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Pharmacist Continuing Education - Pharmacogenomics in Pain Management

17 Jul 2018 12:35 PM | Anonymous

Pharmacogenomics in Pain Management

Author: Lance Schneider, PharmD, PGY2 Internal Medicine Pharmacy Resident, University of Missouri Health Care, Columbia, MO

Preceptor: Ryan Camden, PharmD, BCPS, PGY2 Internal Medicine Residency Program Director, University of Missouri Health Care, Columbia, MO

Program Number: 2018-07-16

Approval Dates: August 1, 2018 - November 1, 2018

Approved Contact Hours: One (1) CE(s) per LIVE session.

Learning Objectives:

1. Describe what pharmacogenomics is and how it relates to medication therapy management.

2. Review the Centers for Disease Control and Prevention guideline for prescribing opioids for chronic pain.

3. Discuss current pharmacogenomic dosing guidelines.

4. Evaluate current literature regarding utilization of pharmacogenomics for opioid prescribing.


Conventional clinical use of drug therapy is based primarily on a ‘one size fits all’ model, meaning medications are utilized and prescribed based off of population outcomes from clinical trials. Pharmacogenomics is the study of how personal genetic traits affect an individual’s response to drug therapy. Clinically, this is the direction medicine is heading and can have a huge impact on patient care. By testing a patient’s DNA for certain genetic variations and combining that information with available medication databases clinicians can personalize a medication regimen to maximize efficacy and safety. For example, approximately 20-40% of the population will have a suboptimal response to clopidogrel due to variations in the CYP2C19 gene.1 Knowing a patient has this variation prior to medication initiation would allow physicians to start an alternative and more efficacious antiplatelet agent. Currently, utilization of pharmacogenomics for patient specific medication regimens is not widespread. Antimicrobials, chemotherapy, and psychotropics are the drug classes with the most pharmacogenomic information available. Opioids have not been in the forefront of pharmacogenomic testing, however, with the recent Centers for Disease Control and Prevention (CDC) campaign to help control the prescription opioid epidemic pharmacogenomics could play an important role in pain management.

CDC Guideline for Prescribing Opioids for Chronic Pain

In response to the quintupling of opioid prescriptions in the U.S. from 1999 to 2016 the CDC has developed a campaign to help combat misuse and overprescribing of opioids. Not coincidentally, prescription opioid overdose deaths during this time-frame similarly increased without an overall change in the amount of pain reported.2 More than 40% of all U.S. opioid overdose deaths in 2016 involved a prescription opioid with more than 46 people dying every day from overdoses involving prescription opioids.3 The CDC sought out to improve opioid prescribing safety and efficacy through the development of clinical practice guidelines. These guidelines, which can be found online at https://www.cdc.gov/drugoverdose/pdf/Guidelines_Factsheet-a.pdf, provide twelve recommendations for health-care providers. These recommendations are broken into three separate sections: determining when to initiate or continue opioids for chronic pain, opioid selection, dosage, duration, follow-up, and discontinuation, and assessing risk and addressing harms of opioid use.4 As pharmacogenomics is still a relatively new field with a limited amount of data, especially regarding opioids, the CDC has yet to implement any recommendations regarding pharmacogenetic testing for opioid utilization.

What is Pharmacogenomics?

Pharmacogenomics is the study of how a person’s genetic makeup (-genomics) can affect their response to a drug (pharmaco-), leading to variations in efficacy and adverse drug effects.5 The idea of pharmacogenomics is far from new, with documentation of landmark discoveries dated from at least the 1950s. However, the rate of new discoveries has significantly increased since the completion and publication of the Human Genome Project (HGP) in 2003.6 The HGP was a collaborative research program with the goal to complete the mapping and understanding of all the genes of human beings. It has truly carved a new path into the future of medicine, giving insights that will help to treat, cure and prevent diseases.7

There are approximately 20,500 genes that make up the human genome. Everyone has two copies of each gene, one inherited from each parent, which are the codes that direct cells how to make proteins. Variations in this genetic code through various mechanisms such as single nucleotide polymorphisms (SNPs), copy number variations (CNVs), insertions, or deletions can lead to differences in response to a medication. Alterations in safety and efficacy arise when these genetic variations occur in genes that code for proteins that effect the pharmacokinetics of medications – absorption, distribution, metabolism, and excretion.1

Pharmacogenomic Dosing Guidelines

The lack of clinical prescribing information regarding pharmacogenomics is a primary contributor to the hesitancy of utilization by health-care providers. For example, the U.S. Food and Drug Administration (FDA) has put out a reference of pharmacogenomic biomarkers in drug labeling with over 100 different medications, but only some of them have actionable recommendations based on the biomarker information. One of the best, and most complete references is PharmGKB. PharmGKB is a National Institute of Health (NIH) funded resource that provides information about how human genetic variation affects response to medications. It incorporates multiple dosing guidelines from various professional societies including the Clinical Pharmacogenetics Implementation Consortium (CPIC), the Royal Dutch Association for the Advancement of Pharmacy – Pharmacogenetics Working Group (DPWG), and the Canadian Pharmacogenomics Network for Drug Safety (CPNDS). Drug label information and primary literature are also included to provide genotype-specific dosing recommendations. 1

