Pharmacogenomics
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Pharmacogenomics is now over 50 years old and has had some impact in clinical practice, through its use to select patient subgroups who will enjoy efficacy without side effects when treated with certain drugs. However, pharmacogenomics, has had less impact than initially predicted. ⋯ A new methodology has emerged, termed pharmacometabonomics that is concerned with the prediction of drug effects through the analysis of predose, biofluid metabolite profiles, which reflect both genetic and environmental influences on human physiology. In this review we will cover what pharmacometabonomics is, how it works, what applications exist and what the future might hold in this exciting new area.
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Review Meta Analysis
µ-opioid receptor gene variant OPRM1 118 A>G: a summary of its molecular and clinical consequences for pain.
The human µ-opioid receptor variant 118 A>G (rs1799971) has become one of the most analyzed genetic variants in the pain field. At the molecular level, the variant reduces opioid receptor signaling efficiency and expression, the latter probably via a genetic-epigenetic interaction. In experimental settings, the variant was reproducibly associated with decreased effects of exogenous opioids. ⋯ An effect can neither be maintained for chronic analgesic therapy nor for opioid side effects. It seems unlikely that further studies will reveal larger effect sizes and, therefore, further analyses appear unwarranted. Thus, due to its small effect size, the SNP is without major clinical relevance as a solitary variant, but should be regarded as a part of complex genotypes underlying pain and analgesia.
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Genetic polymorphisms are thought to contribute to the wide intraindividual variability in antiplatelet and anticoagulant drug response. Pharmacogenetics is the study of how genetic variants influence drug response and how the adoption of a more personalized approach in antiplatelet and anticoagulant therapy may help to minimize harmful drug effects and optimize care for individual patients. ⋯ In this article, we review the genetic mechanisms contributing to the variability in response to three commonly used and emerging antiplatelet and anticoagulant drug therapies, namely clopidogrel, warfarin and dabigatran. We will focus on common genetic variants that influence the absorption, metabolism and/or action of these agents, including CYP2C19 (*2, *3 and *17), CYP3A4, CYP3A5, CYP2C9, ABCB1, P2RY12, CYP2C9 (*2/*3), VKORC1 and CESI.
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Review Meta Analysis
OPRM1 rs1799971 polymorphism and opioid dependence: evidence from a meta-analysis.
The OPRM1 gene encodes the µ-opioid receptor, which is the primary site of action of most opioids. Several studies and three meta-analyses have examined a possible link between the exonic OPRM1 A118G (rs1799971) polymorphism and opioid dependence; however, results have been inconclusive. ⋯ Our meta-analysis showed significant association between this polymorphism and susceptibility to opioid dependence in overall studies under a codominant model, as well as susceptibility to opioid dependence or heroin dependence in Asians under an autosomal dominant model. The nonsynonymous OPRM1 rs1799971 might be a risk factor for addiction to opioids or heroin in an Asian population.
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Inadequate pain relief and adverse effects from analgesics remain common in children and adults during the perioperative period. Opioids are the most commonly used analgesics in children and adults to treat perioperative pain. Narrow therapeutic index and a large interpatient variability in response to opioids are clinically significant, with inadequate pain relief at one end of the spectrum and serious side effects, such as respiratory depression and excessive sedation due to relative overdosing, at the other end. ⋯ We have reviewed the available evidence on improving and personalizing pain management with opioids and the significance of individualizing analgesia, in order to maximize analgesic effect with minimal adverse effects with opioids. While the early evidence on individual genotype associations with pain, analgesia and opioid adverse outcome are promising, the large amount of conflicting data in the literature suggests that there is a need for larger and more robust studies with appropriate population stratification and consideration of nongenetic and other genetic risk factors. Although the clinical evidence and the prospect of being able to provide point-of-care genotyping to enable clinicians to deliver personalized analgesia for individual patients is still not available, positioning our research to identify all possible major genetic and nongenetic risk factors of an individual patient, advancing less expensive point-of-care genotyping technology and developing easy-to-use personalized clinical decision algorithms will help us to improve current clinical and economic outcomes associated with pain and opioid pain management.