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Genotype-based clinical trials in cardiovascular disease

Key Points

  • Substantial progress has been made in the field of pharmacogenomics, with advances in genotyping and sequencing technology, and by the routine collection of DNA samples to study the drug-response phenotype

  • Genetic markers associated with drug toxicity and drug efficacy can be identified by candidate gene, genome-wide association, and next-generation sequencing studies

  • The potential of targeting the right patient with the right drug, and FDA labelling guidance to use pharmacogenetic markers, have provided new impetus to conduct genotype-based randomized clinical trials (RCTs)

  • Prospective approaches using a pharmacogenetic-based strategy with enrichment or adaptive designs are being increasingly used in cardiovascular RCTs

  • Clinical adoption of pharmacogenetics in the practice of cardiovascular medicine will become a reality when a transition has been made from conducting genetic association studies to rigorously performed genotype-based RCTs

Abstract

Consensus practice guidelines and the implementation of clinical therapeutic advances are usually based on the results of large, randomized clinical trials (RCTs). However, RCTs generally inform us on an average treatment effect for a presumably homogeneous population, but therapeutic interventions rarely benefit the entire population targeted. Indeed, multiple RCTs have demonstrated that interindividual variability exists both in drug response and in the development of adverse effects. The field of pharmacogenomics promises to deliver the right drug to the right patient. Substantial progress has been made in this field, with advances in technology, statistical and computational methods, and the use of cell and animal model systems. However, clinical implementation of pharmacogenetic principles has been difficult because RCTs demonstrating benefit are lacking. For patients, the potential benefits of performing such trials include the individualization of therapy to maximize efficacy and minimize adverse effects. These trials would also enable investigators to reduce sample size and hence contain costs for trial sponsors. Multiple ethical, legal, and practical issues need to be considered for the conduct of genotype-based RCTs. Whether pre-emptive genotyping embedded in electronic health records will preclude the need for performing genotype-based RCTs remains to be seen.

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Figure 1: Genotyping and sequencing strategies to identify pharmacogenetic markers associated with drug efficacy (response versus no response) and drug toxicity.
Figure 2: Some possible prospective biomarker-based clinical trial designs.

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References

  1. Pereira, N. L. & Weinshilboum, R. M. Cardiovascular pharmacogenomics and individualized drug therapy. Nat. Rev. Cardiol. 6, 632–638 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Wang, L., McLeod, H. L. & Weinshilboum, R. M. Genomics and drug response. N. Engl. J. Med. 364, 1144–1153 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Pereira, N. L. & Weinshilboum, R. M. The impact of pharmacogenomics on the management of cardiac disease. Clin. Pharmacol. Ther. 90, 493–495 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Koboldt, D. C., Steinberg, K. M., Larson, D. E., Wilson, R. K. & Mardis, E. R. The next-generation sequencing revolution and its impact on genomics. Cell 155, 27–38 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Bowton, E. et al. Biobanks and electronic medical records: enabling cost-effective research. Sci. Transl. Med. 6, 234cm3 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Thorn, C., Klein, T. & Altman, R. PharmGKB: The Pharmacogenomics Knowledge Base. Pharmacogenomics 1015, 311–320 (2013).

    Article  CAS  Google Scholar 

  7. MacRae, C. A. Cardiac arrhythmia: in vivo screening in the zebrafish to overcome complexity in drug discovery. Expert Opin. Drug Discov. 5, 619–632 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Jiang, J. et al. Genome-wide association study for biomarker identification of Rapamycin and Everolimus using a lymphoblastoid cell line system. Front. Genet. 4, 166 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Volzke, H. et al. Personalized cardiovascular medicine: concepts and methodological considerations. Nat. Rev. Cardiol. 10, 308–316 (2013).

    Article  CAS  PubMed  Google Scholar 

  10. Wang, B., Canestaro, W. J. & Choudhry, N. K. Clinical evidence supporting pharmacogenomic biomarker testing provided in US Food and Drug Administration drug labels. JAMA Intern. Med. 174, 1938–1944 (2014).

    Article  PubMed  Google Scholar 

  11. Ahmad, T. et al. Charting a roadmap for heart failure biomarker studies. JACC Heart Fail. 2, 477–488 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Pirmohamed, M. et al. A randomized trial of genotype-guided dosing of warfarin. N. Engl. J. Med. 369, 2294–2303 (2013).

