A recent FDA guidance for industry, titled “In Vitro Drug Interaction Studies – Cytochrome P450 Enzyme- and Transporter- Mediated Drug Interactions”1 includes the following statement:
“A lower cut-off value for the metabolite-to-parent AUC ratio may also be considered for metabolites with structural alerts for potential mechanism-based inhibition (Orr, 2012; Yu, 2013; Yu, 2015)”
Today, a structure activity relationship (SAR) assessment of mechanism-based inhibition (MBI) of cytochrome P450 (CYP450) enzymes without computational support would involve reading a series of publications and visually comparing the alerts mentioned in the papers against your chemicals of interest. The biological and chemical context of the alerts will need to be carefully studied to ensure the validity of such matches. Since this would likely take a considerable amount of time to complete for a single chemical, a more efficient approach would be to use a computational system. However, there are many challenges in developing such a solution.
Firstly, there are numerous publications – at least 14 – that discuss such alerts. These publications describe general bioactivation alerts leading to the formation of reactive metabolites, some of which would cause MBI of CYP450 enzymes. Although the identification of these different types of alerts are helpful for a variety of applications, the FDA guidance specifically singles out “structural alerts for potential mechanism-based inhibition” and so these two classes of alerts need to be clearly differentiated.
Secondly, the different publications describe similar alerts and may use subtly different structural definitions. It is, therefore, challenging to harmonize these alerts over the different sources.
Thirdly, the context of such alerts, including specifics of the structure-activity relationships, is important when performing an expert review to conclude the alert is relevant for the chemicals of interest. This information should ideally be summarized for rapid review, including the reaction schemes underlying bioactivation.
Finally, the process of qualifying and refining these alerts using data is critical. Most of this inhibition data is generated by different companies and cannot easily be shared because of confidentiality concerns. However, it is still possible to use this data, without sharing proprietary information on individual chemicals or study results, using a similar approach to SAR fingerprinting2.
If you are interested in working with us on this collaborative project, please get in touch (Glenn Myatt, firstname.lastname@example.org).
- Ahlberg, E., Amberg, A., Beilke, L.D., Bower, D., Cross, K.P., Custer, L., Ford, K.A., Gompel, J.V., Harvey, J., Honma, M., Jolly, R., Joossens, E., Kemper, R.A., Kenyon, M., Kruhlak, N., Kuhnke, L., Leavitt, P., Naven, R., Neilan, C., Quigley, D.P., Shuey, D., Spirkl, H.-P., Stavitskaya, L., Teasdale, A., White, A., Wichard, J., Zwickl, C., Myatt, G.J., 2016. Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity. Regulatory Toxicology and Pharmacology 77, 1–12. doi:10.1016/j.yrtph.2016.02.003