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Pharmacogenomics: current status and future perspectives

Abstract

Inter-individual variability in drug response, be it efficacy or safety, is common and likely to become an increasing problem globally given the growing elderly population requiring treatment. Reasons for this inter-individual variability include genomic factors, an area of study called pharmacogenomics. With genotyping technologies now widely available and decreasing in cost, implementing pharmacogenomics into clinical practice — widely regarded as one of the initial steps in mainstreaming genomic medicine — is currently a focus in many countries worldwide. However, major challenges of implementation lie at the point of delivery into health-care systems, including the modification of current clinical pathways coupled with a massive knowledge gap in pharmacogenomics in the health-care workforce. Pharmacogenomics can also be used in a broader sense for drug discovery and development, with increasing evidence suggesting that genomically defined targets have an increased success rate during clinical development.

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Fig. 1: The pharmacogenomics landscape.

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References

  1. Nkhoma, E. T., Poole, C., Vannappagari, V., Hall, S. A. & Beutler, E. The global prevalence of glucose-6-phosphate dehydrogenase deficiency: a systematic review and meta-analysis. Blood Cell Mol. Dis. 42, 267–278 (2009).

    Article  CAS  Google Scholar 

  2. Pirmohamed, M. Pharmacogenetics and pharmacogenomics. Br. J. Clin. Pharmacol. 52, 345–347 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Spear, B. B., Heath-Chiozzi, M. & Huff, J. Clinical application of pharmacogenetics. Trends Mol. Med. 7, 201–204 (2001).

    Article  CAS  PubMed  Google Scholar 

  4. Connor, S. Glaxo chief: Our drugs do not work on most patients. Independent (Lond.) https://www.independent.co.uk/news/science/glaxo-chief-our-drugs-do-not-work-on-most-patients-5508670.html (8 December 2003).

  5. Schork, N. J. Personalized medicine: time for one-person trials. Nature 520, 609–611 (2015).

    Article  CAS  PubMed  Google Scholar 

  6. Michel, M. C. & Staskin, D. Study designs for evaluation of combination treatment: focus on individual patient benefit. Biomedicines 10, 270 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Snapinn, S. M. & Jiang, Q. Responder analyses and the assessment of a clinically relevant treatment effect. Trials 8, 31 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Senn, S. Individual response to treatment: is it a valid assumption? BMJ 329, 966–968 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Lonergan, M. et al. Defining drug response for stratified medicine. Drug Discov. Today 22, 173–179 (2017).

    Article  PubMed  Google Scholar 

  10. Pirmohamed, M. et al. Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients. BMJ 329, 15–19 (2004). The largest epidemiological study of ADRs causing hospital admission.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Osanlou, R., Walker, L., Hughes, D. A., Burnside, G. & Pirmohamed, M. Adverse drug reactions, multimorbidity and polypharmacy: a prospective analysis of 1 month of medical admissions. BMJ Open 12, e055551 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Davies, E. C. et al. Adverse drug reactions in hospital in-patients: a prospective analysis of 3695 patient-episodes. PLoS ONE 4, e4439 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Alhawassi, T. M., Krass, I., Bajorek, B. V. & Pont, L. G. 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, 2079–2086 (2014).

    PubMed  PubMed Central  Google Scholar 

  14. Soiza, R. L. Global pandemic — the true incidence of adverse drug reactions. Age Ageing 49, 934–935 (2020).

    Article  PubMed  Google Scholar 

  15. Mostafa, S., Kirkpatrick, C. M. J., Byron, K. & Sheffield, L. An analysis of allele, genotype and phenotype frequencies, actionable pharmacogenomic (PGx) variants and phenoconversion in 5408 Australian patients genotyped for CYP2D6, CYP2C19, CYP2C9 and VKORC1 genes. J. Neural Transm. 126, 5–18 (2019).

    Article  CAS  PubMed  Google Scholar 

  16. Cohn, I. et al. Genome sequencing as a platform for pharmacogenetic genotyping: a pediatric cohort study. NPJ Genom. Med. 2, 19 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Reisberg, S. et al. Translating genotype data of 44,000 biobank participants into clinical pharmacogenetic recommendations: challenges and solutions. Genet. Med. 21, 1345–1354 (2019).

    Article  PubMed  Google Scholar 

  18. Alshabeeb, M. A., Deneer, V. H. M., Khan, A. & Asselbergs, F. W. Use of pharmacogenetic drugs by the Dutch population. Front. Genet. 10, 567 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Jithesh, P. V. et al. A population study of clinically actionable genetic variation affecting drug response from the Middle East. NPJ Genom. Med. 7, 10 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. McInnes, G. et al. Pharmacogenetics at scale: an analysis of the UK Biobank. Clin. Pharmacol. Ther. 109, 1528–1537 (2021).

