1932

Abstract

The clinical implementation of pharmacogenetic biomarkers continues to grow as new genetic variants associated with drug outcomes are discovered and validated. The number of drug labels that contain pharmacogenetic information also continues to expand. Published, peer-reviewed clinical practice guidelines have also been developed to support the implementation of pharmacogenetic tests. Incorporating pharmacogenetic information into health care benefits patients as well as clinicians by improving drug safety and reducing empiricism in drug selection. Barriers to the implementation of pharmacogenetic testing remain. This review explores current pharmacogenetic implementation initiatives with a focus on the challenges of pharmacogenetic implementation and potential opportunities to overcome these challenges.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-pharmtox-030920-025745
2021-01-06
2024-06-18
Loading full text...

Full text loading...

/deliver/fulltext/pharmtox/61/1/annurev-pharmtox-030920-025745.html?itemId=/content/journals/10.1146/annurev-pharmtox-030920-025745&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Motulsky AG. 1957. Drug reactions, enzymes, and biochemical genetics. J. Am. Med. Assoc. 165:7835–37
    [Google Scholar]
  2. 2. 
    Kalow W. 1961. Unusual responses to drugs in some hereditary conditions. Can. Anaesth. Soc. J. 8:143–52
    [Google Scholar]
  3. 3. 
    Nebert DW. 1999. Pharmacogenetics and pharmacogenomics: Why is this relevant to the clinical geneticist. ? Clin. Genet. 56:4247–58
    [Google Scholar]
  4. 4. 
    Pirmohamed M. 2001. Pharmacogenetics and pharmacogenomics. Br. J. Clin. Pharmacol. 52:4345–47
    [Google Scholar]
  5. 5. 
    Whirl-Carrillo M, McDonagh EM, Hebert JM, Gong L, Sangkuhl K et al. 2012. Pharmacogenomics knowledge for personalized medicine. Clin. Pharmacol. Ther. 92:4414–17
    [Google Scholar]
  6. 6. 
    Gaedigk A, Ingelman-Sundberg M, Miller NA, Leeder JS, Whirl-Carrillo M et al. 2018. The Pharmacogene Variation (PharmVar) Consortium: incorporation of the Human Cytochrome P450 (CYP) Allele Nomenclature Database. Clin. Pharmacol. Ther. 103:3399–401
    [Google Scholar]
  7. 7. 
    González-Galarza FF, Takeshita LY, Santos EJ, Kempson F, Maia MH et al. 2015. Allele frequency net 2015 update: new features for HLA epitopes, KIR and disease and HLA adverse drug reaction associations. Nucleic Acids Res 43:D784–88
    [Google Scholar]
  8. 8. 
    Rehm HL, Berg JS, Brooks LD, Bustamante CD, Evans JP et al. 2015. ClinGen—the Clinical Genome Resource. N. Engl. J. Med. 372:232235–42
    [Google Scholar]
  9. 9. 
    Landrum MJ, Kattman BL. 2018. ClinVar at five years: delivering on the promise. Hum. Mutat. 39:111623–30
    [Google Scholar]
  10. 10. 
    Blagec K, Koopmann R, Crommentuijn-van Rhenen M, Holsappel I, van der Wouden CH et al. 2018. Implementing pharmacogenomics decision support across seven European countries: the Ubiquitous Pharmacogenomics (U-PGx) project. J. Am. Med. Inform. Assoc. 25:7893–98
    [Google Scholar]
  11. 11. 
    Relling MV, Klein TE. 2011. CPIC: Clinical Pharmacogenetics Implementation Consortium of the Pharmacogenomics Research Network. Clin. Pharmacol. Ther. 89:3464–67
    [Google Scholar]
  12. 12. 
    Relling MV, Klein TE, Gammal RS, Whirl-Carrillo M, Hoffman JM, Caudle KE 2020. The Clinical Pharmacogenetics Implementation Consortium: 10 years later. Clin. Pharmacol. Ther. 107:1171–75
    [Google Scholar]
  13. 13. 
