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      Aggregating Data for Computational Toxicology Applications: The U.S. Environmental Protection Agency (EPA) Aggregated Computational Toxicology Resource (ACToR) System

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          Computational toxicology combines data from high-throughput test methods, chemical structure analyses and other biological domains (e.g., genes, proteins, cells, tissues) with the goals of predicting and understanding the underlying mechanistic causes of chemical toxicity and for predicting toxicity of new chemicals and products. A key feature of such approaches is their reliance on knowledge extracted from large collections of data and data sets in computable formats. The U.S. Environmental Protection Agency (EPA) has developed a large data resource called ACToR (Aggregated Computational Toxicology Resource) to support these data-intensive efforts. ACToR comprises four main repositories: core ACToR (chemical identifiers and structures, and summary data on hazard, exposure, use, and other domains), ToxRefDB (Toxicity Reference Database, a compilation of detailed in vivo toxicity data from guideline studies), ExpoCastDB (detailed human exposure data from observational studies of selected chemicals), and ToxCastDB (data from high-throughput screening programs, including links to underlying biological information related to genes and pathways). The EPA DSSTox (Distributed Structure-Searchable Toxicity) program provides expert-reviewed chemical structures and associated information for these and other high-interest public inventories. Overall, the ACToR system contains information on about 400,000 chemicals from 1100 different sources. The entire system is built using open source tools and is freely available to download. This review describes the organization of the data repository and provides selected examples of use cases.

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          Most cited references 39

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          The KEGG database.

          KEGG (http://www.genome.ad.jp/kegg/) is a suite of databases and associated software for understanding and simulating higher-order functional behaviours of the cell or the organism from its genome information. First, KEGG computerizes data and knowledge on protein interaction networks (PATHWAY database) and chemical reactions (LIGAND database) that are responsible for various cellular processes. Second, KEGG attempts to reconstruct protein interaction networks for all organisms whose genomes are completely sequenced (GENES and SSDB databases). Third, KEGG can be utilized as reference knowledge for functional genomics (EXPRESSION database) and proteomics (BRITE database) experiments. I will review the current status of KEGG and report on new developments in graph representation and graph computations.
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            In Vitro Screening of Environmental Chemicals for Targeted Testing Prioritization: The ToxCast Project

            Background Chemical toxicity testing is being transformed by advances in biology and computer modeling, concerns over animal use, and the thousands of environmental chemicals lacking toxicity data. The U.S. Environmental Protection Agency’s ToxCast program aims to address these concerns by screening and prioritizing chemicals for potential human toxicity using in vitro assays and in silico approaches. Objectives This project aims to evaluate the use of in vitro assays for understanding the types of molecular and pathway perturbations caused by environmental chemicals and to build initial prioritization models of in vivo toxicity. Methods We tested 309 mostly pesticide active chemicals in 467 assays across nine technologies, including high-throughput cell-free assays and cell-based assays, in multiple human primary cells and cell lines plus rat primary hepatocytes. Both individual and composite scores for effects on genes and pathways were analyzed. Results Chemicals displayed a broad spectrum of activity at the molecular and pathway levels. We saw many expected interactions, including endocrine and xenobiotic metabolism enzyme activity. Chemicals ranged in promiscuity across pathways, from no activity to affecting dozens of pathways. We found a statistically significant inverse association between the number of pathways perturbed by a chemical at low in vitro concentrations and the lowest in vivo dose at which a chemical causes toxicity. We also found associations between a small set of in vitro assays and rodent liver lesion formation. Conclusions This approach promises to provide meaningful data on the thousands of untested environmental chemicals and to guide targeted testing of environmental contaminants.
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              The Toxicity Data Landscape for Environmental Chemicals

              Objective Thousands of chemicals are in common use, but only a portion of them have undergone significant toxicologic evaluation, leading to the need to prioritize the remainder for targeted testing. To address this issue, the U.S. Environmental Protection Agency (EPA) and other organizations are developing chemical screening and prioritization programs. As part of these efforts, it is important to catalog, from widely dispersed sources, the toxicology information that is available. The main objective of this analysis is to define a list of environmental chemicals that are candidates for the U.S. EPA screening and prioritization process, and to catalog the available toxicology information. Data sources We are developing ACToR (Aggregated Computational Toxicology Resource), which combines information for hundreds of thousands of chemicals from > 200 public sources, including the U.S. EPA, National Institutes of Health, Food and Drug Administration, corresponding agencies in Canada, Europe, and Japan, and academic sources. Data extraction ACToR contains chemical structure information; physical–chemical properties; in vitro assay data; tabular in vivo data; summary toxicology calls (e.g., a statement that a chemical is considered to be a human carcinogen); and links to online toxicology summaries. Here, we use data from ACToR to assess the toxicity data landscape for environmental chemicals. Data synthesis We show results for a set of 9,912 environmental chemicals being considered for analysis as part of the U.S. EPA ToxCast screening and prioritization program. These include high-and medium-production-volume chemicals, pesticide active and inert ingredients, and drinking water contaminants. Conclusions Approximately two-thirds of these chemicals have at least limited toxicity summaries available. About one-quarter have been assessed in at least one highly curated toxicology evaluation database such as the U.S. EPA Toxicology Reference Database, U.S. EPA Integrated Risk Information System, and the National Toxicology Program.

                Author and article information

                Int J Mol Sci
                International Journal of Molecular Sciences
                Molecular Diversity Preservation International (MDPI)
                9 February 2012
                : 13
                : 2
                : 1805-1831
                [1 ]U.S. EPA, National Center for Computational Toxicology, Research Triangle Park, NC 27709, USA; E-Mails: martin.matt@ 123456epa.gov (M.T.M.); sumitgangwal@ 123456gmail.com (S.G.); reif.david@ 123456epa.gov (D.M.R.); kothiya.parth@ 123456epa.gov (P.K.); smith.doris@ 123456epa.gov (D.S.); vail.james@ 123456epa.gov (J.V.); frame.alicia@ 123456epa.gov (A.F.); mosher.shad@ 123456epa.gov (S.M.); hubal.elaine@ 123456epa.gov (E.A.C.H.); richard.ann@ 123456epa.gov (A.M.R.)
                [2 ]U.S. EPA, National Exposure Research Laboratory, Research Triangle Park, NC 27709, USA; E-Mail: egeghy.peter@ 123456epa.gov
                [3 ]Lockheed Martin, Research Triangle Park, NC, USA; E-Mails: maritja.a.wolf@ 123456lmco.com (M.W.); thomas.r.transue@ 123456lmco.com (T.C.) tommy.cathey@ 123456lmco.com (T.T.)
                Author notes
                [* ]Author to whom correspondence should be addressed; E-Mail: judson.richard@ 123456epa.gov ; Tel.: +1-919-541-3085.
                © 2012 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.

                This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/).



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