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Original article| Volume 89, ISSUE 1, P25-33, January 01, 2014

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

DOI:https://doi.org/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
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      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.

      Abbreviations and Acronyms:

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

        • Weinshilboum R.
        Inheritance and drug response.
        N Engl J Med. 2003; 348: 529-537
        View in Article
        • Weinshilboum R.
        • Wang L.
        Pharmacogenomics: bench to bedside.
        Nat Rev Drug Discov. 2004; 3: 739-748
        View in Article
        • Weinshilboum R.M.
        • Wang L.
        Pharmacogenetics and pharmacogenomics: development, science, and translation.
        Annu Rev Genomics Hum Genet. 2006; 7: 223-245
        View in Article
        • Wang L.
        • McLeod H.L.
        • Weinshilboum R.M.
        Genomics and drug response.
        N Engl J Med. 2011; 364: 1144-1153
        View in Article

      5. US Food and Drug Administration. Table of pharmacogenomic biomarkers in drug labels. http://www.fda.gov/drugs/scienceresearch/researchareas/pharmacogenetics/ucm083378.htm. Updated June 19, 2013. Accessed July 10, 2013.

        View in Article
        • Farrugia G.
        • Weinshilboum R.M.
        Challenges in implementing genomic medicine: the Mayo Clinic Center for Individualized Medicine.
        Clin Pharmacol Ther. 2013; 94: 204-206
        View in Article

      7. Pharmacogenomics Research Network. http://www.pgrn.org. Accessed November 26, 2013.

        View in Article

      8. Electronic Medical Records and Genomics (eMERGE) Network. http://emerge.mc.vanderbilt.edu. Accessed November 26, 2013.

        View in Article
        • Kho A.N.
        • Pacheco J.A.
        • Peissig P.L.
        • et al.
        Electronic medical records for genetic research: results of the eMERGE consortium.
        Sci Transl Med. 2011; 3: 79re1
        View in Article
        • Olson J.E.
        • Ryu E.
        • Johnson K.J.
        • et al.
        The Mayo Clinic Biobank: a building block for individualized medicine.
        Mayo Clin Proc. 2013; 88: 952-962
        View in Article

      11. Gordon AS, Smith JD, Xiang Q, et al. PGRNseq: a new sequencing-based platform for high-throughput pharmacogenomic implementation and discovery. Abstract presented at the 62nd Annual Meeting of the American Society of Human Genetics: November 6-8, 2012; San Francisco, CA. Program #244. http://www.ashg.org/2012meeting/abstracts/fulltext/f120122669.htm. Accessed May 25, 2013.

        View in Article
        • Wilke R.A.
        • Ramsey L.B.
        • Johnson S.G.
        • et al.
        • Clinical Pharmacogenomics Implementation Consortium (CPIC)
        The Clinical Pharmacogenomics Implementation Consortium: CPIC guideline for SLCO1B1 and simvastatin-induced myopathy.
        Clin Pharmacol Ther. 2012; 92: 112-117
        View in Article
        • Rocca W.A.
        • Yawn B.P.
        • St. Sauver J.L.
        • Grossardt B.R.
        • Melton III, L.J.
        History of the Rochester Epidemiology Project: half a century of medical records linkage in a US population.
        Mayo Clin Proc. 2012; 87: 1202-1213
        View in Article

      14. New York Department of Health. Oncology—molecular and cellular tumor markers: “next generation” sequencing (NGS) guidelines for somatic genetic variant detection. http://www.wadsworth.org/labcert/TestApproval/forms/NextGenSeq_ONCO_Guidelines.pdf. Updated November 26, 2012. Accessed October 10, 2013.

        View in Article
        • Altman R.B.
        PharmGKB: a logical home for knowledge relating genotype to drug response phenotype.
        Nat Genet. 2007; 39: 426
        View in Article

      16. Flockhart DA. Drug interactions: cytochrome p450 drug interaction table. Indiana University School of Medicine website. http://medicine.iupui.edu/clinpharm/ddis/clinical-table/. Updated July 12, 2013. Accessed August 1, 2013.

        View in Article
        • Relling M.V.
        • Klein T.E.
        CPIC: Clinical Pharmacogenetics Implementation Consortium of the Pharmacogenomics Research Network.
        Clin Pharmacol Ther. 2011; 89: 464-467
        View in Article
        • Haga S.B.
        • Burke W.
        • Ginsburg G.S.
        • Mills R.
        • Agans R.
        Primary care physicians' knowledge of and experience with pharmacogenetic testing.
        Clin Genet. 2012; 82: 388-394
        View in Article

      Article Info

      Footnotes

      Grant Support: This work was supported in part by the Mayo Clinic Center for Individualized Medicine, National Institutes of Health grants U19 GM61388 (The Pharmacogenomics Research Network), R01 GM28157 , U01 HG005137 , R01 CA138461 , R01 AG034676 (The Rochester Epidemiology Project [Principal Investigators: Walter A. Rocca, MD, and Barbara P. Yawn, MD, MSc]), and U01 HG06379 and U01 HG06379 Supplement (The Electronic Medical Record and Genomics [eMERGE] Network).

      Identification

      DOI: https://doi.org/10.1016/j.mayocp.2013.10.021

      Copyright

      © 2014 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.

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