Literature DB >> 35188632

In Silico Prediction of Chemically Induced Mutagenicity: A Weight of Evidence Approach Integrating Information from QSAR Models and Read-Across Predictions.

Enrico Mombelli1, Giuseppa Raitano2, Emilio Benfenati2.   

Abstract

Information on genotoxicity is an essential piece of information in the framework of several regulations aimed at evaluating chemical toxicity. In this context, QSAR models that can predict Ames genotoxicity can conveniently provide relevant information. Indeed, they can be straightforwardly and rapidly used for predicting the presence or absence of genotoxic hazards associated with the interactions of chemicals with DNA. Nevertheless, and despite their ease of use, the main interpretative challenge is related to a critical assessment of the information that can be gathered, thanks to these tools. This chapter provides guidance on how to use freely available QSAR and read-across tools provided by VEGA HUB and on how to interpret their predictions according to a weight-of-evidence approach.
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Ames test; Mutagenicity; Predictive reliability; QSAR; Structural alerts

Mesh:

Substances:

Year:  2022        PMID: 35188632     DOI: 10.1007/978-1-0716-1960-5_7

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


  12 in total

1.  Benchmark data set for in silico prediction of Ames mutagenicity.

Authors:  Katja Hansen; Sebastian Mika; Timon Schroeter; Andreas Sutter; Antonius ter Laak; Thomas Steger-Hartmann; Nikolaus Heinrich; Klaus-Robert Müller
Journal:  J Chem Inf Model       Date:  2009-09       Impact factor: 4.956

2.  Evaluation of the OECD (Q)SAR Application Toolbox and Toxtree for predicting and profiling the carcinogenic potential of chemicals.

Authors:  E Mombelli; J Devillers
Journal:  SAR QSAR Environ Res       Date:  2010-10       Impact factor: 3.000

3.  Derivation and validation of toxicophores for mutagenicity prediction.

Authors:  Jeroen Kazius; Ross McGuire; Roberta Bursi
Journal:  J Med Chem       Date:  2005-01-13       Impact factor: 7.446

4.  Automatic knowledge extraction from chemical structures: the case of mutagenicity prediction.

Authors:  T Ferrari; D Cattaneo; G Gini; N Golbamaki Bakhtyari; A Manganaro; E Benfenati
Journal:  SAR QSAR Environ Res       Date:  2013-05-28       Impact factor: 3.000

Review 5.  Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy.

Authors:  Emilio Benfenati; Qasim Chaudhry; Giuseppina Gini; Jean Lou Dorne
Journal:  Environ Int       Date:  2019-08-01       Impact factor: 9.621

6.  Use of in silico systems and expert knowledge for structure-based assessment of potentially mutagenic impurities.

Authors:  Andreas Sutter; Alexander Amberg; Scott Boyer; Alessandro Brigo; Joseph F Contrera; Laura L Custer; Krista L Dobo; Veronique Gervais; Susanne Glowienke; Jacky van Gompel; Nigel Greene; Wolfgang Muster; John Nicolette; M Vijayaraj Reddy; Veronique Thybaud; Esther Vock; Angela T White; Lutz Müller
Journal:  Regul Toxicol Pharmacol       Date:  2013-05-10       Impact factor: 3.271

7.  An open source multistep model to predict mutagenicity from statistical analysis and relevant structural alerts.

Authors:  Thomas Ferrari; Giuseppina Gini
Journal:  Chem Cent J       Date:  2010-07-29       Impact factor: 4.215

8.  Searches for ultimate chemical carcinogens and their reactions with cellular macromolecules.

Authors:  E C Miller; J A Miller
Journal:  Cancer       Date:  1981-05-15       Impact factor: 6.860

Review 9.  A novel approach: chemical relational databases, and the role of the ISSCAN database on assessing chemical carcinogenicity.

Authors:  Romualdo Benigni; Cecilia Bossa; Ann M Richard; Chihae Yang
Journal:  Ann Ist Super Sanita       Date:  2008       Impact factor: 1.663

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