1. Home
  2. Member Resources
  3. Articles
  4. Clinical Implementation of Pharmacogenomic Testing

Clinical Implementation of Pharmacogenomic Testing

Adverse medication reactions are a common source of morbidity and mortality in the US and throughout the world. Often these reactions are of multifactorial etiology; however, in many cases inherited genetic variation in genes that encode enzymes that metabolize medications and/or proteins that are drug targets may also contribute. Pharmacogenetics or pharmacogenomics refers to genetic testing that is performed in order to predict which patients are at risk for adverse medication reactions and/or lack of efficacy (see figure). Prescribers can use these test results to select the most appropriate medication and dose for individual patients.

The focus of my article is germline genetic variants that can be used to identify the most appropriate medication and dose for a patient. Germline genetic variants are those that can be inherited and passed down to the next generation; these genetic variants are generally present in cells throughout the body. In contrast, somatic genetic variants that arise during the process of oncogenesis are also useful to predict to which medication(s) a patient’s tumor is most like to respond or be resistant. Often, these variants are the basis for companion diagnostics used when prescribing a chemotherapeutic or targeted therapy. The testing/use of somatic variants to tailor therapy may be referred to as pharmacogenomics, but is also commonly referred to as theranostics.

Figure: When multiple individuals with the same diagnosis are prescribed the standard dose of the same medication, some individuals achieve the desired response while others will not.
Basic Concepts and Terminology

The majority of the genes of interest in pharmacogenomics are those that encode enzymes and other proteins involved in pharmacokinetic processes including drug metabolism (e.g. cytochrome P450s, thiopurine methyltransferase, etc) and drug transport (e.g. OAT1P1 encoded by SLCO1B1). Many of these genes are highly expressed in the liver, but may be expressed elsewhere in the body as well. In addition, there are genes that encode proteins that are part of pharmacodynamic pathways/drug targets as well (e.g. VKORC1, etc). A selection of commonly tested genes and how they impact medications is presented in the table.

Several recent studies have demonstrated that most individuals (>90%) harbor genetic variants that would be clinically actionable if prescribed a medication that is metabolized by that enzyme1. Considering that most Americans will ultimately take medications throughout their lifetime and 35% of Americans over approximately age 70 use 5 or more prescription medications daily2, pharmacogenomics is one aspect of personalized medicine that will likely impact most if not all patients.

Often the genes that encode enzymes involved in drug metabolism, such as the cytochrome P450s, are highly genetically polymorphic and individual patients may inherit multiple SNPs together on the same allele as a haplotype. These haplotypes are referred to as “star alleles” (i.e. *1)3. The genetic testing results are typically reported as the diplotype (the two predicted haplotypes present) and a predicted phenotype. The predicted phenotype may range from poor metabolizer (often those with homozygous deleterious variants, compound heterozygotes, or those with gene deletions) to intermediate metabolizer (typically those who are heterozygous for a deleterious variant or potentially homozygous/compound heterozygous for partial function alleles) to normal metabolizers (previously referred to as “extensive” metabolizers) to ultrarapid metabolizers (typically those with gene duplications). A similar continuum exists to discuss the encoded proteins that are not enzymes, but instead refers to poor/intermediate/normal function. To facilitate consistency in reporting, pharmacogenetics allele nomenclature guidelines were recently published4.

Next, based on the patient’s metabolizer/functional status, medication recommendations may be provided. Reports may include a few medications based on available guidelines or may include a large number of medications, for which there is more limited data on the importance of genetic variation. The Clinical Pharmacogenetics Implementation Consortium (CPIC) and the Dutch Pharmacogenetics Working Group (DPWG) are the primary organizations that have evaluated the available literature and produced guidelines for clinical use, while PharmGKB curates information and rates level of evidence, but does not publish guidelines5-7. Few other professional societies have published guidelines related to pharmacogenomics. Of note, these organizations have focused primarily on how to use test results if they are available—not on whether testing should be performed.