    Article  CAS  PubMed  Google Scholar 

  13. Kimmel, S. E. et al. A pharmacogenetic versus a clinical algorithm for warfarin dosing. N. Engl. J. Med. 369, 2283–2293 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Bergmeijer, T. O. et al. CYP2C19 genotype–guided antiplatelet therapy in ST-segment elevation myocardial infarction patients—rationale and design of the Patient Outcome after primary PCI (POPular) Genetics study. Am. Heart J. 168, 16–22.e1 (2014).

    Article  CAS  PubMed  Google Scholar 

  15. Takeuchi, F. et al. A genome-wide association study confirms VKORC1, CYP2C9, and CYP4F2 as principal genetic determinants of warfarin dose. PLoS Genet. 5, e1000433 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Liggett, S. B. et al. A polymorphism within a conserved β1-adrenergic receptor motif alters cardiac function and β-blocker response in human heart failure. Proc. Natl Acad. Sci. 103, 11288–11293 (2006).

    Article  CAS  PubMed  Google Scholar 

  17. SEARCH Collaborative Group. SLCO1B1 variants and statin-induced myopathy—a genomewide study. N. Engl. J. Med. 359, 789–799 (2008).

  18. Daneshjou, R. et al. Genetic variant in folate homeostasis is associated with lower warfarin dose in African Americans. Blood 124, 2298–2305 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Myocardial Infarction Genetics Consortium Investigators. Inactivating mutations in NPC1L1 and protection from coronary heart disease. N. Engl. J. Med. 371, 2072–2082 (2014).

  20. Bollag, G. et al. Clinical efficacy of a RAF inhibitor needs broad target blockade in BRAF-mutant melanoma. Nature 467, 596–599 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Aithal, G. P., Day, C. P., Kesteven, P. J. L. & Daly, A. K. Association of polymorphisms in the cytochrome P450 CYP2C9 with warfarin dose requirement and risk of bleeding complications. Lancet 353, 717–719 (1999).

    Article  CAS  PubMed  Google Scholar 

  22. Rost, S. et al. Mutations in VKORC1 cause warfarin resistance and multiple coagulation factor deficiency type 2. Nature 427, 537–541 (2004).

    Article  CAS  PubMed  Google Scholar 

  23. Zhang, J. E. et al. Effects of CYP4F2 genetic polymorphisms and haplotypes on clinical outcomes in patients initiated on warfarin therapy. Pharmacogenet. Genomics 19, 781–789 (2009).

    Article  CAS  PubMed  Google Scholar 

  24. Ioannidis, J. P. To replicate or not to replicate: the case of pharmacogenetic studies: have pharmacogenomics failed, or do they just need larger-scale evidence and more replication? Circ. Cardiovasc. Genet. 6, 413–418 (2013).

    Article  PubMed  Google Scholar 

  25. Aslibekyan, S., Claas, S. A. & Arnett, D. K. To replicate or not to replicate: the case of pharmacogenetic studies establishing validity of pharmacogenomic findings: from replication to triangulation. Circ. Cardiovasc. Genet. 6, 409–412 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Weeke, P. et al. Exome sequencing implicates an increased burden of rare potassium channel variants in the risk of drug-induced long QT interval syndrome. J. Am. Coll. Cardiol. 63, 1430–1437 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Ashley, E. A. et al. Clinical assessment incorporating a personal genome. Lancet 375, 1525–1535 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Biesecker, L. G. & Green, R. C. Diagnostic clinical genome and exome sequencing. N. Engl. J. Med. 370, 2418–2425 (2014).

    Article  CAS  PubMed  Google Scholar 

  30. Green, R. C. et al. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet. Med. 15, 565–574 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Goldstein, D. B. et al. Sequencing studies in human genetics: design and interpretation. Nat. Rev. Genet. 14, 460–470 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Scriver, C. R. & Childs, B. (eds) Garrod's Inborn Factors in Disease (Oxford University Press, 1989).

