1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Pharmacology and Toxicology
  4. Volume 65, 2025
  5. Article

Annual Review of Pharmacology and Toxicology

Volume 65, 2025

Pharmacogenetic Panel Testing: A Review of Current Practice and Potential for Clinical Implementation

Abstract

Pharmacogenetics (PGx) aims to optimize drug treatment outcomes by using a patient's genetic profile for individualized drug and dose selection. Currently, reactive and pretherapeutic single-gene PGx tests are increasingly applied in clinical practice in several countries and institutions. With over 95% of the population carrying at least one actionable PGx variant, and with drugs impacted by these genetic variants being in common use, pretherapeutic or preemptive PGx panel testing appears to be an attractive option for better-informed drug prescribing. Here, we discuss the current state of PGx panel testing and explore the potential for clinical implementation. We conclude that available evidence supports the implementation of pretherapeutic PGx panel testing for drugs covered in the PGx guidelines, yet identification of specific patient populations that benefit most and cost-effectiveness data are necessary to support large-scale implementation.

Keyword(s): adverse drug reactions, implementation, panel testing, personalized medicine, pharmacogenetics, precision medicine
Loading

Article metrics loading...

/content/journals/10.1146/annurev-pharmtox-061724-080935
2025-01-23
2025-02-18
Loading full text...

Full text loading...

/deliver/fulltext/pharmtox/65/1/annurev-pharmtox-061724-080935.html?itemId=/content/journals/10.1146/annurev-pharmtox-061724-080935&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Pirmohamed M, James S, Meakin S, Green C, Scott AK, et al. 2004.. Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients. . BMJ 329::15–19
     [Crossref] [Google Scholar]
  2. 2.
    Davies EC, Green CF, Taylor S, Williamson PR, Mottram DR, Pirmohamed M. 2009.. Adverse drug reactions in hospital in-patients: a prospective analysis of 3695 patient-episodes. . PLOS ONE 4::e4439
     [Crossref] [Google Scholar]
  3. 3.
    Alhawassi TM, Krass I, Bajorek BV, Pont LG. 2014.. A systematic review of the prevalence and risk factors for adverse drug reactions in the elderly in the acute care setting. . Clin. Interv. Aging 9::2079–86
     [Google Scholar]
  4. 4.
    Crispo JAG, Thibault DP, Willis AW. 2019.. Adverse drug events as a reason for adult hospitalization: a nationwide readmission study. . Ann. Pharmacother. 53::557–66
     [Crossref] [Google Scholar]
  5. 5.
    Johnson JA, Bootman JL. 1995.. Drug-related morbidity and mortality. A cost-of-illness model. . Arch. Intern. Med. 155::1949–56
     [Crossref] [Google Scholar]
  6. 6.
    Crews KR, Hicks JK, Pui CH, Relling MV, Evans WE. 2012.. Pharmacogenomics and individualized medicine: translating science into practice. . Clin. Pharmacol. Ther. 92::467–75
     [Google Scholar]
  7. 7.
    Kalow W, Tang BK, Endrenyi L. 1998.. Hypothesis: comparisons of inter- and intra-individual variations can substitute for twin studies in drug research. . Pharmacogenetics 8::283–89
     [Crossref] [Google Scholar]
  8. 8.
    Pirmohamed M. 2023.. Pharmacogenomics: current status and future perspectives. . Nat. Rev. Genet. 24::350–62
     [Crossref] [Google Scholar]
  9. 9.
    Peck RW. 2018.. Precision medicine is not just genomics: the right dose for every patient. . Annu. Rev. Pharmacol. Toxicol. 58::105–22
     [Crossref] [Google Scholar]
  10. 10.
    Swen JJ, Wilting I, de Goede AL, Grandia L, Mulder H, et al. 2008.. Pharmacogenetics: from bench to byte. . Clin. Pharmacol. Ther. 83::781–87
     [Crossref] [Google Scholar]
  11. 11.
    Relling MV, Klein TE. 2011.. CPIC: Clinical Pharmacogenetics Implementation Consortium of the Pharmacogenomics Research Network. . Clin. Pharmacol. Ther. 89::464–67
     [Crossref] [Google Scholar]
  12. 12.