Opioids with Pharmacogenomic Information

Pharmacogenomic information is scarce regarding opioids and most data come from case control studies and case reports. Codeine and tramadol are the only two opioid medications with pharmacogenomic biomarkers in the drug labeling. The pharmacogenomic information on these medications are in relation to CYP2D6.8 CPY2D6 is an enzyme within the cytochrome P450 (CYPs) superfamily of microsomal drug-metabolizing enzymes. CYPs possess many physiological functions including synthesis of steroid hormones, cholesterol and other fatty acids, and bile acids, and metabolism of exogenous and endogenous substances including drugs and toxins.9 There have been 57 cytochrome P450 genes identified in humans with a small number appearing to contribute to the metabolism of drugs, mainly CYP1, CYP2, and CYP3 families.10 Each CYP gene is given a number associated with a specific group within the gene family, a letter representing the gene’s subfamily, and a number assigned to the specific gene within the subfamily. Therefore, CYP2D6 is the cytochrome P450 gene in group 2, subfamily D, and gene 6.9

Codeine is metabolized by CYP2D6 into morphine, a much more potent opioid. An individual carrying a normal CYP2D6 genotype, also known as an extensive metabolizer (77-92% of patients), will have normal morphine formation and thus the label recommendations for dosing may be followed. Genetic polymorphisms of the CYP2D6 gene result in clinically significant phenotypes: ultra-rapid metabolizer (1-2% of patients), intermediate metabolizer (2-11% of patients), and poor metabolizer (5-10% of patients). Poor metabolizers will lack efficacy while ultra-rapid metabolizers are at a higher risk of toxicity due to the increased formation of morphine following codeine administration. The FDA Label has been updated to include the safety concerns related to ultra-rapid metabolizers; respiratory depression, extreme sleepiness, and confusion. Additionally, death has occurred in children who received codeine following tonsillectomy and/or adenoidectomy and had evidence of being ultra-rapid metabolizers of codeine due to a CYP2D6 polymorphism. Lastly, ultra-rapid metabolizing women who are breastfeeding will have higher than expected serum morphine levels leading to potentially high levels within the breastmilk. For these reasons, codeine is not recommended in ultra-rapid metabolizers. 1,8

Tramadol, similar to codeine, is metabolized by CYP2D6 into a more potent active metabolite, O-desmethyltramadol (M1). Tramadol has the same considerations as codeine in regards to the different genotypes. In addition to ultra-rapid metabolizers, tramadol has FDA labeling information regarding poor metabolizers. A phase 1 pharmacokinetic study in healthy subject poor metabolizers revealed an approximately 20% higher serum concentration of tramadol with a 40% lower serum concentration of M1. An important consideration for both tramadol and codeine is drug-drug interactions with CYP2D6 inhibitors such as fluoxetine. With the known alterations from varying genotypes safety and efficacy of these opioids could be affected greatly with the concomitant use of CYP2D6 inhibitors, even in extensive metabolizers. 1,8

Opioid Pharmacogenomics in Cancer Patients

Opioid analgesics are widely used for the treatment of chronic pain in patients with cancer. Efficacy and safety of these opioids vary widely between patients, and despite their utilization a significant number of patients still experience moderate to severe pain.11 These factors contribute to cancer patients being an optimal population for opioid pharmacogenomic research.

Andreassen et al. identified CYP2D6 polymorphisms in patients being treated with oxycodone for cancer pain to evaluate an association between observed pharmacokinetic alterations and the pharmacodynamic response. CYP2D6 is responsible for metabolizing approximately 11% of the parent compound oxycodone into the more potent oxymorphone.12 The study included 27 poor metabolizers (PM), 413 extensive metabolizers (EM), and 10 ultra-rapid metabolizers (URM). PM patients had a statistically significant lower serum concentration oxymorphone to oxycodone ratio than EM and URM (0.0028, 0.0172, and 0.244; p = <0.05). This pharmacokinetic difference did not correlate with pharmacodynamic outcomes. The median pain intensity was 4 on the numerical rating scale for PM and URM, and 3 for EM with a non-significant difference between groups (p = 0.8). Differences in pain intensity (p = 0.7), nausea (p = 0.6), and cognitive function (p = 0.8) were also non-significant.13

In 2015, Bell et al. performed a clinical review of current pharmacogenomic studies in patients with cancer pain. This review focused on the four most studied pharmacogenomic markers in opioid therapy. Adenosine triphosphate-binding cassette, sub-family B, member 1 (ABCB1), also known as P-glycoprotein, is a transporter that facilitates absorption, distribution, and elimination of opioids within the body including transport across the blood-brain barrier. CYPs, as previously described, are responsible for the metabolism of medications. Specifically, CYP3A4 plays an important role in methadone, oxycodone, hydrocodone, and fentanyl, while CYP2D6 influences codeine, hydrocodone, oxycodone, and tramadol. Another enzyme involved in metabolism is the catechol-O-methyltransferase (COMT) enzyme responsible for metabolizing catecholamines, which play an integral role in pain modulation. Lastly, the µ-opioid receptor gene (OPRM1) is the primary binding site for endogenous opioid peptides and opioid analgesics, thus making it a good target to evaluate for genetic variations and opioid response. The summary of select genetic variants within these targets associated with response in cancer pain have varying outcomes (Table12). Several studies included in the review indicated certain polymorphisms, such as OPRM1 A118G, require statistically different opioid dosages, while other studies looking at the same polymorphism demonstrated no difference. This lack of reproducibility is one of the major flaws associated with opioid genetic research. 12

