FOUNDATIONS OF PHARMACOLOGY

Pharmacogenomics

Core concept: Pharmacogenomics examines how genetic variation influences medication response by connecting gene expression with drug efficacy or toxicity (Belle & Singh, 2008; Woo & Robinson, 2019).

Key clinical distinction: Genetic variation can change pharmacokinetics by altering drug metabolism or transport. It can also change pharmacodynamics by altering receptors, signaling pathways, or susceptibility to adverse effects (Belle & Singh, 2008; Woo & Robinson, 2019).

Prescribing priority: Interpret genetic information with the patient’s medication list, clinical characteristics, environmental exposures, and therapeutic response. Genetic variation is one contributor to medication response, not a complete prediction of effectiveness or toxicity (Belle & Singh, 2008).

Pharmacogenetics and Pharmacogenomics

  • Pharmacogenetics examines how variation in an individual gene affects drug response. Pharmacogenomics applies the concept more broadly across genetic variation and gene expression (Belle & Singh, 2008; Woo & Robinson, 2019).
  • A genetic polymorphism is a variation in the allele or alleles responsible for a difference in drug metabolism or response (Belle & Singh, 2008).
  • Single-nucleotide polymorphisms are substitutions of one nucleotide for another and account for much of the genetic variation discussed in pharmacogenomics (Mercier & Issa, 2022).
  • A genotype identifies the alleles detected at a gene. A diplotype is the pair of haplotypes, often reported as two star alleles, used to represent the inherited allele combination for a pharmacogene. A phenotype is the expected functional category inferred from that diplotype (Caudle et al., 2017).
  • Frequencies of genetic variants differ among populations, but race and ethnicity do not directly identify a patient’s genotype. Genetic and clinical information provide a stronger basis for individualization than broad population categories alone (Belle & Singh, 2008).

How Genetic Variation Changes Drug Response

  • Genetic variation in a metabolic enzyme can change the speed at which a drug is metabolized. Slow metabolism may increase exposure to an active drug, while rapid metabolism may reduce exposure and therapeutic effect (Belle & Singh, 2008).
  • The expected result reverses for some prodrugs. A person with reduced enzyme activity may convert less prodrug to its active form and have an inadequate response (Belle & Singh, 2008).
  • Genetic differences in drug targets can alter pharmacodynamic response even when drug concentration is unchanged (Belle & Singh, 2008).
  • P-glycoprotein is a membrane-bound efflux transporter that affects drug movement across cell membranes. Along the gastrointestinal tract, it can limit absorption and bioavailability. Some substances affect both P-glycoprotein and CYP450 activity (Woo & Robinson, 2019).

Metabolizer Phenotypes

  • Poor metabolizers have absent or markedly reduced function for the specified enzyme based on the functional effect of their diplotype. The exact allele combination varies by gene (Caudle et al., 2017).
  • Intermediate metabolizers have reduced function relative to normal metabolizers. This category cannot be inferred simply by counting one normal and one variant allele because allele function and scoring systems differ by gene (Caudle et al., 2017).
  • Normal metabolizers have the activity expected for the reference functional category. Older literature uses the term extensive metabolizer; current CPIC terminology uses normal metabolizer (Caudle et al., 2017).
  • Rapid and ultrarapid metabolizers have function greater than the normal category when those terms are defined for the gene. Increased function may reflect increased-function alleles, gene duplication, or another gene-specific diplotype pattern (Caudle et al., 2017).
  • Phenotype categories and their clinical meaning are gene specific. A patient can have different phenotypes across different CYP pathways, and not every gene uses every category (Caudle et al., 2017).

Genetic and Nongenetic Influences

  • Genes provide the coding for CYP450 enzymes, but medications, sex-related physiology, environmental factors, diet, alcohol use, disease, and other exposures can also alter drug response (Belle & Singh, 2008).
  • CYP450 substrates, inhibitors, and inducers must be reviewed together. An inhibitor generally decreases metabolism of an active substrate and increases its effect, while an inducer generally increases metabolism and decreases its effect (Peterson & Randazzo, 2022; Woo & Robinson, 2019).
  • Current enzyme activity therefore reflects both inherited variation and the patient’s present clinical and medication context.