    Swen JJ, Nijenhuis M, van Rhenen M, de Boer-Veger NJ, Buunk AM, et al. 2018.. Pharmacogenetic information in clinical guidelines: the European perspective. . Clin. Pharmacol. Ther. 103::795–801
     [Crossref] [Google Scholar]
  13. 13.
    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::171–75
     [Crossref] [Google Scholar]
  14. 14.
    Tsermpini EE, Al-Mahayri ZN, Ali BR, Patrinos GP. 2022.. Clinical implementation of drug metabolizing gene-based therapeutic interventions worldwide. . Hum. Genet. 141::1137–57
     [Crossref] [Google Scholar]
  15. 15.
    Abdullah-Koolmees H, van Keulen AM, Nijenhuis M, Deneer VHM. 2020.. Pharmacogenetics guidelines: overview and comparison of the DPWG, CPIC, CPNDS, and RNPGx guidelines. . Front. Pharmacol. 11::595219
     [Crossref] [Google Scholar]
  16. 16.
    van der Wouden CH, Guchelaar HJ, Swen JJ. 2022.. Precision medicine using pharmacogenomic panel-testing: current status and future perspectives. . Clin. Lab. Med. 42::587–602
     [Crossref] [Google Scholar]
  17. 17.
    Pirmohamed M, Burnside G, Eriksson N, Jorgensen AL, Toh CH, et al. 2013.. A randomized trial of genotype-guided dosing of warfarin. . N. Engl. J. Med. 369::2294–303
     [Crossref] [Google Scholar]
  18. 18.
    Wu AH. 2015.. Pharmacogenomic testing and response to warfarin. . Lancet 385::2231–32
     [Crossref] [Google Scholar]
  19. 19.
    Coenen MJ, de Jong DJ, van Marrewijk CJ, Derijks LJ, Vermeulen SH, et al. 2015.. Identification of patients with variants in TPMT and dose reduction reduces hematologic events during thiopurine treatment of inflammatory bowel disease. . Gastroenterology 149::907–17.e7
     [Crossref] [Google Scholar]
  20. 20.
    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::568–79
     [Crossref] [Google Scholar]
  21. 21.
    Claassens DMF, Vos GJA, Bergmeijer TO, Hermanides RS, van ’t Hof AWJ, et al. 2019.. A genotype-guided strategy for oral P2Y12 inhibitors in primary PCI. . N. Engl. J. Med. 381::1621–31
     [Crossref] [Google Scholar]
  22. 22.
    Somogy A. 2008.. Evolution of pharmacogenomics. . Proc. West Pharmacol. Soc. 51::1–4
     [Google Scholar]
  23. 23.
    Deenen MJ, Meulendijks D, Cats A, Sechterberger MK, Severens JL, et al. 2016.. Upfront genotyping of DPYD*2A to individualize fluoropyrimidine therapy: a safety and cost analysis. . J. Clin. Oncol. 34::227–34
     [Crossref] [Google Scholar]
  24. 24.
    Lam SW, Guchelaar HJ, Boven E. 2016.. The role of pharmacogenetics in capecitabine efficacy and toxicity. . Cancer Treat Rev. 50::9–22
     [Crossref] [Google Scholar]
  25. 25.
    Lunenburg CA, van Staveren MC, Gelderblom H, Guchelaar HJ, Swen JJ. 2016.. Evaluation of clinical implementation of prospective DPYD genotyping in 5-fluorouracil- or capecitabine-treated patients. . Pharmacogenomics 17::721–29
     [Crossref] [Google Scholar]
  26. 26.
    Magnani E, Farnetti E, Nicoli D, Casali B, Savoldi L, et al. 2013.. Fluoropyrimidine toxicity in patients with dihydropyrimidine dehydrogenase splice site variant: the need for further revision of dose and schedule. . Intern. Emerg. Med. 8::417–23
     [Crossref] [Google Scholar]
  27. 27.
    Henricks LM, Lunenburg C, de Man FM, Meulendijks D, Frederix GWJ, et al. 2018.. DPYD genotype-guided dose individualisation of fluoropyrimidine therapy in patients with cancer: a prospective safety analysis. . Lancet Oncol. 19::1459–67
     [Crossref] [Google Scholar]
  28. 28.