    KNMP. 2020. Pharmacogenetics. Dutch Pharmacogenetic Working Group of the KNMP https://www.knmp.nl/patientenzorg/medicatiebewaking/farmacogenetica/pharmacogenetics-1/pharmacogenetics
    [Google Scholar]
  14. 14. 
    Swen JJ, Nijenhuis M, de Boer A, Grandia L, Maitland-van der Zee AH et al. 2011. Pharmacogenetics: from bench to byte—an update of guidelines. Clin. Pharmacol. Ther. 89:5662–73
    [Google Scholar]
  15. 15. 
    KNMP. 2020. The pharmacogenetic recommendations. Dutch Pharmacogenetic Working Group of the KNMP https://www.knmp.nl/downloads/pharmacogenetic-recommendations-february-2020.pdf
    [Google Scholar]
  16. 16. 
    Tanoshima R, Khan A, Biala AK, Trueman JN, Drögemöller BI et al. 2019. Analyses of adverse drug reactions–nationwide active surveillance network: Canadian Pharmacogenomics Network for Drug Safety database. J. Clin. Pharmacol. 59:3356–63
    [Google Scholar]
  17. 17. 
    Cluzeau F, Burgers J, Brouwers M, Grol R, Mäkelä M et al. 2003. Development and validation of an international appraisal instrument for assessing the quality of clinical practice guidelines: the AGREE project. Qual. Saf. Health Care 12:118–23
    [Google Scholar]
  18. 18. 
    Brouwers MC, Kho ME, Browman GP, Burgers JS, Cluzeau F et al. 2010. Development of the AGREE II, part 1: performance, usefulness and areas for improvement. CMAJ 182:101045–52
    [Google Scholar]
  19. 19. 
    Brouwers MC, Kho ME, Browman GP, Burgers JS, Cluzeau F et al. 2010. Development of the AGREE II, part 2: assessment of validity of items and tools to support application. CMAJ 182:10E472–78
    [Google Scholar]
  20. 20. 
    Mendrick DL, Brazell C, Mansfield EA, Pietrusko R, Barilero I et al. 2006. Pharmacogenomics and regulatory decision making: an international perspective. Pharmacogenom. J. 6:3154–57
    [Google Scholar]
  21. 21. 
    PharmGKB. 2020. Drug label annotations. PharmGKB, updated Apr https://www.pharmgkb.org/labelAnnotations
  22. 22. 
    US Food Drug Adm. 2020. Table of pharmacogenomic biomarkers in drug labeling. US Food Drug Adm., updated Apr https://www.fda.gov/drugs/science-and-research-drugs/table-pharmacogenomic-biomarkers-drug-labeling
  23. 23. 
    Pui CH, Evans WE. 2006. Treatment of acute lymphoblastic leukemia. N. Engl. J. Med. 354:2166–78
    [Google Scholar]
  24. 24. 
    Karran P, Attard N. 2008. Thiopurines in current medical practice: molecular mechanisms and contributions to therapy-related cancer. Nat. Rev. Cancer 8:124–36
    [Google Scholar]
  25. 25. 
    Salzer WL, Devidas M, Carroll WL, Winick N, Pullen J et al. 2010. Long-term results of the pediatric oncology group studies for childhood acute lymphoblastic leukemia 1984–2001: a report from the children's oncology group. Leukemia 24:2355–70
    [Google Scholar]
  26. 26. 
    Schmiegelow K, Nielsen SN, Frandsen TL, Nersting J 2014. Mercaptopurine/methotrexate maintenance therapy of childhood acute lymphoblastic leukemia: clinical facts and fiction. J. Pediatr. Hematol. Oncol. 36:7503–17
    [Google Scholar]
  27. 27. 
    Lennard L, Lilleyman JS, Van Loon J, Weinshilboum RM 1990. Genetic variation in response to 6-mercaptopurine for childhood acute lymphoblastic leukaemia. Lancet 336:8709225–29
    [Google Scholar]
  28. 28. 
