Literature DB >> 35188634

In Silico Methods for Carcinogenicity Assessment.

Azadi Golbamaki1, Emilio Benfenati1, Alessandra Roncaglioni2.   

Abstract

Screening compounds for potential carcinogenicity is of major importance for prevention of environmentally induced cancers. A large sequence of predictive models, ranging from short-term biological assays (e.g., mutagenicity tests) to theoretical models, has been attempted in this field. Theoretical approaches such as (Q)SAR are highly desirable for identifying carcinogens, since they actively promote the replacement, reduction, and refinement of animal tests. This chapter reports and describes some of the most noted (Q)SAR models based on human expert knowledge and statistical approaches, aiming at predicting the carcinogenicity of chemicals. Additionally, the performance of the selected models has been evaluated, and the results are interpreted in details by applying these predictive models to some pharmaceutical molecules.
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Applicability domain index; Carcinogenicity; Genotoxicity; In silico; Nongenotoxicity; QSAR; SARpy; Structural alerts; Toxtree

Mesh:

Substances:

Year:  2022        PMID: 35188634     DOI: 10.1007/978-1-0716-1960-5_9

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  7 in total

1.  Data mining and machine learning techniques for the identification of mutagenicity inducing substructures and structure activity relationships of noncongeneric compounds.

Authors:  Christoph Helma; Tobias Cramer; Stefan Kramer; Luc De Raedt
Journal:  J Chem Inf Comput Sci       Date:  2004 Jul-Aug

Review 2.  Alternatives to the carcinogenicity bioassay: in silico methods, and the in vitro and in vivo mutagenicity assays.

Authors:  Romualdo Benigni; Cecilia Bossa; Olga Tcheremenskaia; Alessandro Giuliani
Journal:  Expert Opin Drug Metab Toxicol       Date:  2010-07       Impact factor: 4.481

3.  In silico predictive toxicology: the state-of-the-art and strategies to predict human health effects.

Authors:  Christoph Helma
Journal:  Curr Opin Drug Discov Devel       Date:  2005-01

4.  Structure-activity relationship analysis tools: validation and applicability in predicting carcinogens.

Authors:  J Mayer; M A Cheeseman; M L Twaroski
Journal:  Regul Toxicol Pharmacol       Date:  2007-11-19       Impact factor: 3.271

Review 5.  Predictive models for carcinogenicity and mutagenicity: frameworks, state-of-the-art, and perspectives.

Authors:  E Benfenati; R Benigni; D M Demarini; C Helma; D Kirkland; T M Martin; P Mazzatorta; G Ouédraogo-Arras; A M Richard; B Schilter; W G E J Schoonen; R D Snyder; C Yang
Journal:  J Environ Sci Health C Environ Carcinog Ecotoxicol Rev       Date:  2009-04       Impact factor: 3.781

6.  Can in vitro mammalian cell genotoxicity test results be used to complement positive results in the Ames test and help predict carcinogenic or in vivo genotoxic activity? II. Construction and analysis of a consolidated database.

Authors:  David Kirkland; Errol Zeiger; Federica Madia; Raffaella Corvi
Journal:  Mutat Res Genet Toxicol Environ Mutagen       Date:  2014-10-23       Impact factor: 2.873

7.  Evaluation of the ability of a battery of three in vitro genotoxicity tests to discriminate rodent carcinogens and non-carcinogens I. Sensitivity, specificity and relative predictivity.

Authors:  David Kirkland; Marilyn Aardema; Leigh Henderson; Lutz Müller
Journal:  Mutat Res       Date:  2005-07-04       Impact factor: 2.433

  7 in total

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