    Henricks LM, Lunenburg C, de Man FM, Meulendijks D, Frederix GWJ, et al. 2019.. A cost analysis of upfront DPYD genotype-guided dose individualisation in fluoropyrimidine-based anticancer therapy. . Eur. J. Cancer 107::60–67
     [Crossref] [Google Scholar]
  29. 29.
    Knikman JE, Wilting TA, Lopez-Yurda M, Henricks LM, Lunenburg C, et al. 2023.. Survival of patients with cancer with DPYD variant alleles and dose-individualized fluoropyrimidine therapy—a matched-pair analysis. . J. Clin. Oncol. 41::5411–21
     [Crossref] [Google Scholar]
  30. 30.
    Caudle KE, Thorn CF, Klein TE, Swen JJ, McLeod HL, et al. 2013.. Clinical Pharmacogenetics Implementation Consortium guidelines for dihydropyrimidine dehydrogenase genotype and fluoropyrimidine dosing. . Clin. Pharmacol. Ther. 94::640–45
     [Crossref] [Google Scholar]
  31. 31.
    Hertz DL. 2022.. Assessment of the clinical utility of pretreatment DPYD testing for patients receiving fluoropyrimidine chemotherapy. . J. Clin. Oncol. 40::3882–92
     [Crossref] [Google Scholar]
  32. 32.
    FDA (US Food Drug Admin.). 2024.. FDA approves safety labeling changes regarding DPD deficiency for fluorouracil injection products. Resour., FDA, Silver Spring, MD:. https://www.fda.gov/drugs/resources-information-approved-drugs/fda-approves-safety-labeling-changes-regarding-dpd-deficiency-fluorouracil-injection-products
    [Google Scholar]
  33. 33.
    FDA (US Food Drug Admin.). 2024.. FDA approves updated drug labeling including new indications and dosing regimens for capecitabine tablets under Project Renewal. Resour., FDA, Silver Spring, MD:. https://www.fda.gov/drugs/resources-information-approved-drugs/fda-approves-updated-drug-labeling-including-new-indications-and-dosing-regimens-capecitabine
    [Google Scholar]
  34. 34.
    Pereira NL, Farkouh ME, So D, Lennon R, Geller N, et al. 2020.. Effect of genotype-guided oral P2Y12 inhibitor selection versus conventional clopidogrel therapy on ischemic outcomes after percutaneous coronary intervention: the TAILOR-PCI randomized clinical trial. . JAMA 324::761–71
     [Crossref] [Google Scholar]
  35. 35.
    Pereira NL, Rihal C, Lennon R, Marcus G, Shrivastava S, et al. 2021.. Effect of CYP2C19 genotype on ischemic outcomes during oral P2Y12 inhibitor therapy: a meta-analysis. . JACC Cardiovasc. Interv. 14::739–50
     [Crossref] [Google Scholar]
  36. 36.
    Van Driest SL, Shi Y, Bowton EA, Schildcrout JS, Peterson JF, et al. 2014.. Clinically actionable genotypes among 10,000 patients with preemptive pharmacogenomic testing. . Clin. Pharmacol. Ther. 95::423–31
     [Crossref] [Google Scholar]
  37. 37.
    Bank PCD, Swen JJ, Guchelaar HJ. 2019.. Estimated nationwide impact of implementing a preemptive pharmacogenetic panel approach to guide drug prescribing in primary care in The Netherlands. . BMC Med. 17::110
     [Crossref] [Google Scholar]
  38. 38.
    Bush WS, Crosslin DR, Owusu-Obeng A, Wallace J, Almoguera B, et al. 2016.. Genetic variation among 82 pharmacogenes: the PGRNseq data from the eMERGE network. . Clin. Pharmacol. Ther. 100::160–69
     [Crossref] [Google Scholar]
  39. 39.
    McInnes G, Lavertu A, Sangkuhl K, Klein TE, Whirl-Carrillo M, Altman RB. 2021.. Pharmacogenetics at scale: an analysis of the UK Biobank. . Clin. Pharmacol. Ther. 109::1528–37
     [Crossref] [Google Scholar]
  40. 40.
