Abstract
Screening compounds for potential carcinogenicity is of major importance for prevention of environmentally induced cancers. A large sequence of alternative predictive models, ranging from short-term biological assays (e.g. mutagenicity tests) to theoretical models, have 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 the human expert knowledge and statistically approach, 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 prediction models to some pharmaceutical molecules.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arcos JC (1995) Chemical induction of cancer: modulation and combination effects. An inventory of the many factors which influence carcinogenesis. Springer Science & Business Media, New York
Woo Y, Lai D (2003) Mechanisms of action of chemical carcinogens, and their role in Structure-Activity Relationships (SAR) analysis and risk assessment. In: Benigni R (ed) Quantitative Structure-Activity Relationship (QSAR) models of mutagens and carcinogens. CRC, Boca Raton, FL, pp 41–80
Miller J, Miller E (1977) Origins of human cancer. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, pp 605–627
Miller EC, Miller JA (1981) Searches for ultimate chemical carcinogens and their reactions with cellular macromolecules. Cancer 47(10):2327–2345
Ames BN (1979) Identifying environmental chemicals causing mutations and cancer. Science 204(4393):587–593
OECD Test No. 487: In Vitro Mammalian Cell Micronucleus Test. OECD Publishing
Benfenati E (2013) Theory, guidance and applications on QSAR and REACH. ORCHESTRA consortium
Dearden JC, Barratt MD, Benigni R, Bristol DW, Combes RD, Cronin MT, Judson PN, Payne MP, Richard AM, Tichy M (1997) The development and validation of expert systems for predicting toxicity. Workshop (ECVAM Workshop 24). p 2
Benigni R, Bossa C, Tcheremenskaia O, Giuliani A (2010) Alternatives to the carcinogenicity bioassay: in silico methods, and the in vitro and in vivo mutagenicity assays. Expert Opin Drug Metab Toxicol 6(7):809–819. doi:10.1517/17425255.2010.486400
Toropov AA, Toropova AP, Benfenati E (2009) Additive SMILES-based carcinogenicity models: probabilistic principles in the search for robust predictions. Int J Mol Sci 10(7):3106–3127
Rositsa S, Mojca FG, Andrew W (2010) Review of QSAR models and software tools for predicting genotoxicity and carcinogenicity. JRC scientific and technical reports-EUR 24427 EN. Publications Office of the European Union, Luxembourg
Benfenati E, Benigni R, Demarini DM, Helma C, Kirkland D, Martin TM, Mazzatorta P, Ouédraogo-Arras G, Richard AM, Schilter B, Schoonen WG, Snyder RD, Yang C (2009) Predictive models for carcinogenicity and mutagenicity: frameworks, state-of-the-art, and perspectives. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev 27(2):57–90. doi:10.1080/10590500902885593
Ferrari T, Gini G, Benfenati E (2009) Support vector machines in the prediction of mutagenicity of chemical compounds. Proc NAFIPS 2009, June 14–17, Cincinnati, USA. pp 1–6
Ferrari T, Gini G, Bakhtyari NG, Benfenati E (2011) Mining toxicity structural alerts from SMILES: a new way to derive structure activity relationships. Computational Intelligence and Data Mining (CIDM), 2011 IEEE Symposium. pp 120–127. doi:10.1109/cidm.2011.5949444
Toxtree v, Ideaconsult Ltd, Sofia BAahtsn
Woo Y-T, Lai DY, Argus MF, Arcos JC (1995) Development of structure-activity relationship rules for predicting carcinogenic potential of chemicals. Toxicol Lett 79(1):219–228
OECD (2010) OECD Toolbox. http://www.oecd.org/chemicalsafety/risk-assessment/theoecdqsartoolbox.htm. Accessed 20 June 2015
Helma C (2006) Lazy structure-activity relationships (lazar) for the prediction of rodent carcinogenicity and Salmonella mutagenicity. Mol Divers 10(2):147–158
Klopman G, Rosenkranz HS (1994) Approaches to SAR in carcinogenesis and mutagenesis. Prediction of carcinogenicity/mutagenicity using MULTI-CASE. Mutat Res 305(1):33–46
Enslein K, Gombar VK, Blake BW (1994) Use of SAR in computer-assisted prediction of carcinogenicity and mutagenicity of chemicals by the TOPKAT program. Mutat Res 305(1):47–61
Smithing MP, Darvas F (1992) HazardExpert: an expert system for predicting chemical toxicity. ACS Symposium series American Chemical Society
Sanderson D, Earnshaw C (1991) Computer prediction of possible toxic action from chemical structure; the DEREK system. Hum Exp Toxicol 10(4):261–273
Ridings J, Barratt M, Cary R, Earnshaw C, Eggington C, Ellis M, Judson P, Langowski J, Marchant C, Payne M (1996) Computer prediction of possible toxic action from chemical structure: an update on the DEREK system. Toxicology 106(1):267–279
Helma C (2005) In silico predictive toxicology: the state-of-the-art and strategies to predict human health effects. Curr Opin Drug Discov Dev 8(1):27–31
Helma C, Cramer T, Kramer S, De Raedt L (2004) Data mining and machine learning techniques for the identification of mutagenicity inducing substructures and structure activity relationships of noncongeneric compounds. J Chem Inf Comput Sci 44(4):1402–1411. doi:10.1021/ci034254q
VEGA hwv-qe
Benigni R (2008) The Benigni/Bossa rulebase for mutagenicity and carcinogenicity—a module of Toxtree. JRC Scientific and Technical Reports, pp 1–78
ANTARES (2012) http://www.antares-life.eu/. Accessed 20 June 2015
Chemical carcinogens structures and experimental data (ISSCAN) (2008) ISS. http://www.epa.gov/ncct/dsstox/sdf_isscan_external.html. Accessed 20 June 2015
Kirkland D, Aardema M, Henderson L, Müller L (2005) 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. Mutat Res 584(1):1–256
Mayer J, Cheeseman MA, Twaroski ML (2008) Structure-activity relationship analysis tools: validation and applicability in predicting carcinogens. Regul Toxicol Pharmacol 50(1):50–58. doi:10.1016/j.yrtph.2007.09.005
EPA Environmental Protection Agency. http://www.epa.gov/nrmrl/std/qsar/qsar.html. Accessed 20 June 2015
IdeaConsult (2009) Toxtree user manual. IdeaConsult. https://eurl-ecvam.jrc.ec.europa.eu/laboratories-research/predictive_toxicology/doc/Toxtree_user_manual.pdf. Accessed 20 June 2015
The Carcinogenic Potency Database (CPDB) B, CA (USA). http://potency.berkeley.edu
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media New York
About this protocol
Cite this protocol
Golbamaki, A., Benfenati, E. (2016). In Silico Methods for Carcinogenicity Assessment. In: Benfenati, E. (eds) In Silico Methods for Predicting Drug Toxicity. Methods in Molecular Biology, vol 1425. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3609-0_6
Download citation
DOI: https://doi.org/10.1007/978-1-4939-3609-0_6
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-3607-6
Online ISBN: 978-1-4939-3609-0
eBook Packages: Springer Protocols