Original ArticleFirst Report of Pharmacogenomic Profiling in an Outpatient Spine Setting: Preliminary Results from a Pilot Study
Introduction
As massively parallel sequencing advances, individual genomic profiling has become more affordable for members of the broader population.1 To this end, more people worldwide are having their genomes sequenced, and many health systems are beginning to acquire these data from their patients.1 The increased availability of genetic data holds great promise for personalized medicine, including medication selection, because it is recognized that a single regimen may not work for all patients.2
The study of how genes dictate individual drug responses is known as pharmacogenomics.3,4 Patient polymorphisms can alter both drug pharmacokinetic and pharmacodynamic properties, which determine drug metabolism and efficacy, respectively.4 For many medications (e.g., opioids), the 2 parameters are intertwined, because drug modifications by the cytochrome P450 (CYP450) system in the liver alter the biological activity of the compound.5 Previous studies have suggested such polymorphisms to be uncommon5; however, their prevalence among patients with spine diseases is unknown. Understanding the prevalence of these mutations may facilitate superior outcomes for patients, because recognizing suboptimal medication analgesic regimens during clinical evaluation could help maximize nonoperative management. This development, in turn, could decrease costs to the health care system and produce better patient outcomes by sparing some patients from risks associated with surgical intervention.
In this original pilot study, we provide a preliminary pharmacogenetic profiling of spine outpatients, with the objectives of 1) showing the prevalence of these differences and 2) providing preliminary evidence that these polymorphisms may help explain interpatient differences in preoperative pain refractory to conservative management.
Section snippets
Patient Enrollment
After receiving institutional review board approval, we prospectively enrolled patients 18 years of age or older who presented to our outpatient spine clinic during 10 typical days over a 9-month period for the chief concern of axial neck and/or back pain. Consecutive patients were invited to participate during the clinical encounter by the resident or attending physician. Patients included in this study provided buccal swab samples for genomic analysis and described their pain using the
Results
Thirty patients were included; the demographics of these patients are presented in Table 1. The mean age at evaluation was 60.6 ± 15.3 years, the mean body mass index was 29.1 ± 6.5 kg/m2, 16/30 (53%) of patients were female, and 9/30 (30%) had undergone previous spinal surgery (≥3 years earlier in all cases). On average, patients were taking 13 ± 8 total medications (median, 12), including 3 ± 1 total pain medications (median, 3) and 1 ± 1 opioid pain medication (median, 1). Nineteen patients
Discussion
In this pilot study, we provide the first pharmacogenetic profile of an outpatient spine population and provide preliminary evidence suggesting that many spine outpatients may use an analgesic regimen comprising ≥1 agent for which they are poor metabolizers. Specifically, we found that nearly 50% of patients were treated with ≥1 analgesic for which they had genetic polymorphisms causing them to be suboptimal metabolizers of the drug. More notable in light of the ongoing opioid crisis, 13 of the
Conclusions
This study describes the first use of pharmacogenomics testing to profile analgesic regimen efficacy in spine clinic patients presenting for degenerative diseases. Among included patients, 47% were being treated with ≥1 medication for which they were genetically predisposed to be a suboptimal metabolizer. More importantly, 68% of patients being treated with opioids at baseline were rapid metabolizers of those medications and therefore potentially being treated with subtherapeutic doses.
CRediT authorship contribution statement
Ethan Cottrill: Conceptualization, Data curation, Formal analysis, Writing - original draft, Writing - review & editing. Zach Pennington: Conceptualization, Data curation, Formal analysis, Writing - original draft. A. Karim Ahmed: Conceptualization, Data curation, Writing - review & editing, Resources. Bowen Jiang: Conceptualization, Data curation, Writing - review & editing, Resources. Jeff Ehresman: Formal analysis, Writing - review & editing, Resources. Alex Zhu: Data curation, Writing -
References (31)
- et al.
Pharmacogenes (PGx-genes): current understanding and future directions
Gene
(2019) - et al.
Pharmacogenomics
Lancet
(2019) Opioid metabolism
Mayo Clin Proc
(2009)- et al.
Cytochrome P4502C19 polymorphism in young patients treated with clopidogrel after myocardial infarction: a cohort study
Lancet
(2009) - et al.
Multisite Investigation of Outcomes With Implementation of CYP2C19 Genotype-Guided Antiplatelet Therapy After Percutaneous Coronary Intervention
JACC Cardiovasc Interv
(2018) - et al.
The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future
Genet Med
(2013) - et al.
Epidemiology of regular prescribed opioid use: results from a national, population-based survey
J Pain Symptom Manage
(2008) - et al.
Precision medicine, genome sequencing, and improved population health
JAMA
(2018) - et al.
Precision medicine and advancing clinical care: insights from Iceland
JAMA Intern Med
(2019) - et al.
Pain in children: comparison of assessment scales
Pediatr Nurs
(1988)
Responsiveness of the numeric pain rating scale in patients with low back pain
Spine
Inhibition of CYP2D6 activity by bupropion
J Cli Psychopharmacol
Precision of VerifyNowP2Y12 assessment of clopidogrel response in patients undergoing cerebral aneurysm flow diversion
Neurosurgery
CYP2C9 polymorphisms and phenytoin metabolism: implications for adverse effects
Expert Opin Drug Metab Toxicol
Pharmacogenomic testing: clinical evidence and implementation challenges
J Pers Med
Cited by (0)
Conflict of interest statement: Funding for this study was received from Advanced Genomic Solutions (AGS) Ltd., Scottsdale, Arizona, USA. E.C. receives non-study-related grant support from the National Institute on Aging. D.M.S. is a consultant for Baxter, DePuy-Synthes, Globus, K2M, Medtronic, NuVasive, and Stryker and receives non–study-related grant support from Baxter and Stryker. T.W. receives non–study-related grant support from Gordon and Marilyn Macklin Foundation and is an advisory board member for Augmedics and an investor in Augmedics. K.MacD., C.H.L., and C.W.J.L. are employees of Advanced Genomic Solutions LLC. N.T. receives royalties from Depuy and Globus and is a consultant for Globus. All other authors declare that the article content was composed in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Ethan Cottrill and Zach Pennington contributed equally to this work.