Skip to main content
SpringerLink
Log in

Use of In Silico Methods for Regulatory Toxicological Assessment of Pharmaceutical Impurities

  • Protocol
  • First Online:
In Silico Methods for Predicting Drug Toxicity

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2425))

Abstract

The use of novel non-testing methodologies to support the toxicological assessment of drug impurities is having a growing impact in the regulatory framework for pharmaceutical development and marketed products. For DNA reactive (mutagenic) impurities specific recommendations for the use of in silico structure-based approaches (namely (Q)SAR methodologies) are provided in the ICH M7 guideline. In 2018 a draft reflection paper has been published by EMA addressing open issues in the qualification approach of non-genotoxic impurities (NGI) according to the ICH Q3A/Q3B guidelines, and proposing the use of alternative testing strategies, including TTC, (Q)SAR, read-across, and in vitro approaches, to gather impurity-specific safety information.

In the present chapter we describe a workflow to perform the safety assessment of drug impurities based on non-testing in silico methodologies. The proposed approach consists of a stepwise decision scheme including three key phases: PHASE 1: assessment of bacterial mutagenicity and consequent classification of impurities according to ICH M7; PHASE 2: risk characterization of mutagenic impurities (Classes 1, 2 or 3); PHASE 3: qualification of non-mutagenic impurities (Classes 4 or 5). The proposed decision scheme offers the possibility to acquire impurity-specific data, also if testing is not feasible, and to decide on further in vitro testing, besides meeting 3R’s principle.

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 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
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

Similar content being viewed by others

References

  1. ECHA—European Chemical Agency (2008) Guidance on information requirements and chemical safety assessment. Chapter R.6: QSARs and grouping of chemicals. https://echa.europa.eu/documents/10162/13632/information_requirements_r6_en.pdf/77f49f81-b76d-40ab-8513-4f3a533b6ac9. Accessed 31 Mar 2021

  2. ICH—International Conference on Harmonisation (2006) Impurities in new drug substances—Q3A(R2). Current Step 4 version. http://www.ich.org/products/guidelines.html. Accessed 31 Mar 2021

  3. ICH—International Conference on Harmonisation (2006) Impurities in new drug products—Q3B(R2). Current Step 4 version. http://www.ich.org/products/guidelines.html. Accessed 31 Mar 2021

  4. ICH Harmonised Tripartite Guideline (2009) Guidance on nonclinical safety studies for the conduct of human clinical trials and marketing authorization for pharmaceuticals—M3(R2). Current Step 4 version. http://www.ich.org/products/guidelines.html. Accessed 6 May 2015

  5. ICH Harmonised Tripartite Guideline (2017) Assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals to limit potential carcinogenic risk—M7. Current Step 4 version. http://www.ich.org/products/guidelines.html. Accessed 31 Mar 2021

  6. EMA—European Medicines Agency (2018) Reflection paper on the qualification of non-genotoxic impurities. Draft, 15 November 2018. https://www.ema.europa.eu/en/documents/scientific-guideline/reflection-paper-qualification-non-genotoxic-impurities_en.pdf. Accessed 31 Mar 2021

  7. EFSA—European Food Safety Authority and WHO—World Health Organization (2016) Review of the Threshold of Toxicological Concern (TTC) approach and development of new TTC decision tree. EFSA Support Pub 13(3):1006E

    Google Scholar 

  8. Cramer GM, Ford RA, Hall RL (1978) Estimation of toxic hazard—a decision tree approach. Food Cosmet Toxicol 16(3):255–276

    Article  CAS  PubMed  Google Scholar 

  9. Toxtree (Toxic Hazard Estimation by decision tree approach), version 3.1.0 (2018) Ideaconsult, Sofia, Bulgaria. http://toxtree.sourceforge.net/index.html

  10. OECD (2020) (Q)SAR application toolbox, version 4.4. Organization for Economic Co-operation and Development, Helsinki. https://www.oecd.org/chemicalsafety/risk-assessment/oecd-qsar-toolbox.htm

