Pharmacists Play Role in Pharmacogenomics Clinical Services
They should consider many factors and use clear guidance on predicting phenotypes from genotypes to intervene optimally based on specific drug-gene interactions.
More than 90% of patients harbor DNA variants that could affect their response to a medication.1
Each individual has a unique genetic makeup that affects drug absorption, efficacy, metabolism, and response. Pharmacogenomics (PGx) addresses how an individual’s DNA influences the response to a medication. With recent advances in health sciences, we can predict a patient’s phenotype, such as metabolic activity, from the genotype, such as genetic variants, to prevent serious adverse effects (AEs) and therapeutic failure or to optimize treatment efficacy.
Determining the presence of a certain genotype, such as thiopurine methyltransferase for patients treated with thiopurine drugs2 or HLA-B*57:01 for patients receiving abacavir,3 can help prevent potentially fatal adverse reactions. Furthermore, because enzymes are critical in the metabolism of many drugs, understanding the enzyme phenotypes is crucial to predicting adverse drug events (ADEs) or therapeutic outcomes. Many drug-metabolizing enzymes and drug transporters are subject to genetic polymorphisms.
Specifically, Cytochrome P450 (CYP) enzymes, such as CYP2C9, CYP2C19, and CYP2D6, are highly polymorphic and are associated with distinct phenotypes ranging from highly increased activity to no activity. This translates to individuals having intermediate, normal, rapid, poor, or ultrarapid metabolism of drugs.
A poor metabolizer of an enzyme that inactivates a drug may be at higher risk of AEs with standard dosing, because of supratherapeutic active drug concentration in the serum. By contrast, a poor metabolizer of an enzyme responsible for activating a drug may not get the therapeutic benefit of the drug, because of the subtherapeutic concentration of the active metabolites, whereas an ultrarapid metabolizer of the same enzyme may be at increased risk of AEs of the active compound (see the clinical example below).
Thus, pharmacists need to consider many factors and use clear guidance on predicting a phenotype from a genotype to intervene optimally based on the specific drug-gene interaction.
Predicting a phenotype from a genotype is becoming increasingly challenging, considering the ever-growing knowledge in PGx. There are PGx guidelines available from various consortia and regulatory agencies, including the FDA, to help translate the phenotype from genotype to guide intervention. The most well-accepted guidelines in the United States are provided by the Clinical Pharmacogenetic Implementation Consortium (CPIC).4
The CPIC evaluates evidence and provides specific recommendations regarding whether to alter dosing or avoid a drug. Pharmacists also need guidance on which PGx test to use based on the genes and allele coverage provided in each test.5 The Association of Molecular Pathology provides such guidance on allele selection for individual gene testing.6,7
As medication experts with adequate PGx training, pharmacists are best suited to identify patients at risk and use appropriate PGx tests to evaluate patients’ medications across multiple specialties, including cardiology, mental health, pain management, and oncology, to ensure patient safety and optimize care.
Clinical Case of Pharmacogenomics
MG, a 42-year-old Caucasian woman with recurrent major depressive disorder, takes a maximum dose of escitalopram 20 mg/day without benefit. She was recently switched from paroxetine (a CYP2D6 substrate and mechanism-based inhibitor) to escitalopram (a CYP2C19 substrate), because of a potential drug interaction with her metoprolol (a CYP2D6 substrate).
For the past 15 years, her dose of paroxetine was maintained at a low dose of 10 mg/day, with good efficacy for her major depression. Her PGx test results show that she is a poor metabolizer of CYP2D6 and an ultrarapid metabolizer of CYP2C19.
MG’s PGx test results explain why she is not responding to the maximum dose of escitalopram. MG is expected to have subtherapeutic escitalopram concentration as a CYP2C19 ultrarapid metabolizer. On the other hand, paroxetine at a low daily dose of 10 mg worked well for MG, because the paroxetine concentration was elevated, because she is a CYP2D6 poor metabolizer.
Escitalopram should be avoided, and paroxetine should be reduced to 50% of the standard dose to avoid side effects for MG, according to CPIC recommendations.8
Other treatments to adjust or avoid include citalopram, fluvoxamine, sertraline, and venlafaxine. In this case, 10 mg of paroxetine once a day with continued monitoring may be appropriate for MG. Bradycardia related to metoprolol should be monitored, and a lower dose of metoprolol should be considered, because the metabolism of metoprolol is impaired in this patient (CYP2D6 poor metabolizer), in addition to paroxetine being a CYP2D6 mechanism-based inhibitor.
