ELSIE Webinar on a Framework for Sensitization Assessment for E&L and Practical Application

We are delighted to be participating in an upcoming webinar organized by the Extractables and Leachables Safety Information Exchange (ELSIE) Consortium on the topic of a framework for sensitization assessment for Extractables and Leachables (E&L) on 22 September 2022, 9:30 – 11:00 AM ET1.

We will be describing the development of a representative database of E&L from the combined ELSIE Consortium and the Product Quality Research Institute (PQRI) published datasets. An analysis using chemical structure-based clustering will be outlined to show the similarities and differences between the two datasets. The presentation will also describe classes containing chemicals that were flagged as potentially mutagenic as well as potent (strong or extreme) dermal sensitizers by the Leadscope in silico tools.

Please get in touch if you would like to discuss this work in more detail (Glenn Myatt; glenn.myatt@instem.com).

References

1.            Home | ELSIE (elsiedata.org)

New paper on the use of the Ames assay to predict carcinogenicity of N-Nitrosamines

This week, we are pleased to welcome Dr. Kevin P. Cross, Instem’s Principal Investigator with U.S. FDA Collaborations and VP of Product Engineering and Production, as a guest contributor to the blog.

We were happy to contribute to a recent publication titled “Use of the Bacterial Reverse Mutation Assay to Predict Carcinogenicity of N-Nitrosamines”.1

N-Nitrosamines (NAs) are a cohort of concern in the ICH M7 guideline2 and have recently come under increased regulatory scrutiny, mainly because of their potential to be highly potent mutagenic carcinogens in rodent bioassays.

This publication performed an analysis of public databases with both bacterial reverse mutation (Ames) assay and rodent carcinogenicity data, and the sensitivity of the Ames assay to predict the carcinogenic outcome was examined. The paper demonstrated that the Ames assay conducted under OECD 471 guidelines is highly sensitive for detecting the carcinogenic hazards of NAs.   

Please feel free to contact me (Kevin Cross; kevin.cross@instem.com) if you would like any additional information.

References

  1. Alejandra Trejo-Martin et al., 2022, Use of the Bacterial Reverse Mutation Assay to Predict Carcinogenicity of N-Nitrosamines, Regulatory Toxicology and Pharmacology, 105247. https://doi.org/10.1016/j.yrtph.2022.105247
  2. ICH, M7 (R1) Assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals to limit potential carcinogenic risk, European Medicines Agency, 2017. https://database.ich.org/sites/default/files/M7_R1_Guideline.pdf.

Publication of the special issue of computational toxicology on the in silico toxicology protocol initiative

In 2016, we initiated a new project to develop a standardized procedure for in silico experts performing assessments, similar to in vitro or in vivo test guidelines. This would streamline the application of in silico methods, ensure best practices are adopted, and defend its use to colleagues, peers, and regulators.

The general framework paper1 was published in 2018, followed by a protocol for genetic toxicology2 in 2019, and a protocol for skin sensitization3 in 2020.

Since then, there has been considerable activity developing additional position papers and protocols, and much of this work is now included in a special issue of computational toxicology “In silico toxicology protocol initiative”4. The special issue includes 8 papers along with an editorial.

This body of work reflects an enormous effort on the part of the hundreds of collaborators on this project and will support the further acceptance and routine use of in silico methods at the same time as avoiding unnecessary animal studies.

We wish to thank all the collaborators and congratulate them on the completion of this special issue.

If you are interested in discussing this project in more detail, please contact me (Glenn Myatt; glenn.myatt@instem.com).

References

  1. G.J. Myatt et al., In silico toxicology protocols, Regul. Toxicol. Pharmacol. 96 (2018) 1–17. https://doi.org/10.1016/j.yrtph.2018.04.014.
  2. C. Hasselgren et al., Genetic toxicology in silico protocol, Regul. Toxicol. Pharmacol. 107 (2019) 104403. https://doi.org/10.1016/j.yrtph.2019.104403.
  3. C. Johnson et al., Skin sensitization in silico protocol, Regul. Toxicol. Pharmacol. 116 (2020) 104688. https://doi.org/10.1016/j.yrtph.2020.104688.
  4. https://www.sciencedirect.com/journal/computational-toxicology/special-issue/10ZD1MJ9982

The Leadscope bacterial mutagenicity expert alerts journey to universal regulatory acceptance

It has taken over 10 years, but now Leadscope’s expert rule-based methodology is used every day to support submissions to regulatory agencies around the world, including pharmaceutical impurity assessments aligned with the ICH M7 guideline1, assessment of extractable and leachables, along with other applications. The following blog reviews this journey.