Testing Methodology and Implementation

Currently, most pharmacogenomic tests use targeted genotyping techniques to identify single nucleotide polymorphisms (SNPs). Therefore, only the genetic variants that are being targeted in the test will be identified. This may lead to false negative test results for patients who harbor rare variants that are not included in most currently available tests. Of particular concern, this may disproportionately affect patients who are of populations that have been less well characterized genetically because the minor allele frequency of many clinically actionable common variants differs by population8. As costs of sequencing have been decreasing in recent years, a transition in pharmacogenomics from targeted genotyping to sequencing may be on the horizon. While sequencing should decrease the rate of false negative tests due to rare variants, it brings the possibility of identifying the rare variants and the potential uncertainty of how to interpret them. In addition, there are likely additional genes and perhaps intergenic regions that may be important in drug response/toxicity, but are not yet identified and would not be included in sequencing-based tests at present. Pharmacogenomics remains an active area of research for many medications and genes to further expand on our knowledge.

Although pharmacogenomic testing has the potential to benefit patients, its use is not yet widespread. This is in part due to a lack of guidelines on when testing should be used, reimbursement challenges, and potentially a lack of familiarity with testing among health care providers9. In addition, many panel testing options are available; however, there is wide variability in which genes, variants, and medications are included in each test and reporting structures vary significantly, which also may lead to difficulties in adoption of pharmacogenomic testing4. While traditionally testing has been performed a single gene at a time at the time of medication prescription, a move toward “preemptive” testing so that the information is readily available in the electronic health record when needed is underway. Although this practice promises to reduce costs by testing many genes at once and using that information repeatedly in the future, it also brings some challenges10. For example, the data must be stored in such a way that it is readily accessible at the time that it is needed and future providers, who likely didn’t order the test, must know that it is available. The data must be portable as patients often do not live in the same location/see the same health care provider over the course of a lifetime. Currently, the variability among tests is also a barrier that makes preemptive testing more difficult; however, initiatives are underway to address the lack of standardization.

Conclusion

Despite the challenges, there is significant interest in pharmacogenomic testing among patients. Due to technological advances, the cost of testing is rapidly decreasing. With its potential to improve patient care by avoiding adverse medication reactions and facilitating selection of the most efficacious medication for an individual patient, pharmacogenomic testing may become more widespread in the near future.

