Article | Published:

A pathway-driven predictive model of tramadol pharmacogenetics

European Journal of Human Genetics (2019) | Download Citation

Abstract

Predicting metabolizer phenotype (MP) is typically performed using data from a single gene. Cytochrome p450 family 2 subfamily D polypeptide 6 (CYP2D6) is considered the primary gene for predicting MP in reference to approximately 30% of marketed drugs and endogenous toxins. CYP2D6 predictions have proven clinically effective but also have well-documented inaccuracies due to relatively high genotype–phenotype discordance in certain populations. Herein, a pathway-driven predictive model employs genetic data from uridine diphosphate glucuronosyltransferase, family 1, polypeptide B7 (UGT2B7), adenosine triphosphate (ATP)-binding cassette, subfamily B, number 1 (ABCB1), opioid receptor mu 1 (OPRM1), and catechol-O-methyltransferase (COMT) to predict the tramadol to primary metabolite ratio (T:M1) and the resulting toxicologically inferred MP (t-MP). These data were then combined with CYP2D6 data to evaluate performance of a fully combinatorial model relative to CYP2D6 alone. These data identify UGT2B7 as a potentially significant explanatory marker for T:M1 variability in a population of tramadol-exposed individuals of Finnish ancestry. Supervised machine learning and feature selection were used to demonstrate that a set of 16 loci from 5 genes can predict t-MP with over 90% accuracy, depending on t-MP category and algorithm, which was significantly greater than predictions made by CYP2D6 alone.

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References

  1. 1.

    Gaedigk A, Sangkuhl K, Whirl-Carrillo M, Klein T, Leeder JS. Prediction of CYP2D6 phenotype from genotype across world populations. Genet Med. 2017;19:69–76.

  2. 2.

    Ingelman-Sundberg M. Genetic polymorphisms of cytochrome P450 2D6 (CYP2D6): clinical consequences, evolutionary aspects and functional diversity. Pharm J. 2005;5:6–13.

  3. 3.

    Leppert W. CYP2D6 in the metabolism of opioids for mild to moderate pain. Pharmacology. 2011;87:274–85.

  4. 4.

    Sistonen J, Fuselli S, Palo JU, Chauhan N, Padh H, Sajantila A. Pharmacogenetic variation at CYP2C9, CYP2C19, and CYP2D6 at global and microgeographic scales. Pharmacogenet Genomics. 2009;19:170–9.

  5. 5.

    Koski A, Sistonen J, Ojanpera I, Gergov M, Vuori E, Sajantila A. CYP2D6 and CYP2C19 genotypes and amitriptyline metabolite ratios in a series of medicolegal autopsies. Forensic Sci Int. 2006;158:177–83.

  6. 6.

    Levo A, Koski A, Ojanpera I, Vuori E, Sajantila A. Post-mortem SNP analysis of CYP2D6 gene reveals correlation between genotype and opioid drug (tramadol) metabolite ratios in blood. Forensic Sci Int. 2003;135:9–15.

  7. 7.

    Mas S, Gasso P, Torra M, Bioque M, Lobo A, Gonzalez-Pinto A, et al. Intuitive pharmacogenetic dosing of risperidone according to CYP2D6 phenotype extrapolated from genotype in a cohort of first episode psychosis patients. Eur Neuropsychopharmacol. 2017;27:647–56.

  8. 8.

    De Andres F, Teran S, Hernandez F, Teran E, LL A. To genotype or phenotype for personalized medicine? CYP450 drug metabolizing enzyme genotype-phenotype concordance and discordance in the ecuadorian population. OMICS. 2016;20:699–710.

  9. 9.

    Gaedigk A, Bradford LD, Marcucci KA, Leeder JS. Unique CYP2D6 activity distribution and genotype-phenotype discordance in black Americans. Clin Pharmacol Ther. 2002;72:76–89.

  10. 10.

    Shiran MR, Chowdry J, Rostami-Hodjegan A, Ellis SW, Lennard MS, Iqbal MZ, et al. A discordance between cytochrome P450 2D6 genotype and phenotype in patients undergoing methadone maintenance treatment. Br J Clin Pharmacol. 2003;56:220–4.

  11. 11.

    Altar CA, Carhart J, Allen JD, Hall-Flavin D, Winner J, Dechairo B. Clinical utility of combinatorial pharmacogenomics-guided antidepressant therapy: evidence from three clinical studies. Mol Neuropsychiatry. 2015;1:145–55.

  12. 12.

    Altar CA, Carhart JM, Allen JD, Hall-Flavin DK, Dechairo BM, Winner JG. Clinical validity: combinatorial pharmacogenomics predicts antidepressant responses and healthcare utilizations better than single gene phenotypes. Pharm J. 2015;15:443–51.

  13. 13.

    Baber M, Chaudhry S, Kelly L, Ross C, Carleton B, Berger H, et al. The pharmacogenetics of codeine pain relief in the postpartum period. Pharm J. 2015;15:430–5.

  14. 14.

    Bastami S, Gupta A, Zackrisson AL, Ahlner J, Osman A, Uppugunduri S. Influence of UGT2B7, OPRM1 and ABCB1 gene polymorphisms on postoperative morphine consumption. Basic Clin Pharmacol Toxicol. 2014;115:423–31.

  15. 15.

    Sistonen J, Madadi P, Ross CJ, Yazdanpanah M, Lee JW, Landsmeer ML, et al. Prediction of codeine toxicity in infants and their mothers using a novel combination of maternal genetic markers. Clin Pharmacol Ther. 2012;91:692–9.

  16. 16.

