Category: Professional Posters
Purpose: Pharmacogenomics, or the use of genetic information to predict and optimize response to drug therapies, is a clinical tool to advance personalized medicine. Mayo Clinic utilized a collaborative approach to the implementation of pharmacogenomics testing from the lab to the bedside with involvement of pharmacists, Center for Individualized Medicine leadership, Department of Laboratory Medicine and Pathology, general internal medicine providers [who care for Executive Health patients] and OneOme®. We sought to demonstrate the potential impact of preemptive pharmacogenomics testing in optimizing current and future medication therapies.
Methods: 85 Mayo Clinic Executive Health patients consented from 2015-2017 were genotyped for 9 genes encoding cytochrome P450 enzymes (CYP1A2, CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5), solute carrier organic anion transporter (SLCO1B1), major histocompatibility complex variant (HLA-B*58:01), and vitamin K epoxide reductase enzyme (VKORC1). Recruitment was conducted in the Executive Health Clinic using an informational video and appointment request forms. This study was reviewed and approved by Mayo Clinic IRB. A pharmacy consult was ordered by the clinician for interested patients. All participants received an initial visit with the pharmacogenomics pharmacist and a follow-up phone call or face-to-face visit once results were available. During the initial visit, the pharmacist provided a comprehensive overview of the value of pharmacogenomics, obtained a comprehensive medication list with emphasis on current and past medication efficacy and intolerances. Due to the high cost of the test, pharmacists ordered the 9 gene panel test for patients who elected to proceed with testing. After testing was ordered, a cascade of the events occurred:
- A buccal swab was collected
- genotyping was conducted internally.
- patient data were de-identified and sent to OneOme for a patient-friendly final report.
At the follow-up visit, pharmacists provided recommendations for current medications or considerations for future therapy based on the patient’s pharmacogenomics results. These recommendations were documented in the electronic medical record and sent to the ordering provider.
Results: Overall, the phenotype findings from this study were reflective of the general population. CYP1A2 rapid metabolizer the most common phenotype seen in the general Caucasian population; 93% of the 85 patients were rapid metabolizers. CYP3A4 normal metabolizers consisted of 91% of patients and 88% were CYP3A5 poor metabolizers. CYP3A4 is the predominant cytochrome P450 enzyme expressed in the adult human liver, specifically in Caucasian populations, whereas CYP3A5 is the predominant enzyme in African American populations. There was wide variability in phenotype frequencies for CYP2C19 and CYP2D6, which is consistent with the highly polymorphic nature of these genes. With regards to SLCO1B1, 72% of the study participants had normal function. HLA-B*58:01 is associated with risk of severe cutaneous adverse reactions with allopurinol; 3% of the participants carried the risk HLA variant. Lastly, VKORC1 and CYP2C9 data were compounded to predict warfarin sensitivity and 36% of study participants carried variants that increased their sensitivity to usual warfarin dosing. For 54% of the patients, the pharmacist identified gene-drug associations and provided recommendations based on the patient’s pharmacogenomics results with regards to past medication experiences as well as considerations for current medication therapy. The average number of pharmacogenomics-related recommendations sent was two.
Conclusion: Implementation of the 9 gene panel project was successful as evidenced by the number of patients tested. Collaboration with various groups was helpful in implementation at a large institution such as Mayo Clinic. Pharmacogenomics testing results reflected the general population frequencies. Pharmacists were a value added to the care team by providing gene-drug recommendations for current medications and further guided future individualized medication selection.