ToxCast: Predicting Toxicity Potential Through High-Throughput Bioactivity Profiling (original) (raw)

Steinberg/High-Throughput, 2013

Abstract

ABSTRACT The field of toxicology is in transition from one based on an in vivo, chemical-by-chemical approach to one utilizing tools of modern molecular, cellular, and systems biology, providing the opportunity for greatly increased testing efficiency and improved modeling capabilities. Such an increase in testing efficiency is necessary to address the lack of toxicity information on the large chemical inventories that present potential human and environmental exposure scenarios, as well as to provide sufficient coverage of chemical and biological space to enable effective modeling of toxicity. Utilizing high-throughput screening methods developed in the drug discovery community, this in vitro-based approach focuses on the mechanisms of chemico-biological interactions by using large chemical libraries to probe key molecular targets and signaling pathways. Computational toxicology has emerged as a critical component in handling the large and complex datasets required for developing and validating this approach. Insight into these interactions will greatly enhance our understanding of the molecular and biological basis of toxicity and result in greater confidence in extrapolations, as well as factors critical to risk assessment such as significant susceptibility factors, subpopulations at risk, and influences on the shape of the dose–response curve.

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