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.

Over the last 15 years I have been working closely with the US Food and Drug Administration (FDA) as a principal investigator on several Research Collaboration Agreements (RCAs). We recently extended another 5-year RCA with the Center for Drug Evaluation and Research (CDER) and have additional on-going RCAs with the Center for Food Safety and Applied Nutrition, the Center for Veterinary Medicine, and the Center for Devices and Radiological Health. This has been a long, very productive, and mutually beneficial relationship.

You may have heard of our collaboration to develop new and updated mutagenicity prediction models that are used as part of regulatory approvals for pharmaceutical impurities (ICH M7) and for the assessment of extractables and leachables in medical devices, as well as other applications. In support of this work, we have collaborated on numerous publications documenting these models1 as well as publications outlining the principles and procedures for (Q)SAR assessments that include case studies covering expert review of (Q)SAR results.2,3,4,5

However, you may not be aware of other toxicological endpoints where we have been developing predictive models through the RCAs. This has included the development of new and updated models for many genetic toxicology endpoints,6 organ toxicity, carcinogenicity7, reproductive and developmental toxicity, and neurotoxicity. We also have an on-going collaboration focusing on developing computational translational models that incorporate experimental data in SEND submissions from sponsors alongside traditional QSAR descriptors as part of models to identify clinical liver safety signals using non-clinical data.8 Recently, we have started some new collaborations in the areas of assessing bioactivation (to support the in vitro drug interaction studies guidance9), abuse liability and sex-specific toxicological assessments (for the Office of Women’s Health).

In the past few years, we have also collaborated on the development of an updated version of Leadscope Enterprise that is installed behind the FDA’s firewall at CDER that contains both Leadscope models and databases alongside FDA’s proprietary data. This system supports their computational toxicology group to register new chemicals into their internal chemical dictionary and manage FDA-generated (Q)SAR consultation reports (expert reviewed assessments) that are linked to chemicals in the database. This content is also provided to CDER drug reviewers through a simple and easy-to-use web browser that allows for the searching of the databases and immediate access to these (Q)SAR consultation reports.10

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

References

1. Landry, C., Kim, M.T., Kruhlak, N.L., Cross, K.P., Saiakhov, R., Chakravarti, S., Stavitskaya, L., (2019) Transitioning to composite bacterial mutagenicity models in ICH M7 (Q)SAR analyses. Regulatory Toxicology and Pharmacology 109, 104488.

2. Amberg, A., Beilke, L., Bercu, et al., 2016. Principles and procedures for implementation of ICH M7 recommended (Q)SAR analyses. Regulatory Toxicology and Pharmacology 77, 13–24. doi:10.1016/j.yrtph.2016.02.004 

Open access: https://www.sciencedirect.com/science/article/pii/S0273230016300277 

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. Regulatory Toxicology and Pharmacology 102, 53–64. doi:10.1016/j.yrtph.2018.12.007 

Open access: https://www.sciencedirect.com/science/article/pii/S0273230018303143 

4. Hasselgren C., et al., (2020) Management of Pharmaceutical ICH M7 (Q)SAR Predictions – The Impact of Model Updates. Regulatory Toxicology and Pharmacology,118, October 2020.

5. Myatt, G.J., Ahlberg, E., Akahori, Y., et al., 2018. In silico toxicology protocols. Regulatory Toxicology and Pharmacology 96, 1–17. doi:10.1016/j.yrtph.2018.04.014 

Open access: https://www.sciencedirect.com/science/article/pii/S0273230018301144 

6. Yoo, J.W., Kruhlak, N.L., Landry, C., Cross, K.P., Sedykh, A., Stavitskaya, L., (2020). Development of improved QSAR models for predicting the outcome of the in vivo micronucleus genetic toxicity assay. Regulatory Toxicology and Pharmacology 113, 104620.

7. Guo, D., Kruhlak, N.L., Stavitskaya, L., Cross, K.P., Bower, D.A., “Characterizing Compound Classes by Rodent Carcinogenicity Tumor Severity and Type”, American College of Toxicology 36th Annual Meeting, November 6-10, 2016, Baltimore, MD.

8. Daniel P. Russo, Kevin P. Cross, Frederic Moulin, Kevin Snyder, Characterizing rat clinical chemistry tests for liver disease phenotypes: a large-scale, cross-study analysis using standardized electronic submission data, in submission.

9. In Vitro Drug Interaction Studies — Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions Guidance for Industry https://www.fda.gov/media/134582/download

10. Kevin P. Cross, Marlene T. Kim, Naomi L., Kruhlak, The FDA/CDER Chemical Dictionary, Genetic Toxicity Association Meeting, 2019.

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.