The first step in this process was to collect and consolidate alerts reported in the literature, including the seminal publications by John Ashby and Raymond Tennant.2,3 These alerts were then qualified, refined and new alerts identified using a large database of chemicals with bacterial mutagenicity study results assembled from quality public sources. Over the years, it has also been possible to further validate and refine the alerts using proprietary data. A new methodology, referred to as SAR fingerprinting, was developed to allow companies to share structure-activity (mutagenicity) knowledge without disclosing proprietary chemicals or study results.4,5 The latest version made use of around 50,000 chemicals. These exercises have identified mitigating factors which significantly minimize the number of false positives results as compared to using public domain alerts alone.

At the same time, individual alerts have been annotated with knowledge of their mechanistic basis. This is generally a highly time-consuming manual activity to summarize the literature. This has been supplemented with automated annotation of tester strain sensitivities associated with each alert, since knowing the tester strains likely to be positive (based on any alerts that fire) may support an expert review or streamlined in vitro testing. In the latest version of the alerts, the system can now even predict direct- versus indirect-acting mutagens.

Over the years we have also developed computational tools to support applying the alerts as part of an overall hazard assessment (including statistical-based methods as well as bacterial mutagenicity and carcinogenicity database searching). Since most regulatory guidelines emphasize the importance of being aligned with OECD validation principles6, a new methodology for applicability domain assessment was developed. Another important consideration was computational tools to ensure the results are totally transparent, including access to all chemicals matching the alerts from our database of around 20,000 chemicals alongside information on their mechanistic basis. Such methods are critical to enable an expert review of the prediction.

We have been engaged in a series of collaborative working groups to both assess the performance of the technology7,8, while also developing principles and procedures for performing such computational toxicology assessments9,10,11.

It has been rewarding to see how such methods have evolved where they are now relied upon for international regulatory submissions. Many lessons have been learned along this journey which are being applied in the development of new in silico approaches to support emerging regulatory and industrial applications.

If you would like to discuss in more detail or collaborate on emerging in silico methods, please get in touch (Glenn J. Myatt; glenn.myatt@instem.com).

References

  1. ICH, M7 (R1) Assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals to limit potential carcinogenic risk, European Medicines Agency, 2017. https://database.ich.org/sites/default/files/M7_R1_Guideline.pdf.
  2. Ashby, J., Tennant, R.W., 1988. Chemical structure, Salmonella mutagenicity and extent of carcinogenicity as indicators of genotoxic carcinogenesis among 222 chemicals tested in rodents by the U.S. NCI/NTP. Mutat. Res. 204, 17–115.
  3. Myatt, G.J., Beilke, L.D., Cross, K.P. (2016) In Silico Tools and their Application in: Reference Module in Chemistry, Molecular Sciences and Chemical Engineering (eds J. Reedijk). Elsevier. 10.1016/B978-0-12-409547-2.12379-0
  4. E. Ahlberg, et al., Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity, Regul. Toxicol. Pharmacol. 77 (2016) 1–12. https://doi.org/10.1016/j.yrtph.2016.02.003.
  5. Amberg, A., et al. (2018) Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: is aromatic N-oxide a structural alert for predicting DNA-reactive mutagenicity? Mutagenesis 34, 67–82. 10.1093/mutage/gey020
  6. OECD, Guidance Document on the Validation of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models, OECD Environment, Health and Safety Publications, Paris, 2007. https://doi.org/10.1787/9789264085442-en.
  7. Honma, M., Kitazawa, A., Cayley, A., et al., (2019) Improvement of quantitative structure–activity relationship (QSAR) tools for predicting Ames mutagenicity: outcomes of the Ames/QSAR International Challenge Project, Mutagenesis, 34(1), 3–16, https://doi.org/10.1093/mutage/gey031
  8. Myatt, G.J., et al., 2022. Implementation of in silico toxicology protocols within a visual and interactive hazard assessment platform. Comput. Toxicol. 21, 100201. https://doi.org/10.1016/j.comtox.2021.100201
  9. Amberg, A., et al., 2016. Principles and procedures for implementation of ICH M7 recommended (Q)SAR analyses. Regul. Toxicol. Pharmacol. 77, 13–24. https://doi.org/10.1016/j.yrtph.2016.02.004
  10. A. Amberg, et al., Principles and procedures for handling out-of-domain and indeterminate results as part of ICH M7 recommended (Q)SAR analyses, Regul. Toxicol. Pharmacol. 102 (2019) 53–64. https://doi.org/10.1016/j.yrtph.2018.12.007.
  11. Hasselgren, C., et al., (2020) Management of Pharmaceutical ICH M7 (Q)SAR Predictions – The Impact of Model Updates, Regul. Toxicol. Pharmacol., 118, 104807. https://doi.org/10.1016/j.yrtph.2020.104807

