Group-based pharmacogenetic prediction: is it feasible and do current NHS England ethnic classifications provide appropriate data?

Abstract

Inter-individual variation of drug metabolising enzymes (DMEs) leads to variable efficacy of many drugs and even adverse drug responses. Consequently, it would be desirable to test variants of many DMEs before drug treatment. Inter-ethnic differences in frequency mean that the choice of SNPs to test may vary across population groups. Here we examine the utility of testing representative groups as a way of assessing what variants might be tested. We show that publicly available population information is potentially useful for determining loci for pre-treatment genetic testing, and for determining the most prevalent risk haplotypes in defined groups. However, we also show that the NHS England classifications have limitations for grouping for these purposes, in particular for people of African descent. We conclude: (1) genotyping of hospital patients and people from the hospital catchment area confers no advantage over using samples from appropriate existing ethnic group collections or publicly available data, (2) given the current NHS England Black African grouping, a decision as to whether to test, would have to apply to all patients of recent Black African ancestry to cover reported risk alleles and (3) the current scarcity of available genome and drug effect data from Africans is a problem for both testing and treatment decisions.

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Code availability

Data pertaining to the 1000G SNPs were extracted using VCFtools version 0.1.13 (https://vcftools.github.io/index.html), from the 1000G Phase 3 VCF files for the relevant chromosomes (ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/). For systematic and automated haplotype analysis of large SNP data, we developed an R-based tool to convert PLINK PED/MAP files to PHASE input, and to summarise haplotype inference results in multiple groups from PHASE output. This tool is now publicly available on Github (https://github.com/nansari-pour/PLINKtoPHASE).

References

  1. 1.

    Weinshilboum RM, Wang L. Pharmacogenetics and pharmacogenomics: development, science, and translation. Annu Rev Genom Hum Genet. 2006;7:223–45.

    CAS  Google Scholar 

  2. 2.

    Daly AK. Pharmacogenetics: a general review on progress to date. Br Med Bull. 2017;124:65–79.

    CAS  PubMed  Google Scholar 

  3. 3.

    Browning LA, Kruse JA. Hemolysis and methemoglobinemia secondary to rasburicase administration. Ann Pharmacother. 2005;39:1932–5.

    PubMed  Google Scholar 

  4. 4.

    Khan S, Mandal RK, Elasbali AM, Dar SA, Jawed A, Wahid M, et al. Pharmacogenetic association between NAT2 gene polymorphisms and isoniazid induced hepatotoxicity: trial sequence meta-analysis as evidence. Biosci Rep. 2019;39:1–15.

    Google Scholar 

  5. 5.

    Lonjou C, Borot N, Sekula P, Ledger N, Thomas L, Halevy S, et al. A European study of HLA-B in Stevens-Johnson syndrome and toxic epidermal necrolysis related to five high-risk drugs. Pharmacogenet Genom. 2008;18:99–107.

    CAS  Google Scholar 

  6. 6.

    Perry CM. Maraviroc: a review of its use in the management of CCR5-tropic HIV-1 infection. Drugs. 2010;70:1189–213.

    CAS  PubMed  Google Scholar 

  7. 7.

    Stehle S, Kirchheiner J, Lazar A, Fuhr U. Pharmacogenetics of oral anticoagulants: a basis for dose individualization. Clin Pharmacokinet. 2008;47:565–94.

    CAS  PubMed  Google Scholar 

  8. 8.

    Johnson JA, Cavallari LH. Warfarin pharmacogenetics. Trends Cardiovasc Med. 2015;25:33–41.

    CAS  PubMed  Google Scholar 

  9. 9.

    Davies EC, Green CF, Taylor S, Williamson PR, Mottram DR, Pirmohamed M. Adverse drug reactions in hospital in-patients: a prospective analysis of 3695 patient-episodes. PLoS ONE. 2009;4:e4439.

