLA
2011-01-01
Abstract
Polybrominated diphenyl ethers (PBDEs) are persistent chemicals that have been among the most marketed flame retardants used all over the world in the last decades. PBDEs have been detected in all environmental compartments, as well as in humans and wildlife, where they are able to accumulate and exert their toxic effects. At present only a limited amount of experimental data is available to characterize the physico-chemical and toxicological behavior of PBDEs and similar brominated flame retardants. QSA(P)R approaches are very useful tools to predict missing data starting from the chemical structure of compounds. In this study several local QSPR models, developed specifically for the prediction of logKoa, logKow and melting point of PBDEs, were compared with predictions by global QSPR models, such as KoaWIN, KowWIN and MPBPWIN from the EPI Suite package, and AlogP and MlogP from DRAGON software, which were trained on heterogeneous and large datasets. The analysis addressed in the paper supported the identification of points of strength and weaknesses of both local models, and global models. The results are relevant to support decisions made by general QSAR users and regulators, when they have to select and apply one of the analyzed models to predict properties for PBDEs.
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