2 research outputs found
Use of SAR to Assess Impact of Metabolism in ToxCast modeling of Rat Carcinogenicity
Presented by Dr. Richar
ToxCast Chemical Landscape: Paving the Road to 21st Century Toxicology
The
U.S. Environmental Protection Agency’s (EPA) ToxCast
program is testing a large library of Agency-relevant chemicals using <i>in vitro</i> 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