    Article  PubMed  Google Scholar 

  21. Turner, R. M., de Koning, E. M., Fontana, V., Thompson, A. & Pirmohamed, M. Multimorbidity, polypharmacy, and drug-drug-gene interactions following a non-ST elevation acute coronary syndrome: analysis of a multicentre observational study. BMC Med. 18, 367 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Van Driest, S. L. et al. Clinically actionable genotypes among 10,000 patients with preemptive pharmacogenomic testing. Clin. Pharmacol. Ther. 95, 423–431 (2014).

    Article  PubMed  Google Scholar 

  23. Ji, Y. et al. Preemptive pharmacogenomic testing for precision medicine: a comprehensive analysis of five actionable pharmacogenomic genes using next-generation DNA sequencing and a customized CYP2D6 genotyping cascade. J. Mol. Diagn. 18, 438–445 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Dunnenberger, H. M. et al. Preemptive clinical pharmacogenetics implementation: current programs in five US medical centers. Annu. Rev. Pharmacol. Toxicol. 55, 89–106 (2015).

    Article  CAS  PubMed  Google Scholar 

  25. Kimpton, J. E. et al. 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, 2734–2746 (2019). A large database analysis showing exposure to drugs with pharmacogenomic guidance over a lifetime.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Whirl-Carrillo, M. et al. Pharmacogenomics knowledge for personalized medicine. Clin. Pharmacol. Ther. 92, 414–417 (2012).

    Article  CAS  PubMed  Google Scholar 

  27. Whirl-Carrillo, M. et al. An evidence-based framework for evaluating pharmacogenomics knowledge for personalized medicine. Clin. Pharmacol. Ther. 110, 563–572 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Gaedigk, A., Whirl-Carrillo, M., Pratt, V. M., Miller, N. A. & Klein, T. E. PharmVar and the landscape of pharmacogenetic resources. Clin. Pharmacol. Ther. 107, 43–46 (2020).

    Article  PubMed  Google Scholar 

  29. FDA. Table of Pharmacogenomic Biomarkers in Drug Labeling. https://www.fda.gov/drugs/science-and-research-drugs/table-pharmacogenomic-biomarkers-drug-labeling (2022).

  30. FDA. Table of Pharmacogenetic Associations. https://www.fda.gov/medical-devices/precision-medicine/table-pharmacogenetic-associations (2022).

  31. Electronic Medicines Compendium. Tamoxifen 20mg film-coated tablets. https://www.medicines.org.uk/emc/product/2248/smpc#gref (2022).

  32. Koopmans, A. B., Braakman, M. H., Vinkers, D. J., Hoek, H. W. & van Harten, P. N. Meta-analysis of probability estimates of worldwide variation of CYP2D6 and CYP2C19. Transl. Psychiatry 11, 141 (2021). Meta-analysis detailing the global variation in frequencies of variants in two important cytochrome P450 genes.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Meyer, U. A. Pharmacogenetics — five decades of therapeutic lessons from genetic diversity. Nat. Rev. Genet. 5, 669–676 (2004).

    Article  CAS  PubMed  Google Scholar 

  34. Matthaei, J. et al. Heritability of metoprolol and torsemide pharmacokinetics. Clin. Pharmacol. Ther. 98, 611–621 (2015).

    Article  CAS  PubMed  Google Scholar 

  35. Arnett, D. K. et al. Pharmacogenetic approaches to hypertension therapy: design and rationale for the Genetics of Hypertension Associated Treatment (GenHAT) study. Pharmacogenomics J. 2, 309–317 (2002).

    Article  CAS  PubMed  Google Scholar 

  36. Hawcutt, D. B. et al. Susceptibility to corticosteroid-induced adrenal suppression: a genome-wide association study. Lancet Respir. Med. 6, 442–450 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Bourgeois, S. et al. Genome-wide association between EYA1 and aspirin-induced peptic ulceration. EBioMedicine 74, 103728 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. McInnes, G., Yee, S. W., Pershad, Y. & Altman, R. B. Genomewide association studies in pharmacogenomics. Clin. Pharmacol. Ther. 110, 637–648 (2021). The successes and challenges of undertaking GWAS for pharmacogenomic phenotypes.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Maranville, J. C. & Cox, N. J. Pharmacogenomic variants have larger effect sizes than genetic variants associated with other dichotomous complex traits. Pharmacogenomics J. 16, 388–392 (2016).

    Article  CAS  PubMed  Google Scholar 

  40. Bourgeois, S. et al. A multi-factorial analysis of response to warfarin in a UK prospective cohort. Genome Med. 8, 2 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Relling, M. V. et al. Clinical pharmacogenetics implementation consortium guideline for thiopurine dosing based on TPMT and NUDT15 genotypes: 2018 update. Clin. Pharmacol. Ther. 105, 1095–1105 (2019).

    Article  CAS  PubMed  Google Scholar 

  42. Henricks, L. M. et al. DPYD genotype-guided dose individualisation of fluoropyrimidine therapy in patients with cancer: a prospective safety analysis. Lancet Oncol. 19, 1459–1467 (2018). Evaluation of four variants in the DPYD gene in patients of European descent, and how changes in dose can modulate the occurrence of toxicity.