    Samwald M, Xu H, Blagec K, Empey PE, Malone DC, et al. 2016.. Incidence of exposure of patients in the United States to multiple drugs for which pharmacogenomic guidelines are available. . PLOS ONE 11::e0164972
     [Crossref] [Google Scholar]
  41. 41.
    Kuch W, Rinner C, Gall W, Samwald M. 2016.. How many patients could benefit from pre-emptive pharmacogenomic testing and decision support? A retrospective study based on nationwide Austrian claims data. . Stud. Health Technol. Inform. 223::253–58
     [Google Scholar]
  42. 42.
    Kimpton JE, Carey IM, Threapleton CJD, Robinson A, Harris T, et al. 2019.. Longitudinal exposure of English primary care patients to pharmacogenomic drugs: an analysis to inform design of pre-emptive pharmacogenomic testing. . Br. J. Clin. Pharmacol. 85::2734–46
     [Crossref] [Google Scholar]
  43. 43.
    Schildcrout JS, Denny JC, Bowton E, Gregg W, Pulley JM, et al. 2012.. Optimizing drug outcomes through pharmacogenetics: a case for preemptive genotyping. . Clin. Pharmacol. Ther. 92::235–42
     [Crossref] [Google Scholar]
  44. 44.
    Jarvis JP, Peter AP, Keogh M, Baldasare V, Beanland GM, et al. 2022.. Real-world impact of a pharmacogenomics-enriched comprehensive medication management program. . J. Pers. Med. 12::421
     [Crossref] [Google Scholar]
  45. 45.
    Pratt VM, Cavallari LH, Fulmer ML, Gaedigk A, Hachad H, et al. 2022.. TPMT and NUDT15 genotyping recommendations: a joint consensus recommendation of the Association for Molecular Pathology, Clinical Pharmacogenetics Implementation Consortium, College of American Pathologists, Dutch Pharmacogenetics Working Group of the Royal Dutch Pharmacists Association, European Society for Pharmacogenomics and Personalized Therapy, and Pharmacogenomics Knowledgebase. . J. Mol. Diagn. 24::1051–63
     [Crossref] [Google Scholar]
  46. 46.
    Bank PCD, Swen JJ, Guchelaar HJ. 2018.. Implementation of pharmacogenomics in everyday clinical settings. . Adv. Pharmacol. 83::219–46
     [Crossref] [Google Scholar]
  47. 47.
    van der Drift D, Simoons M, Koch BCP, Brufau G, Bindels P, et al. 2023.. Implementation of pharmacogenetics in first-line care: evaluation of its use by general practitioners. . Genes 14::1841
     [Crossref] [Google Scholar]
  48. 48.
    Saya S, Chondros P, Abela A, Mihalopolous C, Chatterton ML, et al. 2023.. The PRESIDE (PhaRmacogEnomicS In DEpression) trial: a double-blind randomised controlled trial of pharmacogenomic-informed prescribing of antidepressants on depression outcomes in patients with major depressive disorder in primary care. . Trials 24::342
     [Crossref] [Google Scholar]
  49. 49.
    Reizine N, Vokes EE, Liu P, Truong TM, Nanda R, et al. 2020.. Implementation of pharmacogenomic testing in oncology care (PhOCus): study protocol of a pragmatic, randomized clinical trial. . Ther. Adv. Med. Oncol. 12::1758835920974118
     [Crossref] [Google Scholar]
  50. 50.
    Hulshof EC, Deenen MJ, Nijenhuis M, Soree B, de Boer-Veger NJ, et al. 2023.. Correction: Dutch Pharmacogenetics Working Group (DPWG) guideline for the gene-drug interaction between UGT1A1 and irinotecan. . Eur. J. Hum. Genet. 31::1088–89
     [Crossref] [Google Scholar]
  51. 51.
    Hulshof EC, Deenen MJ, Nijenhuis M, Soree B, de Boer-Veger NJ, et al. 2023.. Dutch pharmacogenetics working group (DPWG) guideline for the gene-drug interaction between UGT1A1 and irinotecan. . Eur. J. Hum. Genet. 31::982–87
     [Crossref] [Google Scholar]
  52. 52.