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Dressler LG. Integrating personalized genomic medicine into routine clinical care: addressing the social and policy issues of pharmacogenomic testing. N C Med J. 2013;74:509–13.

    PubMed  Google Scholar 

  11. 11.

    Hovelson DH, Xue Z, Zawistowski M, Ehm MG, Harris EC, Stocker SL, et al. Characterization of ADME gene variation in 21 populations by exome sequencing. Pharmacogenet Genom. 2017;27:89.

    CAS  Google Scholar 

  12. 12.

    Creemer OJ, Ansari-Pour N, Ekong R, Tarekegn A, Plaster C, Bains RK, et al. Contrasting exome constancy and regulatory region variation in the gene encoding CYP3A4: an examination of the extent and potential implications. Pharmacogenet Genom. 2016;26:255–70.

    CAS  Google Scholar 

  13. 13.

    Gurwitz D, Motulsky AG. ‘Drug reactions, enzymes, and biochemical genetics’: 50 years later. Pharmacogenomics. 2007;8:1479–84.

    CAS  PubMed  Google Scholar 

  14. 14.

    Wilson JF, Weale ME, Smith AC, Gratrix F, Fletcher B, Thomas MG, et al. Population genetic structure of variable drug response. Nat Genet. 2001;29:265–9.

    CAS  PubMed  Google Scholar 

  15. 15.

    Burchard EG, Ziv E, Coyle N, Gomez SL, Tang H, Karter AJ, et al. The importance of race and ethnic background in biomedical research and clinical practice. N Engl J Med. 2003;348:1170–5.

    PubMed  Google Scholar 

  16. 16.

    Ferrell PB Jr, McLeod HL. Carbamazepine, HLA-B*1502 and risk of Stevens-Johnson syndrome and toxic epidermal necrolysis: US FDA recommendations. Pharmacogenomics. 2008;9:1543–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature. 2015;526:68–74.

    PubMed Central  Google Scholar 

  18. 18.

    Holmes MV, Shah T, Vickery C, Smeeth L, Hingorani AD, Casas JP. Fulfilling the promise of personalized medicine? Systematic review and field synopsis of pharmacogenetic studies. PLoS ONE. 2009;4:e7960.

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Zanger UM, Raimundo S, Eichelbaum M. Cytochrome P450 2D6: overview and update on pharmacology, genetics, biochemistry. Naunyn Schmiedebergs Arch Pharmacol. 2004;369:23–37.

    CAS  PubMed  Google Scholar 

  20. 20.

    Francois AA, Nishida CR, de Montellano PRO, Phillips IR, Shephard EA. Human flavin-containing monooxygenase 2.1 catalyzes oxygenation of the antitubercular drugs thiacetazone and ethionamide. Drug Metab Dispos. 2009;37:178–86.

    CAS  PubMed  Google Scholar 

  21. 21.

    Veeramah KR, Thomas MG, Weale ME, Zeitlyn D, Tarekegn A, Bekele E, et al. The potentially deleterious functional variant flavin-containing monooxygenase 2*1 is at high frequency throughout sub-Saharan Africa. Pharmacogenet Genom. 2008;18:877–86.

    CAS  Google Scholar 

  22. 22.

    Horsfall LJ, Zeitlyn D, Tarekegn A, Bekele E, Thomas MG, Bradman N, et al. Prevalence of clinically relevant UGT1A alleles and haplotypes in African populations. Ann Hum Genet. 2011;75:236–46.

    CAS  PubMed  Google Scholar 

  23. 23.

    Gammal RS, Court MH, Haidar CE, Iwuchukwu OF, Gaur AH, Alvarellos M, et al. Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for UGT1A1 and Atazanavir prescribing. Clin Pharmacol Ther. 2016;99:363–9.

    CAS  PubMed  Google Scholar 

  24. 24.

    Bains RK. African variation at Cytochrome P450 genes: evolutionary aspects and the implications for the treatment of infectious diseases. Evol Med Public Health. 2013;2013:118–34.

