Dataset Information

Ligand requirements for involvement of PKCε in synergistic analgesic interactions between spinal μ and δ opioid receptors.

ABSTRACT:

Background and purpose

We recently found that PKCε was required for spinal analgesic synergy between two GPCRs, δ opioid receptors and α2 A adrenoceptors, co-located in the same cellular subpopulation. We sought to determine if co-delivery of μ and δ opioid receptor agonists would similarly result in synergy requiring PKCε.

Experimental approach

Combinations of μ and δ opioid receptor agonists were co-administered intrathecally by direct lumbar puncture to PKCε-wild-type (PKCε-WT) and -knockout (PKCε-KO) mice. Antinociception was assessed using the hot-water tail-flick assay. Drug interactions were evaluated by isobolographic analysis.

Key results

All agonists produced comparable antinociception in both PKCε-WT and PKCε-KO mice. Of 19 agonist combinations that produced analgesic synergy, only 3 required PKCε for a synergistic interaction. In these three combinations, one of the agonists was morphine, although not all combinations involving morphine required PKCε. Morphine + deltorphin II and morphine + deltorphin I required PKCε for synergy, whereas a similar combination, morphine + deltorphin, did not. Additionally, morphine + oxymorphindole required PKCε for synergy, whereas a similar combination, morphine + oxycodindole, did not.

Conclusions and implications

We discovered biased agonism for a specific signalling pathway at the level of spinally co-delivered opioid agonists. As the bias is only revealed by an appropriate ligand combination and cannot be accounted for by a single drug, it is likely that the receptors these agonists act on are interacting with each other. Our results support the existence of μ and δ opioid receptor heteromers at the spinal level in vivo.

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.

SUBMITTER: Schuster DJ

PROVIDER: S-EPMC4292975 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundThe analgesic efficacy of opioids is well known to vary widely among individuals, and various factors related to individual differences in opioid sensitivity have been identified. However, a prediction model to calculate appropriate opioid analgesic requirements has not yet been established. The present study sought to construct prediction formulas for individual opioid analgesic requirements based on genetic polymorphisms and clinical data from patients who underwent cosmetic orthognathic surgery and validate the utility of the prediction formulas in patients who underwent major open abdominal surgery.MethodsTo construct the prediction formulas, we performed multiple linear regression analyses using data from subjects who underwent cosmetic orthognathic surgery. The dependent variable was 24-h postoperative or perioperative fentanyl use, and the independent variables were age, gender, height, weight, pain perception latencies (PPL), and genotype data of five single-nucleotide polymorphisms (SNPs). To examine the utility of the prediction formulas, we performed simple linear regression analyses using subjects who underwent major open abdominal surgery. Actual 24-h postoperative or perioperative analgesic use and the predicted values that were calculated using the multiple regression equations were incorporated as dependent and independent variables, respectively.ResultsMultiple linear regression analyses showed that the four SNPs, PPL, and weight were retained as independent predictors of 24-h postoperative fentanyl use (R² = 0.145, P = 5.66 × 10?¹?) and the two SNPs and weight were retained as independent predictors of perioperative fentanyl use (R² = 0.185, P = 1.99 × 10?¹?). Simple linear regression analyses showed that the predicted values were retained as an independent predictor of actual 24-h postoperative analgesic use (R² = 0.033, P = 0.030) and perioperative analgesic use (R² = 0.100, P = 1.09 × 10??), respectively.ConclusionsWe constructed prediction formulas, and the possible utility of these prediction formulas was found in another type of surgery.