The association between endogenous opioid function and morphine responsiveness: a moderating role for endocannabinoids.

Abstract

We sought to replicate previous findings that low endogenous opioid (EO) function predicts greater morphine analgesia and extended these findings by examining whether circulating endocannabinoids and related lipids moderate EO-related predictive effects. Individuals with chronic low-back pain (n = 46) provided blood samples for endocannabinoid analyses, then underwent separate identical laboratory sessions under 3 drug conditions: saline placebo, intravenous (i.v.) naloxone (opioid antagonist; 12-mg total), and i.v. morphine (0.09-mg/kg total). During each session, participants rated low-back pain intensity, evoked heat pain intensity, and nonpain subjective effects 4 times in sequence after incremental drug dosing. Mean morphine effects (morphine-placebo difference) and opioid blockade effects (naloxone-placebo difference; to index EO function) for each primary outcome (low-back pain intensity, evoked heat pain intensity, and nonpain subjective effects) were derived by averaging across the 4 incremental doses. The association between EO function and morphine-induced back pain relief was significantly moderated by endocannabinoids [2-arachidonoylglycerol (2-AG) and N-arachidonoylethanolamine (AEA)]. Lower EO function predicted greater morphine analgesia only for those with relatively lower endocannabinoids. Endocannabinoids also significantly moderated EO effects on morphine-related changes in visual analog scale-evoked pain intensity (2-AG), drug liking (AEA and 2-AG), and desire to take again (AEA and 2-AG). In the absence of significant interactions, lower EO function predicted significantly greater morphine analgesia (as in past work) and euphoria. Results indicate that EO effects on analgesic and subjective responses to opioid medications are greatest when endocannabinoid levels are low. These findings may help guide development of mechanism-based predictors for personalized pain medicine algorithms.