    Google Scholar 

  33. Johnson, J. A. et al. Clinical Pharmacogenetics Implementation Consortium Guidelines for CYP2C9 and VKORC1 genotypes and warfarin dosing. Clin. Pharmacol. Ther. 90, 625–629 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Paré, G. et al. Genetic determinants of dabigatran plasma levels and their relation to bleeding. Circulation 127, 1404–1412 (2013).

    Article  CAS  PubMed  Google Scholar 

  35. Mega, J. L. et al. Cytochrome P-450 polymorphisms and response to clopidogrel. N. Engl. J. Med. 360, 354–362 (2009).

    Article  CAS  PubMed  Google Scholar 

  36. Mega, J. L. et al. Reduced-function CYP2C19 genotype and risk of adverse clinical outcomes among patients treated with clopidogrel predominantly for PCI: a meta-analysis. JAMA 304, 1821–1830 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Wang, L. et al. Human thiopurine S-methyltransferase pharmacogenetics: variant allozyme misfolding and aggresome formation. Proc. Natl Acad. Sci. USA 102, 9394–9399 (2005).

    Article  CAS  PubMed  Google Scholar 

  38. Lennard, L., Van Loon, J. A. & Weinshilboum, R. M. Pharmacogenetics of acute azathioprine toxicity: relationship to thiopurine methyltransferase genetic polymorphism. Clin. Pharmacol. Ther. 46, 149–154 (1989).

    Article  CAS  PubMed  Google Scholar 

  39. Van Loon, J. A. & Weinshilboum, R. M. Human lymphocyte thiopurine methyltransferase pharmacogenetics: effect of phenotype on 6-mercaptopurine-induced inhibition of mitogen stimulation. J. Pharmacol. Exp. Ther. 242, 21–26 (1987).

    CAS  PubMed  Google Scholar 

  40. Liang, J. J. et al. TPMT genetic variants are associated with increased rejection with azathioprine use in heart transplantation. Pharmacogenet. Genomics 23, 658–665 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Price, M. J. et al. Standard- vs high-dose clopidogrel based on platelet function testing after percutaneous coronary intervention: the GRAVITAS randomized trial. JAMA 305, 1097–1105 (2011).

    Article  CAS  PubMed  Google Scholar 

  42. Collet, J.-P. et al. Bedside monitoring to adjust antiplatelet therapy for coronary stenting. N. Engl. J. Med. 367, 2100–2109 (2012).

    Article  CAS  PubMed  Google Scholar 

  43. Martin, A. M. et al. Predisposition to abacavir hypersensitivity conferred by HLA-B*5701 and a haplotypic Hsp70-Hom variant. Proc. Natl Acad. Sci. 101, 4180–4185 (2004).

    Article  CAS  PubMed  Google Scholar 

  44. Mallal, S. et al. Association between presence of HLA-B*5701, HLA-DR7, and HLA-DQ3 and hypersensitivity to HIV-1 reverse-transcriptase inhibitor abacavir. Lancet 359, 727–732 (2002).

    Article  CAS  PubMed  Google Scholar 

  45. Hetherington, S. et al. Genetic variations in HLA-B region and hypersensitivity reactions to abacavir. Lancet 359, 1121–1122 (2002).

    Article  CAS  PubMed  Google Scholar 

  46. Hughes, A. R. et al. Association of genetic variations in HLA-B region with hypersensitivity to abacavir in some, but not all, populations. Pharmacogenomics 5, 203–211 (2004).

    Article  CAS  PubMed  Google Scholar 

  47. Phillips, E. J. et al. Clinical and immunogenetic correlates of abacavir hypersensitivity. AIDS 19, 979–981 (2005).

    Article  CAS  PubMed  Google Scholar 

  48. Mallal, S. et al. HLA-B*5701 screening for hypersensitivity to abacavir. N. Engl. J. Med. 358, 568–579 (2008).

    Article  PubMed  Google Scholar 

  49. Ramsey, B. W. et al. A CFTR potentiator in patients with cystic fibrosis and the G551D mutation. N. Engl. J. Med. 365, 1663–1672 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. US Food and Drug Administration. Table of pharmacogenomic biomarkers in drug labeling [online], (2015).

