Using quantitative structure–activity relationship modeling to quantitatively predict the developmental toxicity of halogenated azole compounds

Authors

  • Evisabel A. Craig,

    1. Oak Ridge Institute for Science and Education, Oak Ridge, TN, USA
    2. National Center for Environmental Assessment, Office of Research Development, U.S. Environmental Protection Agency, Cincinnati, OH, USA
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  • Nina Ching Wang,

    1. National Center for Environmental Assessment, Office of Research Development, U.S. Environmental Protection Agency, Cincinnati, OH, USA
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  • Q. Jay Zhao

    Corresponding author
    1. National Center for Environmental Assessment, Office of Research Development, U.S. Environmental Protection Agency, Cincinnati, OH, USA
    • Correspondence to: Q. Jay Zhao, National Center for Environmental Assessment, Office of Research Development, U.S. Environmental Protection Agency, Cincinnati, OH, USA. Email: zhao.jay@epa.gov

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ABSTRACT

Developmental toxicity is a relevant endpoint for the comprehensive assessment of human health risk from chemical exposure. However, animal developmental toxicity data remain unavailable for many environmental contaminants due to the complexity and cost of these types of analyses. Here we describe an approach that uses quantitative structure–activity relationship modeling as an alternative methodology to fill data gaps in the developmental toxicity profile of certain halogenated compounds. Chemical information was obtained and curated using the OECD Quantitative Structure–Activity Relationship Toolbox, version 3.0. Data from 35 curated compounds were analyzed via linear regression to build the predictive model, which has an R2 of 0.79 and a Q2 of 0.77. The applicability domain (AD) was defined by chemical category and structural similarity. Seven halogenated chemicals that fit the AD but are not part of the training set were employed for external validation purposes. Our model predicted lowest observed adverse effect level values with a maximal threefold deviation from the observed experimental values for all chemicals that fit the AD. The good predictability of our model suggests that this method may be applicable to the analysis of qualifying compounds whenever developmental toxicity information is lacking or incomplete for risk assessment considerations. Copyright © 2013 John Wiley & Sons, Ltd.

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