Literature DB >> 20438313

Alternatives to the carcinogenicity bioassay: in silico methods, and the in vitro and in vivo mutagenicity assays.

Romualdo Benigni1, Cecilia Bossa, Olga Tcheremenskaia, Alessandro Giuliani.   

Abstract

IMPORTANCE OF THE FIELD: Carcinogenicity and mutagenicity are toxicological end points posing considerable concern for human health. Due to the cost in animal lives, time and money, alternative approaches to the rodent bioassay were designed based on: i) identification of mutations and ii) structure-activity relationships. AREAS COVERED IN THIS REVIEW: Evidence on i) and ii) is summarized, covering 4 decades (1971 - 2010). WHAT THE READER WILL GAIN: A comprehensive, state-of-the-art perspective on alternatives to the carcinogenicity bioassay. TAKE HOME MESSAGE: Research to develop mutagenicity-based tests to predict carcinogenicity has generated useful results only for a limited area of the chemical space, that is, for the DNA-reactive chemicals (able to induce cancer, together with a wide spectrum of mutations). The most predictive mutagenicity-based assay is the Ames test. For non-DNA-reactive chemicals, that are Ames-negative and mutagenic in other in vitro assays (e.g., clastogenicity), no correlation with carcinogenicity is apparent. The knowledge on DNA reactivity permits the identification of genotoxic carcinogens with the same efficiency of the Ames test. Thus, a chemical mutagenic in Salmonella and/or with structural alerts should be seriously considered as a potential carcinogen. No reliable mutagenicity-based alternative tools are available to assess the risk of non-DNA-reactive chemicals.

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Year:  2010        PMID: 20438313     DOI: 10.1517/17425255.2010.486400

Source DB:  PubMed          Journal:  Expert Opin Drug Metab Toxicol        ISSN: 1742-5255            Impact factor:   4.481


  10 in total

1.  Global structure-activity relationship model for nonmutagenic carcinogens using virtual ligand-protein interactions as model descriptors.

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Journal:  Carcinogenesis       Date:  2012-06-07       Impact factor: 4.944

2.  In Silico Methods for Carcinogenicity Assessment.

Authors:  Azadi Golbamaki; Emilio Benfenati; Alessandra Roncaglioni
Journal:  Methods Mol Biol       Date:  2022

3.  Artificial intelligence uncovers carcinogenic human metabolites.

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Journal:  Nat Chem Biol       Date:  2022-08-11       Impact factor: 16.174

4.  Chemical structure determines target organ carcinogenesis in rats.

Authors:  C A Carrasquer; N Malik; G States; S Qamar; S L Cunningham; A R Cunningham
Journal:  SAR QSAR Environ Res       Date:  2012-10-16       Impact factor: 3.000

5.  Structure-activity relationship models for rat carcinogenesis and assessing the role mutagens play in model predictivity.

Authors:  C A Carrasquer; K Batey; S Qamar; A R Cunningham; S L Cunningham
Journal:  SAR QSAR Environ Res       Date:  2014-04-04       Impact factor: 3.000

Review 6.  In silico toxicology models and databases as FDA Critical Path Initiative toolkits.

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Authors:  Patrick McCarren; Clayton Springer; Lewis Whitehead
Journal:  J Cheminform       Date:  2011-11-22       Impact factor: 5.514

8.  Mutagenicity in a Molecule: Identification of Core Structural Features of Mutagenicity Using a Scaffold Analysis.

Authors:  Kuo-Hsiang Hsu; Bo-Han Su; Yi-Shu Tu; Olivia A Lin; Yufeng J Tseng
Journal:  PLoS One       Date:  2016-02-10       Impact factor: 3.240

9.  CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods.

Authors:  Li Zhang; Haixin Ai; Wen Chen; Zimo Yin; Huan Hu; Junfeng Zhu; Jian Zhao; Qi Zhao; Hongsheng Liu
Journal:  Sci Rep       Date:  2017-05-18       Impact factor: 4.379

Review 10.  Machine learning models for classification tasks related to drug safety.

Authors:  Anita Rácz; Dávid Bajusz; Ramón Alain Miranda-Quintana; Károly Héberger
Journal:  Mol Divers       Date:  2021-06-10       Impact factor: 3.364

  10 in total

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