Global and local quantitative structure–property relationship models to predict the impact sensitivity of nitro compounds

Authors

  • Guillaume Fayet,

    1. INERIS, Direction des Risques Accidentels, Parc Technologique Alata, BP2, 60550 Verneuil-en-Halatte, France
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  • Patricia Rotureau,

    Corresponding author
    1. INERIS, Direction des Risques Accidentels, Parc Technologique Alata, BP2, 60550 Verneuil-en-Halatte, France
    • INERIS, Direction des Risques Accidentels, Parc Technologique Alata, BP2, 60550 Verneuil-en-Halatte, France
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  • Vinca Prana,

    1. INERIS, Direction des Risques Accidentels, Parc Technologique Alata, BP2, 60550 Verneuil-en-Halatte, France
    2. Laboratoire d'Electrochimie, Chimie des Interfaces et Modélisation pour l'Energie, CNRS UMR-7575, Chimie ParisTech, 75231 Paris Cedex 05, France
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  • Carlo Adamo

    1. Laboratoire d'Electrochimie, Chimie des Interfaces et Modélisation pour l'Energie, CNRS UMR-7575, Chimie ParisTech, 75231 Paris Cedex 05, France
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Abstract

New quantitative structure–property relationships were developed to predict accurately the impact sensitivity of nitro compounds from their molecular structures. Such predictive approaches represent good alternative to complete experimental testing in development process or for regulatory issues (e.g., within the European REACH regulation).

To achieve highly predictive models, two approaches were used to explore the whole diversity of nitro compounds included in a dataset of 161 molecules. In the first step, local models, dedicated to nitramines, nitroaliphatics, and nitroaromatics, were proposed. After that, a global model was developed to be applicable for the whole range of the nitro compounds of the dataset.

In both cases, large series of molecular descriptors were calculated from quantum chemically calculated molecular structures, and multilinear regressions were computed to correlate them with experimental impact sensitivities. Both the global and local models could predict nitramines and nitroaliphatics in high accuracy whereas nitroaromatics were more difficult to be predicted due to their complex decomposition mechanisms.

The proposed models were validated in the perspective of potential regulatory use according to the OECD principles, including internal, external validation, and the definition of their applicability domain. So, they could then be used for prediction either separately or in a consensus approach. © 2012 American Institute of Chemical Engineers Process Saf Prog, 2012

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