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

  • Cecilia BossaEmail author
  • Romualdo Benigni
  • Olga Tcheremenskaia
  • Chiara Laura Battistelli
Protocol
Part of the Methods in Molecular Biology book series (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.

Key words

QSAR Structure–activity relationship Predictive toxicology Genotoxicity Carcinogenicity Expert systems Human health Alternative testing 

References

  1. 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 96:23–31CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Huff J, Haseman J, Rall D (1991) Scientific concepts, value, and significance of chemical carcinogenesis studies. Annu Rev Pharmacol Toxicol 31:621–652CrossRefPubMedGoogle Scholar
  3. 3.
    EFSA (2011) Scientific opinion on genotoxicity testing strategies applicable to food and feed safety assessment. EFSA J 9:2379Google Scholar
  4. 4.
    Benigni R, Bossa C (2011) Mechanisms of chemical carcinogenicity and mutagenicity: a review with implications for predictive toxicology. Chem Rev 111:2507–2536CrossRefPubMedGoogle Scholar
  5. 5.
    OECD (2007) Detailed review paper on cell transformation assays for detection of chemical carcinogens. OECD Publishing, Paris. ENV/JM/MONO(2007)18Google Scholar
  6. 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 779:35–38CrossRefPubMedGoogle Scholar
  7. 7.
    OECD (2016) Guidance document on the in vitro Bhas 42 cell transformation assay (BHAS 42 CTA). OECD Publishing, Paris. ENV/JM/MONO(2016)1Google Scholar
  8. 8.
    OECD (2015) Guidance document on the in vitro syrian hamster embryo (SHE) cell transformation assay. OECD Publishing, Paris. ENV/JM/MONO(2015)18Google Scholar
  9. 9.
    Cherkasov A, Muratov EN, Fourches D et al (2014) QSAR modeling: where have you been? Where are you going to? J Med Chem 57:4977–5010CrossRefPubMedPubMedCentralGoogle Scholar
  10. 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 19:1757–1768CrossRefPubMedGoogle Scholar
  11. 11.
    OECD (2014) Guidance on grouping of chemicals, 2nd edn. OECD Publishing, Paris. ENV/JM/MONO(2014)4CrossRefGoogle Scholar
  12. 12.
    ECETOC (2012) Category approaches, Read-across, (Q)SAR. Technical Report no. 116. BrusselsGoogle Scholar
  13. 13.
    Benigni R, Bossa C (2008) Predictivity and reliability of QSAR models: the case of mutagens and carcinogens. Toxicol Mech Methods 18:137–147CrossRefPubMedGoogle Scholar
  14. 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 ENGoogle Scholar
  15. 15.
    OECD (2008) Report of a Workshop on Integrated Approaches to Testing and Assessment (IATA). OECD Publishing, Paris. ENV/JM/MONO(2008)10Google Scholar
  16. 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 ProgramsGoogle Scholar
  17. 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 70:629–640CrossRefPubMedGoogle Scholar
  18. 18.
    Benigni R, Battistelli CL, Bossa C, Colafranceschi M, Tcheremenskaia O (2013) Mutagenicity, carcinogenicity, and other end points. Methods Mol Biol 930:67–98CrossRefPubMedGoogle Scholar
  19. 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 ENGoogle Scholar
  20. 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 26797Google Scholar
  21. 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 32:273–298CrossRefGoogle Scholar
  22. 22.
    OECD (2007) Guidance document on the validation of (quantitative) structure-activity relationship [(Q)SAR] models, vol ENV/JM/MONO(2007)2. OECD Publishing, ParisGoogle Scholar
  23. 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 19:495–524CrossRefPubMedGoogle Scholar
  24. 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 659:248–261CrossRefPubMedGoogle Scholar
  25. 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 ENGoogle Scholar
  26. 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 CrossRefPubMedGoogle Scholar
  27. 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 27:87–92CrossRefPubMedGoogle Scholar
  28. 28.
    Lai D, Woo Y-T (2005) OncoLogic. In: Predictive toxicology. CRC Press, Boca Raton, FL, pp 385–413CrossRefGoogle Scholar
