Literature DB >> 20574897

Computational toxicology as implemented by the U.S. EPA: providing high throughput decision support tools for screening and assessing chemical exposure, hazard and risk.

Robert Kavlock1, David Dix.   

Abstract

Computational toxicology is the application of mathematical and computer models to help assess chemical hazards and risks to human health and the environment. Supported by advances in informatics, high-throughput screening (HTS) technologies, and systems biology, the U.S. Environmental Protection Agency EPA is developing robust and flexible computational tools that can be applied to the thousands of chemicals in commerce, and contaminant mixtures found in air, water, and hazardous-waste sites. The Office of Research and Development (ORD) Computational Toxicology Research Program (CTRP) is composed of three main elements. The largest component is the National Center for Computational Toxicology (NCCT), which was established in 2005 to coordinate research on chemical screening and prioritization, informatics, and systems modeling. The second element consists of related activities in the National Health and Environmental Effects Research Laboratory (NHEERL) and the National Exposure Research Laboratory (NERL). The third and final component consists of academic centers working on various aspects of computational toxicology and funded by the U.S. EPA Science to Achieve Results (STAR) program. Together these elements form the key components in the implementation of both the initial strategy, A Framework for a Computational Toxicology Research Program (U.S. EPA, 2003), and the newly released The U.S. Environmental Protection Agency's Strategic Plan for Evaluating the Toxicity of Chemicals (U.S. EPA, 2009a). Key intramural projects of the CTRP include digitizing legacy toxicity testing information toxicity reference database (ToxRefDB), predicting toxicity (ToxCast) and exposure (ExpoCast), and creating virtual liver (v-Liver) and virtual embryo (v-Embryo) systems models. U.S. EPA-funded STAR centers are also providing bioinformatics, computational toxicology data and models, and developmental toxicity data and models. The models and underlying data are being made publicly available through the Aggregated Computational Toxicology Resource (ACToR), the Distributed Structure-Searchable Toxicity (DSSTox) Database Network, and other U.S. EPA websites. While initially focused on improving the hazard identification process, the CTRP is placing increasing emphasis on using high-throughput bioactivity profiling data in systems modeling to support quantitative risk assessments, and in developing complementary higher throughput exposure models. This integrated approach will enable analysis of life-stage susceptibility, and understanding of the exposures, pathways, and key events by which chemicals exert their toxicity in developing systems (e.g., endocrine-related pathways). The CTRP will be a critical component in next-generation risk assessments utilizing quantitative high-throughput data and providing a much higher capacity for assessing chemical toxicity than is currently available.

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Year:  2010        PMID: 20574897     DOI: 10.1080/10937404.2010.483935

Source DB:  PubMed          Journal:  J Toxicol Environ Health B Crit Rev        ISSN: 1093-7404            Impact factor:   6.393


  24 in total

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Journal:  Carcinogenesis       Date:  2012-06-07       Impact factor: 4.944

2.  Identification of Environmental Quaternary Ammonium Compounds as Direct Inhibitors of Cholesterol Biosynthesis.

Authors:  Josi Herron; Rosalyn C Reese; Keri A Tallman; Rohini Narayanaswamy; Ned A Porter; Libin Xu
Journal:  Toxicol Sci       Date:  2016-02-26       Impact factor: 4.849

3.  Extending an in vitro panel for estrogenicity testing: the added value of bioassays for measuring antiandrogenic activities and effects on steroidogenesis.

Authors:  Si Wang; Jeroen C W Rijk; Harrie T Besselink; René Houtman; Ad A C M Peijnenburg; Abraham Brouwer; Ivonne M C M Rietjens; Toine F H Bovee
Journal:  Toxicol Sci       Date:  2014-06-13       Impact factor: 4.849

4.  In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning.

Authors:  Qingda Zang; Kamel Mansouri; Antony J Williams; Richard S Judson; David G Allen; Warren M Casey; Nicole C Kleinstreuer
Journal:  J Chem Inf Model       Date:  2017-01-09       Impact factor: 4.956

5.  A Reduced Transcriptome Approach to Assess Environmental Toxicants Using Zebrafish Embryo Test.

Authors:  Pingping Wang; Pu Xia; Jianghua Yang; Zhihao Wang; Ying Peng; Wei Shi; Daniel L Villeneuve; Hongxia Yu; Xiaowei Zhang
Journal:  Environ Sci Technol       Date:  2018-01-02       Impact factor: 9.028

6.  In vitro bioassays to evaluate complex chemical mixtures in recycled water.

Authors:  Ai Jia; Beate I Escher; Frederic D L Leusch; Janet Y M Tang; Erik Prochazka; Bingfeng Dong; Erin M Snyder; Shane A Snyder
Journal:  Water Res       Date:  2015-05-14       Impact factor: 11.236

7.  Quantitative non-targeted analysis: Bridging the gap between contaminant discovery and risk characterization.

Authors:  James P McCord; Louis C Groff; Jon R Sobus
Journal:  Environ Int       Date:  2021-12-02       Impact factor: 9.621

8.  Predicting Organ Toxicity Using in Vitro Bioactivity Data and Chemical Structure.

Authors:  Jie Liu; Grace Patlewicz; Antony J Williams; Russell S Thomas; Imran Shah
Journal:  Chem Res Toxicol       Date:  2017-10-09       Impact factor: 3.739

9.  Wind of change challenges toxicological regulators.

Authors:  Tewes Tralau; Christian Riebeling; Ralph Pirow; Michael Oelgeschläger; Andrea Seiler; Manfred Liebsch; Andreas Luch
Journal:  Environ Health Perspect       Date:  2012-08-07       Impact factor: 9.031

10.  Tox-database.net: a curated resource for data describing chemical triggered in vitro cardiac ion channels inhibition.

Authors:  Sebastian Polak; Barbara Wiśniowska; Anna Glinka; Miłosz Polak
Journal:  BMC Pharmacol Toxicol       Date:  2012-08-13       Impact factor: 2.483

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