ArticleEuropean journal of pain supplements2011
Elucidation of mu-Opioid Gene Structure: How Genetics Can Help Predict Responses to Opioids.
Article in European journal of pain supplements, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 21 citations in OpenAlex.
- Non-spinal low back pain: Global epidemiology, trends, and risk factors.Health science reports · 2023Article
- No Association between Genetic Variants of theGenes · 2022Article
- Pain polymorphisms and opioids: An evidence based review.Molecular medicine reports · 2019Article
- Regulation and Functional Implications of Opioid Receptor Splicing in Opioid Pharmacology and HIV Pathogenesis.Journal of cellular physiology · 2016Review
- Polymorphisms in genes of respiratory control and sudden infant death syndrome.International journal of legal medicine · 2015Article
- A6V polymorphism of the human μ-opioid receptor decreases signalling of morphine and endogenous opioids in vitro.British journal of pharmacology · 2015Article
- Mu Opioid Splice Variant MOR-1K Contributes to the Development of Opioid-Induced Hyperalgesia.PloS one · 2015Article
- Cellular signalling of non-synonymous single-nucleotide polymorphisms of the human μ-opioid receptor (OPRM1).British journal of pharmacology · 2015Review
- Buprenorphine signalling is compromised at the N40D polymorphism of the human μ opioid receptor in vitro.British journal of pharmacology · 2014Article
- Opioid therapy pharmacogenomics for noncancer pain: efficacy, adverse events, and costs.Pain research and treatment · 2013Review
- Gene polymorphism impact on opioid analgesic usage.Journal of advanced pharmaceutical technology & researchReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Opioid drugs are among the most commonly used and effective human analgesics. To date, the clinical benefits of opioid analgesics have not been fully realized due to substantial individual variations in the responses to opioids, insufficient drug dosing, and a high rate (up to 66%) of adverse events. As such, there is a substantial need to identify the genetic and molecular biological mechanisms that mediate individual responses to opioid therapy. Recent discoveries show that genetic variations in the μ-opioid receptor (OPRM1) gene locus play an essential role in inter-individual responses. The majority of genetic association studies have focused on the A118G polymorphism, which codes for a non-synonymous change in OPRM1 exon 1. In addition to the A118G polymorphism, another functional SNP (rs563649), which is located within an alternatively-spliced OPRM1 isoform (MOR-1K), has been identified. The MOR-1k isoform codes for 6TM OPRM1 isoforms that display excitatory rather than the inhibitory cellular effects, which are characteristic of the canonical 7TM isoforms. Thus, stimulation of the 6TM isoforms may engage the molecular mechanisms mediating opioid-dependent hyperalgesia, tolerance and dependence. Future clinical and basic studies that seek to identify the functional genetic variants within OPRM1 locus, and associated molecular mechanisms, will result in a better understanding of individual responses to opioid therapy and ultimately to the development new pharmacotherapeutics and diagnostic tools.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.