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ToxCast: PREDICTING TOXICITY POTENTIAL THROUGH HIGH-THROUGHPUT BIOACTIVITY PROFILING
Citation:
HOUCK, K. A., A. M. RICHARD, R. S. JUDSON, M. T. MARTIN, D. REIF, AND I. A. SHAH. ToxCast: PREDICTING TOXICITY POTENTIAL THROUGH HIGH-THROUGHPUT BIOACTIVITY PROFILING. 1st Edition, , Chapter 1, Pablo Steinberg (ed.), High-Throughput Screening Methods in Toxicity Testing. John Wiley & Sons, Inc, Hoboken, NJ, , 3-31, (2013).
Impact/Purpose:
This chapter provides an overview of the emerging field of Computational Toxicology (CT) and discusses: • Goals of computational toxicology research and development • Types of data generated and used •Organization of CT data into databases and knowledgebases •Applications Computational Toxicology is an emerging field that combines in vitro and computationally-generated data on chemicals, information on biological targets (genes, proteins), pathways and processes, and informatics methods to model and understand the mechanistic basis of chemical toxicity. CT often looks at trends across large sets of chemicals, large sets of data on a single chemical or a combination of the two. Much of the computational effort focuses on organizing these data and using statistical and modeling methods to interpret them. Two other areas of research that often fall under the CT heading, but which will not be covered here, are systems biology modeling and quantitative structure activity relationship (QSAR) modeling. These purely computational approaches are complementary to the in vitro, data-centered approach described here, but are sufficiently different to warrant their own in-depth discussion. This chapter will introduce the goals, tools and approaches used by CT practitioners and will illustrate them through several examples.
Description:
Book Chapter