    Article  CAS  PubMed  Google Scholar 

  43. Hulshof, E. C. et al. UGT1A1 genotype-guided dosing of irinotecan: a prospective safety and cost analysis in poor metaboliser patients. Eur. J. Cancer 162, 148–157 (2022).

    Article  CAS  PubMed  Google Scholar 

  44. Rawlins, M. D. & Thompson, J. W. in Textbook of Adverse Drug Reactions (ed. Davies, D. M.) 18–45 (Oxford University Press, Oxford, 1991).

  45. Kuruvilla, R., Scott, K. & Pirmohamed, S. M. Pharmacogenomics of drug hypersensitivity: technology and translation. Immunol. Allergy Clin. North. Am. 42, 335–355 (2022).

    Article  PubMed  Google Scholar 

  46. Daly, A. K. et al. HLA-B*5701 genotype is a major determinant of drug-induced liver injury due to flucloxacillin. Nat. Genet. 41, 816–819 (2009).

    Article  CAS  PubMed  Google Scholar 

  47. McCormack, M. et al. HLA-A*3101 and carbamazepine-induced hypersensitivity reactions in Europeans. N. Engl. J. Med. 364, 1134–1143 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Phillips, E. & Mallal, S. Successful translation of pharmacogenetics into the clinic: the abacavir example. Mol. Diagn. Ther. 13, 1–9 (2009).

    Article  PubMed  Google Scholar 

  49. Mallal, S. et al. HLA-B*5701 screening for hypersensitivity to abacavir. N. Engl. J. Med. 358, 568–579 (2008). Randomized controlled trial showing the utility of pre-prescription genotyping for HLA-B*57:01 in preventing abacavir hypersensitivity.

    Article  PubMed  Google Scholar 

  50. Illing, P. T. et al. Immune self-reactivity triggered by drug-modified HLA-peptide repertoire. Nature 486, 554–558 (2012). Paper detailing the mechanisms by which abacavir binds to HLA-B*57:01 and alters the repertoire of endogenous peptides leading to immune self-reactivity.

    Article  CAS  PubMed  Google Scholar 

  51. White, K. D., Chung, W. H., Hung, S. I., Mallal, S. & Phillips, E. J. Evolving models of the immunopathogenesis of T cell-mediated drug allergy: the role of host, pathogens, and drug response. J. Allergy Clin. Immunol. 136, 219–234 (2015). quiz 235.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Jaruthamsophon, K., Thomson, P. J., Sukasem, C., Naisbitt, D. J. & Pirmohamed, M. HLA allele-restricted immune-mediated adverse drug reactions: framework for genetic prediction. Annu. Rev. Pharmacol. Toxicol. 62, 509–529 (2021).

    Article  PubMed  Google Scholar 

  53. Nelson, M. R. et al. The genetics of drug efficacy: opportunities and challenges. Nat. Rev. Genet. 17, 197–206 (2016).

    Article  CAS  PubMed  Google Scholar 

  54. Holmes, R. D., Tiwari, A. K. & Kennedy, J. L. Mechanisms of the placebo effect in pain and psychiatric disorders. Pharmacogenomics J. 16, 491–500 (2016).

    Article  CAS  PubMed  Google Scholar 

  55. Jorgensen, A. L. et al. Adherence and variability in warfarin dose requirements: assessment in a prospective cohort. Pharmacogenomics 14, 151–163 (2013).

    Article  CAS  PubMed  Google Scholar 

  56. Agache, I. & Akdis, C. A. Precision medicine and phenotypes, endotypes, genotypes, regiotypes, and theratypes of allergic diseases. J. Clin. Invest. 129, 1493–1503 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Brown, L. C. et al. Pharmacogenomic testing and depressive symptom remission: a systematic review and meta-analysis of prospective, controlled clinical trials. Clin. Pharmacol. Ther. https://doi.org/10.1002/cpt.2748 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Pereira, N. L. et al. Clopidogrel pharmacogenetics. Circ. Cardiovasc. Interv. 12, e007811 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Shuldiner, A. R. et al. Association of cytochrome P450 2C19 genotype with the antiplatelet effect and clinical efficacy of clopidogrel therapy. JAMA 302, 849–857 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Beitelshees, A. L. et al. CYP2C19 genotype-guided antiplatelet therapy after percutaneous coronary intervention in diverse clinical settings. J. Am. Heart Assoc. 11, e024159 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Minderhoud, C., Otten, L. S., Hilkens, P. H. E., van den Broek, M. P. H. & Harmsze, A. M. Increased frequency of CYP2C19 loss-of-function alleles in clopidogrel-treated patients with recurrent cerebral ischemia. Br. J. Clin. Pharmacol. 88, 3335–3340 (2022).

