Prediction of opioid dose in cancer pain patients using genetic profiling: not yet an option with support vector machine learning

BMC Res Notes. 2018 Jan 27;11(1):78. doi: 10.1186/s13104-018-3194-z.

Abstract

Objective: Use of opioids for pain management has increased over the past decade; however, inadequate analgesic response is common. Genetic variability may be related to opioid efficacy, but due to the many possible combinations and variables, statistical computations may be difficult. This study investigated whether data processing with support vector machine learning could predict required opioid dose in cancer pain patients, using genetic profiling. Eighteen single nucleotide polymorphisms (SNPs) within the µ and δ opioid receptor genes and the catechol-O-methyltransferase gene were selected for analysis.

Results: Data from 1237 cancer pain patients were included in the analysis. Support vector machine learning did not find any associations between the assessed SNPs and opioid dose in cancer pain patients, and hence, did not provide additional information regarding prediction of required opioid dose using genetic profiling.

Keywords: Cancer pain; Genetics; SNPs; Support vector machine.

MeSH terms

  • Aged
  • Analgesics, Opioid / administration & dosage
  • Analgesics, Opioid / therapeutic use*
  • Cancer Pain / drug therapy*
  • Cancer Pain / genetics*
  • Catechol O-Methyltransferase / genetics
  • Dose-Response Relationship, Drug
  • Female
  • Gene Frequency
  • Genotype
  • Humans
  • Male
  • Middle Aged
  • Morphine / administration & dosage
  • Morphine / therapeutic use
  • Polymorphism, Single Nucleotide
  • Receptors, Opioid, delta / genetics
  • Receptors, Opioid, mu / genetics
  • Support Vector Machine*

Substances

  • Analgesics, Opioid
  • Receptors, Opioid, delta
  • Receptors, Opioid, mu
  • Morphine
  • Catechol O-Methyltransferase