Table
Gene Drug Genotypes References
CFTR Ivacaftor G551D, G1244E, G1349D, G178R, G551S, S1251N, S1255P, S549N, S549R - effective; F508del homozygotes - not effective Clin Pharmacol Ther. 2014 Jun;95(6):592-7. doi: 10.1038/clpt.2014.54. Epub 2014 Mar 5.; Genome Med. 2015 Sep 24;7:101. doi: 10.1186/s13073-015-0223-6.
CYP2C9 Phenytoin reduced activity (e.g. *2, *3) - increased risk of toxicity Clin Pharmacol Ther. 2014 Nov;96(5):542-8. doi: 10.1038/clpt.2014.159. Epub 2014 Aug 6.
Warfarin reduced activity (e.g.*2, *3) - increased risk of bleeding, lower starting dose recommended, VKORC1 genotype also of importance Clin Pharmacol Ther. 2011 Oct;90(4):625-9. doi: 10.1038/clpt.2011.185. Epub 2011 Sep 7.; J Hum Genet. 2016 Jan;61(1):79-85. doi: 10.1038/jhg.2015.78. Epub 2015 Jul 16.
CYP2C19 Tricyclic Antidepressants increased activity (e.g.*17) - increased metabolism and possibly decreased efficacy, consider different drug; decreased activity (e.g.*2-8) -reduced metabolism and increased risk of side effects Clin Pharmacol Ther. 2013 May;93(5):402-8. doi: 10.1038/clpt.2013.2. Epub 2013 Jan 16.
Clopidogrel increased activity (e.g. *17) - increased platelet inhibition; decreased activity (e.g. *2-8) -reduced platelet inhibition/risk for cardiovascular events Clin Pharmacol Ther. 2013 Sep;94(3):317-23. doi: 10.1038/clpt.2013.105. Epub 2013 May 22.; Mega J, Close S, Wiviott D, et al: Cytochrome P-450 polymorphisms and response to clopidogrel. N Engl J Med 2009;360:354-362; J Hum Genet. 2016 Jan;61(1):79-85. doi: 10.1038/jhg.2015.78. Epub 2015 Jul 16.
Selective Serotonin Reuptake Inhibitors increased activity (e.g. *17) - increased metabolism and possibly decreased efficacy, consider different drug; decreased activity (e.g. *2-8) -reduced metabolism and increased risk of side effects Clin Pharmacol Ther. 2015 Aug;98(2):127-34. doi: 10.1002/cpt.147. Epub 2015 Jun 29.
Voriconazole decreased activity variants - increased risk of adverse effects Clin Pharmacol Ther. 2011 May;89(5):662-73. doi: 10.1038/clpt.2011.34. Epub 2011 Mar 16.; Int J Antimicrob Agents. 2016 Feb;47(2):124-31. doi: 10.1016/j.ijantimicag.2015.12.003. Epub 2015 Dec 21.; Biol Blood Marrow Transplant. 2016 Mar;22(3):482-6. doi: 10.1016/j.bbmt.2015.11.011. Epub 2015 Nov 23.
CYP2D6 Tricyclic Antidepressants increased activity (e.g. *1xN, *2xN, multiple functional copies) - increased metabolism and decreased efficacy; decreased activity (e.g. *4, *5, *6) - reduced metabolism and increased side effects Clin Pharmacol Ther. 2013 May;93(5):402-8. doi: 10.1038/clpt.2013.2. Epub 2013 Jan 16.
Codeine increased activity (e.g. *1xN, *2xN, multiple functional copies) - increased formation of morphine and higher risk of toxicity; decreased activity (e.g. *4, *5, *6) - decreased morphine formation and insufficient pain relief Clin Pharmacol Ther. 2014 Apr;95(4):376-82. doi: 10.1038/clpt.2013.254. Epub 2014 Jan 23.; http://www.fda.gov/drugs/drugsafety/ucm339112.htm
Selective Serotonin Reuptake Inhibitors decreased/inactive (e.g.*3-*17, *19-21, *29, *38, *40, *42) - reduced metabolism and increased side effects Clin Pharmacol Ther. 2015 Aug;98(2):127-34. doi: 10.1002/cpt.147. Epub 2015 Jun 29.
oxycodone poor metabolizers (e.g. 2 copies of *3-8, *11-16, *19-21, *38, *40, *42) - potentially less pain relief, neutropenia, leukopenia, moderate diarrhea Br J Pharmacol. 2010 Jun;160(4):919-30. doi: 10.1111/j.1476-5381.2010.00709.x.; Clin Pharmacol Ther. 2014 Apr;95(4):376-82. doi: 10.1038/clpt.2013.254. Epub 2014 Jan 23
tramadol decreased/inactive (e.g. *17) - increased metabolism and possibly decreased efficacy, consider different drug; decreased activity (e.g. *3-*17, *19-21, *29, *38, *40, *42) - increased drug concentrations and possibly increased risk of adverse events Clin Pharmacol Ther. 2014 Apr;95(4):376-82. doi: 10.1038/clpt.2013.254. Epub 2014 Jan 23