    Article  CAS  PubMed  Google Scholar 

  62. Wang, Y. et al. Ticagrelor versus clopidogrel in CYP2C19 loss-of-function carriers with stroke or TIA. N. Engl. J. Med. 385, 2520–2530 (2021).

    Article  CAS  PubMed  Google Scholar 

  63. Nofziger, C. et al. PharmVar GeneFocus: CYP2D6. Clin. Pharmacol. Ther. 107, 154–170 (2020).

    Article  CAS  PubMed  Google Scholar 

  64. Carranza-Leon, D., Dickson, A. L., Gaedigk, A., Stein, C. M. & Chung, C. P. CYP2D6 genotype and reduced codeine analgesic effect in real-world clinical practice. Pharmacogenomics J. 21, 484–490 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Koren, G., Cairns, J., Chitayat, D., Gaedigk, A. & Leeder, S. J. Pharmacogenetics of morphine poisoning in a breastfed neonate of a codeine-prescribed mother. Lancet 368, 704 (2006).

    Article  PubMed  Google Scholar 

  66. Kelly, L. E. et al. More codeine fatalities after tonsillectomy in North American children. Pediatrics 129, e1343–e1347 (2012).

    Article  PubMed  Google Scholar 

  67. Volpi, S. et al. Research directions in the clinical implementation of pharmacogenomics: an overview of US programs and projects. Clin. Pharmacol. Ther. 103, 778–786 (2018).

    Article  PubMed  Google Scholar 

  68. Magavern, E. F. et al. Challenges in cardiovascular pharmacogenomics implementation: a viewpoint from the European Society of Cardiology Working Group on Cardiovascular Pharmacotherapy. Eur. Heart J. Cardiovasc. Pharmacother. 8, 100–103 (2022).

    Article  PubMed  Google Scholar 

  69. Pirmohamed, M. & Hughes, D. A. Pharmacogenetic tests: the need for a level playing field. Nat. Rev. Drug Discov. 12, 3–4 (2013).

    Article  CAS  PubMed  Google Scholar 

  70. Concato, J. Observational versus experimental studies: what’s the evidence for a hierarchy? NeuroRx 1, 341–347 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Huddart, R., Sangkuhl, K., Whirl-Carrillo, M. & Klein, T. E. Are randomized controlled trials necessary to establish the value of implementing pharmacogenomics in the clinic? Clin. Pharmacol. Ther. 106, 284–286 (2019).

    Article  PubMed  Google Scholar 

  72. Padmanabhan, S. in Handbook of Pharmacogenomics and Stratified Medicine (ed. Padmanabhan, S.) 309–320 (Academic Press, San Diego, 2014).

  73. Speich, B. et al. Systematic review on costs and resource use of randomized clinical trials shows a lack of transparent and comprehensive data. J. Clin. Epidemiol. 96, 1–11 (2018).

    Article  PubMed  Google Scholar 

  74. Rawlins, M. De testimonio: on the evidence for decisions about the use of therapeutic interventions. Lancet 372, 2152–2161 (2008).

    Article  PubMed  Google Scholar 

  75. Royal College of Physicians and British Pharmacological Society. Personalised prescribing: using pharmacogenomics to improve patient outcomes. Report of a working party. (RCP and BPS, London, 2022). Report detailing the steps needed in the implementation of pharmacogenomics into clinical practice.

  76. Turner, R. M. et al. Pharmacogenomics in the UK National Health Service: opportunities and challenges. Pharmacogenomics 21, 1237–1246 (2020).

    Article  CAS  PubMed  Google Scholar 

  77. Hoffman, J. M. et al. PG4KDS: a model for the clinical implementation of pre-emptive pharmacogenetics. Am. J. Med. Genet. C. Semin. Med. Genet. 166c, 45–55 (2014).

    Article  PubMed  Google Scholar 

  78. Matey, E. T. et al. Nine-gene pharmacogenomics profile service: the Mayo Clinic experience. Pharmacogenomics J. 22, 69–74 (2022).

    Article  CAS  PubMed  Google Scholar 

  79. van der Wouden, C. H. et al. Implementing pharmacogenomics in Europe: design and implementation strategy of the Ubiquitous Pharmacogenomics Consortium. Clin. Pharmacol. Ther. 101, 341–358 (2017).

    Article  PubMed  Google Scholar 

  80. van der Wouden, C. H. et al. Generating evidence for precision medicine: considerations made by the Ubiquitous Pharmacogenomics Consortium when designing and operationalizing the PREPARE study. Pharmacogenet. Genomics 30, 131–144 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Swen, J.J. et al. A 12-gene pharmacogenetic panel to prevent adverse drug reactions: an open-label, multicentre, controlled, cluster-randomised crossover implementation study. Lancet https://doi.org/10.1016/S0140-6736(22)01841-4 (2023). The first large-scale prospective randomized study to show that a panel pharmacogenomics approach can reduce adverse drug reactions.

