Preemptive Genotyping for Personalized Medicine: Design of the Right Drug, Right Dose, Right Time-Using Genomic Data to Individualize Treatment Protocol

被引:227
作者
Bielinski, Suzette J. [1 ]
Olson, Janet E. [1 ]
Pathak, Jyotishman [1 ]
Weinshilboum, Richard M. [2 ,3 ]
Wang, Liewei [2 ]
Lyke, Kelly J. [1 ]
Ryu, Euijung [1 ]
Targonski, Paul V. [4 ]
Van Norstrand, Michael D. [11 ]
Hathcock, Matthew A. [1 ]
Takahashi, Paul Y. [4 ]
McCormick, Jennifer B. [1 ,3 ,5 ]
Johnson, Kiley J. [3 ]
Maschke, Karen J. [12 ]
Vitek, Carolyn R. Rohrer
Ellingson, Marissa S.
Wieben, Eric D. [6 ]
Farrugia, Gianrico [7 ]
Morrisette, Jody A. [1 ]
Kruckeberg, Keri J. [8 ]
Bruflat, Jamie K. [8 ]
Peterson, Lisa M. [8 ]
Blommel, Joseph H. [8 ]
Skierka, Jennifer M. [8 ]
Ferber, Matthew J. [8 ]
Black, John L. [8 ]
Baudhuin, Linnea M. [8 ]
Klee, Eric W. [1 ]
Ross, Jason L. [9 ]
Veldhuizen, Tamra L.
Schultz, Cloann G.
Caraballo, Pedro J. [5 ]
Freimuth, Robert R. [1 ]
Chute, Christopher G. [1 ]
Kullo, Iftikhar J. [10 ]
机构
[1] Mayo Clin, Dept Hlth Sci Res, Rochester, MN 55905 USA
[2] Mayo Clin, Dept Mol Pharmacol & Expt Therapeut, Rochester, MN USA
[3] Mayo Clin, Ctr Individualized Med, Rochester, MN USA
[4] Mayo Clin, Div Primary Care Internal Med, Rochester, MN USA
[5] Mayo Clin, Div Gen Internal Med, Rochester, MN USA
[6] Mayo Clin, Dept Biochem & Mol Biol, Rochester, MN USA
[7] Mayo Clin, Div Gastroenterol, Rochester, MN USA
[8] Mayo Clin, Dept Lab Med & Pathol, Rochester, MN USA
[9] Mayo Clin, Dept Informat Technol, Rochester, MN USA
[10] Mayo Clin, Div Cardiovasc Dis, Rochester, MN USA
[11] Mayo Clin Hlth Syst Franciscan Healthcare, Dept Gastroenterol, La Crosse, WI USA
[12] Hastings Ctr, Garrison, NY USA
基金
美国国家卫生研究院;
关键词
IMPLEMENTATION CONSORTIUM; PHARMACOGENOMICS; KNOWLEDGE;
D O I
10.1016/j.mayocp.2013.10.021
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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. (C) 2014 Mayo Foundation for Medical Education and Research
引用
收藏
页码:25 / 33
页数:9
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