    Relling MV, Hancock ML, Rivera GK, Sandlund JT, Ribeiro RC et al. 1999. Mercaptopurine therapy intolerance and heterozygosity at the thiopurine S-methyltransferase gene locus. J. Natl. Cancer Inst. 91:232001–8
    [Google Scholar]
  29. 29. 
    Yang S-K, Hong M, Baek J, Choi H, Zhao W et al. 2014. A common missense variant in NUDT15 confers susceptibility to thiopurine-induced leukopenia. Nat. Genet. 46:91017–20
    [Google Scholar]
  30. 30. 
    Yang JJ, Landier W, Yang W, Liu C, Hageman L et al. 2015. Inherited NUDT15 variant is a genetic determinant of mercaptopurine intolerance in children with acute lymphoblastic leukemia. J. Clin. Oncol. 33:111235–42
    [Google Scholar]
  31. 31. 
    Relling MV, Schwab M, Whirl-Carrillo M, Suarez-Kurtz G, Pui CH et al. 2019. Clinical Pharmacogenetics Implementation Consortium guideline for thiopurine dosing based on TPMT and NUDT15 genotypes: 2018 update. Clin. Pharmacol. Ther. 105:51095–105
    [Google Scholar]
  32. 32. 
    Nguyen CM, Mendes MA, Ma JD 2011. Thiopurine methyltransferase (TPMT) genotyping to predict myelosuppression risk. PLOS Curr 3:RRN1236
    [Google Scholar]
  33. 33. 
    Moriyama T, Nishii R, Perez-Andreu V, Yang W, Klussmann FA et al. 2016. NUDT15 polymorphisms alter thiopurine metabolism and hematopoietic toxicity. Nat. Genet. 48:4367–73
    [Google Scholar]
  34. 34. 
    Koutsilieri S, Caudle KE, Alzghari SK, Monte AA, Relling MV, Patrinos GP 2019. Optimizing thiopurine dosing based on TPMT and NUDT15 genotypes: It takes two to tango. Am. J. Hematol. 94:7737–740
    [Google Scholar]
  35. 35. 
    Chung WH, Hung SI, Hong HS, Hsih MS, Yang LC et al. 2004. Medical genetics: a marker for Stevens-Johnson syndrome. Nature 428:6982486
    [Google Scholar]
  36. 36. 
    Ferrell PB, McLeod HL. 2008. Carbamazepine, HLA-B*1502 and risk of Stevens-Johnson syndrome and toxic epidermal necrolysis: US FDA recommendations. Pharmacogenomics 9:101543–46
    [Google Scholar]
  37. 37. 
    Pan RY, Dao RL, Hung SI, Chung WH 2017. Pharmacogenomic advances in the prediction and prevention of cutaneous idiosyncratic drug reactions. Clin. Pharmacol. Ther. 102:186–97
    [Google Scholar]
  38. 38. 
    Chen P, Lin JJ, Lu CS, Ong CT, Hsieh PF et al. 2011. Carbamazepine-induced toxic effects and HLA-B*1502 screening in Taiwan. N. Engl. J. Med. 364:121126–33
    [Google Scholar]
  39. 39. 
    Thong BY-H. 2013. Stevens-Johnson syndrome / toxic epidermal necrolysis: an Asia-Pacific perspective. Asia Pac. Allergy 3:4215–23
    [Google Scholar]
  40. 40. 
    Lin CW, Huang WI, Chao PH, Chen WW, Hsiao FY 2018. Temporal trends and patterns in carbamazepine use, related severe cutaneous adverse reactions, and HLA-B*15:02 screening: a nationwide study. Epilepsia 59:122325–39
    [Google Scholar]
  41. 41. 
    AFND (Allele Freq. Net Database). 2020. Allele Freq. Net Database, Liverpool, UK, updated Apr. http://www.allelefrequencies.net
  42. 42. 
