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ToxCast and Virtual Embryo: in vitro data and in silico models for predictive toxicology
Citation:
KNUDSEN, T. B. ToxCast and Virtual Embryo: in vitro data and in silico models for predictive toxicology. Chapter 3.3, Troy Seidle and Horst Spielmann (ed.), AXLR8-3 Alternative Testing Strategies; Progress Report 2012. Springer-Verlag, Berlin, Germany, , 193-200, (2012).
Impact/Purpose:
This chapter addresses reviews some of the challenges and opportunities for computational approaches focusing specifically on key milestones achieved by EPA’s computational toxicology research program in 2011-2012 toward predictive toxicology of developmental and fertility outcomes. Recent advances in computing power allow for the integration and correlation of vast amounts of data - this greatly extends our ability to identify and understand those biological pathways leading to adverse impacts, to make better predictions about human health risk and to model developing systems with an unprecedented degree of complexity [Knudsen et al. 2012].
Description:
Human populations may be exposed to thousands of chemicals only a fraction of which have detailed toxicity data. Traditional in vivo animal testing is costly, lengthy and normally conducted with dosages that exceed relatively insensitive to concentrations of chemicals at realistic exposure scenarios. These limitations give way to a new paradigm in which chemical biological activity is compiled from responses across high-throughput screening (HTS) assays and alternative models (e.g., embryonic stem cells, zebrafish embryos) to develop in vitro signatures of in vivo outcomes. With high-dimensional data at the molecular and cellular level now in hand, a grand challenge is to translate in vitro signatures into quantitative models that predict toxicity at progressively higher levels of biological organization.
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ToxCast and Virtual Embryo: in vitro data and in silico models for predictive toxicology