  51. Scott, S. A. et al. Clinical Pharmacogenetics Implementation Consortium guidelines for CYP2C19 genotype and clopidogrel therapy: 2013 update. Clin. Pharmacol. Ther. 94, 317–323 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Teutsch, S. M. et al. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) initiative: methods of the EGAPP Working Group. Genet. Med. 11, 3–14 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Roberts, J. D. et al. Point-of-care genetic testing for personalisation of antiplatelet treatment (RAPID GENE): a prospective, randomised, proof-of-concept trial. Lancet 379, 1705–1711 (2012).

    Article  CAS  PubMed  Google Scholar 

  54. Lala, A. et al. Genetic testing in patients with acute coronary syndrome undergoing percutaneous coronary intervention: a cost-effectiveness analysis. J. Thromb. Haemost. 11, 81–91 (2013).

    Article  CAS  PubMed  Google Scholar 

  55. Kazi, D. S. et al. Cost-effectiveness of genotype-guided and dual antiplatelet therapies in acute coronary syndrome. Ann. Intern. Med. 160, 221–232 (2014).

    Article  PubMed  Google Scholar 

  56. Urban, T. J. & Goldstein, D. B. Pharmacogenetics at 50: genomic personalization comes of age. Sci. Transl. Med. 6, 220ps1 (2014).

    Article  CAS  PubMed  Google Scholar 

  57. Simon, R. M., Paik, S. & Hayes, D. F. Use of archived specimens in evaluation of prognostic and predictive biomarkers. J. Natl Cancer Inst. 101, 1446–1452 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Douillard, J.-Y. et al. Panitumumab–FOLFOX4 treatment and RAS mutations in colorectal cancer. N. Engl. J. Med. 369, 1023–1034 (2013).

    Article  CAS  PubMed  Google Scholar 

  59. Wallentin, L. et al. Effect of CYP2C19 and ABCB1 single nucleotide polymorphisms on outcomes of treatment with ticagrelor versus clopidogrel for acute coronary syndromes: a genetic substudy of the PLATO trial. Lancet 376, 1320–1328 (2010).

    Article  CAS  PubMed  Google Scholar 

  60. Sorich, M. J., Vitry, A., Ward, M. B., Horowitz, J. D. & McKinnon, R. A. Prasugrel vs. clopidogrel for cytochrome P450 2C19-genotyped subgroups: integration of the TRITON-TIMI 38 trial data. J. Thromb. Haemost. 8, 1678–1684 (2010).

    Article  CAS  PubMed  Google Scholar 

  61. US National Library of Medicine. ClinicalTrials.gov [online], (2015).

  62. US National Library of Medicine. ClinicalTrials.gov [online], (2014).

  63. Maitournam, A. & Simon, R. On the efficiency of targeted clinical trials. Stat. Med. 24, 329–339 (2005).

    Article  CAS  PubMed  Google Scholar 

  64. Freidlin, B., Korn, E. L. & Gray, R. Marker Sequential Test. (MaST) design. Clin. Trials 11, 19–27 (2014).

    Article  PubMed  Google Scholar 

  65. Mandrekar, S. J. & Sargent, D. J. Clinical trial designs for predictive biomarker validation: theoretical considerations and practical challenges. J. Clin. Oncol. 27, 4027–4034 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Le Tourneau, C. et al. Randomised proof-of-concept phase II trial comparing targeted therapy based on tumour molecular profiling vs conventional therapy in patients with refractory cancer: results of the feasibility part of the SHIVA trial. Br. J. Cancer 111, 17–24 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Kim, E. S. et al. The BATTLE trial: personalizing therapy for lung cancer. Cancer Discov. 1, 44–53 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Barker, A. D. et al. I-SPY 2: an adaptive breast cancer trial design in the setting of neoadjuvant chemotherapy. Clin. Pharmacol. Ther. 86, 97–100 (2009).

    Article  CAS  PubMed  Google Scholar 

  69. Korn, E. L. & Freidlin, B. Outcome-adaptive randomization: is it useful? J. Clin. Oncol. 29, 771–776 (2011).

    Article  PubMed  Google Scholar 

  70. Abrams, J. et al. National Cancer Institute's Precision Medicine Initiatives for the new National Clinical Trials Network. Am. Soc. Clin. Oncol. Educ. Book 34, 71–76 (2014).

