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QSAR modeling in ecotoxicological risk assessment: application to the prediction of acute contact toxicity of pesticides on bees (Apis mellifera L.)

Abstract : Despite their indisputable importance around the world, the pesticides can be dangerous for a range of species of ecological importance such as honeybees (Apis mellifera L.). Thus, a particular attention should be paid to their protection, not only for their ecological importance by contributing to the maintenance of wild plant diversity, but also for their economic value as honey producers and crop-pollinating agents. For all these reasons, the environmental protection requires the resort of risk assessment of pesticides. The goal of this work was therefore to develop a validated QSAR model to predict contact acute toxicity (LD) of 111 pesticides to bees because the QSAR models devoted to this species are very scarce. The analysis of the statistical parameters of this model and those published in the literature shows that our model is more efficient. The QSAR model was assessed according to the OECD principles for the validation of QSAR models. The calculated values for the internal and external validation statistic parameters (Q and [Formula: see text] are greater than 0.85. In addition to this validation, a mathematical equation derived from the ANN model was used to predict the LD of 20 other pesticides. A good correlation between predicted and experimental values was found (R  = 0.97 and RMSE = 0.14). As a result, this equation could be a means of predicting the toxicity of new pesticides.
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-01696986
Contributor : Laurent Jonchère <>
Submitted on : Tuesday, January 30, 2018 - 5:14:57 PM
Last modification on : Thursday, March 5, 2020 - 2:03:27 PM

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Mabrouk Hamadache, Othmane Benkortbi, Salah Hanini, Abdeltif Amrane. QSAR modeling in ecotoxicological risk assessment: application to the prediction of acute contact toxicity of pesticides on bees (Apis mellifera L.). Environmental Science and Pollution Research, Springer Verlag, 2018, 25 (1), pp.896-907. ⟨10.1007/s11356-017-0498-9⟩. ⟨hal-01696986⟩

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