Citation:

Allen, T., M. Nelms, S. Edwards, J. Goodman, S. Gutsell, AND P. Russell. In Silico Guidance for In Vitro Androgen and Glucocorticoid Receptor ToxCast Assays. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, 54(12):7461-7470, (2020). https://doi.org/10.1021/acs.est.0c01105

Impact/Purpose:

Computational approaches to support chemical risk assessment have the advantage of being faster and less expensive than in vitro or in vivo experiments. The US EPA ToxCast database provides a large amount of high quality data for the construction of such computational models. In this study, models have been developed for the human androgen and glucocorticoid receptors using the chemistry of molecules to distinguish between binders and non-binders. It has been found that the different chemical substructures used to construct these models align well with the different ToxCast in vitro assays, which determine chemical activity at the androgen or glucocorticoid receptors experimentally. Therefore, the computational model can provide guidance on which in vitro assays should be conducted first to confirm if a molecule is active at its given target. In this manner, the number of necessary experiments required to gain toxicological insight can be reduced. This work represents an important step in the use of computational models to guide and target experimental studies, and is of interest to those in risk assessment who require rapid and inexpensive toxicological results, by reducing the number of experiments required. In addition, this methodology can be built upon in the future to provide in silico guidance for other experimental procedures.

Description:

Molecular initiating events (MIEs) are key events in adverse outcome pathways (AOPs) that link molecular chemistry to target biology. As they are based in chemistry, these interactions are excellent targets for computational chemistry approaches to in silico modelling. In this work, we aim to link ligand chemical structure to MIEs for androgen and glucocorticoid receptor binding using ToxCast data. This has been done using an automated computational algorithm to perform maximal common substructure searches on chemical binders for each target from the ToxCast dataset. The 2D structural alerts developed can be used as in silico models to predict these MIEs, and as guidance for in vitro ToxCast assays to confirm hits. These models can target such experimental work, reducing the number of assays to be performed to gain required toxicological insight. Development of these models has also allowed some structural alerts to be identified as predictors for agonist or antagonist behavior at the receptor target. This work represents a first step in using computational methods to guide and target experimental approaches.

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