  82. Relling, M. V. et al. The Clinical Pharmacogenetics Implementation Consortium: 10 years later. Clin. Pharmacol. Ther. 107, 171–175 (2020).

    Article  PubMed  Google Scholar 

  83. Plumpton, C. O., Roberts, D., Pirmohamed, M. & Hughes, D. A. A systematic review of economic evaluations of pharmacogenetic testing for prevention of adverse drug reactions. Pharmacoeconomics 34, 771–793 (2016).

    Article  PubMed  Google Scholar 

  84. Plumpton, C. O., Pirmohamed, M. & Hughes, D. A. Cost-effectiveness of panel tests for multiple pharmacogenes associated with adverse drug reactions: an evaluation framework. Clin. Pharmacol. Ther. 105, 1429–1438 (2019).

    Article  PubMed  Google Scholar 

  85. Plumpton, C. O., Alfirevic, A., Pirmohamed, M. & Hughes, D. A. Cost effectiveness analysis of HLA-B*58:01 genotyping prior to initiation of allopurinol for gout. Rheumatology 56, 1729–1739 (2017).

    Article  CAS  PubMed  Google Scholar 

  86. Dong, D., Sung, C. & Finkelstein, E. A. Cost-effectiveness of HLA-B*1502 genotyping in adult patients with newly diagnosed epilepsy in Singapore. Neurology 79, 1259–1267 (2012).

    Article  PubMed  Google Scholar 

  87. Hingorani, A. D. et al. Improving the odds of drug development success through human genomics: modelling study. Sci. Rep. 9, 18911 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Wouters, O. J., McKee, M. & Luyten, J. Estimated research and development investment needed to bring a new medicine to market, 2009–2018. JAMA 323, 844–853 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Nelson, M. R. et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47, 856–860 (2015).

    Article  CAS  PubMed  Google Scholar 

  90. King, E. A., Davis, J. W. & Degner, J. F. Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval. PLoS Genet. 15, e1008489 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  91. Ochoa, D. et al. Human genetics evidence supports two-thirds of the 2021 FDA-approved drugs. Nat. Rev. Drug Discov. 21, 551 (2022).

    Article  CAS  PubMed  Google Scholar 

  92. Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. https://doi.org/10.1200/PO.17.00011 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  93. O’Brien, S. G. et al. Imatinib compared with interferon and low-dose cytarabine for newly diagnosed chronic-phase chronic myeloid leukemia. N. Engl. J. Med. 348, 994–1004 (2003).

    Article  PubMed  Google Scholar 

  94. Bower, H. et al. Life expectancy of patients with chronic myeloid leukemia approaches the life expectancy of the general population. J. Clin. Oncol. 34, 2851–2857 (2016).

    Article  CAS  PubMed  Google Scholar 

  95. Inzoli, E., Aroldi, A., Piazza, R. & Gambacorti-Passerini, C. Tyrosine kinase inhibitor discontinuation in chronic myeloid leukemia: eligibility criteria and predictors of success. Am. J. Hematol. 97, 1075–1085 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Sosman, J. A. et al. Survival in BRAF V600-mutant advanced melanoma treated with vemurafenib. N. Engl. J. Med. 366, 707–714 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Denison, H. et al. Diacylglycerol acyltransferase 1 inhibition with AZD7687 alters lipid handling and hormone secretion in the gut with intolerable side effects: a randomized clinical trial. Diabetes Obes. Metab. 16, 334–343 (2014).

    Article  CAS  PubMed  Google Scholar 

  98. Haas, J. T. et al. DGAT1 mutation is linked to a congenital diarrheal disorder. J. Clin. Invest. 122, 4680–4684 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Alves, C., Mendes, D. & Batel Marques, F. Statins and risk of cataracts: a systematic review and meta-analysis of observational studies. Cardiovasc. Ther. 36, e12480 (2018).

    Article  PubMed  Google Scholar 

  100. Ghouse, J., Ahlberg, G., Skov, A. G., Bundgaard, H. & Olesen, M. S. Association of common and rare genetic variation in the 3-hydroxy-3-methylglutaryl coenzyme A reductase gene and cataract risk. J. Am. Heart Assoc. 11, e025361 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  101. 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 

  102. Nicoletti, P. et al. Beta-lactam-induced immediate hypersensitivity reactions: a genome-wide association study of a deeply phenotyped cohort. J. Allergy Clin. Immunol. 147, 1830–1837 (2020).

    Article  PubMed  Google Scholar 

  103. Krebs, K. et al. Genome-wide study identifies association between HLA-B*55:01 and self-reported penicillin allergy. Am. J. Hum. Genet. 107, 612–621 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Castells, M., Khan, D. A. & Phillips, E. J. Penicillin allergy. N. Engl. J. Med. 381, 2338–2351 (2019).

