The structural basis of the mutagenicity of chemicals in Salmonella typhimurium: The Gene-Tox Data Base

Klopman, Gilles; Frierson, Manton R.; Rosenkranz, Herbert S.

doi:10.1016/0027-5107(90)90013-t

Cited by 111 publications

(39 citation statements)

References 17 publications

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“…For instance, the aromatic nitro, aromatic amine, nitrosamine, epoxide, aziridine, aromatic azo, nitrogen or sulfur mustard, R, -unsatured aldehyde, -propiolactone, and several aliphatic halide and heteroatombonded heteroatom derivatives are examples of welldefined known toxicophores, which in our study were identified and validated in agreement with expert analysis [11][12][13][14][15] and statistics-driven methods. 16,17 On the other hand, the R, -unsaturated alkoxy toxicophore, the detoxifying substructures for the aromatic nitro and amine groups, and, in particular, the polycyclic planar system toxicophore belong to a novel set of additional toxicophores, which fulfills the aim of our study.…”

Section: Resultsmentioning

confidence: 98%

“…11,17 The nitroso and azo-type groups and the three-member heterocycles are moieties that are similar to existing toxicophores. 11,17 Two other simple substructure representations that detected over 70 mutagens with an accuracy of about 70% are the aliphatic halide group (excluding the fluorine atom) [12][13][14][15][16][17] and the unsubstituted heteroatom-bonded heteroatom group 11 (a substructure that contains an unsubstituted heteroatom that is attached with a single bond to another heteroatom). Finally, one general toxicophore was represented by large polycyclic aromatic systems, i.e., systems of three or more fused aromatic rings, whose corresponding substructure representation consists of one aromatic atom that is connected to at least two atoms belonging to multiple aromatic rings.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Derivation and Validation of Toxicophores for Mutagenicity Prediction

Kazius¹,

McGuire²,

Bursi³

2004

J. Med. Chem.

560

473

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Mutagenicity is one of the numerous adverse properties of a compound that hampers its potential to become a marketable drug. Toxic properties can often be related to chemical structure, more specifically, to particular substructures, which are generally identified as toxicophores. A number of toxicophores have already been identified in the literature. This study aims at increasing the current degree of reliability and accuracy of mutagenicity predictions by identifying novel toxicophores from the application of new criteria for toxicophore rule derivation and validation to a considerably sized mutagenicity dataset. For this purpose, a dataset of 4337 molecular structures with corresponding Ames test data (2401 mutagens and 1936 nonmutagens) was constructed. An initial substructure-search of this dataset showed that most mutagens were detected by applying only eight general toxicophores. From these eight, more specific toxicophores were derived and approved by employing chemical and mechanistic knowledge in combination with statistical criteria. A final set of 29 toxicophores containing new substructures was assembled that could classify the mutagenicity of the investigated dataset with a total classification error of 18%. Furthermore, mutagenicity predictions of an independent validation set of 535 compounds were performed with an error percentage of 15%. Since these error percentages approach the average interlaboratory reproducibility error of Ames tests, which is 15%, it was concluded that these toxicophores can be applied to risk assessment processes and can guide the design of chemical libraries for hit and lead optimization.

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Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

Derivation and Validation of Toxicophores for Mutagenicity Prediction

Kazius¹,

McGuire²,

Bursi³

2004

J. Med. Chem.

560

473

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show abstract

“…In fact, using a database ofonly TA100 in the presence of S9 as against one of only TA98 in the presence of S9, the effect of mutagenic specificity by aromatic amines at a single guanine-cytosine (G-C) base pair (as in TA100) as opposed to the specificity ata series ofalternating G-C pairs (as in TA98) was amenable to analysis (5,6). c) On the other hand, if we wish to study and evaluate how Salmonella mutagenicity compares in its predictivity to the results of rodent carcinogenicity bioassays, then for the Salmonella data we might define as a positive response a response in any one of the tester strains obtained either in the presence or absence of S9 (2,7). Under these conditions, of course, it must be ascertained that before a chemical is designated as negative that it has indeed been tested in the complete panel oftester strains both in the presence and absence of S9.…”

Section: Nature Of the Databasementioning

confidence: 99%

“…We stress that our analysis is, of course, influenced by the structure-activity relationship (SAR) methodology we employ, namely, CASE (1,2). Howvver, CASE, as an artificial intelligence/expert system, is probably one ofthe most advanced SAR methods available and a harbinger of the developments that are to come to this methodology in general.…”

Section: Introductionmentioning

confidence: 99%

Structure-activity relations: maximizing the usefulness of mutagenicity and carcinogenicity databases.

Klopman

Rosenkranz

1991

Environ Health Perspect

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The most important criteria for the development and analysis of databases for elucidating the structural bases of toxicological activity include the integrity of the databases with respect to uniformity of the experimental protocol and interpretation ofthe test results and inclusion ofch cls representin different chemical classs and differingmechanisms of action. Within these criteria, it is de_mostrated that when the chemials are chosen at random, the larer the database, the better the pedictvity ofchemials not inlud in the learning sL It is shownhower, thatwhenchemicasare selected on the basis of structural features, that a learning set of approxmately 180 chemicals is as informative as a database consisting of 800 chemicals chosen at random.

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“…Since 1984, many studies have been published by Klopman and Rosenkranz (3)(4)(5)(6)(7)(8)(9)(10)(11) on this subject: sets of congeneric and noncongeneric compounds have been tested for several biological endpoints (mutagenicity, carcinogenicity, etc.). We have selected for discussion in this report some papers among the most pertinent to our work.…”

mentioning

confidence: 99%