Evaluation of the TOPKAT system for predicting the carcinogenicity of chemicals

Prival, Michael J.

doi:10.1002/1098-2280(2001)37:1<55::aid-em1006>3.0.co;2-5

Cited by 57 publications

(24 citation statements)

References 12 publications

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“…According to Enslein (1994), the accuracy of mutagenicity and carcinogenicity predictions are extremely high: 98% (against a mutagenicity dataset of 1083 chemicals) and 99.6% (against a carcinogenicity dataset of 705 chemicals), respectively. However, some subsequent evaluation studies (Prival 2001) indicate that for external sets of chemicals, the accuracy of TOPKAT prediction is considerably lower (40-75% against datasets of 30-40 chemicals) and these results were not significantly better when the analyses were restricted to predictions made inside the OPS.…”

Section: Topkatmentioning

confidence: 99%

Applicability of QSAR analysis to the evaluation of the toxicological relevance of metabolites and degradates of pesticide active substances for dietary risk assessment

2010

EFSA Supporting Publications

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Section: Topkatmentioning

confidence: 99%

Applicability of QSAR analysis to the evaluation of the toxicological relevance of metabolites and degradates of pesticide active substances for dietary risk assessment

2010

EFSA Supporting Publications

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“…These four classifi ers are based on structural descriptors computed from atomic and bond E -state indices. Prival tested the TOPKAT system by determining its ability to predict the chronic rodent carcinogenicity of 28 chemical compounds tested by the National Toxicology Program [51] . Although the sample used in this test is small, the classifi cation results suggest that the predictions of the TOPKAT carcinogenicity modules do not agree with the experimental fi ndings of the National Toxicology Program.…”

Section: Mutagenicity and Carcinogenicitymentioning

confidence: 99%

Electrotopological State Indices

Ivanciuc

2007

Methods and Principles in Medicinal Chemistry

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Abbreviations3D three -dimensional AC accuracy ADMET absorption, distribution, metabolism, excretion and toxicity ALL -QSAR automated lazy learning quantitative structure -activity relationships ANN artifi cial neural networks AUROC area under the receiver operator characteristic BBB blood -brain barrier Caco -2 adenocarcinoma cell line derived from human colon CART classifi cation and regression trees CL tot total clearance COX cyclooxygenase CYP cytochrome P450 DA dopamine antagonists DF decision forest Dual serotonin -dopamine dual antagonists ER estrogen receptor E -state electrotopological state HE -state hydrogen electrotopological state HIA human intestinal absorption HSA human serum albumin IAM immobilized artifi cial membrane KHE Kier -Hall electronegativity k NN k -nearest neighbors LDA linear discriminant analysis LLNA local lymph node assay MARS multivariate adaptive regression splines MCC Matthews correlation coeffi cient MLR multiple linear regression NPY neuropeptide Y 85 Molecular Drug Properties. Measurement and Prediction. R. Mannhold (Ed.)

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“…The results of a trial using TOPKAT to predict the carcinogenicity of chemicals tested by the U.S. NTP were disappointing, with a low rate of successful prediction (Prival 2001). It should be emphasized that the results of this trial should not be taken in isolation.…”

Section: Regulatory Usementioning

confidence: 99%

Use of QSARs in international decision-making frameworks to predict health effects of chemical substances.

Cronin

Jaworska

Walker

et al. 2003

Environ Health Perspect

245

122

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This article is a review of the use of quantitative (and qualitative) structure-activity relationships (QSARs and SARs) by regulatory agencies and authorities to predict acute toxicity, mutagenicity, carcinogenicity, and other health effects. A number of SAR and QSAR applications, by regulatory agencies and authorities, are reviewed. These include the use of simple QSAR analyses, as well as the use of multivariate QSARs, and a number of different expert system approaches.

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Evaluation of the TOPKAT system for predicting the carcinogenicity of chemicals

Cited by 57 publications

References 12 publications

Applicability of QSAR analysis to the evaluation of the toxicological relevance of metabolites and degradates of pesticide active substances for dietary risk assessment

Applicability of QSAR analysis to the evaluation of the toxicological relevance of metabolites and degradates of pesticide active substances for dietary risk assessment

Electrotopological State Indices

Use of QSARs in international decision-making frameworks to predict health effects of chemical substances.

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