Medication Examples

  • Codeine and CYP2D6: CYP2D6 converts codeine to morphine. Poor metabolizers may have reduced conversion and inadequate analgesia. Ultrarapid metabolizers may convert codeine to morphine quickly and experience increased opioid effects or toxicity (Belle & Singh, 2008; Woo & Robinson, 2019).
  • Warfarin, CYP2C9, and VKORC1: CYP2C9 variation can reduce S-warfarin clearance and increase bleeding risk, while VKORC1 variation contributes to differences in warfarin response and dose requirements (Belle & Singh, 2008; Woo & Robinson, 2019).
  • Clopidogrel and CYP2C19: Clopidogrel is a prodrug that requires CYP2C19-mediated activation. CYP2C19 intermediate and poor metabolizers form less active metabolite and have reduced platelet inhibition. For acute coronary syndrome or percutaneous coronary intervention, current CPIC guidance recommends considering an alternative P2Y12 inhibitor when clinically appropriate and not contraindicated. Normal, rapid, and ultrarapid metabolizers generally receive standard label-directed dosing (Lee et al., 2022).
  • Carbamazepine and HLA-B*15:02: HLA-B*15:02 is associated with carbamazepine-induced Stevens-Johnson syndrome and toxic epidermal necrolysis. Before initial therapy, testing is particularly relevant for patients from populations in which the allele is more prevalent. Ancestry can inform testing decisions but does not establish genotype; if HLA-B*15:02 is present, carbamazepine should generally be avoided in a treatment-naive patient unless the benefit clearly outweighs the risk (Phillips et al., 2018).

Applying Pharmacogenomic Information

  • Identify whether the genetic difference affects an active drug, a prodrug, a transporter, or a pharmacodynamic target.
  • Determine whether the predicted effect is reduced efficacy, increased toxicity, altered dose requirements, or risk of a specific adverse reaction.
  • Review the full medication list for substrates, inhibitors, and inducers that may change the expected response.
  • Integrate the result with kidney and liver function, age, pregnancy when relevant, adherence, environmental factors, and observed clinical response.
  • Document the genetic finding, its interpretation, and how it affected drug selection, dosing, education, or monitoring.

Patient Education

  • Explain in plain language how the genetic difference may change drug activation, metabolism, effectiveness, or toxicity.
  • State whether the information changes the medication choice, starting dose, titration, or monitoring plan.
  • Clarify that the result does not guarantee that a medication will work or cause harm because nongenetic factors also affect response.
  • Reinforce that the patient should not change or stop medication without contacting the prescriber.
  • Encourage the patient to retain the result for future medication decisions and share it with relevant prescribers and pharmacists.

Common Interpretation Errors

  • Assuming the same metabolizer phenotype applies to every CYP450 enzyme.
  • Ignoring whether the medication is active when administered or requires metabolic activation.
  • Using race or ethnicity as if it identifies an individual’s genotype.
  • Interpreting genetic variation without reviewing current substrates, inhibitors, inducers, organ function, and environmental factors.
  • Treating a genetic result as a guarantee of response rather than one component of individualized pharmacotherapy.

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References

Belle, D. J., & Singh, H. (2008). Genetic factors in drug metabolism. American Family Physician, 77(11), 1553–1560. https://www.aafp.org/pubs/afp/issues/2008/0601/p1553.html

Caudle, K. E., Dunnenberger, H. M., Freimuth, R. R., Peterson, J. F., Burlison, J. D., Whirl-Carrillo, M., Scott, S. A., Rehm, H. L., Williams, M. S., Klein, T. E., Relling, M. V., & Hoffman, J. M. (2017). Standardizing terms for clinical pharmacogenetic test results: Consensus terms from the Clinical Pharmacogenetics Implementation Consortium (CPIC). Genetics in Medicine, 19(2), 215–223. https://doi.org/10.1038/gim.2016.87

Lee, C. R., Luzum, J. A., Sangkuhl, K., Gammal, R. S., Sabatine, M. S., Stein, C. M., Kisor, D. F., Limdi, N. A., Lee, Y. M., Scott, S. A., Hulot, J.-S., Roden, D. M., Gaedigk, A., Caudle, K. E., Klein, T. E., Johnson, J. A., & Shuldiner, A. R. (2022). Clinical Pharmacogenetics Implementation Consortium guideline for CYP2C19 genotype and clopidogrel therapy: 2022 update. Clinical Pharmacology & Therapeutics, 112(5), 959–967. https://doi.org/10.1002/cpt.2526

Mercier, I., & Issa, A. M. (2022). Pharmacogenomics. In V. P. Arcangelo, A. M. Peterson, V. F. Wilbur, & T. M. Kang (Eds.), Pharmacotherapeutics for advanced practice: A practical approach (5th ed.). Wolters Kluwer.

Phillips, E. J., Sukasem, C., Whirl-Carrillo, M., Müller, D. J., Dunnenberger, H. M., Chantratita, W., Goldspiel, B., Chen, Y.-T., Carleton, B. C., George, A. L., Jr., Mushiroda, T., Klein, T., Gammal, R. S., & Pirmohamed, M. (2018). Clinical Pharmacogenetics Implementation Consortium guideline for HLA genotype and use of carbamazepine and oxcarbazepine: 2017 update. Clinical Pharmacology & Therapeutics, 103(4), 574–581. https://doi.org/10.1002/cpt.1004

Woo, T. M., & Robinson, M. V. (2019). An introduction to pharmacogenomics [PowerPoint slides]. F. A. Davis.