Literature DB >> 19412856

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

E Benfenati1, 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.   

Abstract

Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include Vitotox, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-throughput assays combined with innovative data-mining and in silico methods. Various initiatives in this regard have begun, including CAESAR, OSIRIS, CHEMOMENTUM, CHEMPREDICT, OpenTox, EPAA, and ToxCast. In silico methods can be used for priority setting, mechanistic studies, and to estimate potency. Ultimately, such efforts should lead to improvements in application of in silico methods for predicting carcinogenicity to assist industry and regulators and to enhance protection of public health.

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Year:  2009        PMID: 19412856     DOI: 10.1080/10590500902885593

Source DB:  PubMed          Journal:  J Environ Sci Health C Environ Carcinog Ecotoxicol Rev        ISSN: 1059-0501            Impact factor:   3.781


  21 in total

1.  Some findings relevant to the mechanistic interpretation in the case of predictive models for carcinogenicity based on the counter propagation artificial neural network.

Authors:  Natalja Fjodorova; Marjana Novič
Journal:  J Comput Aided Mol Des       Date:  2011-12-03       Impact factor: 3.686

2.  Evaluating the applicability domain in the case of classification predictive models for carcinogenicity based on the counter propagation artificial neural network.

Authors:  Natalja Fjodorova; Marjana Novič; Alessandra Roncaglioni; Emilio Benfenati
Journal:  J Comput Aided Mol Des       Date:  2011-12-03       Impact factor: 3.686

3.  Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling.

Authors:  Kazutoshi Tanabe; Bono Lučić; Dragan Amić; Takio Kurita; Mikio Kaihara; Natsuo Onodera; Takahiro Suzuki
Journal:  Mol Divers       Date:  2010-02-26       Impact factor: 2.943

4.  Integrated in silico approaches for the prediction of Ames test mutagenicity.

Authors:  Sandeep Modi; Jin Li; Sophie Malcomber; Claire Moore; Andrew Scott; Andrew White; Paul Carmichael
Journal:  J Comput Aided Mol Des       Date:  2012-08-24       Impact factor: 3.686

5.  Preliminary Safety Assessment of New Azinesulfonamide Analogs of Aripiprazole using Prokaryotic Models.

Authors:  Beata Powroźnik; Karolina Słoczyńska; Krzysztof Marciniec; Paweł Zajdel; Elżbieta Pękala
Journal:  Adv Pharm Bull       Date:  2016-09-25

6.  Chemical toxicity prediction for major classes of industrial chemicals: Is it possible to develop universal models covering cosmetics, drugs, and pesticides?

Authors:  Vinicius M Alves; Eugene N Muratov; Alexey Zakharov; Nail N Muratov; Carolina H Andrade; Alexander Tropsha
Journal:  Food Chem Toxicol       Date:  2017-04-12       Impact factor: 6.023

7.  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

8.  New public QSAR model for carcinogenicity.

Authors:  Natalja Fjodorova; Marjan Vracko; Marjana Novic; Alessandra Roncaglioni; Emilio Benfenati
Journal:  Chem Cent J       Date:  2010-07-29       Impact factor: 4.215

9.  Preliminary mutagenicity and genotoxicity evaluation of selected arylsulfonamide derivatives of (aryloxy)alkylamines with potential psychotropic properties.

Authors:  Beata Powroźnik; Karolina Słoczyńska; Vittorio Canale; Katarzyna Grychowska; Paweł Zajdel; Elżbieta Pękala
Journal:  J Appl Genet       Date:  2015-10-06       Impact factor: 3.240

10.  Do cancer cells in human and meristematic cells in plant exhibit similar responses toward plant extracts with cytotoxic activities?

Authors:  Noha S Khalifa; Hoda S Barakat; Salwa Elhallouty; Dina Salem
Journal:  Cytotechnology       Date:  2014-04-05       Impact factor: 2.058

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