Metabolic clearance of select opioids and opioid antagonists using hepatic spheroids and recombinant cytochrome P450 enzymes

The opioid crisis is a pressing public health issue, exacerbated by the emergence of more potent synthetic opioids, particularly fentanyl and its analogs. While competitive antagonists exist, their efficacy against synthetic opioids is largely unknown. Furthermore, due to the short durations of action of current antagonists, renarcotization remains a concern. In this study, metabolic activity was characterized for fentanyl‐class opioids and common opioid antagonists using multiple in vitro systems, namely, cytochrome P450 (CYP) enzymes and hepatic spheroids, after which an in vitro‐in vivo correlation was applied to convert in vitro metabolic activity to predictive in vivo intrinsic clearance. For all substrates, intrinsic hepatic metabolism was higher than the composite of CYP activities, due to fundamental differences between whole cells and single enzymatic reactions. Of the CYP isozymes investigated, 3A4 yielded the highest absolute and relative metabolism across all substrates, with largely negligible contributions from 2D6 and 2C19. Comparative analysis highlighted elevated lipophilicity and diminished CYP3A4 activity as potential considerations for the development of more efficacious opioid antagonists. Finally, antagonists with a high degree of molecular similarity exhibited comparable clearance, providing a basis for structure‐metabolism relationships. Together, these results provide multiple screening criteria for early stage drug discovery involving opioid countermeasures. This study represents the first characterization of metabolic clearance for multiple common opioid antagonists and fentanyl‐class opioids using the same in vitro systems. The direct comparability enables a thorough understanding of the relative stability of opioids and opioid antagonists for the development of more efficacious countermeasures, and measured metabolic clearances can be incorporated into pharmacokinetic models.