Establishing potency categories for Nitrosamine impurities

Nitrosamine impurities currently belong to a “cohort of concern” because of their potential to be potent mutagenic carcinogens, as described in the ICH M7 guideline1.

They are also coming under increasing regulatory scrutiny with the US Food and Drug Administration and European Medicines Agency recently issuing new guidelines2,3 for examination of this class. These guidelines include computational approaches to establish limits for N-Nitrosamine impurities.

In supporting these important guidelines, Kevin Cross from Leadscope (an Instem company) in combination with others has established a Nitrosamine SAR working group comprising over 46 members and 20 companies. One important activity is the development of predictive strategies for N-Nitrosamine carcinogenicity potency, including documenting a comprehensive understanding of the structure-activity relationships (SAR) for N-Nitrosamines. This assessment is based upon analysis of carcinogenicity and genotoxicity data, reaction mechanisms and structural similarity. It is leading to the definition of categorical alerts to predict several carcinogenic potency categories.

SAR analysis is focused on understanding the impact of several reactivity sites near the N-Nitrosamine functional group that involve different reaction mechanisms leading to differences in potency severity. These reactivity sites include the nitrogen-nitrogen bond, the α-carbon and the β-carbon. Access to these sites (e.g. steric hinderance), along with electronic effects impacts reactivity and consequently, potency. By considering both reaction mechanism and specific substituents at these positions, N-Nitrosamines may be assigned to very-high, high, medium, or low potency categories4.

Through examination of both structural similarity and an examination of the dominant mechanism, categorical alerts can be established to predict N-Nitrosamine potency. This approach will provide a scientifically defendable methodology for establishing potency categories and corresponding exposure limits and will also avoid “activity cliffs” where the structural similarity concept breaks down.

If you would like to learn more, or collaborate on this important project, please reach out to me at gmyatt@leadscope.com or Kevin at kcross@leadscope.com.

References

1. Assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals to limit potential carcinogenic risk M7(R1)

2. US Food and Drug Administration’s Guidance for Industry: US FDA Control of Nitrosamine Impurities in Human Drugs, September 2020

3. European’s Medicines Agency, Nitrosamine impurities in human medicinal products, EMA/369136/2020, 25th June 2020

4. K.P. Cross “Predicting Nitrosamine Activity from Structure-Activity Relationships”, Informa Nitrosamines Impurities Forum.  26th August 2020. Slides with audio presentation available info@leadscope.com.

So many pieces of information!

The movement of toxicology away from an observational-based paradigm and towards a mechanism-based one is ongoing. One pertinent question is how to combine data across mechanistic pathways to derive an overall assessment of hazard and what level of confidence should be placed in such a result. Further, where data gaps exist, how could in silico tools be used to support an assessment.

The area of skin sensitization is exemplary. Hinging on knowledge of the adverse outcome pathway (AOP) for skin sensitization, key events across the AOP could be assessed and integrated to derive an overall assessment. But what if information is missing? Could I utilize the power of existing knowledge- stored in a database- to analyze reactive features, statistical correlations, chemical and mechanistic similarity to facilitate an assessment? Could I really ‘pull out all the stops’ as they say? And if I did (congrats to you on getting this far!), and managed to standardize reporting for the various assessments, would I be able to reproducibly justify the overall conclusion across hundreds of chemicals and effectively (don’t forget reproducibly) communicate the confidence in the results?

There is so much to unravel here. A major plus is that we have a starting point. Documents such as the OECD’s Guidance Document on the Reporting of Defined Approaches and Individual Information Sources to Be Used within Integrated Approaches to Testing and Assessment (IATA) for Skin Sensitization1  are an excellent resource. The recently published skin sensitization in silico protocol2 is also a good resource for expert review considerations and guidelines on how to assess the reliability and confidence of an in silico assessment. If you would like to talk more about implementing the principles outlined in the skin sensitization in silico protocol, we would like to hear from you. Together, we could explore the solutions to many of the questions above.

  1. OECD. Guidance Document on the Reporting of Defined Approaches and Individual Information Sources to Be Used within Integrated Approaches to Testing and Assessment (IATA) for Skin Sensitisation. OECD; 2017. doi:10.1787/9789264279285-en  
  2. Johnson C, Ahlberg E, Anger LT, et al. Skin sensitization in silico protocol. Regul Toxicol Pharmacol. 2020;116:104688. doi:https://doi.org/10.1016/j.yrtph.2020.104688

Can the burden on industry and regulators be reduced?


About 6 years ago, the International Conference on Harmonization (ICH) published the M7 guideline “Assessment and Control of DNA reactive (mutagenic) Impurities in Pharmaceuticals to limit Potential Carcinogenic Risk”.[1] This was a landmark moment for computational toxicology, as it was the first time such methods were recognized by the ICH as a regulatory test.