The translation of a genotype into a phenotype for a drug considering inter-subject variability in drug pharmacokinetics or pharmacodynamics is complex.9-13 The complexity increases further when several genes are involved in the pharmacology of a drug or when multiple drugs affect the same enzyme, such as competing substrates, inducers, or inhibitor effects, as in MG’s case.
Coadministration of drugs that modulate drug-metabolizing enzyme activities, whether competing substrates, inducers, inhibitors, or mechanism-based inhibitors, can lead to a phenomenon known as phenoconversion.14-17
Phenoconversion occurs when a patient’s observed phenotype differs from the genotype-predicted phenotype. The mismatch between the genotype-based prediction of CYP drug metabolism and the real-time capacity of an individual to metabolize drugs (ie, a phenotype) can be because of nongenetic factors, such as concomitant medications, disease, the environment, and food. The effect of phenoconversion may also differ across age, genotype, race, and sex.15-17
Consequently, interpreting PGx results in patients with multiple chronic diseases and polypharmacy requires the intervention of drug experts. This is where pharmacists’ unique training can provide expert clinical guidance.
Pharmacists as Medication experts
Undoubtedly, PGx offers an excellent opportunity for personalized medicine. However, no test, including the PGx tests, should be ordered unless the ordering authority is able to interpret the final clinical results, develop a meaningful diagnosis, intervention, or prognosis, and implement an appropriate action plan with the results of such test.18,19
We think that the time has come for pharmacists to be recognized as the experts and owners for ordering and interpreting PGx test results.
On February 28, 2022, a federal bipartisan legislation called the Right Drug Dose Now Act was introduced in Congress to address drug-gene interactions and enable the use of PGx testing to ensure that patients receive the most appropriate prescription medications based on their genetic makeups.
The bill will also create an educational campaign to prevent ADEs, improve electronic health record systems to ensure that health care providers are alerted to drug-gene interactions, update the US Department of Health and Human Services’ National Action Plan for Adverse Drug Events Prevention. Furthermore, the bill will allocate additional funding to the Genomic Community Resources program at the National Institutes of Health to integrate PGx testing into patient care.20
Similar legislation was introduced in the California senate on February 12, 2022, which amended Senate Bill 1191, known as the Utilizing Pharmacogenomics to Greatly Reduce Adverse Drug Events Act, to include PGx testing as a covered benefit under Medi-Cal insurance. The bill defines PGx testing as genetic panel testing by a laboratory with specified accreditation and licensing to identify how an individual’s genetics may affect the efficacy, safety, and toxicity of medications.
The bill would cover the benefit of a medication under Medi-Cal if it is approved or considered for use in treating the beneficiary’s condition, known to have a drug-gene or drug-drug-gene interaction that is shown to be clinically actionable and ordered by an enrolled Medi-Cal clinician or pharmacist within their scope of practice.21
Pharmacists are best equipped to implement PGx and support health care decision-making to help patients benefit from personalized medicine. By interpreting PGx test results, pharmacists could recognize the risk of ADEs or treatment failure and provide alternatives based on patients’ genetics. Legislation is also increasingly supporting PGx implementation by allowing pharmacists to take the leadership role in PGx implementation and reimbursing for their services in this field.
1. Ji Y, Skierka JM, Blommel JH, 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):438-445. doi:10.1016/j.jmoldx.2016.01.003
2. Relling MV, Gardner EE, Sandborn WJ, et al. Clinical pharmacogenetics implementation consortium guidelines for thiopurine methyltransferase genotype and thiopurine dosing. Clin Pharmacol Ther. 2011;89(3):387-391. doi:10.1038/clpt.2013.4
3. Lam JT. Infectious Diseases Pharmacogenomics. In: Lam JT GMA, Shah S, eds. Pharmacogenomics: A Primer for Clinicians. 1st ed. McGraw Hill. 2021:81-90.