    Article  CAS  PubMed  Google Scholar 

  105. Muhammad, A. et al. Genome-wide approach to measure variant-based heritability of drug outcome phenotypes. Clin. Pharmacol. Ther. 110, 714–722 (2021).

    Article  CAS  PubMed  Google Scholar 

  106. Zhou, Y., Tremmel, R., Schaeffeler, E., Schwab, M. & Lauschke, V. M. Challenges and opportunities associated with rare-variant pharmacogenomics. Trends Pharmacol. Sci. 43, 852–865 (2022).

    Article  CAS  PubMed  Google Scholar 

  107. Ingelman-Sundberg, M., Mkrtchian, S., Zhou, Y. & Lauschke, V. M. Integrating rare genetic variants into pharmacogenetic drug response predictions. Hum. Genomics 12, 26 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  108. Zhou, Y., Koutsilieri, S., Eliasson, E. & Lauschke, V. M. A paradigm shift in pharmacogenomics: from candidate polymorphisms to comprehensive sequencing. Basic. Clin. Pharmacol. Toxicol. 131, 452–464 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Zhou, Y., Mkrtchian, S., Kumondai, M., Hiratsuka, M. & Lauschke, V. M. An optimized prediction framework to assess the functional impact of pharmacogenetic variants. Pharmacogenomics J. 19, 115–126 (2019).

    Article  CAS  PubMed  Google Scholar 

  110. van der Lee, M. et al. Toward predicting CYP2D6-mediated variable drug response from CYP2D6 gene sequencing data. Sci. Transl. Med. 13, eabf3637 (2021).

    Article  PubMed  Google Scholar 

  111. Kreimer, A., Yan, Z., Ahituv, N. & Yosef, N. Meta-analysis of massively parallel reporter assays enables prediction of regulatory function across cell types. Hum. Mutat. 40, 1299–1313 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  112. Fowler, D. M. & Fields, S. Deep mutational scanning: a new style of protein science. Nat. Methods 11, 801–807 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Kullo, I. J. et al. Polygenic scores in biomedical research. Nat. Rev. Genet. 23, 524–532 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. 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 

  115. Lewis, J. P. et al. Pharmacogenomic polygenic response score predicts ischaemic events and cardiovascular mortality in clopidogrel-treated patients. Eur. Heart J. Cardiovasc. Pharmacother. 6, 203–210 (2020).

    Article  PubMed  Google Scholar 

  116. Lanfear, D. E. et al. Polygenic score for β-blocker survival benefit in European ancestry patients with reduced ejection fraction heart failure. Circ. Heart Fail. 13, e007012 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Koido, M. et al. Polygenic architecture informs potential vulnerability to drug-induced liver injury. Nat. Med. 26, 1541–1548 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Sun, L. et al. Polygenic risk scores in cardiovascular risk prediction: a cohort study and modelling analyses. PLoS Med. 18, e1003498 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Kiflen, M. et al. Cost-effectiveness of polygenic risk scores to guide statin therapy for cardiovascular disease. Prev. Circ. Genom. Precis. Med. 15, e003423 (2022).

    Google Scholar 

  120. Mills, M. C. & Rahal, C. The GWAS Diversity Monitor tracks diversity by disease in real time. Nat. Genet. 52, 242–243 (2020). Paper detailing the lack of genetic diversity in the GWAS undertaken to date.

    Article  CAS  PubMed  Google Scholar 

  121. Prive, F. et al. Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort. Am. J. Hum. Genet. 109, 12–23 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Asiimwe, I. G., Zhang, E. J., Osanlou, R., Jorgensen, A. L. & Pirmohamed, M. Warfarin dosing algorithms: a systematic review. Br. J. Clin. Pharmacol. 87, 1717–1729 (2021).

    Article  CAS  PubMed  Google Scholar 

  123. Asiimwe, I. G. et al. Genetic factors influencing warfarin dose in Black-African patients: a systematic review and meta-analysis. Clin. Pharmacol. Ther. 107, 1420–1433 (2020).

    Article  CAS  PubMed  Google Scholar 

  124. Asiimwe, I. G. & Pirmohamed, M. Ethnic diversity and warfarin pharmacogenomics. Front. Pharmacol. 13, 866058 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Electronic Medicines Compendium. Mayzent 0.25 mg film-coated tablets. https://www.medicines.org.uk/emc/product/11019/smpc#gref (2022).

  126. Amezcua, L., Rivera, V. M., Vazquez, T. C., Baezconde-Garbanati, L. & Langer-Gould, A. Health disparities, inequities, and social determinants of health in multiple sclerosis and related disorders in the US: a review. JAMA Neurol. 78, 1515–1524 (2021).

