Combining Experiments, Simulations, and AI to Interrogate the Pharmacology of Fentanyl and Nitazene Derivatives

The opioid epidemic is ongoing. While fentanyl, an ultra-potent synthetic (UPS) opioid, is involved in the majority of the overdose deaths, fentanyl analogs and the newly emerged nitazene derivatives have caused additional deaths. Fentanyls and nitazenes pose not only public health risks but also potential threats as chemical agents used by the military or civilians. Intriguingly, the reported binding affinities of fentanyl and etonitazene (the most potent nitazene derivative) at µ-opioid receptor are similar to the natural opioid morphine in the nanomolar range; however, fentanyl is 100 times morphine equivalency and etonitazene’s potency is even higher. The extreme potencies of fentanyls and nitazenes and their mechanisms of action (MOA) are poorly understood. The objective of this project is to advance the basic understanding of the MOA and structure-activity relationship (SAR) of the nitazenes using state-of-the-art simulations, machine learning (ML), and experiments (Abbas lab). Building on the strong preliminary data and leveraging the cutting-edge experiments, simulations, and ML, we will accomplish the project objectives by pursuing the following specific aims. The first aim is to determine the receptor-ligand interactions and elucidate the MOA of nitazenes in contrast to fentanyl. The second aim is to elucidate the mechanistic basis of the structure-activity relationship of the ultrapotent nitazenes. This project will provide novel mechanistic insights into how ultrapotent nitazenes differ from fentanyl in their MOAs and how nitazenes’ potency is modulated by structural modifications. These insights may help develop countermeasures to prevent and reduce overdose deaths involving UPS opioids. Furthermore, the experimental and computational approaches developed can be broadly applied to study future emerging UPS opioids.