tamoxifen decreased/inactive (e.g. *3-*17, *19-21, *29, *38, *40, *42) - increased risk of breast cancer relapse, consider aromatase inhibitor for post-menopausal women Pharmacol Res. 2016 May;107:398-406. doi: 10.1016/j.phrs.2016.03.025. Epub 2016 Apr 8.; Clin. Pharmacol. Ther., 89 (May (5)) (2011), pp. 662–673; Cell Oncol (Dordr). 2015 Feb;38(1):65-89. doi: 10.1007/s13402-014-0214-4. Epub 2015 Jan 9.
CYP3A5 Tacrolimus extensive and intermediate metabolizers (e.g. *1/*1, *1/*3, *1/*6, *1/*7) - decreased chance of achieving target concentrations, increased starting dose recommended Clin Pharmacol Ther. 2015 Jul;98(1):19-24. doi: 10.1002/cpt.113. Epub 2015 Jun 3.; Pharmacogenomics J. 2015 Feb;15(1):38-48. doi: 10.1038/tpj.2014.38. Epub 2014 Sep 9.
DPYD Fluoropyrimidines non-functional variants (e.g. *2A, *13, rs67376798 A) - increased risk for severe/fatal drug toxicity Clin Pharmacol Ther. 2013 Dec;94(6):640-5. doi: 10.1038/clpt.2013.172. Epub 2013 Aug 29.; Cell Oncol (Dordr). 2015 Feb;38(1):65-89. doi: 10.1007/s13402-014-0214-4. Epub 2015 Jan 9.
G6PD Rasburicase deficiency (male with class I, II, or III allele; female with two class I-III alleles) - increased risk of acute hemolytic anemia Clin Pharmacol Ther. 2014 Aug;96(2):169-74. doi: 10.1038/clpt.2014.97. Epub 2014 May 2.; Br J Haematol. 2014 Feb;164(4):469-80. doi: 10.1111/bjh.12665. Epub 2013 Dec 28.
PEG Interferon-Alpha Ivacaftor rs12979860 CC - increased likelihood of response in patients with HCV genotype 1 Clin Pharmacol Ther. 2014 Feb;95(2):141-6. doi: 10.1038/clpt.2013.203. Epub 2013 Oct 4.
SLCO1B1 Simvastatin reduced function/expression (e.g. *5, *15, *17) - increased risk of myopathy Clin Pharmacol Ther. 2014 Oct;96(4):423-8. doi: 10.1038/clpt.2014.125. Epub 2014 Jun 11.; J Hum Genet. 2016 Jan;61(1):79-85. doi: 10.1038/jhg.2015.78. Epub 2015 Jul 16.
TPMT Thiopurines reduced activity (e.g. *2, *3A, *3B, *3C, *4) - increased risk for myelosuppression, fatal toxicity possible Clin Pharmacol Ther. 2013 Apr;93(4):324-5. doi: 10.1038/clpt.2013.4. Epub 2013 Jan 17.; Cell Oncol (Dordr). 2015 Feb;38(1):65-89. doi: 10.1007/s13402-014-0214-4. Epub 2015 Jan 9.
UGT1A1 Atazanavir reduced activity (e.g. *28 [TA7], *37 [TA8], *6, *80) - increased likelihood of bilirubin-related discontinuation of atazanavir Clin Pharmacol Ther. 2016 Apr;99(4):363-9. doi: 10.1002/cpt.269. Epub 2015 Nov 9.
Irinotecan reduced activity (e.g. *28 [TA7], *37 [TA8], *6, *80) - increased risk for neutropenia Int J Clin Oncol. 2015 Dec 28. [Epub ahead of print]; Acta Oncol. 2016;55(3):318-28. doi: 10.3109/0284186X.2015.1053983. Epub 2015 Jun 22.; Fundam Clin Pharmacol. 2015 Jun;29(3):219-37. doi: 10.1111/fcp.12117. Epub 2015 May 4.
VKORC1 Warfarin rs9923231 (-1639G>A) - A allele associated with reduced VKORC1 (target of warfarin), reduced dose of warfarin necessary Clin Pharmacol Ther. 2011 Oct;90(4):625-9. doi: 10.1038/clpt.2011.185. Epub 2011 Sep 7.; J Hum Genet. 2016 Jan;61(1):79-85. doi: 10.1038/jhg.2015.78. Epub 2015 Jul 16.
HLA-B Abacavir *57:01 - increased risk of hypersensitivity Clin Pharmacol Ther. 2014 May;95(5):499-500. doi: 10.1038/clpt.2014.38. Epub 2014 Feb 21.; J Immunol Res. 2014;2014:565320. doi: 10.1155/2014/565320. Epub 2014 May 8.
Allopurinol *58:01 - increased risk of severe cutaneous adverse reactions Clin Pharmacol Ther. 2016 Jan;99(1):36-7. doi: 10.1002/cpt.161. Epub 2015 Jul 16.; J Immunol Res. 2014;2014:565320. doi: 10.1155/2014/565320. Epub 2014 May 8.
Carbamazepine *15:02 - increased risk of Stevens-Johnson Syndrome/Toxic Epidermal Necrolysis Clin Pharmacol Ther. 2013 Sep;94(3):324-8. doi: 10.1038/clpt.2013.103. Epub 2013 May 21.; J Immunol Res. 2014;2014:565320. doi: 10.1155/2014/565320. Epub 2014 May 8.
Phenytoin *15:02 - increased risk of Stevens-Johnson Syndrome/Toxic Epidermal Necrolysis Clin Pharmacol Ther. 2014 Nov;96(5):542-8. doi: 10.1038/clpt.2014.159. Epub 2014 Aug 6.; J Immunol Res. 2014;2014:565320. doi: 10.1155/2014/565320. Epub 2014 May 8.

