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



  • 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.


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

publication date

  • January 1, 2014



  • Genetic Testing
  • Pharmacogenetics
  • Practice Guidelines as Topic
  • Precision Medicine


PubMed Central ID

  • PMC3932754

Scopus Document Identifier

  • 84893097016

Digital Object Identifier (DOI)

  • 10.1016/j.mayocp.2013.10.021

PubMed ID

  • 24388019

Additional Document Info


  • 89


  • 1