    Amstutz U, Shear NH, Rieder MJ, Hwang S, Fung V et al. 2014. Recommendations for HLA-B*15:02 and HLA-A*31:01 genetic testing to reduce the risk of carbamazepine-induced hypersensitivity reactions. Epilepsia 55:4496–506
    [Google Scholar]
  43. 43. 
    Phillips EJ, Sukasem C, Whirl-Carrillo M, Müller DJ, Dunnenberger HM et al. 2018. Clinical Pharmacogenetics Implementation Consortium guideline for HLA genotype and use of carbamazepine and oxcarbazepine: 2017 update. Clin. Pharmacol. Ther. 103:4574–81
    [Google Scholar]
  44. 44. 
    McCormack M, Alfirevic A, Bourgeois S, Farrell JJ, Kasperavičiūtė D et al. 2011. HLA-A*3101 and carbamazepine-induced hypersensitivity reactions in Europeans. N. Engl. J. Med. 364:121134–43
    [Google Scholar]
  45. 45. 
    Amstutz U, Ross CJ, Castro-Pastrana LI, Rieder MJ, Shear NH et al. 2013. HLA-A*31:01 and HLA-B*15:02 as genetic markers for carbamazepine hypersensitivity in children. Clin. Pharmacol. Ther. 94:1142–49
    [Google Scholar]
  46. 46. 
    Ozeki T, Mushiroda T, Yowang A, Takahashi A, Kubo M et al. 2011. Genome-wide association study identifies HLA-A*3101 allele as a genetic risk factor for carbamazepine-induced cutaneous adverse drug reactions in Japanese population. Hum. Mol. Genet. 20:51034–41
    [Google Scholar]
  47. 47. 
    Porcelli S, Fabbri C, Serretti A 2012. Meta-analysis of serotonin transporter gene promoter polymorphism (5-HTTLPR) association with antidepressant efficacy. Eur. Neuropsychopharmacol. 22:4239–58
    [Google Scholar]
  48. 48. 
    Roberts JD, Wells GA, Le May MR, Labinaz M, Glover C et al. 2012. Point-of-care genetic testing for personalisation of antiplatelet treatment (RAPID GENE): a prospective, randomised, proof-of-concept trial. Lancet 379:98271705–11
    [Google Scholar]
  49. 49. 
    Mallal S, Phillips E, Carosi G, Molina JM, Workman C et al. 2008. HLA-B*5701 screening for hypersensitivity to abacavir. N. Engl. J. Med. 358:6568–79
    [Google Scholar]
  50. 50. 
    Sistonen J, Madadi P, Ross CJ, Yazdanpanah M, Lee JW et al. 2012. Prediction of codeine toxicity in infants and their mothers using a novel combination of maternal genetic markers. Clin. Pharmacol. Ther. 91:4692–99
    [Google Scholar]
  51. 51. 
    Mockenhaupt M, Messenheimer J, Tennis P, Schlingmann J 2005. Risk of Stevens-Johnson syndrome and toxic epidermal necrolysis in new users of antiepileptics. Neurology 64:71134–38
    [Google Scholar]
  52. 52. 
    Morris ZS, Wooding S, Grant J 2011. The answer is 17 years, what is the question: understanding time lags in translational research. J. R. Soc. Med. 104:12510–20
    [Google Scholar]
  53. 53. 
    Klein ME, Parvez MM, Shin J-G 2017. Clinical implementation of pharmacogenomics for personalized precision medicine: barriers and solutions. J. Pharm. Sci. 106:92368–79
    [Google Scholar]
  54. 54. 
    Manolio TA, Murray MF. 2014. The growing role of professional societies in educating clinicians in genomics. Genet. Med. 16:8571–72
    [Google Scholar]
  55. 55. 
    Stanek EJ, Sanders CL, Taber KA, Khalid M, Patel A et al. 2012. Adoption of pharmacogenomic testing by US physicians: results of a nationwide survey. Clin. Pharmacol. Ther. 91:3450–58
    [Google Scholar]
  56. 56. 