New in silico paper on acute toxicity

We are pleased to announce the publication of a new paper “Principles and Procedures for Assessment of Acute Toxicity Incorporating In Silico Methods”.1 This paper presents the results from a significant cross-industry collaboration to support the application of in silico methods for (1) product research and development, (2) product approval and registration as well as (3) the transport, storage and handling of chemicals.

The paper also complements the recent paper assessing whether (Q)SAR models are fit-for-purpose for classification and labelling.2

The new paper describes:

  • a framework for hazard assessment of acute toxicity using different sources of information including in silico methods alongside in vitro or in vivo experiments
  • the endpoints from in vitro studies commonly used for predicting acute toxicity
  • key pathways and key triggering mechanisms underlying acute toxicity
  • the state-of-the-art in prediction of acute toxicity
  • an expert review using weight-of-evidence considerations
  • diverse and practical use cases using in silico approaches

We wish to thank and congratulate all collaborators on this important project supporting the 3Rs.

Please get in touch if you would like to discuss this paper or get involved with other collaborative working groups (Glenn Myatt; glenn.myatt@instem.com).

Reference

  1. Zwickl, C. et al., Principles and Procedures for Assessment of Acute Toxicity Incorporating In Silico Methods, Computational Toxicology, 100237. https://doi.org/10.1016/j.comtox.2022.100237
  2. Bercu, J. et al., A cross-industry collaboration to assess if acute oral toxicity (Q)SAR models are fit-for-purpose for GHS classification and labelling, Regulatory Toxicology and Pharmacology, 120, 2021, 104843. https://doi.org/10.1016/j.yrtph.2020.104843

Neurotoxicity hazard assessment framework that integrates in silico approaches

Earlier this year, we described the results from a collaborative working group that developed a neurotoxicity hazard assessment framework that integrates in silico approaches1. This cross-industry collaboration resulted in a publication within a special issue of the Journal of Computational Toxicology2.

Dr. Arianna Bassan will be presenting the project on July 22nd, 2022 at 9:00 am EST in an upcoming webinar organized by International Neurotoxicology Association titled “Seizing the moment: Issues and technological advancements in mitigation strategies for drugs with seizure liability” 3. In this talk, in silico methods available today that support the assessment of neurotoxicity based on knowledge of chemical structure will be reviewed, followed by the presentation of a conceptual framework for the integration of in silico methods with experimental information.

Please get in touch with me (Glenn Myatt; glenn.myatt@instem.com) if you would like more information on assessment of neurotoxicity.

References

  1. https://insilicoinsider.blog/2022/04/07/neurotoxicity-hazard-assessment-framework-that-integrates-in-silico-approaches/
  2. Crofton et al (2022) Current status and future directions for a neurotoxicity hazard assessment framework that integrates in silico approaches, Computational Toxicology, 22, 100223. https://doi.org/10.1016/j.comtox.2022.100223
  3. https://www.neurotoxicology.org/ina-webinars/

Novel application of in silico methods in the assessment of Extractables and Leachables

New therapeutic modalities play a critical role in our health and safety. Novel therapeutics may be comprised of biologically based molecules including peptides, monoclonal antibodies, and genetic materials. The quality and safety of these products can be assessed using experimental systems; however, it is important to ask whether in silico methods can add value to the analysis workflow. In silico methods have an advantage of providing a rapid screening of effects that could be used in early stages of development to triage and prioritize chemicals based on the likelihood that they may lead to the assessed effect.