    Google Scholar 

  11. Munro IC, Ford RA, Kennepohl E et al (1996) Correlation of structural class with 271 no-observed-effect levels: a proposal for establishing a threshold of toxicological concern. Food Chem Toxicol 34:829–867

    Article  CAS  PubMed  Google Scholar 

  12. Kroes R, Renwick AG, Cheeseman M et al (2004) Structure-based thresholds of toxicological concern (TTC): guidance for application to substances present at low levels in the diet. Food Chem Toxicol 42(1):65–83

    Article  CAS  PubMed  Google Scholar 

  13. EFSA—European Food Safety Authority Scientific Committee (2012) Scientific opinion on exploring options for providing advice about possible human health risks based on the concept of Threshold of Toxicological Concern (TTC). EFSA J 10(7):2750

    Google Scholar 

  14. Tluczkiewicz I, Buist HE, Martin MT et al (2011) Improvement of the Cramer classification for oral exposure using the database TTC Repdose—a strategy description. Regul Toxicol Pharmacol 61:340–350

    Article  CAS  PubMed  Google Scholar 

  15. Drew R, Frangos J (2007) The concentration of no toxicological concern (CoNTC): a risk assessment screening tool for air toxics. J Toxicol Environ Health Part A 70:1584–1593

    Article  CAS  Google Scholar 

  16. Carthew P, Clapp C, Gutsell S (2009) Exposure based waiving: the application of the toxicological threshold of concern (TTC) to inhalation exposure for aerosol ingredients in consumer products. Food Chem Toxicol 47:1287–1295

    Article  CAS  PubMed  Google Scholar 

  17. Escher SE, Tluczkiewicz I, Batke M et al (2010) Evaluation of inhalation TTC values with the database RepDose. Regul Toxicol Pharmacol 58:259–274

    Article  CAS  PubMed  Google Scholar 

  18. Barle E, Winkler GC, Glowienke S et al (2016) Setting occupational exposure limits for genotoxic substances in the pharmaceutical industry. Toxicol Sci 151:2–9

    Article  CAS  Google Scholar 

  19. Schuurmann G, Ebert RU, Tluczkiewicz I et al (2016) Inhalation threshold of toxicological concern (TTC)—structural alerts discriminate high from low repeated-dose inhalation toxicity. Environ Int 88:123–132

    Article  PubMed  Google Scholar 

  20. Tluczkiewicz I, Kuhne R, Ebert RU et al (2016) Inhalation TTC values: a new integrative grouping approach considering structural, toxicological and mechanistic features. Regul Toxicol Pharmacol 78:8–23

    Article  CAS  PubMed  Google Scholar 

  21. Chebekoue SF, Krishnan K (2017) Derivation of occupational thresholds of toxicological concern for systemically acting noncarcinogenic organic chemicals. Toxicol Sci 160:47–56

    Article  CAS  PubMed  Google Scholar 

  22. Safford RJ (2008) The dermal sensitisation threshold—a TTC approach for allergic contact dermatitis. Regul Toxicol Pharmacol 51:195–200

    Article  CAS  PubMed  Google Scholar 

  23. Safford RJ, Aptula AO, Gilmour N (2011) Refinement of the dermal sensitisation threshold (DST) approach using a larger dataset and incorporating mechanistic chemistry domains. Regul Toxicol Pharmacol 60:218–224

    Article  CAS  PubMed  Google Scholar 

  24. Williams FM, Rothe H, Barrett G et al (2016) Assessing the safety of cosmetic chemicals: consideration of a flux decision tree to predict dermally delivered systemic dose for comparison with oral TTC (Threshold of Toxicological Concern). Regul Toxicol Pharmacol 76:174–186

    Article  CAS  PubMed  Google Scholar 

  25. Partosch F, Mielke H, Stahlmann R et al (2015) Internal threshold of toxicological concern values: enabling route-to-route extrapolation. Arch Toxicol 89:941–948

    Article  CAS  PubMed  Google Scholar 

  26. Ellison CA, Blackburn KL, Carmichael PL et al (2019) Challenges in working towards an internal threshold of toxicological concern (iTTC) for use in the safety assessment of cosmetics: discussions from the cosmetics Europe iTTC Working Group workshop. Regul Toxicol Pharmacol 103:63–72