    Bank PC, Swen JJ, Guchelaar HJ 2017. A nationwide survey of pharmacists' perception of pharmacogenetics in the context of a clinical decision support system containing pharmacogenetics dosing recommendations. Pharmacogenomics 18:3215–25
    [Google Scholar]
  57. 57. 
    Verbelen M, Weale ME, Lewis CM 2017. Cost-effectiveness of pharmacogenetic-guided treatment: Are we there yet. ? Pharmacogenom. J. 17:5395–402
    [Google Scholar]
  58. 58. 
    CMS (Cent. Medicare Medicaid Serv.). 2020. Pharmacogenomic testing for warfarin response. CMS https://www.cms.gov/Medicare/Coverage/Coverage-with-Evidence-Development/Pharmacogenomic-Testing-for-Warfarin-Response
    [Google Scholar]
  59. 59. 
    Hughes DA, Vilar FJ, Ward CC, Alfirevic A, Park BK, Pirmohamed M 2004. Cost-effectiveness analysis of HLA B*5701 genotyping in preventing abacavir hypersensitivity. Pharmacogenetics 14:6335–42
    [Google Scholar]
  60. 60. 
    Nieves Calatrava D, de la Calle-Martín Ó, Iribarren-Loyarte JA, Rivero-Román A, García-Bujalance L et al. 2010. Cost-effectiveness analysis of HLA-B*5701 typing in the prevention of hypersensitivity to abacavir in HIV patients in Spain. Enferm. Infecc. Microbiol. Clin. 28:9590–95
    [Google Scholar]
  61. 61. 
    Kauf TL, Farkouh RA, Earnshaw SR, Watson ME, Maroudas P, Chambers MG 2010. Economic efficiency of genetic screening to inform the use of abacavir sulfate in the treatment of HIV. Pharmacoeconomics 28:111025–39
    [Google Scholar]
  62. 62. 
    Wolf E, Blankenburg M, Bogner JR, Becker W, Gorriahn D et al. 2010. Cost impact of prospective HLA-B*5701-screening prior to abacavir/lamivudine fixed dose combination use in Germany. Eur. J. Med. Res. 15:4145–51
    [Google Scholar]
  63. 63. 
    Goh KS, Kapoor R, Lee CC, Ng CY, Leong KP 2019. HLA-B*5701 genotyping for abacavir prescription: re-examination of its cost-effectiveness in Singapore. Ann. Acad. Med. Singap. 48:4133–38
    [Google Scholar]
  64. 64. 
    Locharernkul C, Loplumlert J, Limotai C, Korkij W, Desudchit T et al. 2008. Carbamazepine and phenytoin induced Stevens-Johnson syndrome is associated with HLA-B*1502 allele in Thai population. Epilepsia 49:122087–91
    [Google Scholar]
  65. 65. 
    Rattanavipapong W, Koopitakkajorn T, Praditsitthikorn N, Mahasirimongkol S, Teerawattananon Y 2013. Economic evaluation of HLA-B*15:02 screening for carbamazepine-induced severe adverse drug reactions in Thailand. Epilepsia 54:91628–38
    [Google Scholar]
  66. 66. 
    Tiamkao S, Jitpimolmard J, Sawanyawisuth K, Jitpimolmard S 2013. Cost minimization of HLA-B*1502 screening before prescribing carbamazepine in Thailand. Int. J. Clin. Pharm. 35:4608–12
    [Google Scholar]
  67. 67. 
    Kim E, McCrossin I, Frew JW 2018. HLA-B*1502 haplotype screening prior to carbamazepine administration in individuals of south-east Asian ancestry nears cost-effectiveness in preventing severe cutaneous adverse drug reactions. Australas. J. Dermatol. 59:3245–46
    [Google Scholar]
  68. 68. 
    Dong D, Sung C, Finkelstein EA 2012. Cost-effectiveness of HLA-B*1502 genotyping in adult patients with newly diagnosed epilepsy in Singapore. Neurology 79:121259–67
    [Google Scholar]
  69. 69. 