In considering how in silico models could be used to support the development and quality of therapies consisting of biomolecules, we can leverage the ability of models to predict chemical reactivity. Active pharmaceutical ingredients which consist of biomolecules (biomolecule-APIs) may be exposed to leachables from container/closure systems, delivery devices or manufacturing systems. The leachables may react with the biomolecule and potentially impact the safety and/or quality of the drug product. In this context we investigated the use of in silico methods which either predict peptide reactivity, direct acting mutagenicity (as a surrogate for reactivity with a DNA or RNA containing API) or assigns a reaction domain (description of the nature by which a covalent interaction may or may not occur) to predict the potential chemical reactivity of leachables in an extractables and leachables (E&L) database. The prevalence of potentially reactive chemicals was low with 16.7% of the set of 801 leachables predicted as reactive based on assignment to a domain; whereas, 16.1% and 5.4% were considered reactive based on the peptide reactivity model and direct acting mutagenicity alerts respectively. To validate the use of such models in early screening, we selected 22 representative E&L chemicals and experimentally determined the reactivity of these chemicals with insulin glargine. The peptide reactivity and reaction domain models were used to predict the observed reactivity. A consensus approach based on the model results, was found to be conservative; that is, most of the reactive chemicals were flagged for reactivity. Factors such as steric hindrance was identified as an important consideration for expert review.

Leachables often exist at very low concentration, and a flag by the in silico models may not necessarily translate into a safety or quality issue. As such, a workflow, which entails a review of model predictions to ensure reliability of the prediction, as well as product quality and safety attributes could be referenced to determine whether follow-up studies are necessary.

This work was conducted by members of the Biomolecule Reactivity Consortium: a group of experts with diverse experience and expertise in the assessment of extractables and leachables. A manuscript is being prepared for publication. Click here to access a recorded presentation on this work which was presented at the E&L USA 2022 conference.

If you would like to learn more about this initiative, or the group’s activities, please feel free to contact me at candice.johnson@instem.com.

Special issue of the Journal of Computational Toxicology

A special issue of the Journal of Computational Toxicology on the in silico toxicology protocol initiative1 is currently being finalized. Myself (Glenn Myatt), Kevin Cross and Candice Johnson from Instem were happy to support this effort as guest editors and many of the articles are already available on-line.

The in silico toxicology protocol initiative was established to create a clear framework and process for performing a computational toxicology assessment to ensure such assessments are performed in a consistent and repeatable manner based on the best science and best practices in the field. These protocols also help to defend the use of in silico models to colleagues, peers, and regulators. To date, publications detailing the protocol framework2 as well as two protocols to support genetic toxicology3 and skin sensitization4 have been published.

This special edition covers a series of new publications describing the projects goals and overriding framework, how confidence in any assessments can be systemically and transparently determined and reviews the current state of the science in the prediction of liver, heart, lung, and kidney toxicity as well as carcinogenicity and neurotoxicity. A paper also introduces a new visual and interactive platform to support a rapid execution and documentation aligned with the protocols.

There are currently seven publications in this edition covering:

  1. Increasing the acceptance of in silico toxicology through development of protocols and position papers
  2. Evaluating confidence in toxicity assessments based on experimental data and in silico predictions
  3. In silico approaches in organ toxicity hazard assessment: current status and future needs in predicting liver toxicity
  4. In silico approaches in organ toxicity hazard assessment: current status and future needs for predicting heart, kidney and lung toxicities
  5. In silico approaches in carcinogenicity hazard assessment: current status and future needs
  6. Current status and future needs for a neurotoxicity hazard assessment framework that integrates in silico approaches
  7. Implementation of in silico toxicology protocols within a visual and interactive hazard assessment platform

If you would like to discuss any of these publications or any upcoming collaborative working groups, please get in touch (Glenn Myatt; glenn.myatt@instem.com).

References

  1. https://www.sciencedirect.com/journal/computational-toxicology/special-issue/10ZD1MJ9982
  2. G.J. Myatt et al., In silico toxicology protocols, Regul. Toxicol. Pharmacol. 96 (2018) 1–17. https://doi.org/10.1016/j.yrtph.2018.04.014.
  3. C. Hasselgren et al., Genetic toxicology in silico protocol, Regul. Toxicol. Pharmacol. 107 (2019) 104403. https://doi.org/10.1016/j.yrtph.2019.104403.
  4. C. Johnson et al., Skin sensitization in silico protocol, Regul. Toxicol. Pharmacol. 116 (2020) 104688. https://doi.org/10.1016/j.yrtph.2020.104688.