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Campbell MC, Tishkoff SA. African genetic diversity: implications for human demographic history, modern human origins, and complex disease mapping. Annu Rev Genom Hum Genet. 2008;9:403–33.

    CAS  Google Scholar 

  26. 26.

    Choudhury A, Aron S, Sengupta D, Hazelhurst S, Ramsay M. African genetic diversity provides novel insights into evolutionary history and local adaptations. Hum Mol Genet. 2018;27(R2):R209–R218.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Gurdasani D, Carstensen T, Tekola-Ayele F, Pagani L, Tachmazidou I, Hatzikotoulas K, et al. The African Genome Variation Project shapes medical genetics in Africa. Nature. 2015;517:327–32.

    CAS  PubMed  Google Scholar 

  28. 28.

    de Man FM, Goey AKL, van Schaik RHN, Mathijssen RHJ, Bins S. Individualization of Irinotecan treatment: a review of pharmacokinetics, pharmacodynamics, and pharmacogenetics. Clin Pharmacokinet. 2018;57:1229–54.

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Sugatani J, Mizushima K, Osabe M, Yamakawa K, Kakizaki S, Takagi H, et al. Transcriptional regulation of human UGT1A1 gene expression through distal and proximal promoter motifs: implication of defects in the UGT1A1 gene promoter. Naunyn Schmiedebergs Arch Pharmacol. 2008;377:597–605.

    CAS  PubMed  Google Scholar 

  30. 30.

    Han FF, Guo CL, Yu D, Zhu J, Gong LL, Li GR, et al. Associations between UGT1A1*6 or UGT1A1*6/*28 polymorphisms and irinotecan-induced neutropenia in Asian cancer patients. Cancer Chemother Pharmacol. 2014;73:779–88.

    CAS  PubMed  Google Scholar 

  31. 31.

    Cui C, Shu C, Cao D, Yang Y, Liu J, Shi S, et al. UGT1A1*6, UGT1A7*3 and UGT1A9*1b polymorphisms are predictive markers for severe toxicity in patients with metastatic gastrointestinal cancer treated with irinotecan-based regimens. Oncol Lett. 2016;12:4231–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7.

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Ansari Pour N, Plaster CA, Bradman N. Evidence from Y-chromosome analysis for a late exclusively eastern expansion of the Bantu-speaking people. Eur J Hum Genet. 2013;21:423–9.

    CAS  PubMed  Google Scholar 

  34. 34.

    R-Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2014.

  35. 35.

    Stephens M, Donnelly P. A comparison of bayesian methods for haplotype reconstruction from population genotype data. Am J Hum Genet. 2003;73:1162–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Lee CR, Pieper JA, Frye RF, Hinderliter AL, Blaisdell JA, Goldstein JA. Differences in flurbiprofen pharmacokinetics between CYP2C9*1/*1, *1/*2, and *1/*3 genotypes. Eur J Clin Pharmacol. 2003;58:791–4.

    CAS  PubMed  Google Scholar 

  37. 37.

    Perini JA, Vianna-Jorge R, Brogliato AR, Suarez-Kurtz G. Influence of CYP2C9 genotypes on the pharmacokinetics and pharmacodynamics of piroxicam. Clin Pharmacol Ther. 2005;78:362–9.

    CAS  PubMed  Google Scholar 

  38. 38.

    Rettie AE, Haining RL, Bajpai M, Levy RH. A common genetic basis for idiosyncratic toxicity of warfarin and phenytoin. Epilepsy Res. 1999;35:253–5.

    CAS  PubMed  Google Scholar 

  39. 39.

    Tang C, Shou M, Mei Q, Rushmore TH, Rodrigues AD. Major role of human liver microsomal cytochrome P450 2C9 (CYP2C9) in the oxidative metabolism of celecoxib, a novel cyclooxygenase-II inhibitor. J Pharmacol Exp Ther. 2000;293:453–9.