    Seya MJ, Gelders SF, Achara OU, Milani B, Scholten WK. A first comparison between the consumption of and the need for opioid analgesics at country, regional, and global levels. J Pain Palliat Care Pharmacother. 2011;25:6–18.

  17. 17.

    Solanki DR, Koyyalagunta D, Shah RV, Silverman SM, Manchikanti L. Monitoring opioid adherence in chronic pain patients: assessment of risk of substance misuse. Pain Physician. 2011;14:E119–31.

  18. 18.

    Wendt FR, Novroski NMM, Rahikainen AL, Sajantila A, Budowle B. Supervised classification of CYP2D6 genotype and metabolizer phenotype with postmortem tramadol-exposed Finns. Am J Forensic Med Pathol. 2019;40:8–18.

  19. 19.

    Rahikainen AL, Palo JU, de Leeuw W, Budowle B, Sajantila A. DNA quality and quantity from up to 16 years old post-mortem blood stored on FTA cards. Forensic Sci Int. 2016;261:148–53.

  20. 20.

    Scrucca L, Fop M, Murphy TB, Raftery AE. mclust 5: clustering, classification and density estimation using gaussian finite mixture models. R J. 2016;8:289–317.

  21. 21.

    Owusu Obeng A, Hamadeh I, Smith M. Review of opioid pharmacogenetics and considerations for pain management. Pharmacotherapy. 2017;37:1105–21.

  22. 22.

    Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics. 2011;27:2987–93.

  23. 23.

    Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60.

  24. 24.

    Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9.

  25. 25.

    McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20:1297–303.

  26. 26.

    Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, et al. The variant call format and VCFtools. Bioinformatics. 2011;27:2156–8.

  27. 27.

    Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88:76–82.

  28. 28.

    Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–75.

  29. 29.

    Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009;5:e1000529.

  30. 30.

    Schmedes SE, Woerner AE, Budowle B. Forensic human identification using skin microbiomes. Appl Environ Microbiol. 2017.

  31. 31.

    Schmedes SE, Woerner AE, Novroski NMM, Wendt FR, King JL, Stephens KM, et al. Targeted sequencing of clade-specific markers from skin microbiomes for forensic human identification. Forensic Sci Int Genet. 2018;32:50–61.

  32. 32.

    Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, et al. A global reference for human genetic variation. Nature. 2015;526:68–74.

  33. 33.

    McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, et al. The Ensembl Variant Effect Predictor. Genome Biol. 2016;17:122.

  34. 34.

    McLaren W, Pritchard B, Rios D, Chen Y, Flicek P, Cunningham F. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics. 2010;26:2069–70.

  35. 35.

    Ingelman-Sundberg M, Mkrtchian S, Zhou Y, Lauschke VM. Integrating rare genetic variants into pharmacogenetic drug response predictions. Hum Genomics. 2018;12:26.

  36. 36.

    Kircher M, Witten DM, Jain P, O’Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. 2014;46:310–5.

  37. 37.

    Dhaliwal AK, Mohan A, Gill KS. Comparative analysis of ABCB1 reveals novel structural and functional conservation between monocots and dicots. Front Plant Sci. 2014;5:657.

  38. 38.

    Loo TW, Bartlett MC, Clarke DM. The “LSGGQ” motif in each nucleotide-binding domain of human P-glycoprotein is adjacent to the opposing walker A sequence. J Biol Chem. 2002;277:41303–6.

  39. 39.

    Sauna ZE, Ambudkar SV. About a switch: how P-glycoprotein (ABCB1) harnesses the energy of ATP binding and hydrolysis to do mechanical work. Mol Cancer Ther. 2007;6:13–23.

  40. 40.

    Gaedigk A, Simon SD, Pearce RE, Bradford LD, Kennedy MJ, Leeder JS. The CYP2D6 activity score: translating genotype information into a qualitative measure of phenotype. Clin Pharmacol Ther. 2008;83:234–42.

  41. 41.

    Sistonen J, Sajantila A, Lao O, Corander J, Barbujani G, Fuselli S. CYP2D6 worldwide genetic variation shows high frequency of altered activity variants and no continental structure. Pharmacogenet Genomics. 2007;17:93–101.

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Acknowledgements

The authors thank Jenny Atanasov and Mehdi Keddache from Illumina, Inc. and Cydne Holt, John Walsh, and Kameran Wong from Verogen, Inc. for library preparation product support, and Jerry Boonyaratanakornkit and Wahaj Zuberi from Exact Diagnostics for their technical assistance generating TruSeq data. We also thank Medicinska Understödsföreningen Liv och Hälsa r.f. and Helsinki University Doctoral Programme in Population Health for support for AS and A-LR, respectively.

Funding

Support for this work was provided by the Center for Human Identification at the University of North Texas Health Science Center.

Author information

Author notes

  1. These authors contributed equally: A. Sajantila, B. Budowle

Affiliations

  1. Graduate School of Biomedical Sciences, University of North Texas Health Science Center, 3500 Camp Bowie Blvd., Fort Worth, TX, 76107, USA

    • Frank R. Wendt
    • , Nicole M. M. Novroski
    •  & Bruce Budowle
  2. Center for Human Identification, University of North Texas Health Science Center, 3500 Camp Bowie Blvd., Fort Worth, TX, 76107, USA

    • Frank R. Wendt
    • , Nicole M. M. Novroski
    •  & Bruce Budowle
  3. Department of Forensic Medicine, University of Helsinki, P.O. Box 40, 00014, Helsinki, Finland

    • Anna-Liina Rahikainen
    •  & Antti Sajantila

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Conflict of interest

The authors declare that they have no conflict of interest.

Corresponding author

Correspondence to Frank R. Wendt.

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DOI

https://doi.org/10.1038/s41431-019-0369-6