    Article  PubMed  Google Scholar 

  127. Wall, J. D. et al. The GenomeAsia 100K Project enables genetic discoveries across Asia. Nature 576, 106–111 (2019).

    Article  CAS  Google Scholar 

  128. Zhou, W. et al. Global Biobank Meta-analysis Initiative: powering genetic discovery across human disease. Cell Genomics 2, 100192 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Hung, S. I. et al. HLA-B*5801 allele as a genetic marker for severe cutaneous adverse reactions caused by allopurinol. Proc. Natl Acad. Sci. USA 102, 4134–4139 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Ozeki, T. et al. 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, 1034–1041 (2011).

    Article  CAS  PubMed  Google Scholar 

  131. Hung, S. I. et al. Genetic susceptibility to carbamazepine-induced cutaneous adverse drug reactions. Pharmacogenet. Genomics 16, 297–306 (2006).

    Article  CAS  PubMed  Google Scholar 

  132. Capule, F. et al. Association of carbamazepine-induced Stevens–Johnson syndrome/toxic epidermal necrolysis with the HLA-B75 serotype or HLA-B*15:21 allele in Filipino patients. Pharmacogenomics J. 20, 533–541 (2020).

    Article  CAS  PubMed  Google Scholar 

  133. Nicoletti, P. et al. Shared genetic risk factors across carbamazepine-induced hypersensitivity reactions. Clin. Pharmacol. Ther. 106, 1028–1036 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Zhang, F. R. et al. HLA-B*13:01 and the dapsone hypersensitivity syndrome. N. Engl. J. Med. 369, 1620–1628 (2013).

    Article  CAS  PubMed  Google Scholar 

  135. Tangamornsuksan, W. & Lohitnavy, M. Association between HLA-B*1301 and dapsone-induced cutaneous adverse drug reactions: a systematic review and meta-analysis. JAMA Dermatol. 154, 441–446 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  136. Carr, D. F. et al. Genome-wide association study of nevirapine hypersensitivity in a sub-Saharan African HIV-infected population. J. Antimicrob. Chemother. 72, 1152–1162 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  137. Ciccacci, C. et al. A multivariate genetic analysis confirms rs5010528 in the human leucocyte antigen-C locus as a significant contributor to Stevens-Johnson syndrome/toxic epidermal necrolysis susceptibility in a Mozambique HIV population treated with nevirapine. J. Antimicrob. Chemother. 73, 2137–2140 (2018).

    Article  CAS  PubMed  Google Scholar 

  138. Hung, S. I. et al. Common risk allele in aromatic antiepileptic-drug induced Stevens–Johnson syndrome and toxic epidermal necrolysis in Han Chinese. Pharmacogenomics 11, 349–356 (2010).

    Article  CAS  PubMed  Google Scholar 

  139. 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 

  140. Konvinse, K. C. et al. HLA-A*32:01 is strongly associated with vancomycin-induced drug reaction with eosinophilia and systemic symptoms. J. Allergy Clin. Immunol. 144, 183–192 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Lucena, M. I. et al. Susceptibility to amoxicillin-clavulanate-induced liver injury is influenced by multiple HLA class I and II alleles. Gastroenterology 141, 338–347 (2011).

    Article  CAS  PubMed  Google Scholar 

  142. Hautekeete, M. L. et al. HLA association of amoxicillin-clavulanate–induced hepatitis. Gastroenterology 117, 1181–1186 (1999).

    Article  CAS  PubMed  Google Scholar 

  143. O’Donohue, J. et al. Co-amoxiclav jaundice: clinical and histological features and HLA class II association. Gut 47, 717–720 (2000).

    Article  PubMed  PubMed Central  Google Scholar 

  144. Hirata, K. et al. Ticlopidine-induced hepatotoxicity is associated with specific human leukocyte antigen genomic subtypes in Japanese patients: a preliminary case-control study. Pharmacogenomics J. 8, 29–33 (2008).

    Article  CAS  PubMed  Google Scholar 

  145. Ariyoshi, N. et al. Enhanced susceptibility of HLA-mediated ticlopidine-induced idiosyncratic hepatotoxicity by CYP2B6 polymorphism in Japanese. Drug. Metab. Pharmacokinet. 25, 298–306 (2010).

    Article  CAS  PubMed  Google Scholar 

  146. Goldstein, J. I. et al. Clozapine-induced agranulocytosis is associated with rare HLA-DQB1 and HLA-B alleles. Nat. Commun. 5, 4757 (2014).

    Article  CAS  PubMed  Google Scholar 

  147. Dettling, M., Cascorbi, I., Opgen-Rhein, C. & Schaub, R. Clozapine-induced agranulocytosis in schizophrenic Caucasians: confirming clues for associations with human leukocyte class I and II antigens. Pharmacogenomics J. 7, 325–332 (2007).

    Article  CAS  PubMed  Google Scholar 

  148. Oussalah, A. et al. Genetic variants associated with drugs-induced immediate hypersensitivity reactions: a PRISMA-compliant systematic review. Allergy 71, 443–462 (2016).