    CAS  PubMed  Google Scholar 

  40. 40.

    Furuta T, Ohashi K, Kosuge K, Zhao XJ, Takashima M, Kimura M, et al. CYP2C19 genotype status and effect of omeprazole on intragastric pH in humans. Clin Pharmacol Ther. 1999;65:552–61.

    CAS  PubMed  Google Scholar 

  41. 41.

    Goldstein JA, Faletto MB, Romkes-Sparks M, Sullivan T, Kitareewan S, Raucy JL, et al. Evidence that CYP2C19 is the major (S)-mephenytoin 4’-hydroxylase in humans. Biochemistry. 1994;33:1743–52.

    CAS  PubMed  Google Scholar 

  42. 42.

    Hirani VN, Raucy JL, Lasker JM. Conversion of the HIV protease inhibitor nelfinavir to a bioactive metabolite by human liver CYP2C19. Drug Metab Dispos. 2004;32:1462–7.

    CAS  PubMed  Google Scholar 

  43. 43.

    Inomata S, Nagashima A, Itagaki F, Homma M, Nishimura M, Osaka Y, et al. CYP2C19 genotype affects diazepam pharmacokinetics and emergence from general anesthesia. Clin Pharmacol Ther. 2005;78:647–55.

    CAS  PubMed  Google Scholar 

  44. 44.

    Scott SA, Sangkuhl K, Stein CM, Hulot JS, Mega JL, Roden DM, et al. Clinical Pharmacogenetics Implementation Consortium guidelines for CYP2C19 genotype and clopidogrel therapy: 2013 update. Clin Pharmacol Ther. 2013;94:317–23.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Tseng E, Walsky RL, Luzietti RA Jr, Harris JJ, Kosa RE, Goosen TC, et al. Relative contributions of cytochrome CYP3A4 versus CYP3A5 for CYP3A-cleared drugs assessed in vitro using a CYP3A4-selective inactivator (CYP3cide). Drug Metab Dispos. 2014;42:1163–73.

    PubMed  Google Scholar 

  46. 46.

    Phillips IR, Shephard EA. Drug metabolism by flavin-containing monooxygenases of human and mouse. Expert Opin Drug Metab Toxicol. 2017;13:167–81.

    CAS  PubMed  Google Scholar 

  47. 47.

    McDonagh EM, Boukouvala S, Aklillu E, Hein DW, Altman RB, Klein TE. PharmGKB summary: very important pharmacogene information for N-acetyltransferase 2. Pharmacogenet Genom. 2014;24:409–25.

    CAS  Google Scholar 

  48. 48.

    Innocenti F, Ratain MJ. Pharmacogenetics of irinotecan: clinical perspectives on the utility of genotyping. Pharmacogenomics. 2006;7:1211–21.

    CAS  PubMed  Google Scholar 

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Acknowledgements

We thank all the sample donors who participated in this study and the UCLH clinicians, Aroon Hingorani, Alastair Forbes, Simon Woldman, Steve Hurel, Clare Dollery and others who helped us with access to patient volunteers in their clinics. We also thank Mark Thomas for access to some of the samples and Pieta Nosanea, Esther Williams, Sarah Steward and Ayele Tarekegn for help with sample collections from African and Indian volunteers in their native countries; Ranji Areseratnam for technical assistance. This research was funded by the University College London Hospitals Comprehensive Biomedical Research Centre.

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Correspondence to Dallas M. Swallow.

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During this study NB had a controlling interest in a company interested in developing diagnostic technology to identify variation in drug metabolising enzymes to improve healthcare. Neither NB nor the company now have that objective. None of the other authors have any potential conflicts of interest to declare.

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Ingram, C.J.E., Ekong, R., Ansari-Pour, N. et al. Group-based pharmacogenetic prediction: is it feasible and do current NHS England ethnic classifications provide appropriate data?. Pharmacogenomics J (2020). https://doi.org/10.1038/s41397-020-0175-0

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