Analgesic synergy between opioid and α<sub>2</sub>‐adrenoceptors
Bibliographic record
Abstract
UNLABELLED: Opioid and α2 -adrenoceptor agonists are potent analgesic drugs and their analgesic effects can synergize when co-administered. These supra-additive interactions are potentially beneficial clinically; by increasing efficacy and/or reducing the total drug required to produce sufficient pain relief, undesired side effects can be minimized. However, combination therapies of opioids and α2 -adrenoceptor agonists remain underutilized clinically, in spite of a large body of preclinical evidence describing their synergistic interaction. One possible obstacle to the translation of preclinical findings to clinical applications is a lack of understanding of the mechanisms underlying the synergistic interactions between these two drug classes. In this review, we provide a detailed overview of the interactions between different opioid and α2 -adrenoceptor agonist combinations in preclinical studies. These studies have identified the spinal cord as an important site of action of synergistic interactions, provided insights into which receptors mediate these interactions and explored downstream signalling events enabling synergy. It is now well documented that the activation of both μ and δ opioid receptors can produce synergy with α2 -adrenoceptor agonists and that α2 -adrenoceptor agonists can mediate synergy through either the α2A or the α2C adrenoceptor subtypes. Current hypotheses surrounding the cellular mechanisms mediating opioid-adrenoceptor synergy, including PKC signalling and receptor oligomerization, and the evidence supporting them are presented. Finally, the implications of these findings for clinical applications and drug discovery are discussed. LINKED ARTICLES: This article is part of a themed section on Opioids: New Pathways to Functional Selectivity. To view the other articles in this section visit http://dx.doi.org/10.1111/bph.2015.172.issue-2.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".