    Gammal RS, Court MH, Haidar CE, Iwuchukwu OF, Gaur AH, et al. 2016.. Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for UGT1A1 and atazanavir prescribing. . Clin. Pharmacol. Ther. 99::363–69
     [Crossref] [Google Scholar]
  53. 53.
    Lunenburg CATC, van der Wouden CH, Nijenhuis M, Crommentuijn-van Rhenen MH, de Boer-Veger NJ, et al. 2020.. Dutch Pharmacogenetics Working Group (DPWG) guideline for the gene-drug interaction of DPYD and fluoropyrimidines. . Eur. J. Hum. Genet. 28::508–17
     [Crossref] [Google Scholar]
  54. 54.
    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::210–16
     [Crossref] [Google Scholar]
  55. 55.
    van der Lee M, Kriek M, Guchelaar H-J, Swen JJ. 2020.. Technologies for pharmacogenomics: a review. . Genes 11::1456
     [Crossref] [Google Scholar]
  56. 56.
    Elliott LS, Henderson JC, Neradilek MB, Moyer NA, Ashcraft KC, Thirumaran RK. 2017.. Clinical impact of pharmacogenetic profiling with a clinical decision support tool in polypharmacy home health patients: a prospective pilot randomized controlled trial. . PLOS ONE 12::e0170905
     [Crossref] [Google Scholar]
  57. 57.
    Kim K, Magness JW, Nelson R, Baron V, Brixner DI. 2018.. Clinical utility of pharmacogenetic testing and a clinical decision support tool to enhance the identification of drug therapy problems through medication therapy management in polypharmacy patients. . J. Manag. Care Spec. Pharm. 24::1250–59
     [Google Scholar]
  58. 58.
    Brixner D, Biltaji E, Bress A, Unni S, Ye X, et al. 2016.. The effect of pharmacogenetic profiling with a clinical decision support tool on healthcare resource utilization and estimated costs in the elderly exposed to polypharmacy. . J. Med. Econ. 19::213–28
     [Crossref] [Google Scholar]
  59. 59.
    Finkelstein J, Friedman C, Hripcsak G, Cabrera M. 2016.. Pharmacogenetic polymorphism as an independent risk factor for frequent hospitalizations in older adults with polypharmacy: a pilot study. . Pharmacogen. Pers. Med. 9::107–16
     [Google Scholar]
  60. 60.
    Swen JJ, van der Wouden CH, Manson LE, Abdullah-Koolmees H, Blagec K, et al. 2023.. A 12-gene pharmacogenetic panel to prevent adverse drug reactions: an open-label, multicentre, controlled, cluster-randomised crossover implementation study. . Lancet 401::347–56
     [Crossref] [Google Scholar]
  61. 61.
    van der Wouden CH, Cambon-Thomsen A, Cecchin E, Cheung KC, Davila-Fajardo CL, et al. 2017.. Implementing pharmacogenomics in Europe: design and implementation strategy of the ubiquitous Pharmacogenomics Consortium. . Clin. Pharmacol. Ther. 101::341–58
     [Crossref] [Google Scholar]
  62. 62.
    Kabbani D, Akika R, Wahid A, Daly AK, Cascorbi I, Zgheib NK. 2023.. Pharmacogenomics in practice: a review and implementation guide. . Front. Pharmacol. 14::1189976
     [Crossref] [Google Scholar]
  63. 63.
    Cavallari LH, Johnson JA. 2023.. Use of a multi-gene pharmacogenetic panel reduces adverse drug effects. . Cell Rep. Med. 4::101021
     [Crossref] [Google Scholar]
  64. 64.
    Ingelman-Sundberg M, Pirmohamed M. 2024.. Precision medicine in cardiovascular therapeutics: evaluating the role of pharmacogenetic analysis prior to drug treatment. . J. Intern. Med. 295::583–98
     [Crossref] [Google Scholar]
  65. 65.
    Swen JJ, Manson LEN, Bohringer S, Pirmohamed M, Guchelaar HJ. 2023.. The PREPARE study: benefits of pharmacogenetic testing are unclear—authors’ reply. . Lancet 401::1851–52
     [Crossref] [Google Scholar]
  66. 66.