    Choi H, Mohit B. 2019. Cost-effectiveness of screening for HLA-B*1502 prior to initiation of carbamazepine in epilepsy patients of Asian ancestry in the United States. Epilepsia 60:71472–81
    [Google Scholar]
  70. 70. 
    Saokaew S, Tassaneeyakul W, Maenthaisong R, Chaiyakunapruk N 2014. Cost-effectiveness analysis of HLA-B*5801 testing in preventing allopurinol-induced SJS/TEN in Thai population. PLOS ONE 9:4e94294
    [Google Scholar]
  71. 71. 
    Park D-J, Kang J-H, Lee J-W, Lee K-E, Wen L et al. 2015. Cost-effectiveness analysis of HLA-B5801 genotyping in the treatment of gout patients with chronic renal insufficiency in Korea. Arthritis Care Res 67:2280–87
    [Google Scholar]
  72. 72. 
    Ke CH, Chung WH, Wen YH, Huang YB, Chuang HY et al. 2017. Cost-effectiveness analysis for genotyping before allopurinol treatment to prevent severe cutaneous adverse drug reactions. J. Rheumatol. 44:6835–43
    [Google Scholar]
  73. 73. 
    Jutkowitz E, Dubreuil M, Lu N, Kuntz KM, Choi HK 2017. The cost-effectiveness of HLA-B*5801 screening to guide initial urate-lowering therapy for gout in the United States. Semin. Arthritis Rheum. 46:5594–600
    [Google Scholar]
  74. 74. 
    Plumpton CO, Alfirevic A, Pirmohamed M, Hughes DA 2017. Cost effectiveness analysis of HLA-B*58:01 genotyping prior to initiation of allopurinol for gout. Rheumatology 56:101729–39
    [Google Scholar]
  75. 75. 
    Araújo M, Pinto CG. 2014. Cost-effectiveness of routine testing for HLA-B*5801 in Caucasian patients newly diagnosed with gout in Portuguese NHS hospitals. Value Health 17:7A379
    [Google Scholar]
  76. 76. 
    Dong D, Tan-Koi W-C, Teng GG, Finkelstein E, Sung C 2015. Cost-effectiveness analysis of genotyping for HLA-B*5801 and an enhanced safety program in gout patients starting allopurinol in Singapore. Pharmacogenomics 16:161781–93
    [Google Scholar]
  77. 77. 
    Chong HY, Lim YH, Prawjaeng J, Tassaneeyakul W, Mohamed Z, Chaiyakunapruk N 2018. Cost-effectiveness analysis of HLA-B*58:01 genetic testing before initiation of allopurinol therapy to prevent allopurinol-induced Stevens–Johnson syndrome/toxic epidermal necrolysis in a Malaysian population. Pharmacogenet. Genom. 28:256–67
    [Google Scholar]
  78. 78. 
    Chen Z, Liew D, Kwan P 2014. Effects of a HLA-B*15:02 screening policy on antiepileptic drug use and severe skin reactions. Neurology 83:222077–84
    [Google Scholar]
  79. 79. 
    Chen Z, Liew D, Kwan P 2016. Real-world cost-effectiveness of pharmacogenetic screening for epilepsy treatment. Neurology 86:121086–94
    [Google Scholar]
  80. 80. 
    Chong HY, Mohamed Z, Tan LL, Wu DBC, Shabaruddin FH et al. 2017. Is universal HLA-B*15:02 screening a cost-effective option in an ethnically diverse population? A case study of Malaysia. Br. J. Dermatol. 177:41102–12
    [Google Scholar]
  81. 81. 
    Amstutz U, Henricks LM, Offer SM, Barbarino J, Schellens JHM et al. 2018. Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for dihydropyrimidine dehydrogenase genotype and fluoropyrimidine dosing: 2017 update. Clin. Pharmacol. Ther. 103:2210–16
    [Google Scholar]
  82. 82. 
