Drug–drug and drug–gene interactions in a large population undergoing pharmacogenetic testing
As pharmacogenetic data are increasingly being incorporated into the patient care decision making process, additional information is needed to inform clinician strategies and priorities. An important step in obtaining these data are characterizing medication use and potential interactions in patients undergoing pharmacogenetic testing.
In the current study, researchers analyzed genotype, phenotype, and medication use data for patients referred to Genelex for pharmacogenetic testing (including CYP2D6, CYP2C9, CYP2C19, CYP3A4, and CYP3A5 testing) between May 2013 and September 2014. Clinicians employed by the laboratory testing company used a proprietary, web-based software system to classify drug–drug (DD), drug–gene (DG), and drug–drug–gene (DDG) interactions according to the resulting clinical recommendation (i.e., change medication, consider medication change, monitor patient, no change warranted).
Approximately half of the 20,534 study patients were older than 65 years of age, 33% possessed at least one “at-risk” (non-wildtype) phenotype, and 69.1% had at least one identified DD, DG, or DDG interaction. Out of a total of 33,655 reported interactions, investigators rated 16,924 as requiring a medication “change” or “consider change” recommendation (53.0% DD, 24.6% DG, and 22.4% DDG interactions). Approximately 9% of patients had at least one “change” drug therapy recommendation based on any type of interaction, with 5.4% resulting from potential DD interactions and 3.9% from DG and/or DDG interactions.
On average, patients were taking a total of 9 medications each. In patients who were identified as having any potential interaction, the most commonly prescribed interacting medications were warfarin in 96% of patients and clopidogrel in 74%. Other commonly used (i.e., >50%) potentially interacting medications included oxycodone, citalopram, bupropion, and carvedilol.
Study results provide important insight into a population of patients genotyped in a pragmatic, clinical practice setting. Data support the need for clinical pharmacist guidance and intervention to assist providers in managing potential interactions involving CYP enzyme variability in a generalist patient population.
Although advanced age was associated with increased medication use (10.5 medications per patients over age 64 versus 7.2 per patient in those ≤64), authors wrote that the “sheer number of patients with risk phenotypes, as well as the prevalence of DGIs and DDGIs identified, suggests that testing may be beneficial in all patients receiving a large number of medications regardless of patient age.”
Hocum BT et al. Cytochrome P-450 gene and drug interaction analysis in patients referred for pharmacogenetic testing. Am J Health-Sys Pharm. 2016;73:61–7.