Skip to main content
SpringerLink
Log in

(Q)SAR Methods for Predicting Genotoxicity and Carcinogenicity: Scientific Rationale and Regulatory Frameworks

  • Protocol
  • First Online:
Computational Toxicology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1800))

Abstract

Knowledge of the genotoxicity and carcinogenicity potential of chemical substances is one of the key scientific elements able to better protect human health. Genotoxicity assessment is also considered as prescreening of carcinogenicity. The assessment of both endpoints is a fundamental component of national and international legislations, for all types of substances, and has stimulated the development of alternative, nontesting methods. Over the recent decades, much attention has been given to the use and further development of structure–activity relationships-based approaches, to be used in isolation or in combination with in vitro assays for predictive purposes. In this chapter, we briefly introduce the rationale for the main (Q)SAR approaches, and detail the most important regulatory initiatives and frameworks. It appears that the existence and needs of regulatory frameworks stimulate the development of better predictive tools; in turn, this allows the regulators to fine-tune their requirements for an improved defense of human health.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Huff J, Haseman J (1991) Long-term chemical carcinogenesis experiments for identifying potential human cancer hazards: collective database of the National Cancer Institute and National Toxicology Program (1976-1991). Environ Health Perspect 96:23–31

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Huff J, Haseman J, Rall D (1991) Scientific concepts, value, and significance of chemical carcinogenesis studies. Annu Rev Pharmacol Toxicol 31:621–652

    Article  CAS  PubMed  Google Scholar 

  3. EFSA (2011) Scientific opinion on genotoxicity testing strategies applicable to food and feed safety assessment. EFSA J 9:2379

    Google Scholar 

  4. Benigni R, Bossa C (2011) Mechanisms of chemical carcinogenicity and mutagenicity: a review with implications for predictive toxicology. Chem Rev 111:2507–2536

    Article  CAS  PubMed  Google Scholar 

  5. OECD (2007) Detailed review paper on cell transformation assays for detection of chemical carcinogens. OECD Publishing, Paris. ENV/JM/MONO(2007)18

    Google Scholar 

  6. Benigni R, Bossa C, Tcheremenskaia O, Battistelli CL, Giuliani A (2015) The Syrian hamster embryo cells transformation assay identifies efficiently nongenotoxic carcinogens, and can contribute to alternative, integrated testing strategies. Mutat Res Genet Toxicol Environ Mutagen 779:35–38

    Article  CAS  PubMed  Google Scholar 

  7. OECD (2016) Guidance document on the in vitro Bhas 42 cell transformation assay (BHAS 42 CTA). OECD Publishing, Paris. ENV/JM/MONO(2016)1

    Google Scholar 

  8. OECD (2015) Guidance document on the in vitro syrian hamster embryo (SHE) cell transformation assay. OECD Publishing, Paris. ENV/JM/MONO(2015)18

    Google Scholar 

  9. Cherkasov A, Muratov EN, Fourches D et al (2014) QSAR modeling: where have you been? Where are you going to? J Med Chem 57:4977–5010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Nicolotti O, Benfenati E, Carotti A, Gadaleta D, Gissi A, Mangiatordi GF, Novellino E (2014) REACH and in silico methods: an attractive opportunity for medicinal chemists. Drug Discov Today 19:1757–1768

    Article  CAS  PubMed  Google Scholar 

  11. OECD (2014) Guidance on grouping of chemicals, 2nd edn. OECD Publishing, Paris. ENV/JM/MONO(2014)4

    Book  Google Scholar 

  12. ECETOC (2012) Category approaches, Read-across, (Q)SAR. Technical Report no. 116. Brussels

    Google Scholar 

  13. Benigni R, Bossa C (2008) Predictivity and reliability of QSAR models: the case of mutagens and carcinogens. Toxicol Mech Methods 18:137–147

    Article  CAS  PubMed  Google Scholar 

  14. Benigni R, Bossa C, Netzeva T, Worth A (2007) Collection and evaluation of (Q)SAR models for mutagenicity and carcinogenicity. EUR - Scientific and Technical Research Reports. EUR 22772 EN

    Google Scholar 

  15. OECD (2008) Report of a Workshop on Integrated Approaches to Testing and Assessment (IATA). OECD Publishing, Paris. ENV/JM/MONO(2008)10