    Rogers A, Mackenzie IS, MacDonald TM, Doney ASF. 2023.. The PREPARE study: benefits of pharmacogenetic testing are unclear. . Lancet 401::1850
     [Crossref] [Google Scholar]
  67. 67.
    Weitzel KW, Alexander M, Bernhardt BA, Calman N, Carey DJ, et al. 2016.. The IGNITE network: a model for genomic medicine implementation and research. . BMC Med. Genom. 9::1
     [Crossref] [Google Scholar]
  68. 68.
    Fulton CR, Zang Y, Desta Z, Rosenman MB, Holmes AM, et al. 2019.. Drug-gene and drug-drug interactions associated with tramadol and codeine therapy in the INGENIOUS trial. . Pharmacogenomics 20::397–408
     [Crossref] [Google Scholar]
  69. 69.
    Eadon MT, Rosenman MB, Zhang P, Fulton CR, Callaghan JT, et al. 2023.. The INGENIOUS trial: impact of pharmacogenetic testing on adverse events in a pragmatic clinical trial. . Pharmacogenom. J. 23::169–77
     [Crossref] [Google Scholar]
  70. 70.
    Eadon MT, Desta Z, Levy KD, Decker BS, Pierson RC, et al. 2016.. Implementation of a pharmacogenomics consult service to support the INGENIOUS trial. . Clin. Pharmacol. Ther. 100::63–66
     [Crossref] [Google Scholar]
  71. 71.
    Vos CF, de Wildt SN, Kramers C. 2023.. Iedereen een DNA-medicatiepas?. Ned. Tijdschr. Geneeskd. 167::D7584
     [Google Scholar]
  72. 72.
    Swen JJ, Huizinga TW, Gelderblom H, de Vries EG, Assendelft WJ, et al. 2007.. Translating pharmacogenomics: challenges on the road to the clinic. . PLOS Med. 4::e209
     [Crossref] [Google Scholar]
  73. 73.
    Pratt VM, Cavallari LH, Del Tredici AL, Hachad H, Ji Y, et al. 2019.. Recommendations for clinical CYP2C9 genotyping allele selection: a joint recommendation of the Association for Molecular Pathology and College of American Pathologists. . J. Mol. Diagn. 21::746–55
     [Crossref] [Google Scholar]
  74. 74.
    Pratt VM, Cavallari LH, Del Tredici AL, Hachad H, Ji Y, et al. 2020.. Recommendations for clinical warfarin genotyping allele selection: a report of the Association for Molecular Pathology and the College of American Pathologists. . J. Mol. Diagn. 22::847–59
     [Crossref] [Google Scholar]
  75. 75.
    Pratt VM, Cavallari LH, Del Tredici AL, Gaedigk A, Hachad H, et al. 2021.. Recommendations for clinical CYP2D6 genotyping allele selection: a joint consensus recommendation of the Association for Molecular Pathology, College of American Pathologists, Dutch Pharmacogenetics Working Group of the Royal Dutch Pharmacists Association, and the European Society for Pharmacogenomics and Personalized Therapy. . J. Mol. Diagn. 23::1047–64
     [Crossref] [Google Scholar]
  76. 76.
    Pratt VM, Del Tredici AL, Hachad H, Ji Y, Kalman LV, et al. 2018.. Recommendations for clinical CYP2C19 genotyping allele selection: a report of the Association for Molecular Pathology. . J. Mol. Diagn. 20::269–76
     [Crossref] [Google Scholar]
  77. 77.
    Gong L, Klein C, Whirl-Carrillo M, Scott S, Klein T. 2023.. P178: the ClinGen Pharmacogenomics Working Group: developing a framework for gene-drug response clinical validity. . Genet. Med. Open 1:(Suppl.):100207
     [Crossref] [Google Scholar]
  78. 78.
    Caudle KE, Sangkuhl K, Whirl-Carrillo M, Swen JJ, Haidar CE, et al. 2020.. Standardizing CYP2D6 genotype to phenotype translation: consensus recommendations from the Clinical Pharmacogenetics Implementation Consortium and Dutch Pharmacogenetics Working Group. . Clin. Transl. Sci. 13::116–24
     [Crossref] [Google Scholar]
  79. 79.
