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Article Dans Une Revue SAR and QSAR in Environmental Research Année : 2013

External validation of structure-biodegradation relationship (SBR) models for predicting the biodegradability of xenobiotics

Résumé

Biodegradation is an important mechanism for eliminating xenobiotics by biotransforming them into simple organic and inorganic products. Faced with the ever growing number of chemicals available on the market, structure-biodegradation relationship (SBR) and quantitative structure-biodegradation relationship (QSBR) models are increasingly used as surrogates of the biodegradation tests. Such models have great potential for a quick and cheap estimation of the biodegradation potential of chemicals. The Estimation Programs Interface (EPI) Suite includes different models for predicting the potential aerobic biodegradability of organic substances. They are based on different endpoints, methodologies and/or statistical approaches. Among them, Biowin 5 and 6 appeared the most robust, being derived from the largest biodegradation database with results obtained only from the Ministry of International Trade and Industry (MITI) test. The aim of this study was to assess the predictive performances of these two models from a set of 356 chemicals extracted from notification dossiers including compatible biodegradation data. Another set of molecules with no more than four carbon atoms and substituted by various heteroatoms and/or functional groups was also embodied in the validation exercise. Comparisons were made with the predictions obtained with START (Structural Alerts for Reactivity in Toxtree). Biowin 5 and Biowin 6 gave satisfactorily prediction results except for the prediction of readily degradable chemicals. A consensus model built with Biowin 1 allowed the diminution of this tendency.
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Dates et versions

ineris-00963504 , version 1 (21-03-2014)

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James Devillers, Pascal Pandard, B. Richard. External validation of structure-biodegradation relationship (SBR) models for predicting the biodegradability of xenobiotics. SAR and QSAR in Environmental Research, 2013, 24 (12), pp.979-993. ⟨10.1080/1062936X.2013.848632⟩. ⟨ineris-00963504⟩

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