Preemptive genotyping for personalized medicine

Design of the right drug, right dose, right timedusing genomic data to individualize treatment protocol

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

Research output: Contribution to journalArticle

132 Citations (Scopus)

Abstract

Objective: To report the design and implementation of the Right Drug, Right Dose, Right TimedUsing 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.

Original languageEnglish (US)
Pages (from-to)25-33
Number of pages9
JournalMayo Clinic Proceedings
Volume89
Issue number1
DOIs
StatePublished - 2014

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Precision Medicine
Electronic Health Records
Drug Design
Clinical Decision Support Systems
Clinical Protocols
Point-of-Care Systems
Genome
Hydroxymethylglutaryl-CoA Reductase Inhibitors
Cytochrome P-450 CYP2D6
Decision Support Techniques
Hematologic Diseases
Pharmacogenetics
International Classification of Diseases
Therapeutics
Dyslipidemias
Drug Interactions
Proportional Hazards Models
Genes
Coronary Artery Disease
Heart Diseases

ASJC Scopus subject areas

  • Medicine(all)

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Preemptive genotyping for personalized medicine : Design of the right drug, right dose, right timedusing genomic data to individualize treatment protocol. / Bielinski, Suzette J; Olson, Janet E; Pathak, Jyotishman; Weinshilboum, Richard M; Wang, Liewei M; 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; Freimuth, Robert; Chute, Christopher G.; Kullo, Iftikhar Jan.

In: Mayo Clinic Proceedings, Vol. 89, No. 1, 2014, p. 25-33.

Research output: Contribution to journalArticle

Bielinski, SJ, Olson, JE, Pathak, J, Weinshilboum, RM, Wang, LM, Lyke, KJ, Ryu, E, Targonski, PV, Van Norstrand, MD, Hathcock, MA, Takahashi, PY, McCormick, JB, Johnson, KJ, Maschke, KJ, Rohrer Vitek, CR, Ellingson, MS, Wieben, ED, Farrugia, G, Morrisette, JA, Kruckeberg, KJ, Bruflat, JK, Peterson, LM, Blommel, JH, Skierka, JM, Ferber, MJ, Black, JL, Baudhuin, LM, Klee, EW, Ross, JL, Veldhuizen, TL, Schultz, CG, Caraballo, P, Freimuth, R, Chute, CG & Kullo, IJ 2014, 'Preemptive genotyping for personalized medicine: Design of the right drug, right dose, right timedusing genomic data to individualize treatment protocol', Mayo Clinic Proceedings, vol. 89, no. 1, pp. 25-33. https://doi.org/10.1016/j.mayocp.2013.10.021
Bielinski, Suzette J ; Olson, Janet E ; Pathak, Jyotishman ; Weinshilboum, Richard M ; Wang, Liewei M ; 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 ; Freimuth, Robert ; Chute, Christopher G. ; Kullo, Iftikhar Jan. / Preemptive genotyping for personalized medicine : Design of the right drug, right dose, right timedusing genomic data to individualize treatment protocol. In: Mayo Clinic Proceedings. 2014 ; Vol. 89, No. 1. pp. 25-33.
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abstract = "Objective: To report the design and implementation of the Right Drug, Right Dose, Right TimedUsing 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.",
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T1 - Preemptive genotyping for personalized medicine

T2 - Design of the right drug, right dose, right timedusing genomic data to individualize treatment protocol

AU - Bielinski, Suzette J

AU - Olson, Janet E

AU - Pathak, Jyotishman

AU - Weinshilboum, Richard M

AU - Wang, Liewei M

AU - Lyke, Kelly J.

AU - Ryu, Euijung

AU - Targonski, Paul V.

AU - Van Norstrand, Michael D.

AU - Hathcock, Matthew A.

AU - Takahashi, Paul Y

AU - McCormick, Jennifer B.

AU - Johnson, Kiley J.

AU - Maschke, Karen J.

AU - Rohrer Vitek, Carolyn R.

AU - Ellingson, Marissa S.

AU - Wieben, Eric D

AU - Farrugia, Gianrico

AU - Morrisette, Jody A.

AU - Kruckeberg, Keri J.

AU - Bruflat, Jamie K.

AU - Peterson, Lisa M.

AU - Blommel, Joseph H.

AU - Skierka, Jennifer M.

AU - Ferber, Matthew J.

AU - Black, John L.

AU - Baudhuin, Linnea M.

AU - Klee, Eric W

AU - Ross, Jason L.

AU - Veldhuizen, Tamra L.

AU - Schultz, Cloann G.

AU - Caraballo, Pedro

AU - Freimuth, Robert

AU - Chute, Christopher G.

AU - Kullo, Iftikhar Jan

PY - 2014

Y1 - 2014

N2 - Objective: To report the design and implementation of the Right Drug, Right Dose, Right TimedUsing 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.

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