In the largest study to date in this population, Klepstad et al. set out to validate previously tested SNPs associated with cancer pain and opioid efficacy, such as OPRM1 A118G. This was a 17 center study in 11 European countries in adult patients who were using an opioid for moderate to severe pain (step III at the World Health Organization [WHO] treatment ladder for cancer pain). Of the 2201 patients included in analysis, the primary opioids were morphine (n = 827), oxycodone (n = 445), fentanyl (n = 695), or other opioids (n = 234). The median opioid dose per patient was 180 mg morphine equivalence/24 hours. One-hundred and twelve SNPs from 25 genes were tested with none showing a statistically significant association with opioid dose requirements. 11


Utilization of pharmacogenomics has grown substantially in recent years and will continue to develop as additional information becomes available in the near future. Improvement in clinical outcomes utilizing patient specific medication therapies through pharmacogenomics is promising and is already being observed in several fields such as infectious diseases, oncology, and psychiatry. Utilization of pharmacogenetic testing for opioid prescribing in pain management is not routinely recommended due to limited evidence demonstrating disparate results. Further research is needed to elucidate the clinical relevance and cost-effectiveness of pharmacogenetic testing for opioid therapy.

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1. M. Whirl-Carrillo, E.M. McDonagh, J. M. Hebert, L. Gong, K. Sangkuhl, C.F. Thorn, R.B. Altman and T.E. Klein. "Pharmacogenomics Knowledge for Personalized Medicine" Clinical Pharmacology & Therapeutics (2012) 92(4): 414-417.

2. Centers for Disease Control and Prevention. Opioid Overdose: Prescribing Data. https://www.cdc.gov/drugoverdose/data/prescribing.html. Accessed May 22, 2018.

3. Centers for Disease Control and Prevention. Opioid Overdose: Prescription Opioid Overdose Data. https://www.cdc.gov/drugoverdose/data/overdose.html. Accessed May 22, 2018.

4. Centers for Disease Control and Prevention. Opioid Overdose: CDC Guideline for Prescribing Opioids for Chronic Pain. https://www.cdc.gov/drugoverdose/prescribing/guideline.html. Accessed May 22, 2018.

5. U.S. Food and Drug Administration. Pharmacogenomics: Overview of the Genomics and Targeted Therapy Group. https://www.fda.gov/Drugs/ScienceResearch/ucm572617.htm. Accessed May 23, 2018.

6. Felcone LH. Pharmacogenomics: Where Will It Take Us? Biotechnology Healthcare. 2004;1(3):18-28.

7. An Overview of the Human Genome Project. National Human Genome Research Institute (NHGRI). https://www.genome.gov/12011238/an-overview-of-the-human-genome-project/. Accessed May 23, 2018.

8. Center for Drug Evaluation and Research. Science & Research (Drugs) - Table of Pharmacogenomic Biomarkers in Drug Labeling. U S Food and Drug Administration Home Page. https://www.fda.gov/Drugs/ScienceResearch/ucm572698.htm. Accessed May 25, 2018.

9. What is pharmacogenomics? - Genetics Home Reference. U.S. National Library of Medicine. https://ghr.nlm.nih.gov/primer/genomicresearch/pharmacogenomics. Accessed May 25, 2018.

10. Wilkinson GR. Drug metabolism and variability among patients in drug response. N Engl J Med. 2005;352(21):2211–21.

11. Klepstad P, Fladvad T, Skorpen F, et al. Influence from genetic variability on opioid use for cancer pain: A European genetic association study of 2294 cancer pain patients. Pain. 2011;152(5):1139-1145. doi:10.1016/j.pain.2011.01.040.

12. Bell GC, Donovan KA, Mcleod HL. Clinical Implications of Opioid Pharmacogenomics in Patients with Cancer. Cancer Control. 2015;22(4):426-432. doi:10.1177/107327481502200408.

13. Andreassen TN, Eftedal I, Klepstad P, et al. Do CYP2D6 genotypes reflect oxycodone requirements for cancer patients treated for cancer pain? A cross-sectional multicentre study. European Journal of Clinical Pharmacology. 2012;68(1):55-64. doi:10.1007/s00228-011-1093-5.

Appendix 1:

Bell GC, Donovan KA, Mcleod HL. Clinical Implications of Opioid Pharmacogenomics in Patients with Cancer. Cancer Control. 2015;22(4):426-432. doi:10.1177/107327481502200408.

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