6-year anniversary of the ICH M7 principles and procedures publication

Over 6 years ago, the ICH M7 pharmaceutical impurities guideline1 was in its implementation phase, and we were approached to consider writing a cross-industry publication to outline a protocol for performing a (Q)SAR assessment aligned with the guideline. A collaborative working group was established and work began to create this publication. The paper was published in 2016 and outlined considerations for incorporating experimental data on the impurities, rules and principles for performing a (Q)SAR analysis including expert review considerations, recommendations on formats to document the results along with case studies.2

In 2019, it was followed up with a paper outlining how to support indeterminate and out-of-domain (Q)SAR results that included case studies and expert review approaches.3 The paper also outlined an analysis that documented the risk of missing a mutagenic impurity based on the differing results from the two (Q)SAR methodologies.

Both papers are open access and have been widely used and cited, with 77 combined citations including the ICH M7 Q&A4. The principles and procedures have also been incorporated into software applications. For example, the Leadscope model applier includes an ICH M7 protocol implementation that streamlines the application of ICH M7 aligned analyses based on these publications, including tools to guide an expert review.5,6 Instem also uses these publications to support its Predict™ computational toxicology services.7

The development of these papers also helped a series of follow-on activities, including the development of an in silico toxicology protocol framework8 as well as other applications that incorporate similar principles, such as the hazard assessment of extractables and leachables.

If you would like to discuss any of these papers or projects, please get in touch (Glenn Myatt; glenn.myatt@instem.com).

References

  1. ICH, 2017. ICH guideline M7 (R1). Assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals to limit potential carcinogenic risk (No. EMA/CHMP/ICH/83812/2013), ICH Harmonised Guideline. European Medicines Agency. https://database.ich.org/sites/default/files/M7_R1_Guideline.pdf
  2. Amberg, A., et al., 2016. Principles and procedures for implementation of ICH M7 recommended (Q)SAR analyses. Regul. Toxicol. Pharmacol. 77, 13–24. https://doi.org/10.1016/j.yrtph.2016.02.004
  3. A. Amberg 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. https://doi.org/10.1016/j.yrtph.2018.12.007.
  4. ICH M7 Guideline: Assessment and control of DNA reactive (mutagenic) impurities in pharmaceticals to limit potential carcinogenic risk. Questions and Answers. https://www.ich.org/page/multidisciplinary-guidelines#7-3
  5. https://www.instem.com/solutions/insilico/computational-toxicology.php
  6. Myatt, G.J., Bassan, A., Bower, D., Johnson, C., Miller, S., Pavan, M., Cross, K.P., 2022. Implementation of in silico toxicology protocols within a visual and interactive hazard assessment platform. Comput. Toxicol. 21, 100201. https://doi.org/10.1016/j.comtox.2021.100201
  7. https://www.instem.com/solutions/insilico/predict.php
  8. Myatt, G.J., et al., 2018. In silico toxicology protocols. Regul. Toxicol. Pharmacol. 96, 1–17. https://doi.org/10.1016/j.yrtph.2018.04.014

Advancing Prediction of Nitrosamine Carcinogenicity Potency

This week, Dr. Kevin Cross from Instem is presenting at the 2022 Genetic Toxicology Association meeting on recent progress in predicting N-nitrosamine carcinogenicity potency. The presentation outlines progress over the last year and provides an update on different collaborative working groups, including a recently announced EMA-MutAmind project funded by the European Medicine’s Agency and led by Fraunhofer ITEM that includes Instem as a partner.1

The presentation provides an overview of the different reaction mechanisms involved in N-nitrosamines mutagenicity and compares Nitrosamine Drug Substance-Related Impurities (NDSRIs) against historical information on tested nitrosamines. This includes an assessment of historical Ames negative studies that have historically tested positive for rodent carcinogenicity. Recent testing of these ‘false negatives’ are presented and discussed relative to experimental protocols used and structural classes where issues occur, allowing for more directed future assessments of nitrosamines.

The presentation is available to view:

<View presentation>

Please get in touch if we can help answer any questions on this important topic (Glenn Myatt, glenn.myatt@instem.com).

References

  1. Instem Awarded EMA Research Grant. https://www.instem.com/news/articles/0905-instem-awarded-EMA-research-grant.php