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. 2014 Jan;89(1):25-33.
doi: 10.1016/j.mayocp.2013.10.021.

Preemptive genotyping for personalized medicine: design of the right drug, right dose, right time-using genomic data to individualize treatment protocol

Suzette J Bielinski  1 , Janet E Olson  2 , Jyotishman Pathak  2 , Richard M Weinshilboum  3 , Liewei Wang  4 , Kelly J Lyke  2 , Euijung Ryu  2 , Paul V Targonski  5 , Michael D Van Norstrand  6 , Matthew A Hathcock  2 , Paul Y Takahashi  5 , Jennifer B McCormick  7 , Kiley J Johnson  8 , Karen J Maschke  9 , Carolyn R Rohrer Vitek  8 , Marissa S Ellingson  8 , Eric D Wieben  10 , Gianrico Farrugia  11 , Jody A Morrisette  2 , Keri J Kruckeberg  12 , Jamie K Bruflat  12 , Lisa M Peterson  12 , Joseph H Blommel  12 , Jennifer M Skierka  12 , Matthew J Ferber  12 , John L Black  12 , Linnea M Baudhuin  12 , Eric W Klee  2 , Jason L Ross  13 , Tamra L Veldhuizen  8 , Cloann G Schultz  8 , Pedro J Caraballo  14 , Robert R Freimuth  2 , Christopher G Chute  2 , Iftikhar J Kullo  15
Affiliations

Preemptive genotyping for personalized medicine: design of the right drug, right dose, right time-using genomic data to individualize treatment protocol

Suzette J Bielinski et al. Mayo Clin Proc. 2014 Jan.

Abstract

Objective: To report the design and implementation of the Right Drug, Right Dose, Right Time-Using Genomic Data to Individualize Treatment protocol that was developed to test the concept that prescribers can deliver genome-guided therapy at the point of care by using preemptive pharmacogenomics (PGx) data and clinical decision support (CDS) integrated into the electronic medical record (EMR).

Patients and methods: We used a multivariate prediction model to identify patients with a high risk of initiating statin therapy within 3 years. The model was used to target a study cohort most likely to benefit from preemptive PGx testing among the Mayo Clinic Biobank participants, with a recruitment goal of 1000 patients. We used a Cox proportional hazards model with variables selected through the Lasso shrinkage method. An operational CDS model was adapted to implement PGx rules within the EMR.

Results: The prediction model included age, sex, race, and 6 chronic diseases categorized by the Clinical Classifications Software for International Classification of Diseases, Ninth Revision codes (dyslipidemia, diabetes, peripheral atherosclerosis, disease of the blood-forming organs, coronary atherosclerosis and other heart diseases, and hypertension). Of the 2000 Biobank participants invited, 1013 (51%) provided blood samples, 256 (13%) declined participation, 555 (28%) did not respond, and 176 (9%) consented but did not provide a blood sample within the recruitment window (October 4, 2012, through March 20, 2013). Preemptive PGx testing included CYP2D6 genotyping and targeted sequencing of 84 PGx genes. Synchronous real-time CDS was integrated into the EMR and flagged potential patient-specific drug-gene interactions and provided therapeutic guidance.

Conclusion: This translational project provides an opportunity to begin to evaluate the impact of preemptive sequencing and EMR-driven genome-guided therapy. These interventions will improve understanding and implementation of genomic data in clinical practice.

Keywords: CAB; CAP; CDS; CGSL; CLIA; Clinical Genome Sequencing Laboratory; Clinical Laboratory Improvement Amendments; College of American Pathologists; Community Advisory Board; EMR; Electronic Medical Record and Genomics; FDA; Food and Drug Administration; NGS; PGL; PGRN; PGx; Personalized Genomics Laboratory; Pharmacogenomics Research Network; RIGHT; The Right Drug, Right Dose, Right Time—Using Genomic Data to Individualize Treatment; clinical decision support; eMERGE; electronic medical record; next-generation sequencing; pharmacogenomics.

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Figures

Figure 1
Figure 1
Mayo Clinic Center for Individualized Medicine (CIM) Process for Clinical Implementation of Pharmacogenomic Rules
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