    Google Scholar 

  16. USEPA (2011) Integrated approaches to testing and assessment strategy: use of new computational and molecular tools. FIFRA Scientific Advisory Panel Consultation US Environmental Protection Agency, Office of Pesticide Programs

    Google Scholar 

  17. Tollefsen KE, Scholz S, Cronin MT, Edwards SW, de Knecht J, Crofton K, Garcia-Reyero N, Hartung T, Worth A, Patlewicz G (2014) Applying adverse outcome pathways (AOPs) to support integrated approaches to testing and assessment (IATA). Regul Toxicol Pharmacol 70:629–640

    Article  PubMed  Google Scholar 

  18. Benigni R, Battistelli CL, Bossa C, Colafranceschi M, Tcheremenskaia O (2013) Mutagenicity, carcinogenicity, and other end points. Methods Mol Biol 930:67–98

    Article  CAS  PubMed  Google Scholar 

  19. Serafimova R, Gatnik MF, Worth A (2010) Review of QSAR models and software tools for predicting genotoxicity and carcinogenicity. EUR - Scientific and Technical Research Reports. EUR 24427 EN

    Google Scholar 

  20. Worth A, Barroso J, Bremer S, Burton J, Casati S, Coecke S, Corvi R, Desprez B, Dumont C, Gouliarmou V, Goumenou M, Gräpel R, Griesinger C, Halder M, Roi AJ, Kienzler A, Madia F, Munn S, Nepelska M, Paini A, Price A, Prieto P, Rolaki A, Schäffer M, Triebe J, Whelan M, Wittwehr C, Zuang V (2014) Alternative methods for regulatory toxicology – a state-of-the-art review. EUR - Scientific and Technical Research Reports. EUR 26797

    Google Scholar 

  21. Cassano A, Raitano G, Mombelli E, Fernández A, Cester J, Roncaglioni A, Benfenati E (2014) Evaluation of QSAR models for the prediction of ames genotoxicity: a retrospective exercise on the chemical substances registered under the EU REACH regulation. J Environ Sci Health C 32:273–298

    Article  Google Scholar 

  22. OECD (2007) Guidance document on the validation of (quantitative) structure-activity relationship [(Q)SAR] models, vol ENV/JM/MONO(2007)2. OECD Publishing, Paris

    Google Scholar 

  23. Patlewicz G, Jeliazkova N, Safford RJ, Worth AP, Aleksiev B (2008) An evaluation of the implementation of the Cramer classification scheme in the Toxtree software. SAR QSAR Environ Res 19:495–524

    Article  CAS  PubMed  Google Scholar 

  24. Benigni R, Bossa C (2008) Structure alerts for carcinogenicity, and the Salmonella assay system: a novel insight through the chemical relational databases technology. Mutat Res 659:248–261

    Article  CAS  PubMed  Google Scholar 

  25. Benigni R, Bossa C, Jeliazkova N, Netzeva T, Worth A (2008) The Benigni/Bossa rulebase for mutagenicity and carcinogenicity - a module of toxtree. EUR - Scientific and Technical Research Reports. EUR 23241 EN

    Google Scholar 

  26. Benigni R, Bossa C, Tcheremenskaia O (2013) Nongenotoxic carcinogenicity of chemicals: mechanisms of action and early recognition through a new set of structural alerts. Chem Rev 113(5):2940–2957. https://doi.org/10.1021/cr300206t

    Article  PubMed  CAS  Google Scholar 

  27. Benigni R, Bossa C, Tcheremenskaia O, Battistelli CL, Crettaz P (2012) The new ISSMIC database on in vivo micronucleus and its role in assessing genotoxicity testing strategies. Mutagenesis 27:87–92

    Article  CAS  PubMed  Google Scholar 

  28. Lai D, Woo Y-T (2005) OncoLogic. In: Predictive toxicology. CRC Press, Boca Raton, FL, pp 385–413

    Chapter  Google Scholar 

  29. Woo YTLD, Argus MF, Arcos JC (1998) An integrative approach of combining mechanistically complementary short-term predictive tests as a basis for assessing the carcinogenic potential of chemicals. J Environ Sci Health C Environ Carcinog Ecotoxicol Rev C 16(2):101–122

    Article  Google Scholar 

  30. OECD (2004) OECD Principles for the validation, for regulatory purposes, of (quantitative) structure-activity relationship models. OECD Publishing, Paris

    Google Scholar 

  31. OECD (2015) Fundamental and guiding principles for (Q)SAR analysis of chemical carcinogens with mechanistic considerations. OECD Publishing, Paris. ENV/JM/MONO(2015)46

