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The Benigni / Bossa rulebase for mutagenicity and carcinogenicity - a module of Toxtree
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TLDR
This report gives an introduction to currently available QSARs and SAs for carcinogenicity and mutagenicity, and provides details of the Benigni/Bossa rulebase.Abstract:
The Joint Resarch Centre's European Chemicals Bureau has developed a hazard estimation software called Toxtree, capable of making structure-based predictions for a number of toxicological endpoints. One of the modules developed as an extension to Toxtree is aimed at the prediction of carcinogenicity and mutagenicity. This module encodes the Benigni/Bossa rulebase for carcinogenicity and mutagenicity developed by Romualdo Benigni and Cecilia Bossa at the Istituto Superiore di Sanita’, in Rome, Italy. The module was coded by the Toxtree programmer, Ideaconsult Ltd, Bulgaria. In the Toxtree implementation of this rulebase, the processing of a query chemical gives rise to limited number of different outcomes, namely: a) no structural alerts for carcinogenicity are recognised; b) one or more structural alerts (SAs) are recognised for genotoxic or non-genotoxic carcinogenicity; c) SAs relative to aromatic amines or αβ-unsaturated aldehydes are recognised, and the chemical goes through Quantitative Structure-Activity Relationship (QSAR) analysis, which may result in a negative or positive outcome. If the query chemical belongs to the classes of aromatic amines or αβ-unsaturated aldehydes, the appropriate QSAR is applied and provides a more refined assessment than the SAs, and should be given higher importance in a weightof-evidence scheme. This report gives an introduction to currently available QSARs and SAs for carcinogenicity and mutagenicity, and provides details of the Benigni/Bossa rulebase. LIST OF ABBREVIATIONS AD Applicability Domain ECB European Chemicals Bureau FDA US Food and Drug Administration ISSCAN Istituto di Sanita database on chemical carcinogens QSAR Quantitative Structure-Activity Relationship SA Structural Alertread more
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In silico toxicology for the pharmaceutical sciences.
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References
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Journal ArticleDOI
Revised methods for the salmonella mutagenicity test
Dorothy M. Maron,Bruce N. Ames +1 more
TL;DR: Two new tester strains, a frameshift strain and a strain carrying an ochre mutation on a multicopy plasmid (TA102), are added to the standard tester set and two substitutions are made in diagnostic mutagens to eliminate MNNG and 9-aminoacridine.
Journal ArticleDOI
Robust Classification for Imprecise Environments
Foster Provost,Tom Fawcett +1 more
TL;DR: It is shown that it is possible to build a hybrid classifier that will perform at least as well as the best available classifier for any target conditions, and in some cases, the performance of the hybrid actually can surpass that of the best known classifier.
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TL;DR: The ROC convex hull (ROCCH) method as mentioned in this paper combines techniques from ROC analysis, decision analysis and computational geometry, and adapts them to the particulars of analyzing learned classifiers.
Journal ArticleDOI
Searches for ultimate chemical carcinogens and their reactions with cellular macromolecules
TL;DR: Current data are consistent with the idea that the initiation step of chemical carcinogenesis is a mutagenic event and is caused by alteration of DNA by the ultimate carcinogens and there appears to be no requirement that they be electrophilic.
Journal ArticleDOI
Chemical structure, Salmonella mutagenicity and extent of carcinogenicity as indicators of genotoxic carcinogenesis among 222 chemicals tested in rodents by the U.S. NCI/NTP.
John Ashby,Raymond W. Tennant +1 more
TL;DR: The Salmonella assay is found to be a sensitive method of detecting intrinsic genotoxicity in a chemical, and is consistent with tumors being induced in this tissue (and to a lesser extent in other tissues of the mouse and rat) by mechanisms not dependent upon direct interaction of the test chemical with DNA.