Joanne Droney

Background: Pain tolerance is subject to considerable inter-individual variation, which may be influenced by a number of genetic and non-genetic factors. The mu, delta and kappa opioid receptors play a role in pain perception and are thought to mediate different pain modalities. The aim of this study was to explore associations between pain thresholds and gender and genetic variants in the three opioid receptor genes ( OPRM, OPRD and OPRK). Experimental multi-modal pain data from previously published studies carried out in healthy Caucasian volunteers were used in order to limit the number of confounders to the study outcome. Data on thermal skin pain (n=36), muscle pressure pain (n=31) and mechanical visceral pain (n=50)) tolerance thresholds were included. Results: Nineteen genetic polymorphisms were included in linear regression modeling. Males were found to tolerate higher thermal and muscle pressure pain than females (p=0.003 and 0.02). Thirty four percent of variability in thermal skin pain was accounted for by a model consisting of OPRK rs6473799 and gender. This finding was just outside significance when correction for multiple testing was applied. Variability in muscle pressure pain tolerance was associated with OPRK rs7016778 and rs7824175. These SNPs accounted for 43% of variability in muscle pressure pain sensitivity and these findings remained significant after adjustment for multiple testing. No association was found with mechanical visceral pain. Conclusion: This is a preliminary and hypothesis generating study due to the relatively small study size. However, significant association between the opioid receptor genes and experimental pain sensitivity supports the influence of genetic variability in pain perception. These findings may be used to generate hypotheses for testing in larger clinical trials of patients with painful conditions.

Aims: To present a statistical model for defining interindividual variation in response to morphine and to use this model in a preliminary hypothesis‐generating multivariate genetic association study.Methods: Two hundred and sixty‐four cancer patients taking oral morphine were included in a prospective observational study. Pain and morphine side‐effect scores were examined using principal components analysis. The resulting principal components were used in an exploratory genetic association study of single nucleotide polymorphisms across the genes coding for the three opioid receptors,OPRM1,OPRK1andOPRD1. Associations in multivariate models, including potential clinical confounders, were explored.Results: Two principal components corresponding to residual pain and central side‐effects were identified. These components accounted for 42 and 18% of the variability in morphine response, respectively, were independent of each other and only mildly correlated. The genetic and clinical factors associated with these components were markedly different. Multivariate regression modelling, including clinical and genetic factors, accounted for only 12% of variability in residual pain on morphine and 3% of variability in central side‐effects.Conclusions: Although replication is required, this data‐driven analysis suggests that pain and central side‐effects on morphine may be two separate dimensions of morphine response. Larger study samples are necessary to investigate potential genetic and clinical associations comprehensively.

On a population level, there is no difference in terms of efficacy or side-effects between any of the strong opioids. On an individual level, however, there is marked variation in response to opioids. This review presents some of the recent advances in opioid pharmacogenetic studies. A growing number of genes have been studied in a number of different patient populations. Most data have come from candidate-gene studies. There have been two genome-wide association studies in pain and opioid response. The clinical and genetic complexity of response to opioids has limited the clinical applicability of the genetic results. Currently, interindividual variation in opioid response is managed clinically through a process known as opioid switching. The evidence supporting the efficacy of opioid switching is poor, mainly because randomized controlled trials in this area are lacking. Adequately powered studies to allow identification of genetic variants with small effect size and exploration of gene-gene interaction are needed. Integration of genetic analysis in clinical studies with carefully defined outcome measures will increase the likelihood of identifying clinical and genetic factors which can be used to predict opioid response.