    Zierhut HA, Campbell CA, Mitchell AG, Lemke AA, Mills R, Bishop JR. 2017.. Collaborative counseling considerations for pharmacogenomic tests. . Pharmacotherapy 37::990–99
     [Crossref] [Google Scholar]
  80. 80.
    Shuldiner AR, Relling MV, Peterson JF, Hicks JK, Freimuth RR, et al. 2013.. The Pharmacogenomics Research Network Translational Pharmacogenetics Program: overcoming challenges of real-world implementation. . Clin. Pharmacol. Ther. 94::207–10
     [Crossref] [Google Scholar]
  81. 81.
    Ji Y, Si Y, McMillin GA, Lyon E. 2018.. Clinical pharmacogenomics testing in the era of next generation sequencing: challenges and opportunities for precision medicine. . Expert Rev. Mol. Diagn. 18::411–21
     [Crossref] [Google Scholar]
  82. 82.
    Reisberg S, Krebs K, Lepamets M, Kals M, Magi R, et al. 2019.. Translating genotype data of 44,000 biobank participants into clinical pharmacogenetic recommendations: challenges and solutions. . Genet. Med. 21::1345–54
     [Crossref] [Google Scholar]
  83. 83.
    Tremmel R, Pirmann S, Zhou Y, Lauschke VM. 2023.. Translating pharmacogenomic sequencing data into drug response predictions—how to interpret variants of unknown significance. . Br. J. Clin. Pharmacol. In press. https://doi.org/10.1111/bcp.15915
    [Google Scholar]
  84. 84.
    Lauschke VM, Zhou Y, Ingelman-Sundberg M. 2024.. Pharmacogenomics beyond single common genetic variants: the way forward. . Annu. Rev. Pharmacol. Toxicol. 64::33–51
     [Crossref] [Google Scholar]
  85. 85.
    Chong CS, Limviphuvadh V, Maurer-Stroh S. 2021.. Global spectrum of population-specific common missense variation in cytochrome P450 pharmacogenes. . Hum. Mutat. 42::1107–23
     [Crossref] [Google Scholar]
  86. 86.
    Veldman A, Kiewiet MBG, Heiner-Fokkema MR, Nelen MR, Sinke RJ, et al. 2022.. Towards next-generation sequencing (NGS)-based newborn screening: a technical study to prepare for the challenges ahead. . Int. J. Neonatal Screen 8::17
     [Crossref] [Google Scholar]
  87. 87.
    Hollegaard MV, Grauholm J, Nielsen R, Grove J, Mandrup S, Hougaard DM. 2013.. Archived neonatal dried blood spot samples can be used for accurate whole genome and exome-targeted next-generation sequencing. . Mol. Genet. Metab. 110::65–72
     [Crossref] [Google Scholar]
  88. 88.
    Manson LEN, Chan PCY, Bohringer S, Guchelaar HJ. 2022.. Genotyping for HLA risk alleles versus patch tests to diagnose anti-seizure medication induced cutaneous adverse drug reactions. . Front. Pharmacol. 13::1061419
     [Crossref] [Google Scholar]
  89. 89.
    Westlind-Johnsson A, Hermann R, Huennemeyer A, Hauns B, Lahu G, et al. 2006.. Identification and characterization of CYP3A4*20, a novel rare CYP3A4 allele without functional activity. . Clin. Pharmacol. Ther. 79::339–49
     [Crossref] [Google Scholar]
  90. 90.
    Apellániz-Ruiz M, Inglada-Pérez L, Naranjo ME, Sánchez L, Mancikova V, et al. 2015.. High frequency and founder effect of the CYP3A4*20 loss-of-function allele in the Spanish population classifies CYP3A4 as a polymorphic enzyme. . Pharmacogenom. J. 15::288–92
     [Crossref] [Google Scholar]
  91. 91.
    Matimba A, Dhoro M, Dandara C. 2016.. Is there a role of pharmacogenomics in Africa. . Glob. Health Epidemiol. Genom. 1::e9
     [Crossref] [Google Scholar]
  92. 92.