    Article  Google Scholar 

  71. CIBIS-II Investigators and Committees. The Cardiac Insufficiency Bisoprolol Study II (CIBIS-II): a randomised trial. Lancet 353, 9–13 (1999).

  72. Packer, M. et al. The effect of carvedilol on morbidity and mortality in patients with chronic heart failure. N. Engl. J. Med. 334, 1349–1355 (1996).

    Article  CAS  PubMed  Google Scholar 

  73. Hjalmarson, Å. et al. Effects of controlled-release metoprolol on total mortality, hospitalizations, and well-being in patients with heart failure: Tte metoprolol CR/XL randomized intervention trial in congestive heart failure (MERIT-HF). JAMA 283, 1295–1302 (2000).

    Article  CAS  PubMed  Google Scholar 

  74. Beta-Blocker Evaluation of Survival Trial Investigators. A trial of the beta-blocker bucindolol in patients with advanced chronic heart failure. N. Engl. J. Med. 344, 1659–1667 (2001).

  75. Moore, J. D., Mason, D. A., Green, S. A., Hsu, J. & Liggett, S. B. Racial differences in the frequencies of cardiac β1-adrenergic receptor polymorphisms: analysis of c145A>G and c1165G>C. Hum. Mutat. 14, 271 (1999).

    Article  CAS  PubMed  Google Scholar 

  76. Tirona, R. G., Leake, B. F., Merino, G. & Kim, R. B. Polymorphisms in OATP-C: identification of multiple allelic variants associated with altered transport activity among European- and African-Americans. J. Biol. Chem. 276, 35669–35675 (2001).

    Article  CAS  PubMed  Google Scholar 

  77. Pasanen, M. K., Neuvonen, M., Neuvonen, P. J. & Niemi, M. SLCO1B1 polymorphism markedly affects the pharmacokinetics of simvastatin acid. Pharmacogenet. Genomics 16, 873–879 (2006).

    Article  CAS  PubMed  Google Scholar 

  78. Ramsey, L. B. et al. The clinical pharmacogenetics implementation consortium guideline for SLCO1B1 and simvastatin-induced myopathy: 2014 update. Clin. Pharmacol. Ther. 96, 423–428 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Danik, J. S. et al. Lack of association between SLCO1B1 polymorphisms and clinical myalgia following rosuvastatin therapy. Am. Heart J. 165, 1008–1014 (2013).

    Article  CAS  PubMed  Google Scholar 

  80. Brunham, L. R. et al. Differential effect of the rs4149056 variant in SLCO1B1 on myopathy associated with simvastatin and atorvastatin. Pharmacogenomics J. 12, 233–237 (2012).

    Article  CAS  PubMed  Google Scholar 

  81. Voora, D. et al. The SLCO1B1*5 genetic variant is associated with statin-induced side effects. J. Am. Coll. Cardiol. 54, 1609–1616 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. US National Library of Medicine. ClinicalTrials.gov [online], (2014).

  83. Budnitz, D. S., Shehab, N., Kegler, S. R. & Richards, C. L. Medication use leading to emergency department visits for adverse drug events in older adults. Ann. Intern. Med. 147, 755–765 (2007).

    Article  PubMed  Google Scholar 

  84. US Food and Drug Administration. Coumadin (warfarin sodium) tablets label [online], (2011).

  85. Go, A. S. et al. Heart disease and stroke statistics—2014 update: a report from the American Heart Association. Circulation 129, e28–e292 (2014).

    Article  CAS  PubMed  Google Scholar 

  86. de Morais, S. M. et al. The major genetic defect responsible for the polymorphism of S-mephenytoin metabolism in humans. J. Biol. Chem. 269, 15419–15422 (1994).

    CAS  PubMed  Google Scholar 

  87. De Morais, S. M. et al. Identification of a new genetic defect responsible for the polymorphism of (S)-mephenytoin metabolism in Japanese. Mol. Pharmacol. 46, 594–598 (1994).

    CAS  PubMed  Google Scholar 

  88. Scott, S. A. et al. Clinical Pharmacogenetics Implementation Consortium guidelines for cytochrome P450–452C19 (CYP2C19) genotype and clopidogrel therapy. Clin. Pharmacol. Ther. 90, 328–332 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Brandt, J. T. et al. Common polymorphisms of CYP2C19 and CYP2C9 affect the pharmacokinetic and pharmacodynamic response to clopidogrel but not prasugrel. J. Thromb. Haemost. 5, 2429–2436 (2007).

