Big Data in Chemical Toxicity Research: The Use of High-Throughput Screening Assays To Identify Potential Toxicants

Abstract

High-throughput screening (HTS) assays that measure the in vitro toxicity of environmental compounds have been widely applied as an alternative to in vivo animal tests of chemical toxicity. Current HTS studies provide the community with rich toxicology information that has the potential to be integrated into toxicity research. The available in vitro toxicity data is updated daily in structured formats (e.g., deposited into PubChem and other data-sharing web portals) or in an unstructured way (papers, laboratory reports, toxicity Web site updates, etc.). The information derived from the current toxicity data is so large and complex that it becomes difficult to process using available database management tools or traditional data processing applications. For this reason, it is necessary to develop a big data approach when conducting modern chemical toxicity research. In vitro data for a compound, obtained from meaningful bioassays, can be viewed as a response profile that gives detailed information about the compound’s ability to affect relevant biological proteins/receptors. This information is critical for the evaluation of complex bioactivities (e.g., animal toxicities) and grows rapidly as big data in toxicology communities. This review focuses mainly on the existing structured in vitro data (e.g., PubChem data sets) as response profiles for compounds of environmental interest (e.g., potential human/animal toxicants). Potential modeling and mining tools to use the current big data pool in chemical toxicity research are also described.