    Relling MV, McDonagh EM, Chang T, Caudle KE, McLeod HL et al. 2014. Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines for rasburicase therapy in the context of G6PD deficiency genotype. Clin. Pharmacol. Ther. 96:2169–74
    [Google Scholar]
  83. 83. 
    Goetz MP, Sangkuhl K, Guchelaar HJ, Schwab M, Province M et al. 2018. Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for CYP2D6 and tamoxifen therapy. Clin. Pharmacol. Ther. 103:5770–77
    [Google Scholar]
  84. 84. 
    Aminkeng F, Ross CJD, Rassekh SR, Hwang S, Rieder MJ et al. 2016. Recommendations for genetic testing to reduce the incidence of anthracycline‐induced cardiotoxicity. Br. J. Clin. Pharmacol. 82:3683–95
    [Google Scholar]
  85. 85. 
    Lee JW, Pussegoda K, Rassekh SR, Monzon JG, Liu G et al. 2016. Clinical practice recommendations for the management and prevention of cisplatin-induced hearing loss using pharmacogenetic markers. Ther. Drug Monit. 38:4423–31
    [Google Scholar]
  86. 86. 
    Drögemöller BI, Wright GEB, Shih J, Monzon JG, Gelmon KA et al. 2019. CYP2D6 as a treatment decision aid for ER-positive non-metastatic breast cancer patients: a systematic review with accompanying clinical practice guidelines. Breast Cancer Res. Treat. 173:3521–32
    [Google Scholar]
  87. 87. 
    Mockenhaupt M, Viboud C, Dunant A, Naldi L, Halevy S et al. 2008. Stevens-Johnson syndrome and toxic epidermal necrolysis: assessment of medication risks with emphasis on recently marketed drugs: the EuroSCAR-study. J. Investig. Dermatol. 128:135–44
    [Google Scholar]
  88. 88. 
    Blumenthal D, Tavenner M. 2010. The “meaningful use” regulation for electronic health records. N. Engl. J. Med. 363:6501–4
    [Google Scholar]
  89. 89. 
    Ohno-Machado L, Kim J, Gabriel RA, Kuo GM, Hogarth MA 2018. Genomics and electronic health record systems. Hum. Mol. Genet. 27:R1R48–55
    [Google Scholar]
  90. 90. 
    Hicks JK, Stowe D, Willner MA, Wai M, Daly T et al. 2016. Implementation of clinical pharmacogenomics within a large health system: from electronic health record decision support to consultation services. Pharmacotherapy 36:8940–48
    [Google Scholar]
  91. 91. 
    Alanazi A. 2017. Incorporating pharmacogenomics into health information technology, electronic health record and decision support system: an overview. J. Med. Syst. 41:219
    [Google Scholar]
  92. 92. 
    Caraballo PJ, Bielinski SJ,St. Sauver JL, Weinshilboum RM 2017. Electronic medical record-integrated pharmacogenomics and related clinical decision support concepts. Clin. Pharmacol. Ther 102:2254–64
    [Google Scholar]
  93. 93. 
    Kazley AS, Simpson AN, Simpson KN, Teufel R 2014. Association of electronic health records with cost savings in a national sample. Am. J. Manag. Care 20:6e183–90
    [Google Scholar]
  94. 94. 
    Sadoughi F, Nasiri S, Ahmadi H 2018. The impact of health information exchange on healthcare quality and cost-effectiveness: a systematic literature review. Comput. Methods Programs Biomed. 161:209–32
    [Google Scholar]
  95. 95. 
    Poissant L, Pereira J, Tamblyn R, Kawasumi Y 2005. The impact of electronic health records on time efficiency of physicians and nurses: a systematic review. J. Am. Med. Inform. Assoc. 12:5505–16
    [Google Scholar]
  96. 96. 
    Sanger F, Nicklen S, Coulson AR 1977. DNA sequencing with chain-terminating inhibitors. PNAS 74:125463–67
    [Google Scholar]
  97. 97. 
    Collins FS, Lander ES, Rogers J, Waterson RH 2004. Finishing the euchromatic sequence of the human genome. Nature 431:7011931–45
    [Google Scholar]
  98. 98. 
