Literature DB >> 8933058

A mechanism-mediated model for carcinogenicity: model content and prediction of the outcome of rodent carcinogenicity bioassays currently being conducted on 25 organic chemicals.

R Purdy1.   

Abstract

A hierarchical model consisting of quantitative structure-activity relationships based mainly on chemical reactivity was developed to predict the carcinogenicity of organic chemicals to rodents. The model is comprised of quantitative structure-activity relationships, QSARs based on hypothesized mechanisms of action, metabolism, and partitioning. Predictors included octanol/water partition coefficient, molecular size, atomic partial charge, bond angle strain, atomic acceptor delocalizibility, atomic radical superdelocalizibility, the lowest unoccupied molecular orbital (LUMO) energy of hypothesized intermediate nitrenium ion of primary aromatic amines, difference in charge of ionized and unionized carbon-chlorine bonds, substituent size and pattern on polynuclear aromatic hydrocarbons, the distance between lone electron pairs over a rigid structure, and the presence of functionalities such as nitroso and hydrazine. The model correctly classified 96% of the carcinogens in the training set of 306 chemicals, and 90% of the carcinogens in the test set of 301 chemicals. The test set by chance contained 84% of the positive thio-containing chemicals. A QSAR for these chemicals was developed. This posttest set modified model correctly predicted 94% of the carcinogens in the test set. This model was used to predict the carcinogenicity of the 25 organic chemicals the U.S. National Toxicology Program was testing at the writing of this article.

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Year:  1996        PMID: 8933058      PMCID: PMC1469711          DOI: 10.1289/ehp.96104s51085

Source DB:  PubMed          Journal:  Environ Health Perspect        ISSN: 0091-6765            Impact factor:   9.031


  6 in total

1.  Classification according to chemical structure, mutagenicity to Salmonella and level of carcinogenicity of a further 42 chemicals tested for carcinogenicity by the U.S. National Toxicology Program.

Authors:  J Ashby; R W Tennant; E Zeiger; S Stasiewicz
Journal:  Mutat Res       Date:  1989-06       Impact factor: 2.433

2.  Electrophilicity as measured by Ke: molecular determinants, relationship with other physical-chemical and quantum mechanical parameters, and ability to predict rodent carcinogenicity.

Authors:  R Benigni; M Cotta-Ramusino; C Andreoli; A Giuliani
Journal:  Carcinogenesis       Date:  1992-04       Impact factor: 4.944

3.  The structure-activity relationship of skin carcinogenicity of aromatic hydrocarbons and heterocycles.

Authors:  L Zhang; K Sannes; A J Shusterman; C Hansch
Journal:  Chem Biol Interact       Date:  1992-01       Impact factor: 5.192

4.  The utility of computed superdelocalizability for predicting the LC50 values of epoxides to guppies.

Authors:  R Purdy
Journal:  Sci Total Environ       Date:  1991-12       Impact factor: 7.963

5.  Prediction of rodent carcinogenicity for 44 chemicals: results.

Authors:  J Ashby; R W Tennant
Journal:  Mutagenesis       Date:  1994-01       Impact factor: 3.000

Review 6.  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.

Authors:  J Ashby; R W Tennant
Journal:  Mutat Res       Date:  1988-01       Impact factor: 2.433

  6 in total
  5 in total

1.  Development, interpretation and temporal evaluation of a global QSAR of hERG electrophysiology screening data.

Authors:  Claire L Gavaghan; Catrin Hasselgren Arnby; Niklas Blomberg; Gert Strandlund; Scott Boyer
Journal:  J Comput Aided Mol Des       Date:  2007-03-24       Impact factor: 3.686

2.  On the interpretation and interpretability of quantitative structure-activity relationship models.

Authors:  Rajarshi Guha
Journal:  J Comput Aided Mol Des       Date:  2008-09-11       Impact factor: 3.686

3.  The NIEHS Predictive-Toxicology Evaluation Project.

Authors:  D W Bristol; J T Wachsman; A Greenwell
Journal:  Environ Health Perspect       Date:  1996-10       Impact factor: 9.031

Review 4.  In silico prediction of drug toxicity.

Authors:  John C Dearden
Journal:  J Comput Aided Mol Des       Date:  2003 Feb-Apr       Impact factor: 3.686

Review 5.  Use of QSARs in international decision-making frameworks to predict health effects of chemical substances.

Authors:  Mark T D Cronin; Joanna S Jaworska; John D Walker; Michael H I Comber; Christopher D Watts; Andrew P Worth
Journal:  Environ Health Perspect       Date:  2003-08       Impact factor: 9.031

  5 in total

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