  29. 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):101–122CrossRefGoogle Scholar
  30. 30.
    OECD (2004) OECD Principles for the validation, for regulatory purposes, of (quantitative) structure-activity relationship models. OECD Publishing, ParisGoogle Scholar
  31. 31.
    OECD (2015) Fundamental and guiding principles for (Q)SAR analysis of chemical carcinogens with mechanistic considerations. OECD Publishing, Paris. ENV/JM/MONO(2015)46Google Scholar
  32. 32.
    Dimitrov SD, Diderich R, Sobanski T et al (2016) QSAR Toolbox - workflow and major functionalities. SAR QSAR Environ Res:1–17Google Scholar
  33. 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 28:401–409CrossRefPubMedGoogle Scholar
  34. 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):129–137CrossRefPubMedGoogle Scholar
  35. 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 29:139–156CrossRefPubMedGoogle Scholar
  36. 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):S7CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    ECHA (2008) QSARs and grouping of chemicals, vol R.6. Guidance on information requirements and chemical safety assessment. Guidance for the implementation of REACHGoogle Scholar
  38. 38.
    ECHA (2017) The use of alternatives to testing on animals for the REACH Regulation. European Chemicals AgencyGoogle Scholar
  39. 39.
    ECHA (2016) Evaluation under REACH progress report 2016 – executive summary and recommendations to registrants. European Chemicals AgencyGoogle Scholar
  40. 40.
    ECHA (2017) Read-across assessment framework (RAAF). European Chemicals AgencyGoogle Scholar
  41. 41.
    ECHA (2016) Practical guide – how to use and report (Q)SARs. European Chemicals Agenc (ECHA)Google Scholar
  42. 42.
    ECHA (2016) Practical guide: how to use alternatives to animal testing to fulfil the information requirements for REACH registration. European Chemicals AgencyGoogle Scholar
  43. 43.
    NAFTA (2012) (Quantitative) Structure Activity Relationship [(Q)SAR] Guidance Document. US Environmental Protection Agency, Technical Working Group on PesticidesGoogle Scholar
  44. 44.
    EFSA-PPR (2016) Guidance on the establishment of the residue definition for dietary risk assessment. EFSA J 14:4549Google Scholar
  45. 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):50EGoogle Scholar
  46. 46.
    EFSA-PPR (2012) Scientific opinion on evaluation of the toxicological relevance of pesticide metabolites for dietary risk assessment. EFSA J 10(07):2799Google Scholar
  47. 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. 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 72:335–349CrossRefPubMedGoogle Scholar
  49. 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 77:13–24CrossRefPubMedGoogle Scholar
  50. 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 73:367–377CrossRefPubMedGoogle Scholar
  51. 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):7–20CrossRefPubMedGoogle Scholar
  52. 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):124–130CrossRefPubMedGoogle Scholar
  53. 53.
    Cartus A, Schrenk D (2017) Current methods in risk assessment of genotoxic chemicals. Food Chem Toxicol 106(Part B):574–582CrossRefPubMedGoogle Scholar
  54. 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 71:295–300CrossRefPubMedGoogle Scholar
  55. 55.
    Teasdale A (2017) Regulatory highlights. Org Process Res Dev 21:1209–1212CrossRefGoogle Scholar
  56. 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):79–86CrossRefPubMedGoogle Scholar
  57. 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 67:39–52CrossRefPubMedGoogle Scholar
  58. 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 6:39CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Cecilia Bossa
    • 1
    Email author
  • Romualdo Benigni
    • 2
  • Olga Tcheremenskaia
    • 1
  • Chiara Laura Battistelli
    • 1
  1. 1.Environment and Health DepartmentIstituto Superiore di SanitàRomaItaly
  2. 2.Alpha-PretoxRomaItaly

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