    Google Scholar 

  32. Dimitrov SD, Diderich R, Sobanski T et al (2016) QSAR Toolbox - workflow and major functionalities. SAR QSAR Environ Res:1–17

    Google Scholar 

  33. Benigni R, Battistelli CL, Bossa C, Tcheremenskaia O, Crettaz P (2013) New perspectives in toxicological information management, and the role of ISSTOX databases in assessing chemical mutagenicity and carcinogenicity. Mutagenesis 28:401–409

    Article  CAS  PubMed  Google Scholar 

  34. Hardy B, Apic G, Carthew P, Clark D, Cook D, Dix I, Escher S, Hastings J, Heard DJ, Jeliazkova N, Judson P, Matis-Mitchell S, Mitic D, Myatt G, Shah I, Spjuth O, Tcheremenskaia O, Toldo L, Watson D, White A, Yang C (2012) Food for thought ... A toxicology ontology roadmap. ALTEX 29(2):129–137

    Article  PubMed  Google Scholar 

  35. Hardy B, Apic G, Carthew P, Clark D, Cook D, Dix I, Escher S, Hastings J, Heard DJ, Jeliazkova N, Judson P, Matis-Mitchell S, Mitic D, Myatt G, Shah I, Spjuth O, Tcheremenskaia O, Toldo L, Watson D, White A, Yang C (2012) Toxicology ontology perspectives. ALTEX 29:139–156

    Article  PubMed  Google Scholar 

  36. Tcheremenskaia O, Benigni R, Nikolova I, Jeliazkova N, Escher SE, Batke M, Baier T, Poroikov V, Lagunin A, Rautenberg M, Hardy B (2012) OpenTox predictive toxicology framework: toxicological ontology and semantic media wiki-based OpenToxipedia. J Biomed Semantics 3(Suppl 1):S7

    Article  PubMed  PubMed Central  Google Scholar 

  37. ECHA (2008) QSARs and grouping of chemicals, vol R.6. Guidance on information requirements and chemical safety assessment. Guidance for the implementation of REACH

    Google Scholar 

  38. ECHA (2017) The use of alternatives to testing on animals for the REACH Regulation. European Chemicals Agency

    Google Scholar 

  39. ECHA (2016) Evaluation under REACH progress report 2016 – executive summary and recommendations to registrants. European Chemicals Agency

    Google Scholar 

  40. ECHA (2017) Read-across assessment framework (RAAF). European Chemicals Agency

    Google Scholar 

  41. ECHA (2016) Practical guide – how to use and report (Q)SARs. European Chemicals Agenc (ECHA)

    Google Scholar 

  42. ECHA (2016) Practical guide: how to use alternatives to animal testing to fulfil the information requirements for REACH registration. European Chemicals Agency

    Google Scholar 

  43. NAFTA (2012) (Quantitative) Structure Activity Relationship [(Q)SAR] Guidance Document. US Environmental Protection Agency, Technical Working Group on Pesticides

    Google Scholar 

  44. EFSA-PPR (2016) Guidance on the establishment of the residue definition for dietary risk assessment. EFSA J 14:4549

    Google Scholar 

  45. EU-JRC (2010) Applicability of QSAR analysis to the evaluation of the toxicological relevance of metabolites and degradates of pesticide active substances for dietary risk assessment. EFSA Support Publ 7(5):50E

    Google Scholar 

  46. EFSA-PPR (2012) Scientific opinion on evaluation of the toxicological relevance of pesticide metabolites for dietary risk assessment. EFSA J 10(07):2799

    Google Scholar 

  47. ICH-M7 (2017) Assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals to limit potential carcinogenic risk. http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Multidisciplinary/M7/M7_R1_Addendum_Step_4_2017_0331.pdf

  48. Greene N, Dobo KL, Kenyon MO, Cheung J, Munzner J, Sobol Z, Sluggett G, Zelesky T, Sutter A, Wichard J (2015) A practical application of two in silico systems for identification of potentially mutagenic impurities. Regul Toxicol Pharmacol 72:335–349