An implementation period followed the guideline’s publication in which pharmaceutical companies used computational assessments of impurities in their submissions to regulatory authorities. During this period, many questions on how such an assessment should be performed and documented were raised by the stakeholders. And after a series of discussions, we decided to form a consortium to address these issues through the development of a protocol outlining how computational toxicology assessments aligned with ICH M7 should be performed. In 2016 this protocol was published “Principles and procedures for implementation of ICH M7 recommended (Q)SAR analyses” [2] and we were encouraged to see how well received the paper was. It is consistently in the most downloaded articles in Regulatory Toxicology and Pharmacology and is often cited in publications and presentations by regulators and industry scientists. A follow-on paper was published in 2019 to address issues related to handling inconclusive results “Principles and procedures for handling out-of-domain and indeterminate results as part of ICH M7 recommended (Q)SAR analyses”. [3]

Based on the success of this work, we began to wonder – could we repeat this experience for other toxicology endpoints?  This would reduce the burden on industry and regulators to justify their use, as well as ensuring in silico assessments are performed in a consistent and reproducible manner to support good in silico practices.

Propelled by an NIH grant and enthusiasm from a wider consortium of over 60 members, we began to develop a framework for such protocols which was published in 2018 “In silico toxicology protocols”. [4] Individual working groups focusing on the development of the endpoints-specific protocols were established and in 2019, the “Genetic toxicology in silico protocol” [5] was published, followed by the “Skin sensitization in silico protocol” [6] in 2020.

Work continues on the development of other protocols and supporting position papers. It is clear that for endpoints, such as skin sensitization, where there are generally accepted Adverse Outcome Pathways (AOPs), Integrated Approaches to Testing and Assessment (IATAs), Defined Approaches (DAs), etc., that the process of putting together an accepted protocol is more straightforward than other areas, such as carcinogenicity. As such, position papers reflecting the current state-of-the-art, as well as gaps in our current knowledge, are important steps towards in silico protocols for complex toxicological endpoints.

This year also marked another important landmark in the evolution of this project with the complete implementation of the published protocols within Leadscope’s products. The solution provides access to computation methodologies and toxicity databases outlined in the protocols which are incorporated within a visual decision framework to support an inspection of the results, along with the ability to perform an expert review and document the entire assessment process.

We are excited with the progress to date and the momentum of this project to support the more widespread application of in silico methods through adoption of good in silico practices.

Please get in touch if you’d like to collaborate on this project.

References

[1] ICH M7, 2017 (R1) (2017) Assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals to limit potential carcinogenic risk. https://database.ich.org/sites/default/files/M7_R1_Guideline.pdf

[2] 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. doi:10.1016//j.yrtph.2016.02.004

Open access: https://doi.org/10.1016/j.yrtph.2016.02.004

[3] Amberg, A., Andaya, R.V., Anger, L.T., 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. 10.1016/j.yrtph.2018.12.007

Open access: https://doi.org/10.1016/j.yrtph.2018.12.007

[4] Myatt, G.J., Ahlberg, E., Akahori, Y., et al. (2018) In Silico Toxicology Protocols. Regul. Toxicol. Pharmacol. 98, 1-17. doi:10.1016/j.yrtph.2018.04.014

Open access: https://doi.org/10.1016/j.yrtph.2018.04.014

[5] Hasselgren, C., Ahlberg, E., Akahori, Y., et al. (2019) Genetic toxicology in silico protocol. Regul. Toxicol. Pharmacol.  107, 104403. doi:10.1016/j.yrtph.2019.104403 

Open access: https://doi.org/10.1016/j.yrtph.2019.104403

[6] Johnson, C., Ahlberg, E., Anger, L.T., et al. (2020) Skin sensitization in silico protocol. Regul. Toxicol. Pharmacol.  116, October 2020, 104688. doi: 10.1016/j.yrtph.2020.104688 

Open access: https://doi.org/10.1016/j.yrtph.2020.104688

Welcome to In Silico Insider

Welcome to In Silico Insider, a bi-weekly info-Blog for professionals working in the field of Computational Toxicology.

At Instem, through our recent acquisition of Leadscope, Inc., we have the inside track when it comes to In Silico Safety Assessments and we are excited to launch this new resource for scientists and professionals working in the Computational Toxicology field.

Join our bi-weekly blog and step inside our online community, where our subject matter experts will share important insights about key issues facing our industry. From regulatory updates to industry innovations and scientific best practice, we hope this becomes a regular destination for you.

Leadscope’s scientific leadership in computational toxicology has developed through a deep relationship with regulatory authorities, a long-standing, loyal customer base and a wealth of experience in the management of international consortia to develop industry protocols and position papers.

Look for blog posts from us every other Thursday. If you have any suggestions for content or want to comment on any of our topics, you can email Dr Glenn Myatt, Instem’s In Silico Toxicology subject matter expert and In Silico Insider’s primary author, at glenn.myatt@instem.com

Welcome inside and happy reading!