    Article  CAS  PubMed  Google Scholar 

  90. Holmes, M. V., Perel, P., Shah, T., Hingorani, A. D. & Casas, J. P. CYP2C19 genotype, clopidogrel metabolism, platelet function, and cardiovascular events: a systematic review and meta-analysis. JAMA 306, 2704–2714 (2011).

    Article  CAS  PubMed  Google Scholar 

  91. Umemura, K., Furuta, T. & Kondo, K. The common gene variants of CYP2C19 affect pharmacokinetics and pharmacodynamics in an active metabolite of clopidogrel in healthy subjects. J. Thromb. Haemost. 6, 1439–1441 (2008).

    Article  CAS  PubMed  Google Scholar 

  92. US Food and Drug Administration. FDA drug safety communication: reduced effectiveness of Plavix (clopidogrel) in patients who are poor metabolizers of the drug [online], (2014).

  93. Holmes, D. R. Jr et al. ACCF/AHA clopidogrel clinical alert: approaches to the FDA “boxed warning”: a report of the American College of Cardiology Foundation Task Force on Clinical Expert Consensus Documents and the American Heart Association endorsed by the Society for Cardiovascular Angiography and Interventions and the Society of Thoracic Surgeons. J. Am. Coll. Cardiol. 56, 321–341 (2010).

    Article  CAS  PubMed  Google Scholar 

  94. Levine, G. N. et al. 2011 ACCF/AHA/SCAI guideline for percutaneous coronary intervention: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines and the Society for Cardiovascular Angiography and Interventions. J. Am. Coll. Cardiol. 58, e44–e122 (2011).

    Article  PubMed  Google Scholar 

  95. US National Library of Medicine. ClinicalTrials.gov [online], (2013).

  96. Mrazek, D. A. & Lerman, C. Facilitating clinical implementation of pharmacogenomics. JAMA 306, 304–305 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Rothstein, M. A. & Epps, P. G. Ethical and legal implications of pharmacogenomics. Nat. Rev. Genet. 2, 228–231 (2001).

    Article  CAS  PubMed  Google Scholar 

  98. Kohane, I. S. Using electronic health records to drive discovery in disease genomics. Nat. Rev. Genet. 12, 417–428 (2011).

    Article  CAS  PubMed  Google Scholar 

  99. Xu, H. et al. Facilitating pharmacogenetic studies using electronic health records and natural-language processing: a case study of warfarin. J. Am. Med. Inform. Assoc. 18, 387–391 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  100. Rasmussen-Torvik, L. J. et al. Design and anticipated outcomes of the eMERGE-PGx project: a multicenter pilot for preemptive pharmacogenomics in electronic health record systems. Clin. Pharmacol. Ther. 96, 482–489 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Daly, A. K. Genome-wide association studies in pharmacogenomics. Nat. Rev. Genet. 11, 241–246 (2010).

    Article  CAS  PubMed  Google Scholar 

  102. Altman, R. B., Whirl-Carrillo, M. & Klein, T. E. Challenges in the pharmacogenomic annotation of whole genomes. Clin. Pharmacol. Ther. 94, 211–213 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

Supported in part by Mayo Transplant Scholarly Award (N.L.P.), U01 GM61388 (The Pharmacogenetics Research Network). The TAILOR-PCI study is funded in part by the Mayo Clinic Centre for Individualized Medicine, and the Mayo Clinic Division of Cardiology. We thank Ms Luanne Wussow (Mayo Clinic, Rochester, MN, USA) for her assistance with the preparation of this manuscript.

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N.L.P. researched data for the article. N.L.P., D.J.S., and C.S.R. provided substantial contributions to discussion of the content. N.L.P., D.J.S., M.E.F., and C.S.R. wrote and reviewed/edited the article before submission.

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Correspondence to Naveen L. Pereira.

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Pereira, N., Sargent, D., Farkouh, M. et al. Genotype-based clinical trials in cardiovascular disease. Nat Rev Cardiol 12, 475–487 (2015). https://doi.org/10.1038/nrcardio.2015.64

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