MetaPath: An electronic knowledge base for collating, exchanging and analyzing case studies of xenobiotic metabolism

Kolanczyk, Richard C.; Schmieder, Patricia K.; Jones, William J.; Mekenyan, Ovanes G.; Chapkanov, Atanas; Temelkov, Stanislav; Котов, С. В.; Velikova, M.; Kamenska, Verginia; Vasilev, Krasimir; Veith, Gilman D.

doi:10.1016/j.yrtph.2012.02.013

Cited by 25 publications

(15 citation statements)

References 13 publications

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“…Metapath ( 20 ) is closely related to enviPath. Metapath is part of the OECD toolbox (see http://www.qsartoolbox.org/ ) and provides a search engine and database of metabolic maps.…”

Section: Related Workmentioning

confidence: 99%

enviPath – The environmental contaminant biotransformation pathway resource

et al. 2015

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The University of Minnesota Biocatalysis/Biodegradation Database and Pathway Prediction System (UM-BBD/PPS) has been a unique resource covering microbial biotransformation pathways of primarily xenobiotic chemicals for over 15 years. This paper introduces the successor system, enviPath (The Environmental Contaminant Biotransformation Pathway Resource), which is a complete redesign and reimplementation of UM-BBD/PPS. enviPath uses the database from the UM-BBD/PPS as a basis, extends the use of this database, and allows users to include their own data to support multiple use cases. Relative reasoning is supported for the refinement of predictions and to allow its extensions in terms of previously published, but not implemented machine learning models. User access is simplified by providing a REST API that simplifies the inclusion of enviPath into existing workflows. An RDF database is used to enable simple integration with other databases. enviPath is publicly available at https://envipath.org with free and open access to its core data.

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“…Metapath ( 20 ) is closely related to enviPath. Metapath is part of the OECD toolbox (see http://www.qsartoolbox.org/ ) and provides a search engine and database of metabolic maps.…”

Section: Related Workmentioning

confidence: 99%

enviPath – The environmental contaminant biotransformation pathway resource

et al. 2015

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“…A probability of occurrence is ascribed to each principal transformation, which determines its hierarchy in the transformation list. A training set of xenobiotic chemicals of a wide structural diversity, with experimentally observed metabolic reactions and pathways, was built using published data on their metabolism in rodent liver microsomes and S9 fraction, as taken from primary articles, monographs, or websites (Kolanczyk et al, 2012). The organic compounds in the training set belong to different classes of industrial chemicals, including single and fused-ring arenes, phenols, haloalkanes and haloarenes, aromatic and aliphatic amines, nitroarenes, alkanes and cycloalkanes, alkenes, ethers, carboxylic acids and their derivatives, halogenated hydrocarbons, alcohols, epoxides, N-nitrosoamines, and azo chemicals.…”

Section: Reaction Schema Diagramsmentioning

confidence: 99%

“…Overall, the simulator contains 450 -470 enzymatic phase I transformations and 15 -20 enzymatic phase II transformations. The reported average sensitivity and predictivity of the simulator are 81.6% and 39.1% respectively (Kolanczyk et al, 2012;Mekenyan et al, 2012;Serafimova et al, 2007). Within the Toolbox platform, a list of metabolites simulated are provided but without any additional information on the relative proportions or rates.…”

Section: Reaction Schema Diagramsmentioning

confidence: 99%

Retrospective mining of toxicology data to discover multispecies and chemical class effects: Anemia as a case study

Judson

Martin

Patlewicz

et al. 2017

Regulatory Toxicology and Pharmacology

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Predictive toxicity models rely on large amounts of accurate in vivo data. Here, we analyze the quality of in vivo data from the U.S. EPA Toxicity Reference Database (ToxRefDB), using chemical-induced anemia as an example. Considerations include variation in experimental conditions, changes in terminology over time, distinguishing negative from missing results, observer and diagnostic bias, and data transcription errors. Within ToxRefDB, we use hematological data on 658 chemicals tested in one or more of 1738 studies (subchronic rat or chronic rat, mouse, or dog). Anemia was reported most frequently in the rat subchronic studies, followed by chronic studies in dog, rat, and then mouse. Concordance between studies for a positive finding of anemia (same chemical, different laboratories) ranged from 90% (rat subchronic predicting rat chronic) to 40% (mouse chronic predicting rat chronic). Concordance increased with manual curation by 20% on average. We identified 49 chemicals that showed an anemia phenotype in at least two species. These included 14 aniline moiety-containing compounds that were further analyzed for their potential to be metabolically transformed into substituted anilines, which are known anemia-causing chemicals. This analysis should help inform future use of in vivo databases for model development.

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“…52,53 Early efforts inspired by Dr. Veith in the 1980s to establish databases of metabolites and transformation rates 54,55 were later followed by the building of a software tool with the computational capabilities needed to both systematically capture metabolism pathway data and provide data analysis tools to aid risk assessors. 56 The further development of the metabolism pathway (MetaPath) tool is now an internationally supported project of the MetaPath Users Group under the OECD Working Group on Pesticides. The development of metabolism simulators continues today both inside EPA and internationally.…”

Section: -34mentioning

confidence: 99%

Advancing Computational Toxicology in a Regulatory Setting: A Selected Review of the Accomplishments of Gilman D. Veith (1944–2013)

BradburySteven

RussomChristine²,

SchmiederPatricia

et al. 2015

Applied In Vitro Toxicology

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With the passing of Dr. Gilman D. Veith on August 18, 2013, the research community lost one of its true visionaries in the development and implementation of alternative in silico and in vitro toxicology models in human health and ecological risk assessment. His career spanned more than four decades, during which he repeatedly demonstrated vision and leadership to advance alternative testing and assessment research and to guide the adoption of research accomplishments into U.S. and international chemical regulatory programs. His ability to advance toxicological and environmental exposure research, and associated quantitative structure-activity relationships (QSARs), for application in environmental regulatory decision making was achieved by a focus on establishing a transparent, mechanistic understanding of the chemistry and biology underlying alternative experimental and mathematical models. Dr. Veith was a pioneer of the application of alternative methods, especially QSAR to industrial chemicals, which traditionally lacked the experimental data already available for pesticides and pharmaceuticals. His lifelong vision and leadership were recognized by a number of awards, including the Henry J. Heimlich, M.D., Award for Innovative Medicine in 2010. Through Dr. Veith's accomplishments, the use of alternative approaches in toxicity testing and risk assessment was implemented in the 20th century. His leadership also provided the scientific and regulatory framework for developing and applying in vitro and in silico techniques in what is now coined as 21st-century toxicology.

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MetaPath: An electronic knowledge base for collating, exchanging and analyzing case studies of xenobiotic metabolism

Cited by 25 publications

References 13 publications

enviPath – The environmental contaminant biotransformation pathway resource

enviPath – The environmental contaminant biotransformation pathway resource

Retrospective mining of toxicology data to discover multispecies and chemical class effects: Anemia as a case study

Advancing Computational Toxicology in a Regulatory Setting: A Selected Review of the Accomplishments of Gilman D. Veith (1944–2013)

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