    Article  CAS  PubMed  Google Scholar 

  149. Claassens, D. M. F. et al. A genotype-guided strategy for oral P2Y(12) inhibitors in primary PCI. N. Engl. J. Med. 381, 1621–1631 (2019). Randomized controlled trial showing non-inferiority of a genotype-guided regimen compared with non-genotype-guided treatment with ticagrelor or prasugrel.

    Article  CAS  PubMed  Google Scholar 

  150. Nishimura, J. et al. Genetic variants in C5 and poor response to eculizumab. N. Engl. J. Med. 370, 632–639 (2014).

    Article  CAS  PubMed  Google Scholar 

  151. Lima, J. J. et al. Clinical pharmacogenetics implementation consortium (CPIC) guideline for CYP2C19 and proton pump inhibitor dosing. Clin. Pharmacol. Ther. 109, 1417–1423 (2021).

    Article  PubMed  Google Scholar 

  152. Zhou, K. et al. Common variants near ATM are associated with glycemic response to metformin in type 2 diabetes. Nat. Genet. 43, 117–120 (2011).

    Article  CAS  PubMed  Google Scholar 

  153. Zhou, K. et al. Variation in the glucose transporter gene SLC2A2 is associated with glycemic response to metformin. Nat. Genet. 48, 1055–1059 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. Kaur, S. D. et al. Recent advances in cancer therapy using PARP inhibitors. Med. Oncol. 39, 241 (2022).

    Article  CAS  PubMed  Google Scholar 

  155. Pearson, E. R. et al. Genetic cause of hyperglycaemia and response to treatment in diabetes. Lancet 362, 1275–1281 (2003). Trial showing marked sensitivity of diabetic patients carrying HNF1A mutations to treatment with sulfonylureas such as gliclazide.

    Article  CAS  PubMed  Google Scholar 

  156. Gloyn, A. L. et al. Activating mutations in the gene encoding the ATP-sensitive potassium-channel subunit Kir6.2 and permanent neonatal diabetes. N. Engl. J. Med. 350, 1838–1849 (2004).

    Article  CAS  PubMed  Google Scholar 

  157. Goetz, M. P. et al. Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for CYP2D6 and tamoxifen therapy. Clin. Pharmacol. Ther. 103, 770–777 (2018).

    Article  PubMed  Google Scholar 

  158. Babenko, A. P. et al. Activating mutations in the ABCC8 gene in neonatal diabetes mellitus. N. Engl. J. Med. 355, 456–466 (2006).

    Article  CAS  PubMed  Google Scholar 

  159. Moriyama, B. et al. Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines for CYP2C19 and voriconazole therapy. Clin. Pharmacol. Ther. 102, 45–51 (2017). Randomized controlled trial showing superiority of genotype-guided dosing with warfarin compared with standard dosing.

    Article  CAS  PubMed  Google Scholar 

  160. Tewkesbury, D. H., Robey, R. C. & Barry, P. J. Progress in precision medicine in cystic fibrosis: a focus on CFTR modulator therapy. Breathe 17, 210112 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  161. Kim, E. J. & Wierzbicki, A. S. The history of proprotein convertase subtilisin kexin-9 inhibitors and their role in the treatment of cardiovascular disease. Ther. Adv. Chronic Dis. 11, 2040622320924569 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  162. Fabre, S., Funck-Brentano, T. & Cohen-Solal, M. Anti-sclerostin antibodies in osteoporosis and other bone diseases. J. Clin. Med. 9, 3439 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  163. Bovijn, J. et al. Evaluating the cardiovascular safety of sclerostin inhibition using evidence from meta-analysis of clinical trials and human genetics. Sci. Transl. Med. 12, eaay6570 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Correspondence to Munir Pirmohamed.

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Competing interests

M.P. has received partnership funding for the following: the Medical Research Council (MRC) Clinical Pharmacology Training Scheme (co-funded by MRC and Roche, UCB, Eli Lilly and Novartis) and a PhD studentship jointly funded by the Engineering and Physical Sciences Research Council (EPSRC) and Astra Zeneca. He also has unrestricted educational grant support for the UK Pharmacogenetics and Stratified Medicine Network from Bristol-Myers Squibb. He has developed an HLA genotyping panel with MC Diagnostics but does not benefit financially from this. He is part of the Innovative Medicines Initiative (IMI) consortium ARDAT (www.ardat.org). None of the funding received is related to the current paper.

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All of Us Research Program: https://allofus.nih.gov/

China Kadoorie Biobank: https://www.ckbiobank.org/

CPIC Guidelines: https://cpicpgx.org/guidelines/

H3Africa: https://h3africa.org/

Open Targets: https://www.opentargets.org/

Our Future Health: https://ourfuturehealth.org.uk/

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Trans-Omic for Precision Medicine (TOPMed) programme: https://topmed.nhlbi.nih.gov/

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Pirmohamed, M. Pharmacogenomics: current status and future perspectives. Nat Rev Genet 24, 350–362 (2023). https://doi.org/10.1038/s41576-022-00572-8

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