A major focus of our work over the last few years has been on the development and publication of in silico toxicology protocols, discussed in previous blog posts1. This has resulted in a paper outlining a framework for such protocols2  as well as two published protocols in the areas of genetic toxicology3 and skin sensitization4. These protocols have been implemented in the Leadscope computational toxicology software to support a fast, defendable, consistent, and fully documented assessment.

We are continually working on the development of new protocols as part of a series of cross-industry working groups. For example, in silico protocols for skin and eye irritation/corrosion as well as endocrine activity are being generated. In addition, many papers outlining the state-of-the-art in the prediction of different toxicological endpoints, such as liver toxicity, are being submitted for publication as part of a special issue on in silico toxicology protocols in the Journal of Computational Toxicology. These newer publications will provide a springboard for the development of new in silico methods and future protocols.

The ICH M7 guideline5 supports the mutagenicity assessment for pharmaceutical impurities and similar approaches are being adopted in many other areas including animal health and pesticide residuals.

Based on the protocol framework, we have now developed an ICH M7 protocol implementation. This pulls together information from the models (statistical QSAR models, expert alerts, cohort-of-concern profilers) and database searches (bacterial mutagenicity, rodent carcinogenicity, acceptable intake limits) which is integrated as part of a documented decision scheme. Figure 1 shows an example of how the new tool summarizes all available information.

Figure 1: Tabular summary of ICH M7 results

In the same manner as the other protocol implementations, it is possible to inspect the underlying decision scheme behind each assessment, perform an expert review (support by a series of guidelines) that may result in modification (such as refuting a (Q)SAR result), as well as documentation of all the results and expert review. Figure 2 shows the decision scheme and expert review guidelines for an individual chemical.

Figure 2: Interactive ICH M7 decision scheme for a single chemical

Please get in touch with me (Glenn Myatt; glenn.myatt@instem.com) if you are interested in discussing this approach to mutagenicity assessment.

References

  1. Can the burden on industry and regulators be reduced? https://insilicoinsider.blog/2020/09/10/can-the-burden-on-industry-and-regulators-be-reduced/
  2. 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
  3. 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 
  4. 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 
  5.  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

Published by Glenn Myatt

Glenn J. Myatt is the co-founder and currently head of Leadscope (An Instem company) with over 25 years’ experience in computational chemistry/toxicology. He holds a Bachelor of Science degree in Computing from Oxford Brookes University, a Master of Science degree in Artificial Intelligence from Heriot-Watt University and a Ph.D. in Chemoinformatics from the University of Leeds. He has published 27 papers, 6 book chapters and three books.