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ellison CM, Sherhod R, Cronin MT et al (2011) Assessment of methods to define the applicability domain of structural alert models. Chem Inf Model 51(5):975–985

    Article  CAS  Google Scholar 

  28. Hillebrecht A, Muster W, Brigo A et al (2011) Comparative evaluation of in silico systems for ames test mutagenicity prediction: scope and limitations. Chem Res Toxicol 24(6):843–854

    Article  CAS  PubMed  Google Scholar 

  29. OECD—Organization for Economic Co-operation and Development (2006) Report on the regulatory uses and applications in OECD member countries of (Quantitative) Structure-Activity Relationship [(Q)SAR] models in the assessment of new and existing chemicals. OECD Environment Health and Safety Publications, Series on Testing and Assessment No. 58. ENV/JM/MONO(2006)25. http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=env/jm/mono(2006)25&doclanguage=en. Accessed 31 Mar 2021

  30. OECD—Organization for Economic Co-operation and Development (2007). Guidance document on the validation of (Quantitative) Structure Activity Relationship [(Q)SAR] models. OECD Environment Health and Safety Publications, Series on Testing and Assessment No. 69. ENV/JM/MONO(2007)2 http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=env/jm/mono(2007)2&doclanguage=en. Accessed 31 Mar 2021

  31. Serafimova R, Gatnik MF, Worth A (2010) Review of QSAR models and software tools for predicting genotoxicity and carcinogenicity. JRC scientific and technical reports EUR 24427 EN. https://publications.jrc.ec.europa.eu/repository/handle/JRC59068. Accessed 31 Mar 2021

  32. Worth AP, Lapenna S, Serafimova R (2013) QSAR and metabolic assessment tools in the assessment of genotoxicity. Methods Mol Biol 930:125–162

    Article  CAS  PubMed  Google Scholar 

  33. Roncaglioni A, Toropov AA, Toropova AP et al (2013) In silico methods to predict drug toxicity. Curr Opin Pharmacol 13(5):802–806

    Article  CAS  PubMed  Google Scholar 

  34. Fioravanzo F, Bassan A, Pavan M et al (2012) Role of in silico genotoxicity tools in the regulatory assessment of pharmaceutical impurities. SAR QSAR Environ Res 23(3-4):257–277

    Article  CAS  PubMed  Google Scholar 

  35. Cassano A, Raitano G, Mombelli E et al (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 Environ Carcinog Ecotoxicol Rev 32(3):273–298

    Article  PubMed  Google Scholar 

  36. ACD/Percepta, release 2018. Advanced Chemistry Development, Toronto, ON. www.acdlabs.com

  37. Lanevskij K, Juska L, Dapkunas J et al (2012) In silico test battery for rapid evaluation of genotoxic and carcinogenic potential of chemicals. Poster presented at 243rd ACS National meeting, San Diego, CA, 25–29 March 2012

    Google Scholar 

  38. ChemTunes/ToxGPS. Database and knowledgebase for safety evaluation and risk assessment, version 1.2, Altamira LLC and Molecular Networks GmbH. https://www.mn-am.com/products/chemtunestoxgps

  39. Leadscope Model Applier, version 3.0 (2020) Leadscope, Columbus, OH. http://www.leadscope.com

  40. TopKat (TOxicity Prediction by Komputer Assisted Technology), release 2020 Dassault Systèmes, BIOVIA Corp., San Diego, CA. http://www.3dsbiovia.com

  41. Ferrari T, Gini G (2010) An open source multistep model to predict mutagenicity from statistical analysis and relevant structural alerts. Chem Cent J 4(Suppl 1):S2

    Article  PubMed  PubMed Central  Google Scholar 

  42. VegaNIC Application (Virtual Models for evaluating the properties of chemicals within a global architecture), version 1.1.5. Laboratory of Environmental Chemistry and Toxicology of Mario Negri Institute of Pharmacological Research, Milano, IT. http://www.vega-qsar.eu/download.html. Accessed 31 Mar 2021