    Koboldt DC, Steinberg KM, Larson DE, Wilson RK, Mardis ER 2013. The next-generation sequencing revolution and its impact on genomics. Cell 155:127–38
    [Google Scholar]
  99. 99. 
    Mu W, Lu HM, Chen J, Li S, Elliott AM 2016. Sanger confirmation is required to achieve optimal sensitivity and specificity in next-generation sequencing panel testing. J. Mol. Diagn. 18:6923–32
    [Google Scholar]
  100. 100. 
    Schadt EE, Turner S, Kasarskis A 2010. A window into third-generation sequencing. Hum. Mol. Genet. 19:R2R227–40
    [Google Scholar]
  101. 101. 
    Schwarz UI, Gulilat M, Kim RB 2019. The role of next-generation sequencing in pharmacogenetics and pharmacogenomics. Cold Spring Harb. Perspect. Med. 9:2a033027
    [Google Scholar]
  102. 102. 
    Wetterstrand KA. 2019. DNA sequencing costs: data. National Human Genome Research Institute: Fact Sheets About Genomics Oct. 30. https://www.genome.gov/about-genomics/fact-sheets/DNA-Sequencing-Costs-Data
    [Google Scholar]
  103. 103. 
    Schreckenberger PC, McAdam AJ. 2015. Point-counterpoint: Large multiplex PCR panels should be first-line tests for detection of respiratory and intestinal pathogens. J. Clin. Microbiol. 53:103110–15
    [Google Scholar]
  104. 104. 
    Hamblin A, Wordsworth S, Fermont JM, Page S, Kaur K et al. 2017. Clinical applicability and cost of a 46-gene panel for genomic analysis of solid tumours: retrospective validation and prospective audit in the UK National Health Service. PLOS Med 14:2e1002230
    [Google Scholar]
  105. 105. 
    Plumpton CO, Pirmohamed M, Hughes DA 2019. Cost-effectiveness of panel tests for multiple pharmacogenes associated with adverse drug reactions: an evaluation framework. Clin. Pharmacol. Ther. 105:61429–38
    [Google Scholar]
  106. 106. 
    Obermeyer Z, Emanuel EJ. 2016. Predicting the future-big data, machine learning, and clinical medicine. N. Engl. J. Med. 375:131216–19
    [Google Scholar]
  107. 107. 
    Chen JH, Asch SM. 2017. Machine learning and prediction in medicine—beyond the peak of inflated expectations. N. Engl. J. Med. 376:262507–9
    [Google Scholar]
  108. 108. 
    Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M et al. 2019. A guide to deep learning in healthcare. Nat. Med. 25:124–29
    [Google Scholar]
  109. 109. 
    Xiang YP, Liu K, Cheng XY, Cheng C, Gong F et al. 2015. Rapid assessment of adverse drug reactions by statistical solution of gene association network. IEEE/ACM Trans. Comput. Biol. Bioinform. 12:4844–50
    [Google Scholar]
  110. 110. 
    Mansouri M, Yuan B, Ross CJD, Carleton BC, Ester M 2018. HUME: large-scale detection of causal genetic factors of adverse drug reactions. Bioinformatics 34:244274–83
    [Google Scholar]
  111. 111. 
    Sharifi-Noghabi H, Zolotareva O, Collins CC, Ester M 2019. MOLI: multi-omics late integration with deep neural networks for drug response prediction. Bioinformatics 35:14i501–9
    [Google Scholar]
  112. 112. 
    Naylor CD. 2018. On the prospects for a (deep) learning health care system. JAMA 320:111099–100
    [Google Scholar]
  113. 113. 
    Hinton G. 2018. Deep learning—a technology with the potential to transform health care. JAMA 320:111101–2
    [Google Scholar]
/content/journals/10.1146/annurev-pharmtox-030920-025745
Loading
/content/journals/10.1146/annurev-pharmtox-030920-025745
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error