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 technology^7,8, while also developing principles and procedures for performing such computational toxicology assessments^9,10,11.

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