Analgesia pp 395-420

Part of the Methods in Molecular Biology book series (MIMB, volume 617)

Genetic Polymorphisms and Human Sensitivity to Opioid Analgesics

  • Daisuke Nishizawa
  • Masakazu Hayashida
  • Makoto Nagashima
  • Hisashi Koga
  • Kazutaka Ikeda
Protocol

Abstract

Opioid analgesics are commonly used for the treatment of acute as well as chronic, moderate to severe pain. Well-known, however, is the wide interindividual variability in sensitivity to opioids that exists, which has often been a critical problem in pain treatment. To date, only a limited number of studies have addressed the relationship between human genetic variations and sensitivity to opioids, and such studies are still in their early stages. Therefore, revealing the relationship between genetic variations in many candidate genes and individual differences in sensitivity to opioids will provide valuable information for appropriate individualization of opioid doses required for adequate pain control. Although the methodologies for such association studies can be diverse, here we summarize protocols for investigating the association between genetic polymorphisms and sensitivity to opioids in human volunteers and patients undergoing painful surgery.

Key words

Analgesics Genetic polymorphisms Single nucleotide polymorphism (SNP) Genotype-phenotype association Haplotype Opioids Opiates Pain relief Personalized medicine Pharmacogenomics 

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Daisuke Nishizawa
    • 1
  • Masakazu Hayashida
    • 2
  • Makoto Nagashima
    • 3
  • Hisashi Koga
    • 4
  • Kazutaka Ikeda
    • 1
  1. 1.Division of PsychobiologyTokyo Institute of PsychiatryTokyoJapan
  2. 2.Department of AnesthesiologySaitama Medical University International Medical CenterHidakaJapan
  3. 3.Department of SurgeryToho University Sakura Medical CenterSakuraJapan
  4. 4.Laboratory of Medical Genomics, Department of Human Genome TechnologyKazusa DNA Research InstituteKisarazuJapan

Personalised recommendations