Elsevier

World Neurosurgery

Volume 145, January 2021, Pages e21-e31
World Neurosurgery

Original Article
First Report of Pharmacogenomic Profiling in an Outpatient Spine Setting: Preliminary Results from a Pilot Study

https://doi.org/10.1016/j.wneu.2020.09.007Get rights and content

Objective

Pharmacogenomics may help personalize medicine and improve therapeutic selection. This is the first study investigating how pharmacogenomic testing may inform analgesic selection in patients with spine disease. We profile pharmacogenetic differences in pain medication–metabolizing enzymes across patients presenting at an outpatient spine clinic and provide preliminary evidence that genetic polymorphisms may help explain interpatient differences in preoperative pain refractory to conservative management.

Methods

Adults presenting to our outpatient spine clinic with chief symptoms of neck and/or back pain were prospectively enrolled over 9 months. Patients completed the Wong-Baker FACES and numeric pain rating scales for their chief pain symptom and provided detailed medication histories and cheek swab samples for genomic analysis.

Results

Thirty adults were included (mean age, 60.6 ± 15.3 years). The chief concern was neck pain in 23%, back pain in 67%, and combined neck/back pain in 10%. At enrollment, patient analgesic regimens comprised 3 ± 1 unique medications, including 1 ± 1 opioids. After genomic analysis, 14/30 patients (47%) were identified as suboptimal metabolizers of ≥1 medications in their analgesic regimen. Of these patients, 93% were suboptimal metabolizers of their prescribed opioid analgesic. Nonetheless, pain scores were similar between optimal and suboptimal metabolizer groups.

Conclusions

This pilot study shows that a large proportion of the spine outpatient population may use pain medications for which they are suboptimal metabolizers. Further studies should assess whether these pharmacogenomic differences indicate differences in odds of receiving therapeutic benefit from surgery or if they can be used to generate more effective postoperative analgesic regimens.

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 -

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  • 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.

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