4. Relling MV, Klein TE. CPIC: clinical pharmacogenetics implementation consortium of the pharmacogenomics research network. Clin Pharmacol Ther. 2011;89(3):464-467. doi:10.1038/clpt.2010.279
5. Sayer M, Duche A, Nguyen TJT, et al. Clinical implications of combinatorial pharmacogenomic tests based on cytochrome P450 variant selection. Frontiers in Genetics. 2021;12(1628). doi:10.3389/fgene.2021.719671
6. Pratt VM, Cavallari LH, Del Tredici AL, et al. Recommendations for Clinical CYP2C9 Genotyping Allele Selection: A Joint Recommendation of the Association for Molecular Pathology and College of American Pathologists. J Mol Diagn. 2019;21(5):746-755.
7. Pratt VM, Del Tredici AL, Hachad H, et al. Recommendations for clinical CYP2C19 genotyping allele selection: a report of the Association for Molecular Pathology. J Mol Diagn. 2018;20(3):269-276. doi:10.1016/j.jmoldx.2018.01.011
8. Hamann K, Daut, RA, Gutierrez MA. Psychiatry and Neurology Pharmacogenomics. In: Lam JT GMA, Shah S , eds. Pharmacogenomics: A Primer for Clinicians. 1st ed. McGraw Hill. 2021:115-145.
9. DPWG: Dutch Pharmacogenetics Working Group DPW. PharmGKB. Accessed April 23, 2022.http://www.pharmgkb.org/page/dpwg
10. Gaedigk A, Simon SD, Pearce RE, Bradford LD, Kennedy MJ, Leeder JS. The CYP2D6 activity score: translating genotype information into a qualitative measure of phenotype. Clin Pharmacol Ther. 2008;83(2):234-242. doi:10.1038/sj.clpt.6100406
11. Kirchheiner J. CYP2D6 phenotype prediction from genotype: which system is the best? Clin Pharmacol Ther. 2008;83(2):225-227. doi:10.1038/sj.clpt.6100455
12. Hicks JK, Swen JJ, Gaedigk A. Challenges in CYP2D6 phenotype assignment from genotype data: a critical assessment and call for standardization. Curr Drug Metab. 2014;15(2):218-232. doi:10.2174/1389200215666140202215316
13. Cicali EJ, Elchynski AL, Cook KJ, et al. How to integrate CYP2D6 phenoconversion into clinical pharmacogenetics: a tutorial. Clin Pharmacol Ther. 2021;110(3):677-687. doi:10.1002/cpt.2354
14. Deodhar M, Al Rihani SB, Arwood MJ, et al. Mechanisms of CYP450 inhibition: understanding drug-drug interactions due to mechanism-based inhibition in clinical practice. Pharmaceutics. 2020;12(9). doi:10.3390/pharmaceutics12090846
15. Del Toro-Pagan NM, Matos A, Thacker D, Turgeon J, Amin NS, Michaud V. Pharmacist-led medication evaluation considering pharmacogenomics and drug-induced phenoconversion in the treatment of multiple comorbidities: a case report. Medicina (Kaunas). 2021;57(9). doi:10.3390/medicina57090955
16. Pizzolato K, Thacker D, Del Toro-Pagán N, et al. Integrating pharmacogenomic results and drug-induced phenoconversion concepts while conducting a medication safety review process. Ann Case Rep. 2022;7:817. doi:10.29011/2574-7754.100817
17. Shah RR, Smith RL. Addressing phenoconversion: the Achilles' heel of personalized medicine. Br J Clin Pharmacol. 2015;79(2):222-240. doi:10.1111/bcp.12441
18. Sikaris KA. Enhancing the clinical value of medical laboratory testing. Clin Biochem Rev. 2017;38(3):107-114.
19. Lee DW, Neumann PJ, Rizzo JA. Understanding the medical and nonmedical value of diagnostic testing. Value Health. 2010;13(2):310-314. doi:10.1111/j.1524-4733.2009.00597.x
20. Emmer, Swalwell introduce legislation to prevent adverse drug effects. Tom Emmer. News release. February 28, 2022. Accessed April 26, 2022. https://emmer.house.gov/2022/2/emmer-swalwell-introduce-legislation-to-prevent-adverse-drug-effects
21. Medi-Cal: pharmacogenomic testing. In: Bill CS, ed. SB 11912022. Accessed April 26, 2022. https://leginfo.legislature.ca.gov/faces/billTextClient.xhtml?bill_id=202120220SB1191