Advancing Pharmacogenomics from Single-Gene to Preemptive Testing

Abstract

Pharmacogenomic testing can be an effective tool to enhance medication safety and efficacy. Pharmacogenomically actionable medications are widely used, and approximately 90-95% of individuals have an actionable genotype for at least one pharmacogene. For pharmacogenomic testing to have the greatest impact on medication safety and clinical care, genetic information should be made available at the time of prescribing (preemptive testing). However, the use of preemptive pharmacogenomic testing is associated with some logistical concerns, such as consistent reimbursement, processes for reporting preemptive results over an individual's lifetime, and result portability. Lessons can be learned from institutions that have implemented preemptive pharmacogenomic testing. In this review, we discuss the rationale and best practices for implementing pharmacogenomics preemptively.

Keywords: clinical decision support; genomic medicine; individualized medicine; personalized medicine; pharmacogenetics; pharmacogenomics; precision medicine.

Figure 1

Proportions of patients preemptively genotyped at St. Jude Children’s Research Hospital with high-risk phenotypes. Data are from the PG4KDS clinical trial and are shown as of November 1, 2021. The study genotyped 5,912 patients for the following 13 pharmacogenes: CACNA1S, CYP2B6, CYP2C19, CYP2C9, CYP2D6, CYP3A5, DPYD, G6PD, mt-RNR1, RYR1, SLCO1B1, TPMT, and UGT1A1. Patients diagnosed with acute lymphoblastic leukemia were also genotyped for NUDT15. A high-risk phenotype is a result that has implications for the use of at least one medication.

Figure 2

Preemptive pharmacogenomics service workflow process. Abbreviations: CDS, clinical decision support; EHR, electronic health record.

Figure 3

Demonstration of the reduction in complexity of pharmacogenomic result interpretation by utilizing phenotype instead of genotype. In this example, 406 unique CYP2D6 genotype (diplotype) results (panel a) can be classified into four distinct phenotypes (panel b), with a small percentage left as indeterminate. This approach simplifies result interpretation for clinicians and enables clinical decision alerts to be triggered based on phenotype instead of diplotype.

Figure 4

Example of a preemptive implementation time line for pharmacogene testing, showing the rollout of gene test results over time in St. Jude Children Research Hospital’s PG4KDS study. All gene test resultswere interrogated starting in 2011 but were placed in the EHR system as CDS for at least one drug affected by that gene had been implemented in the EHR systemAll gene–drug pair rollouts were approved by the hospital’s Pharmacogenomics Oversight Committee. Abbreviations: CDS, clinical decision support; EHR, electronic health record.

Figure 5

Genes and medications implemented at St. Jude Children’s Research Hospital. Test results for some genes (e.g., CYP2C19; yellow bars) were implemented as early as 2013, but CDS for additional drugs was implemented over time for different drugs (clopidogrel in 2013, voriconazole in 2015, and proton pump inhibitors in 2017). Abbreviation: CDS, clinical decision support.

Figure 6

Percentage of thiopurine-naive patients diagnosed with acute lymphocytic leukemia who had a known TPMT and NUDT15 genotype prior to initiating thiopurine therapy at St. Jude Children’s Research Hospital, by quarter. TPMT genotyping was implemented prior to 2012; NUDT15 genotyping was implemented clinically in 2017. The goal quality metric is 100% for the top curve and 0% for the bottom curve. Approximately 22 patients per quarter were included in this metric.