    H3Africa Consort., Rotimi C, Abayomi A, Abimiku A, Adabayeri VM, et al. 2014.. Enabling the genomic revolution in Africa. . Science 344::1346–48
     [Crossref] [Google Scholar]
  93. 93.
    Radouani F, Zass L, Hamdi Y, da Rocha J, Sallam R, et al. 2020.. A review of clinical pharmacogenetics studies in African populations. . Pers. Med. 17::155–70
     [Crossref] [Google Scholar]
  94. 94.
    Samarasinghe SR, Hoy W, Jadhao S, McMorran BJ, Guchelaar HJ, Nagaraj SH. 2023.. The pharmacogenomic landscape of an Indigenous Australian population. . Front. Pharmacol. 14::1180640
     [Crossref] [Google Scholar]
  95. 95.
    van der Lee M, Allard WG, Bollen S, Santen GWE, Ruivenkamp CAL, et al. 2020.. Repurposing of diagnostic whole exome sequencing data of 1,583 individuals for clinical pharmacogenetics. . Clin. Pharmacol. Ther. 107::617–27
     [Crossref] [Google Scholar]
  96. 96.
    Chua EW, Cree SL, Ton KN, Lehnert K, Shepherd P, et al. 2016.. Cross-comparison of exome analysis, next-generation sequencing of amplicons, and the iPLEX® ADME PGx panel for pharmacogenomic profiling. . Front. Pharmacol. 7::1
     [Google Scholar]
  97. 97.
    Shugg T, Ly RC, Osei W, Rowe EJ, Granfield CA, et al. 2023.. Computational pharmacogenotype extraction from clinical next-generation sequencing. . Front. Oncol. 13::1199741
     [Crossref] [Google Scholar]
  98. 98.
    Caspar SM, Schneider T, Meienberg J, Matyas G. 2020.. Added value of clinical sequencing: WGS-based profiling of pharmacogenes. . Int. J. Mol. Sci. 21::2308
     [Crossref] [Google Scholar]
  99. 99.
    van der Lee M, Rowell WJ, Menafra R, Guchelaar HJ, Swen JJ, Anvar SY. 2022.. Application of long-read sequencing to elucidate complex pharmacogenomic regions: a proof of principle. . Pharmacogenom. J. 22::75–81
     [Crossref] [Google Scholar]
  100. 100.
    Lewis CM, Vassos E. 2020.. Polygenic risk scores: from research tools to clinical instruments. . Genome Med. 12::44
     [Crossref] [Google Scholar]
  101. 101.
    Marston NA, Kamanu FK, Nordio F, Gurmu Y, Roselli C, et al. 2020.. Predicting benefit from evolocumab therapy in patients with atherosclerotic disease using a genetic risk score: results from the FOURIER trial. . Circulation 141::616–23
     [Crossref] [Google Scholar]
  102. 102.
    Verhoef TI, Ragia G, de Boer A, Barallon R, Kolovou G, et al. 2013.. A randomized trial of genotype-guided dosing of acenocoumarol and phenprocoumon. . N. Engl. J. Med. 369::2304–12
     [Crossref] [Google Scholar]
  103. 103.
    Hulshof EC, de With M, de Man FM, Creemers GJ, Deiman B, et al. 2022.. UGT1A1 genotype-guided dosing of irinotecan: a prospective safety and cost analysis in poor metaboliser patients. . Eur. J. Cancer 162::148–57
     [Crossref] [Google Scholar]
  104. 104.
    Mega JL, Walker JR, Ruff CT, Vandell AG, Nordio F, et al. 2015.. Genetics and the clinical response to warfarin and edoxaban: findings from the randomised, double-blind ENGAGE AF-TIMI 48 trial. . Lancet 385::2280–87
     [Crossref] [Google Scholar]
/content/journals/10.1146/annurev-pharmtox-061724-080935
Loading
Pharmacogenetic Panel Testing: A Review of Current Practice and Potential for Clinical Implementation
Annual Review of Pharmacology and Toxicology 65, 91 (2025); https://doi.org/10.1146/annurev-pharmtox-061724-080935
/content/journals/10.1146/annurev-pharmtox-061724-080935
/content/journals/10.1146/annurev-pharmtox-061724-080935
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

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