Using Quantitative Structure-Activity Relationship Modeling to Quantitatively Predict the Developmental Toxicity of Halogenated Azole Compounds
Developmental toxicity is a relevant endpoint for the comprehensive assessment of human health risk from chemical exposure. However, animal developmental toxicity studies remain unavailable for many environmental contaminants due to the complexity and cost of these types of analyses. Here we describe an approach that utilizes Quantitative Structure-Activity Relationship (QSAR) 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 QSAR 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. A total of 7 halogenated chemicals that fit the AD but are not part the training set were employed for external validation purposes. Our model predicted LOAEL values reasonably well for all chemicals that fit the AD, with a maximal 3-fold deviation from the observed experimental values. The relatively 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.