ToxCast Chemical Landscape: Paving the Road to 21st Century Toxicology.

By Ann M. Richard, Richard S. Judson, K. Houck, Christopher M. Grulke, P. Volarath, I. Thillainadarajah, Chihae Yang, J. Rathman, Matt T. Martin, J. Wambaugh and 8 more. 2016

652 citations recorded by the source corpus.

The U.S. Environmental Protection Agency's (EPA) ToxCast program is testing a large library of Agency-relevant chemicals using in vitro high-throughput screening (HTS) approaches to support the development of improved toxicity prediction models. Launched in 2007, Phase I of the program screened 310 chemicals, mostly pesticides, across hundreds of ToxCast assay end points. In Phase II, the ToxCast library was expanded to 1878 chemicals, culminating in the public release of screening data at the end of 2013. Subsequent expansion in Phase III has resulted in more than 3800 chemicals actively undergoing ToxCast screening, 96% of which are also being screened in the multi-Agency Tox21 project. The chemical library unpinning these efforts plays a central role in defining the scope and potential application of ToxCast HTS results. The history of the phased construction of EPA's ToxCast library is reviewed, followed by a survey of the library contents from several different vantage points. CAS Registry Numbers are used to assess ToxCast library coverage of important toxicity, regulatory, and exposure inventories. Structure-based representations of ToxCast chemicals are then used to compute physicochemical properties, substructural features, and structural alerts for toxicity and biotransformation. Cheminformatics approaches using these varied representations are applied to defining the boundaries of HTS testability, evaluating chemical diversity, and comparing the ToxCast library to potential target application inventories, such as used in EPA's Endocrine Disruption Screening Program (EDSP). Through several examples, the ToxCast chemical library is demonstrated to provide comprehensive coverage of the knowledge domains and target inventories of potential interest to EPA. Furthermore, the varied representations and approaches presented here define local chemistry domains potentially worthy of further investigation (e.g., not currently covered in the testing library or defined by toxicity "alerts") to strategically support data mining and predictive toxicology modeling moving forward.

Subfields: Computational Theory and Mathematics, Health, Toxicology and Mutagenesis, Small Animals, Computational Chemistry & Drug Discovery, Medical & Health AI

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Citations in the corpus

Strategies for Pre-training Graph Neural Networks (2019, background): “Toxicology measurements based on over 600 in vitro high-throughput screenings (Richard et al., 2016).”
Geometry-enhanced molecular representation learning for property prediction (2021, methodology): “We conduct experiments on multiple molecular benchmarks from 224 the MoleculeNet [53], including both classification and regression tasks [47, 33, 38, 16, 25, 28].”
Graph convolutional networks for computational drug development and discovery (2019, background): “Tox21 [57],ToxCast [87] and ClinTox [63] are datasets containing molecular toxicity information.”
Graph Self-Supervised Learning: A Survey (2021, neutral): “94 12* [10], [16], [70], [75], [101], [108] ToxCast [146] 8,575 18.”
The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency. (2019, methodology): “As a result, the initial chemicals in ToxRefDB were chosen to maximize overlap with ToxCast phase I (Richard et al. , 2016).”
Pre-training Graph Neural Networks (2019, background): “Toxicology measurements based on over 600 in vitro high-throughput screenings [41].”
The CompTox Chemistry Dashboard: a community data resource for environmental chemistry (2017, background): “Richard et al. [38], provides an account of the evolution and application of the chemical library for the ToxCast program].”