    Article  CAS  PubMed  Google Scholar 

  49. Amberg A, Beilke L, Bercu J, Bower D, Brigo A, Cross KP, Custer L, Dobo K, Dowdy E, Ford KA, Glowienke S, Van Gompel J, Harvey J, Hasselgren C, Honma M, Jolly R, Kemper R, Kenyon M, Kruhlak N, Leavitt P, Miller S, Muster W, Nicolette J, Plaper A, Powley M, Quigley DP, Reddy MV, Spirkl HP, Stavitskaya L, Teasdale A, Weiner S, Welch DS, White A, Wichard J, Myatt GJ (2016) Principles and procedures for implementation of ICH M7 recommended (Q)SAR analyses. Regul Toxicol Pharmacol 77:13–24

    Article  CAS  PubMed  Google Scholar 

  50. Barber C, Amberg A, Custer L, Dobo KL, Glowienke S, Van Gompel J, Gutsell S, Harvey J, Honma M, Kenyon MO, Kruhlak N, Muster W, Stavitskaya L, Teasdale A, Vessey J, Wichard J (2015) Establishing best practise in the application of expert review of mutagenicity under ICH M7. Regul Toxicol Pharmacol 73:367–377

    Article  PubMed  Google Scholar 

  51. Barber C, Cayley A, Hanser T, Harding A, Heghes C, Vessey JD, Werner S, Weiner SK, Wichard J, Giddings A, Glowienke S, Parenty A, Brigo A, Spirkl H-P, Amberg A, Kemper R, Greene N (2016) Evaluation of a statistics-based Ames mutagenicity QSAR model and interpretation of the results obtained. Regul Toxicol Pharmacol 76(Suppl C):7–20

    Article  CAS  PubMed  Google Scholar 

  52. Barber C, Hanser T, Judson P, Williams R (2017) Distinguishing between expert and statistical systems for application under ICH M7. Regul Toxicol Pharmacol 84(Suppl C):124–130

    Article  PubMed  Google Scholar 

  53. Cartus A, Schrenk D (2017) Current methods in risk assessment of genotoxic chemicals. Food Chem Toxicol 106(Part B):574–582

    Article  CAS  PubMed  Google Scholar 

  54. Powley MW (2015) (Q)SAR assessments of potentially mutagenic impurities: a regulatory perspective on the utility of expert knowledge and data submission. Regul Toxicol Pharmacol 71:295–300

    Article  CAS  PubMed  Google Scholar 

  55. Teasdale A (2017) Regulatory highlights. Org Process Res Dev 21:1209–1212

    Article  CAS  Google Scholar 

  56. Williams RV, Amberg A, Brigo A, Coquin L, Giddings A, Glowienke S, Greene N, Jolly R, Kemper R, O’Leary-Steele C, Parenty A, Spirkl H-P, Stalford SA, Weiner SK, Wichard J (2016) It’s difficult, but important, to make negative predictions. Regul Toxicol Pharmacol 76(Suppl C):79–86

    Article  CAS  PubMed  Google Scholar 

  57. Sutter A, Amberg A, Boyer S, Brigo A, Contrera JF, Custer LL, Dobo KL, Gervais V, Glowienke S, Gompel JV, Greene N, Muster W, Nicolette J, Reddy MV, Thybaud V, Vock E, White AT, Müller L (2013) Use of in silico systems and expert knowledge for structure-based assessment of potentially mutagenic impurities. Regul Toxicol Pharmacol 67:39–52

    Article  CAS  PubMed  Google Scholar 

  58. Floris M, Manganaro A, Nicolotti O, Medda R, Mangiatordi GF, Benfenati E (2014) A generalizable definition of chemical similarity for read-across. J Cheminformatics 6:39

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Environment and Health Department, Istituto Superiore di Sanità, Roma, Italy

    Cecilia Bossa, Olga Tcheremenskaia & Chiara Laura Battistelli

  2. Alpha-Pretox, Roma, Italy

    Romualdo Benigni

Authors
  1. Cecilia Bossa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Romualdo Benigni
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Olga Tcheremenskaia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chiara Laura Battistelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cecilia Bossa .

Editor information

Editors and Affiliations

  1. Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Bari, Italy

    Orazio Nicolotti

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Bossa, C., Benigni, R., Tcheremenskaia, O., Battistelli, C.L. (2018). (Q)SAR Methods for Predicting Genotoxicity and Carcinogenicity: Scientific Rationale and Regulatory Frameworks. In: Nicolotti, O. (eds) Computational Toxicology. Methods in Molecular Biology, vol 1800. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7899-1_20

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7899-1_20

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7898-4

  • Online ISBN: 978-1-4939-7899-1

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation