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.

Quimioinformática: Uma Introdução

“…116 Essa é uma abordagem bem estabelecida no moderno processo de planejamento e desenvolvimento de fármacos, 116 assim como na avaliação de potenciais compostos perigosos ao meio ambiente. 8 A triagem virtual é uma alternativa rápida e de baixo custo para a triagem e seleção de hits, 117 o que reduz o número de compostos selecionados para avaliação experimental. 118 Habitualmente, a taxa de sucesso da triagem de alta vazão (HTS, do inglês, High Throughput Screening) varia entre 0,01% e 0,14%, enquanto as taxas de sucesso para a VS geralmente varia entre 1% e 40%.…”

“…6,7 Essa mudança de cenário se deve, principalmente, à grande quantidade de dados biológicos e químicos produzidos graças a evolução de ensaios de biológicos de alta vazão (HTS, do inglês, High Throughput Screening) e da química combinatória. 8 A quantidade de dados cresceu tanto que, atualmente, tornou-se inviável extrair informação de bases de dados e, principalmente, transformar essa informação em conhecimento sem o uso de um computador. 6,9 Ainda na década de 1960, quando os primeiros estudos de relação quantitativa estrutura entre atividade foram publicados, as análises eram realizadas a poucos compostos de séries congêneres e as equações deduzidas manualmente.…”

The Future of Chemical Information Is Now

“…[28] Big Data tools are also valuable in chemical toxicology, where the use of high-throughput screening produces both structured and unstructured information that is so large and complex that it is difficult to analyze using traditional methods. [29] Just managing the chemistry data (in terms of chemical compounds and challenges of chemical structure detail vs. the myriad associated identifiers) is enough of a problem. [30] Working to blend the chemistry data with the associated bioassay screening data into a form consumable by scientists as openly accessible data, and for consumption by Big Data algorithmic approaches, is a significant and often underestimated difficulty.…”

CIIPro: a new read-across portal to fill data gaps using public large-scale chemical and biological data

“…PubChem, ChEMBL; Zhu et al, 2014). Computational methods to utilize public bioassay data for toxicity prediction and assessment are being developed (Kim et al, 2014(Kim et al, , 2016Low et al, 2013;Wang et al, 2015).…”

“…PubChem) and use these data for read-across assessment. The profiling and read-across approaches integrated into this portal have been used to develop multiple predictive models for complex bioactivities (Kim et al, 2016;Ribay et al, 2016;Wang et al, 2015;Zhang et al, 2014;Zhu et al, 2009Zhu et al, , 2014 Bioinformatics ;33 (2016), 3. -S. 464-466 https://dx.doi.org/10.1093 2 Methods…”