Dataset of the first report of pharmacogenomics profiling in an outpatient spine setting

• Published in: Data in brief 2021-04, Vol.35, p.106832-106832, Article 106832
• Main Authors: Cottrill, Ethan, Pennington, Zach, Lai, Chun Wan Jeffrey, Ehresman, Jeff, Jiang, Bowen, Ahmed, A. Karim, Zhu, Alex, Perdomo-Pantoja, Alexander, Sciubba, Daniel M., Witham, Timothy, Lee, Chun Hin, MacDonald, Kevin, Theodore, Nicholas
• Format: Article
• Language: English
• Subjects: Analgesic regimen; Data; Medications; Neck and back pain; Personalized medicine; Pharmacogenetics; Pharmacogenomics; Single nucleotide polymorphism; Spine surgery
• ISSN: 2352-3409; 2352-3409
• DOI: 10.1016/j.dib.2021.106832

Here we describe the dataset of the first report of pharmacogenomics profiling in an outpatient spine setting with the primary aims to catalog: 1) the genes, alleles, and associated rs Numbers (accession numbers for specific single-nucleotide polymorphisms) analysed and 2) the genotypes and corresponding phenotypes of the genes involved in metabolizing 37 commonly used analgesic medications. The present description applies to analgesic medication-metabolizing enzymes and may be especially valuable to investigators who are exploring strategies to optimize pharmacologic pain management (e.g., by tailoring analgesic regimens to the genetically identified sensitivities of the patient). Buccal swabs were used to acquire tissue samples of 30 adult patients who presented to an outpatient spine clinic with the chief concern of axial neck and/or back pain. Array-based assays were then used to detect the alleles of genes involved in the metabolism of pain medications, including all common (wild type) and most rare variant alleles with known clinical significance. Both CYP450 isozymes – including CYP1A2, CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A4, and CYP3A5 – and the phase II enzyme UDP-glucuronosyltransferase-2B7 (UGT2B7) were examined. Genotypes/phenotypes were then used to evaluate each patient's relative ability to metabolize 37 commonly used analgesic medications. These medications included both non-opioid analgesics (i.e., aspirin, diclofenac, nabumetone, indomethacin, meloxicam, piroxicam, tenoxicam, lornoxicam, celecoxib, ibuprofen, flurbiprofen, ketoprofen, fenoprofen, naproxen, and mefenamic acid) and opioid analgesics (i.e., morphine, codeine, dihydrocodeine, ethylmorphine, hydrocodone, hydromorphone, oxycodone, oxymorphone, alfentanil, fentanyl, sufentanil, meperidine, ketobemidone, dextropropoxyphene, levacetylmethadol, loperamide, methadone, buprenorphine, dextromethorphan, tramadol, tapentadol, and tilidine). The genes, alleles, and associated rs Numbers that were analysed are provided. Also provided are: 1) the genotypes and corresponding phenotypes of the genes involved in metabolizing 37 commonly used analgesic medications and 2) the mechanisms of metabolism of the analgesic medications by primary and ancillary pathways. In supplemental spreadsheets, the raw and analysed pharmacogenomics data for all 30 patients evaluated in the primary research article are additionally provided. Collectively, the presented data offer significant reuse potential in fu