References
  1. Ji Y, Skierka JM, Blommel JH, Moore BE, VanCuyk DL, Bruflat JK et al. Preemptive Pharmacogenomic Testing for Precision Medicine: A Comprehensive Analysis of Five Actionable Pharmacogenomic Genes Using Next-Generation DNA Sequencing and a Customized CYP2D6 Genotyping Cascade. J Mol Diagn. 2016;18(3):438-45.
  2. Qato DM, Wilder J, Schumm LP, Gillet V, Alexander GC. Changes in Prescription and Over-the-Counter Medication and Dietary Supplement Use Among Older Adults in the United States, 2005 vs 2011. JAMA Intern Med. 2016;176(4):473-82.
  3. Pharmacogene Variation Consortium (PharmVar). www.pharmvar.org.
  4. Kalman LV, Agundez J, Appell ML, Black JL, Bell GC, Boukouvala S et al. Pharmacogenetic allele nomenclature: International workgroup recommendations for test result reporting. Clin Pharmacol Ther. 2016;99(2):172-85.
  5. The Pharmacogenomics Knowledge Base (PharmGKB). www.pharmgkb.org.
  6. Clinical Pharmacogenetics Implementation Consortium (CPIC). www.cpicpgx.org.
  7. Swen JJ, Nijenhuis M, de Boer A, Grandia L, Maitland-van der Zee AH, Mulder H et al. Pharmacogenetics: From Bench to Byte— An Update of Guidelines. Clin Pharmacol Ther. 2011;89(5):662-73.
  8. Zhou Y, Ingelman-Sundberg M, Lauschke VM. Worldwide Distribution of Cytochrome P450 Alleles: A Meta-analysis of Population-scale Sequencing Projects. Clin Pharmacol Ther. 2017;102(4):688-700.
  9. Johnson JA. Pharmacogenetics in clinical practice: how far have we come and where are we going? Pharmacogenomics. 2013;14(7):835-43.
  10. Moyer AM, Caraballo PJ. The challenges of implementing pharmacogenomic testing in the clinic. Expert Rev Pharmacoecon Outcomes Res. 2017;17(6):567-77.

Ann M. Moyer, MD, PhD, FCAP, is a co-director of the Personalized Genomics Laboratory at Mayo Clinic in Rochester, Minnesota. Her clinical and research interests include pharmacogenomics and the genetic basis of primary immunodeficiencies.

Most Recent Articles

  1. Set Up for Success: Workspace Décor and Organization
  2. 5 Lessons for Avoiding Burnout and Finding Fulfillment as a Pathologist
  3. How a Fresh Take on Patient Safety Can Empower Pathologists
  4. Being the New-in-Practice Kid on the Hospital Block
  5. Setting the New-in-Practice Pathologist Up For Success: A Primer for the First Year on the Job
  6. View All