Development of structure-activity relationship rules for predicting carcinogenic potential of chemicals

Woo, Yin-tak; Lai, David Y.; Argus, Mary F.; Arcos, Joseph C.

doi:10.1016/0378-4274(95)03373-s

Cited by 117 publications

(62 citation statements)

References 5 publications

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“…The basic principles of mechanism-based SAR analysis used for categorizing concern levels of DBPs are discussed below. predictive capability of SAR analysis combined with other toxicity information has been demonstrated (25)(26)(27)(28). Currently, SAR analysis is most well developed for chemicals and metabolites believed to initiate carcinogenesis through covalent interaction with DNA (i.e., DNA-reactive, -mutagenic, -electrophilic, or -proelectrophilic chemicals).…”

Section: Prioritization Approachmentioning

confidence: 99%

“…The same approach has been used in design of safer chemicals (33) and pollution prevention (34). An expert system (OncoLogic) has been developed to systematize and codify the agency's SAR expertise in predicting carcinogenic potential of chemicals (26). The principal authors of this present article have been involved in these program activities for more than a decade.…”

Section: Overview Of Basicmentioning

confidence: 99%

“…The structural criteria and methodology for assessing carcinogenic potential of chemicals have been discussed in detail in previous reviews (26,27). Basically, the structural moieties or fragments that may contribute to carcinogenic activity through a perceived or postulated mechanism are identified, and the modifying role of the rest of the molecule to which the structural moiety/fragment is attached is evaluated.…”

Section: Mechanism-based Structure-activity Analysismentioning

confidence: 99%

“…The DBPs were first reviewed to identify chemicals with low concern. Judgments of low cancer concern were based on structural similarity to chemicals with negative cancer data, a lack of structural alert for genotoxicity, or presence of structural features suggestive of low cancer risk via the oral route (26,27,33). Once the DBPs of low concern were removed from the list, a literature search was done for the remaining DBPs.…”

Section: Structure-activity Relationships Cancer Prediction For Disinmentioning

confidence: 99%

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Use of mechanism-based structure-activity relationships analysis in carcinogenic potential ranking for drinking water disinfection by-products.

Woo

Lai

McLain

et al. 2002

Environ Health Perspect

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222

100

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Disinfection by-products (DBPs) are formed when disinfectants such as chlorine, chloramine, and ozone react with organic and inorganic matter in water. The observations that some DBPs such as trihalomethanes (THMs), di-/trichloroacetic acids, and 3-chloro-4-(dichloromethyl)-5-hydroxy-2(5H)-furanone (MX) are carcinogenic in animal studies have raised public concern over the possible adverse health effects of DBPs. To date, several hundred DBPs have been identified. To prioritize research efforts, an in-depth, mechanism-based structure-activity relationship analysis, supplemented by extensive literature search for genotoxicity and other data, was conducted for ranking the carcinogenic potential of DBPs that met the following criteria: a) detected in actual drinking water samples, b) have insufficient cancer bioassay data for risk assessment, and c) have structural features/alerts or short-term predictive assays indicative of carcinogenic potential. A semiquantitative concern rating scale of low, marginal, low-moderate, moderate, high-moderate, and high was used along with delineation of scientific rationale. Of the 209 DBPs analyzed, 20 were of priority concern with a moderate or high-moderate rating. Of these, four were structural analogs of MX and five were haloalkanes that presumably will be controlled by existing and future THM regulations. The other eleven DBPs, which included halonitriles (6), haloketones (2), haloaldehyde (1), halonitroalkane (1), and dialdehyde (1), are suitable priority candidates for future carcinogenicity testing and/or mechanistic studies.

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Section: Prioritization Approachmentioning

confidence: 99%

Section: Overview Of Basicmentioning

confidence: 99%

Section: Mechanism-based Structure-activity Analysismentioning

confidence: 99%

Section: Structure-activity Relationships Cancer Prediction For Disinmentioning

confidence: 99%

See 2 more Smart Citations

Use of mechanism-based structure-activity relationships analysis in carcinogenic potential ranking for drinking water disinfection by-products.

Woo

Lai

McLain

et al. 2002

Environ Health Perspect

Self Cite

222

100

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“…These knowledge-based studies together with Klopman and Rosenkranz's computational studies 16,17 have established many qualitative structure-toxicity relationships for mutagenicity prediction. More recent literature methods [18][19][20] and currently available commercial software packages [21][22][23][24][25][26][27] also apply substructural descriptors for mutagenicity predictions. The differences between these knowledge-based or statistics-driven in silico tools as well as their individual limitations and capabilities have been discussed in numerous reviews.…”

Section: Introductionmentioning

confidence: 99%

Derivation and Validation of Toxicophores for Mutagenicity Prediction

Kazius¹,

McGuire²,

Bursi³

2004

J. Med. Chem.

528

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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Structural Concepts in the Prediction of the Toxicity of Therapeutical Agents

Rosenkranz

2003

Burger's Medicinal Chemistry and Drug Discovery

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Currently available substructure‐based structure‐activity relationship (SAR) methodologies, if expertly used, can be employed to predict the toxicological potential of therapeutic agents. Moreover, with robust SAR models of toxicological phenomena, it is feasible to understand the basis of these toxicities and to ascertain their relevance to possible human health risks. At present, SAR approaches to the evaluation of toxicity must be used in conjunction with human expert knowledge. The currently available technology can be used to design molecules of reduced toxicity which retain their therapeutic effectiveness.

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Development of structure-activity relationship rules for predicting carcinogenic potential of chemicals

Cited by 117 publications

References 5 publications

Use of mechanism-based structure-activity relationships analysis in carcinogenic potential ranking for drinking water disinfection by-products.

Use of mechanism-based structure-activity relationships analysis in carcinogenic potential ranking for drinking water disinfection by-products.

Derivation and Validation of Toxicophores for Mutagenicity Prediction

Structural Concepts in the Prediction of the Toxicity of Therapeutical Agents

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