  43. Benigni R, Bossa C, Jeliazkova N et al (2008) The Benigni/Bossa rulebase for mutagenicity and carcinogenicity—a module of Toxtree. JRC scientific and technical reports, EUR 23241 EN. https://ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-research-reports/benigni-bossa-rulebase-mutagenicity-and-carcinogenicity-module-toxtree. Accessed 31 Mar 2021

  44. Pavan M, Kovarich S, Bassan A et al (2016) The consultancy activity on in silico models for genotoxic prediction of pharmaceutical impurities. In: Benfenati E (ed) In silico methods for predicting drug toxicity. Humana Press, New York

    Google Scholar 

  45. ECHA—European Chemicals Agency (2012) Practical guide 6: how to report read-across and categories. https://echa.europa.eu/documents/6362380/7127661/pg_report_readacross_en.pdf/69860e5b-c669-4a0d-b868-72f5dba5b560. Accessed 31 Mar 2021

  46. ECHA—European Chemicals Agency (2017) Read-across assessment framework (RAAF). https://echa.europa.eu/documents/10162/13628/raaf_en.pdf. Accessed 31 Mar 2021

  47. Kovarich S, Ceriani L, Fuart Gatnik M et al (2019) Filling data gaps by read-across: a mini review on its application, developments and challenges. Mol Inf 38:1800121

    Article  CAS  Google Scholar 

  48. Schultz TW, Amcoff P, Berggren E et al (2015) A strategy for structuring and reporting a read-across prediction of toxicity. Regul Toxicol Pharmacol 72(3):586–601

    Article  CAS  PubMed  Google Scholar 

  49. ToxRead, version 0.17 BETA (2019) Laboratory of environmental chemistry and toxicology of Mario Negri Institute of pharmacological research, Milano, IT. https://www.vegahub.eu/portfolio-item/toxread/. Accessed 31 Mar 2021

  50. Ambit, version 4.0.1 (2021) Ideaconsult, Sofia, Bulgaria. https://ambitlri.ideaconsult.net/tool2

  51. Patlewicz G, Helman G, Pradeep P et al (2017) Navigating through the minefield of read-across tools: a review of in silico tools for grouping. Comp Toxicol 3:1–18

    Article  Google Scholar 

  52. Judson P (2009) Combining predictions. In: Royal Society of Chemistry (ed) Knowledge-based expert systems in chemistry: not counting on computers. Royal Society of Chemistry, GB

    Google Scholar 

  53. Cronin MTD (2010) Prediction of harmful human health effects of chemicals from structure. In: Puzyn T, Leszczynski J, MTD C (eds) Recent advances in QSAR Studies. Challenges and advances in computational chemistry and physics, vol 8. Springer, Dordrecht

    Google Scholar 

  54. Ellison CM, Madden JC, Judson P et al (2010) Using in silico tools in a weight of evidence approach to aid toxicological assessment. Mol Inf 26(1-2):97–110

    Article  Google Scholar 

  55. ECHA—European Chemicals Agency (2016) Practical guide—how to use alternatives to animal testing to fulfil your information requirements for REACH registration. https://echa.europa.eu/documents/10162/13655/practical_guide_how_to_use_alternatives_en.pdf/148b30c7-c186-463c-a898-522a888a4404. Accessed 31 Mar 2021

  56. Sutter A, Amberg A, Boyer S et al (2013) Use of in silico systems and expert knowledge for structure-based assessment of potentially mutagenic impurities. Regul Toxicol Pharmacol 67(1):39–52

    Article  CAS  PubMed  Google Scholar 

  57. Nendza M, Gabbert S, Kühne R et al (2013) A comparative survey of chemistry-driven in silico methods to identify hazardous substances under REACH. Regul Toxicol Pharmacol 66(3):301–314

    Article  PubMed  Google Scholar 

  58. Dobo KL, Greene N, Fred C et al (2012) In silico methods combined with expert knowledge rule out mutagenic potential of pharmaceutical impurities: an industry survey. Regul Toxicol Pharmacol 62(3):449–455

