The EPA tested several thousand chemicals in 700 toxicity-related in-vitro HTS bioassays through the ToxCast and Tox21 projects. However, the chemical space of interest for environmental exposure is much wider than this set of chemicals. Thus, there is a need to fill data gaps with in-silico methods, and quantitative structure-activity relationships (QSARs) are a cost effective approach to predict biological activity. The overall goal of this project was to use QSAR predictions to fill the data gaps in a larger environmental database of ~30K structures. The specific aim of the current work was to build QSAR models for multiple ToxCast assays using a subset of 1800 chemicals tested in 18 G-Protein Coupled Receptor (GPCR) assays. These assays are part of the aminergic category which was among the most active within the biochemical assays. Two QSAR modeling strategies were adopted; a classification into active/non-active chemicals, then regression models were built to predict the AC50 potency values of the bioassays for the active chemicals. Multiple software programs were used to calculate constitutional, topological and substructural molecular descriptors from two-dimensional structures. Model-fitting methods included PLSDA, SVM, MLR, PLS and kNN. Genetic algorithms (GAs) were applied as a variable selection technique to select the most predictive molecular descriptors for each assay. N-fold cross-validation (CV) coupled with multi-criteria decision making (MCDM) fitting criterions was used to evaluation the models. Finally, the applicability domain of the models was defined in order to make predictions within established chemical space limits. Using partial least squares discriminant analysis for the human histamine H1 GPCR assay, the classification accuracy reached 94% with a non-error rate of 89% in fitting and 80% in 5-fold CV, with only 2 latent variables. These results demonstrate the ability of QSAR models to predict bioactivity. This abstract does not necessarily reflect U.S. EPA policy.

In-silico study of ToxCast GPCR assays by quantitative structure-activity relationships (QSARs) modeling

248th National Meeting (2014)

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