Biased Opioid Antagonists as Modulators of Opioid Dependence: Opportunities to Improve Pain Therapy and Opioid Use Management

Abstract

Opioid analgesics are effective pain therapeutics but cause various adverse effects and addiction. For safer pain therapy, biased opioid agonists selectively target distinct m opioid receptor (MOR) conformations, while the potential of biased opioid antagonists has been neglected. Agonists convert a dormant receptor form (MOR-m) to a ligand-free active form (MOR-m*), which mediates MOR signaling. Moreover, MOR-m converts spontaneously to MOR-m* (basal signaling). Persistent upregulation of MOR-m* has been invoked as a hallmark of opioid dependence. Contrasting interactions with both MOR-m and MOR-m* can account for distinct pharmacological characteristics of inverse agonists (naltrexone), neutral antagonists (6b-naltrexol), and mixed opioid agonist-antagonists (buprenorphine). Upon binding to MOR-m*, naltrexone but not 6b-naltrexol suppresses MOR-m*signaling. Naltrexone blocks opioid analgesia non-competitively at MOR-m*with high potency, whereas 6BN must compete with agonists at MOR-m, accounting for ~100-fold lower in vivo potency. Buprenorphine’s bell-shaped dose-response curve may also result from opposing effects on MOR-m and MOR-m*. In contrast, we find that 6b-naltrexol potently prevents dependence, below doses affecting analgesia or causing withdrawal, possibly binding to MOR conformations relevant to opioid dependence. We propose that 6b-naltrexol is a biased opioid antagonist modulating opioid dependence at low doses, opening novel avenues for opioid pain therapy and use management.

Development of a Translational Model to Assess the Impact of Opioid Overdose and Naloxone Dosing on Respiratory Depression and Cardiac Arrest

“…For example, our current in vitro submodel, while able to capture the binding rate constants for buprenorphine, is not designed to recapitulate the partial agonist effect after receptor binding. This may be better described by a submodel with multiple states of the opioid receptor 42 . The modular design of our model makes it straightforward to replace components of the model with alternative submodels.…”

“…Similarly, the in vitro submodel assumes simple competitive binding between opioids and naloxone on the opioid receptor, ignoring intricate mechanisms, such as G protein activation 41 and multiple states of the opioid receptor. 42 We adopted a "middleout" approach 43 by keeping some submodels (such as the in vitro) more empirically derived and others (such as the physiological submodel) more mechanistic, with the aim of balancing biological reality and model complexity. Such a design dictates that the context of use of the model should be relevant to the validation procedure.…”

“…This may be better described by a submodel with multiple states of the opioid receptor. 42 The modular design of our model makes it straightforward to replace components of the model with alternative submodels.…”