    Article  CAS  PubMed  Google Scholar 

  59. Powley MW (2014) (Q)SAR assessments of potentially mutagenic impurities: a regulatory perspective on the utility of expert knowledge and data submission. Regul Toxicol Pharmacol 71(2):295–300

    Article  PubMed  Google Scholar 

  60. Naven RT, Greene N, Williams RV (2012) Latest advances in computational genotoxicity prediction. Expert Opin Drug Metab Toxicol 8(12):1579–1587

    Article  PubMed  Google Scholar 

  61. Williams RV, Amberg A, Brigo A et al (2016) It’s difficult, but important, to make negative predictions. Reg Toxicol Pharmacol 76:79–86

    Article  CAS  Google Scholar 

  62. Nohmi T (2018) Thresholds of genotoxic and non-genotoxic carcinogens. Toxicol Res 34(4):281–290

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. ICH—International Conference on Harmonisation (2016) Impurities: guideline for residual solvents. Q3C(R6). https://database.ich.org/sites/default/files/Q3C-R6_Guideline_ErrorCorrection_2019_0410_0.pdf. Accessed 31 Mar 2021

  64. ICH—International Conference on Harmonisation (2019) Guideline for elemental impurities Q3D(R1). https://database.ich.org/sites/default/files/Q3D-R1EWG_Document_Step4_Guideline_2019_0322.pdf. Accessed 31 Mar 2021

  65. EFSA Scientific Committee, More SJ, Bampidis V et al (2019) Guidance on the use of the Threshold of Toxicological Concern approach in food safety assessment. EFSA J 17(6): 5708. https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/j.efsa.2019.5708. Accessed 31 Mar 2021

  66. Amberg A, Beilke L, Bercu J et al (2016) Principles and procedures for implementation of ICH M7 recommended (Q)SAR analyses. Regul Toxicol Pharmacol 77:13–24

    Article  CAS  PubMed  Google Scholar 

  67. Amberg A, Andaya RV, Anger LT et al (2019) Principles and procedures for handling out-of-domain and indeterminate results as part of ICH M7 recommended (Q)SAR analyses. Regul Toxicol Pharmacol 102:53–64

    Article  CAS  PubMed  Google Scholar 

  68. EMA—European Medicines Agency (2020) Committee for Medicinal Products for Human Use (CHMP) assessment report—nitrosamine impurities in human medicinal products. EMA/369136/2020. https://www.ema.europa.eu/en/documents/referral/nitrosamines-emea-h-a53-1490-assessment-report_en.pdf. Accessed 31 Mar 2021

  69. EMA—European Medicines Agency (2021) Questions and answers for marketing authorisation holders/applicants on the CHMP Opinion for the Article 5(3) of Regulation (EC) No 726/2004 referral on nitrosamine impurities in human medicinal products. EMA/409815/2020 Rev.2. https://www.ema.europa.eu/en/documents/referral/nitrosamines-emea-h-a53-1490-questions-answers-marketing-authorisation-holders/applicants-chmp-opinion-article-53-regulation-ec-no-726/2004-referral-nitrosamine-impurities-human-medicinal-products_en.pdf. Accessed 31 Mar 2021

  70. FDA—Food and Drug Administration (2021) Control of nitrosamine impurities in human drugs—guidance for industries. Revision 1. https://www.fda.gov/media/141720/download. Accessed 31 Mar 2021

Download references

Author information

Authors and Affiliations

  1. S-IN Soluzioni Informatiche SRL, Vicenza, Italy

    Simona Kovarich & Claudia Ileana Cappelli

Authors
  1. Simona Kovarich
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Claudia Ileana Cappelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Simona Kovarich .

Editor information

Editors and Affiliations

  1. Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy

    Emilio Benfenati

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Kovarich, S., Cappelli, C.I. (2022). Use of In Silico Methods for Regulatory Toxicological Assessment of Pharmaceutical Impurities. In: Benfenati, E. (eds) In Silico Methods for Predicting Drug Toxicity. Methods in Molecular Biology, vol 2425. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1960-5_21

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1960-5_21

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1959-9

  • Online ISBN: 978-1-0716-1960-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation