1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Pharmacology and Toxicology
  4. Volume 64, 2024
  5. Article

Annual Review of Pharmacology and Toxicology

Volume 64, 2024

Circulating Biomarkers Instead of Genotyping to Establish Metabolizer Phenotypes

Abstract

Pharmacogenomics (PGx) enables personalized treatment for the prediction of drug response and to avoid adverse drug reactions. Currently, PGx mainly relies on the genetic information of absorption, distribution, metabolism, and excretion (ADME) targets such as drug-metabolizing enzymes or transporters to predict differences in the patient's phenotype. However, there is evidence that the phenotype-genotype concordance is limited. Thus, we discuss different phenotyping strategies using exogenous xenobiotics (e.g., drug cocktails) or endogenous compounds for phenotype prediction. In particular, minimally invasive approaches focusing on liquid biopsies offer great potential to preemptively determine metabolic and transport capacities. Early studies indicate that ADME phenotyping using exosomes released from the liver is reliable. In addition, pharmacometric modeling and artificial intelligence improve phenotype prediction. However, further prospective studies are needed to demonstrate the clinical utility of individualized treatment based on phenotyping strategies, not only relying on genetics. The present review summarizes current knowledge and limitations.

Keyword(s): drug metabolism, exosomes, genomics, liquid biopsy, metabolomics, phenotype
Loading

Article metrics loading...

/content/journals/10.1146/annurev-pharmtox-032023-121106
2024-01-23
2024-04-28
Loading full text...

Full text loading...

/deliver/fulltext/pharmtox/64/1/annurev-pharmtox-032023-121106.html?itemId=/content/journals/10.1146/annurev-pharmtox-032023-121106&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Pirmohamed M. 2023. Pharmacogenomics: current status and future perspectives. Nat. Rev. Genet. 24:350–62
     [Google Scholar]
  2. 2.
    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
     [Google Scholar]
  3. 3.
    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
     [Google Scholar]
  4. 4.
    Krebs K, Milani L. 2019. Translating pharmacogenomics into clinical decisions: Do not let the perfect be the enemy of the good. Hum. Genom. 13:39
     [Google Scholar]
  5. 5.
    Smith DM, Wake DT, Dunnenberger HM. 2022. Pharmacogenomic clinical decision support: a scoping review. Clin. Pharmacol. Ther. 113:803–15
     [Google Scholar]
  6. 6.
    Chenoweth MJ, Giacomini KM, Pirmohamed M, Hill SL, van Schaik RHN et al. 2020. Global pharmacogenomics within precision medicine: challenges and opportunities. Clin. Pharmacol. Ther. 107:57–61
     [Google Scholar]
  7. 7.
    Casalino S, Frangione E, Chung M, MacDonald G, Chowdhary S et al. 2023. Genome screening, reporting, and genetic counseling for healthy populations. Hum. Genet. 142:181–92
     [Google Scholar]
  8. 8.
    Zanger UM, Momoi K, Hofmann U, Schwab M, Klein K. 2021. Tri-allelic haplotypes determine and differentiate functionally normal allele CYP2D6*2 and impaired allele CYP2D6*41. Clin. Pharmacol. Ther. 109:1256–64
     [Google Scholar]
  9. 9.
    Griese EU, Zanger UM, Brudermanns U, Gaedigk A, Mikus G et al. 1998. Assessment of the predictive power of genotypes for the in-vivo catalytic function of CYP2D6 in a German population. Pharmacogenetics 8:15–26
     [Google Scholar]
  10. 10.
    Sachse C, Brockmöller J, Bauer S, Roots I. 1997. Cytochrome P450 2D6 variants in a Caucasian population: allele frequencies and phenotypic consequences. Am. J. Hum. Genet. 60:284–95
     [Google Scholar]
  11. 11.
    Zanger UM, Schwab M. 2013. Cytochrome P450 enzymes in drug metabolism: regulation of gene expression, enzyme activities, and impact of genetic variation. Pharmacol. Ther. 138:103–41
     [Google Scholar]
  12. 12.
    Schaeffeler E, Fischer C, Brockmeier D, Wernet D, Moerike K et al. 2004. Comprehensive analysis of thiopurine S-methyltransferase phenotype-genotype correlation in a large population of German-Caucasians and identification of novel TPMT variants. Pharmacogenetics 14:407–17
     [Google Scholar]
  13. 13.
    Yates CR, Krynetski EY, Loennechen T, Fessing MY, Tai HL et al. 1997. Molecular diagnosis of thiopurine S-methyltransferase deficiency: genetic basis for azathioprine and mercaptopurine intolerance. Ann. Intern. Med. 126:608–14
     [Google Scholar]
  14. 14.
    Schwab M, Schaeffeler E, Marx C, Zanger U, Aulitzky W, Eichelbaum M. 2001. Shortcoming in the diagnosis of TPMT deficiency in a patient with Crohn's disease using phenotyping only. Gastroenterology 121:498–99
     [Google Scholar]
  15. 15.
    Gaedigk A, Dinh JC, Jeong H, Prasad B, Leeder JS. 2018. Ten years' experience with the CYP2D6 activity score: a perspective on future investigations to improve clinical predictions for precision therapeutics. J. Pers. Med. 8:15
     [Google Scholar]
  16. 16.
    Relling MV, Klein TE. 2011. CPIC. Clinical Pharmacogenetics Implementation Consortium of the Pharmacogenomics Research Network. Clin. Pharmacol. Ther. 89:464–67
     [Google Scholar]
  17. 17.
    Emami Riedmaier A, Burk O, van Eijck BAC, Schaeffeler E, Klein K et al. 2016. Variability in hepatic expression of organic anion transporter 7/SLC22A9, a novel pravastatin uptake transporter: impact of genetic and regulatory factors. Pharmacogenom. J. 16:341–51
     [Google Scholar]
  18. 18.
    Nies AT, Niemi M, Burk O, Winter S, Zanger UM et al. 2013. Genetics is a major determinant of expression of the human hepatic uptake transporter OATP1B1, but not of OATP1B3 and OATP2B1. Genome Med 5:1
     [Google Scholar]
  19. 19.
    Klein K, Winter S, Turpeinen M, Schwab M, Zanger UM. 2010. Pathway-targeted pharmacogenomics of CYP1A2 in human liver. Front. Pharmacol. 1:129
     [Google Scholar]
  20. 20.
    Klein K, Thomas M, Winter S, Nussler AK, Niemi M, Schwab M, Zanger UM. 2012. PPARA: a novel genetic determinant of CYP3A4 in vitro and in vivo. Clin. Pharmacol. Ther. 91:1044–52
     [Google Scholar]
  21. 21.
    Hakkola J, Hukkanen J, Turpeinen M, Pelkonen O. 2020. Inhibition and induction of CYP enzymes in humans: an update. Arch. Toxicol. 94:3671–722
     [Google Scholar]
  22. 22.
    van der Lee M, Allard WG, Vossen RHAM, Baak-Pablo RF, Menafra R et al. 2021. Toward predicting CYP2D6-mediated variable drug response from CYP2D6 gene sequencing data. Sci. Transl. Med. 13:eabf3637
     [Google Scholar]
  23. 23.
    Gaedigk A, Simon SD, Pearce RE, Bradford LD, Kennedy MJ, Leeder JS. 2008. The CYP2D6 activity score: translating genotype information into a qualitative measure of phenotype. Clin. Pharmacol. Ther. 83:234–42
     [Google Scholar]
  24. 24.
    Raimundo S, Toscano C, Klein K, Fischer J, Griese E-U et al. 2004. A novel intronic mutation, 2988G>A, with high predictivity for impaired function of cytochrome P450 2D6 in white subjects. Clin. Pharmacol. Ther. 76:128–38
     [Google Scholar]
  25. 25.
    Frederiksen T, Areberg J, Schmidt E, Bjerregaard Stage T, Brøsen K 2021. Quantification of in vivo metabolic activity of CYP2D6 genotypes and alleles through population pharmacokinetic analysis of vortioxetine. Clin. Pharmacol. Ther. 109:150–59
     [Google Scholar]
  26. 26.
    Frederiksen T, Areberg J, Schmidt E, Stage TB, Brøsen K. 2021. Cytochrome P450 2D6 genotype-phenotype characterization through population pharmacokinetic modeling of tedatioxetine. CPT Pharmacometrics Syst. Pharmacol. 10:983–93
     [Google Scholar]
  27. 27.
    Frederiksen T, Areberg J, Raoufinia A, Schmidt E, Stage TB, Brøsen K. 2023. Estimating the in vivo function of CYP2D6 alleles through population pharmacokinetic modeling of brexpiprazole. Clin. Pharmacol. Ther. 113:360–69
     [Google Scholar]
  28. 28.
    Khor CC, Winter S, Sutiman N, Mürdter TE, Chen S et al. 2023. Cross-ancestry genome-wide association study defines the extended CYP2D6 locus as the principal genetic determinant of endoxifen plasma concentrations. Clin. Pharmacol. Ther. 113:712–23
     [Google Scholar]
  29. 29.
    Abduljalil K, Frank D, Gaedigk A, Klaassen T, Tomalik-Scharte D et al. 2010. Assessment of activity levels for CYP2D6*1, CYP2D6*2, and CYP2D6*41 genes by population pharmacokinetics of dextromethorphan. Clin. Pharmacol. Ther. 88:643–51
     [Google Scholar]
  30. 30.
    Hicks JK, Sangkuhl K, Swen JJ, Ellingrod VL, Müller DJ et al. 2017. Clinical pharmacogenetics implementation consortium guideline (CPIC) for CYP2D6 and CYP2C19 genotypes and dosing of tricyclic antidepressants: 2016 update. Clin. Pharmacol. Ther. 102:37–44
     [Google Scholar]
  31. 31.
    Crews KR, Gaedigk A, Dunnenberger HM, Leeder JS, Klein TE et al. 2014. Clinical Pharmacogenetics Implementation Consortium guidelines for cytochrome P450 2D6 genotype and codeine therapy: 2014 update. Clin. Pharmacol. Ther. 95:376–82
     [Google Scholar]
  32. 32.
    Cross B, Turner R, Pirmohamed M. 2022. Polygenic risk scores: an overview from bench to bedside for personalised medicine. Front. Genet. 13:1000667
     [Google Scholar]
  33. 33.
    Cooper-DeHoff RM, Niemi M, Ramsey LB, Luzum JA, Tarkiainen EK et al. 2022. The Clinical Pharmacogenetics Implementation Consortium guideline for SLCO1B1, ABCG2, and CYP2C9 genotypes and statin-associated musculoskeletal symptoms. Clin. Pharmacol. Ther. 111:1007–21
     [Google Scholar]
  34. 34.
    Crews KR, Monte AA, Huddart R, Caudle KE, Kharasch ED et al. 2021. Clinical Pharmacogenetics Implementation Consortium guideline for CYP2D6, OPRM1, and COMT genotypes and select opioid therapy. Clin. Pharmacol. Ther. 110:888–96
     [Google Scholar]
  35. 35.
    Lenoir C, Terrier J, Gloor Y, Curtin F, Rollason V et al. 2021. Impact of SARS-CoV-2 infection (COVID-19) on cytochromes P450 activity assessed by the Geneva cocktail. Clin. Pharmacol. Ther. 110:1358–67
     [Google Scholar]
  36. 36.
    Klomp SD, Meziyerh S, Vissers MFJM, Moes DJAR, Arends EJ et al. 2022. Increased tacrolimus exposure in kidney transplant recipients with COVID-19: inflammation-driven downregulation of metabolism as a potential mechanism. Transpl. Int. 35:10269
     [Google Scholar]
  37. 37.
    Tanaka E, Kurata N, Yasuhara H. 2003. How useful is the “cocktail approach” for evaluating human hepatic drug metabolizing capacity using cytochrome P450 phenotyping probes in vivo?. J. Clin. Pharm. Ther. 28:157–65
     [Google Scholar]
  38. 38.
    Fuhr U, Jetter A, Kirchheiner J. 2007. Appropriate phenotyping procedures for drug metabolizing enzymes and transporters in humans and their simultaneous use in the “cocktail” approach. Clin. Pharmacol. Ther. 81:270–83
     [Google Scholar]
  39. 39.
    de Andrés F, LLerena A. 2016. Simultaneous determination of cytochrome P450 oxidation capacity in humans: a review on the phenotyping cocktail approach. Curr. Pharm. Biotechnol. 17:1159–80
     [Google Scholar]
  40. 40.
    Keller GA, Ferreirós Gago ML, Diez RA, Di Girolamo G. 2017. In vivo phenotyping methods: cytochrome P450 probes with emphasis on the cocktail approach. Curr. Pharm. Des. 23:2035–49
     [Google Scholar]
  41. 41.
    Streetman DS, Bertino JS Jr., Nafziger AN. 2000. Phenotyping of drug-metabolizing enzymes in adults: a review of in-vivo cytochrome P450 phenotyping probes. Pharmacogenetics 10:187–216
     [Google Scholar]
  42. 42.
    Stopfer P, Giessmann T, Hohl K, Hutzel S, Schmidt S et al. 2018. Optimization of a drug transporter probe cocktail. Potential screening tool for transporter-mediated drug-drug interactions. Br. J. Clin. Pharmacol. 84:1941–49
     [Google Scholar]
  43. 43.
    Trueck C, Hsin C-H, Scherf-Clavel O, Schaeffeler E, Lenssen R et al. 2019. A clinical drug-drug interaction study assessing a novel drug transporter phenotyping cocktail with adefovir, sitagliptin, metformin, pitavastatin, and digoxin. Clin. Pharmacol. Ther. 106:1398–407
     [Google Scholar]
  44. 44.
    Lamba V, Panetta JC, Strom S, Schuetz EG. 2010. Genetic predictors of interindividual variability in hepatic CYP3A4 expression. J. Pharmacol. Exp. Ther. 332:1088–99
     [Google Scholar]
  45. 45.
    Matthaei J, Bonat WH, Kerb R, Tzvetkov MV, Strube J et al. 2020. Inherited and acquired determinants of hepatic CYP3A activity in humans. Front. Genet. 11:944
     [Google Scholar]
  46. 46.
    Mahmoudi M, Foerster KI, Burhenne J, Weiss J, Mikus G, Haefeli WE. 2021. Application of microdosed intravenous omeprazole to determine hepatic CYP2C19 activity. J. Clin. Pharmacol. 61:789–98
     [Google Scholar]
  47. 47.
    Hohmann N, Blank A, Burhenne J, Suzuki Y, Mikus G, Haefeli WE. 2019. Simultaneous phenotyping of CYP2E1 and CYP3A using oral chlorzoxazone and midazolam microdoses. Br. J. Clin. Pharmacol. 85:2310–20
     [Google Scholar]
  48. 48.
    Grangeon A, Gravel S, Gaudette F, Turgeon J, Michaud V. 2017. Highly sensitive LC-MS/MS methods for the determination of seven human CYP450 activities using small oral doses of probe-drugs in human. J. Chromatogr. B. Analyt. Technol. Biomed. Life Sci. 1040:144–58
     [Google Scholar]
  49. 49.
    Magliocco G, Rodieux F, Desmeules J, Samer CF, Daali Y. 2020. Toward precision medicine in pediatric population using cytochrome P450 phenotyping approaches and physiologically based pharmacokinetic modeling. Pediatr. Res. 87:441–49
     [Google Scholar]
  50. 50.
    Geist MJP, Bardenheuer HJ, Burhenne J, Mikus G. 2018. In vivo CYP3A activity in palliative care patients: study protocol for a single arm prospective trial. J. Palliat. Med. 21:686–88
     [Google Scholar]
  51. 51.
    Chavez-Eng CM, Lutz RW, Goykhman D, Bateman KP. 2018. Microdosing cocktail assay development for drug-drug interaction studies. J. Pharm. Sci. 107:1973–86
     [Google Scholar]
  52. 52.
    Prueksaritanont T, Tatosian DA, Chu X, Railkar R, Evers R et al. 2017. Validation of a microdose probe drug cocktail for clinical drug interaction assessments for drug transporters and CYP3A. Clin. Pharmacol. Ther. 101:519–30
     [Google Scholar]
  53. 53.
    Rattanacheeworn P, Kerr SJ, Kittanamongkolchai W, Townamchai N, Udomkarnjananun S et al. 2021. Quantification of CYP3A and drug transporters activity in healthy young, healthy elderly and chronic kidney disease elderly patients by a microdose cocktail approach. Front. Pharmacol. 12:726669
     [Google Scholar]
  54. 54.
    Tatosian DA, Yee KL, Zhang Z, Mostoller K, Paul E et al. 2021. A microdose cocktail to evaluate drug interactions in patients with renal impairment. Clin. Pharmacol. Ther. 109:403–15
     [Google Scholar]
  55. 55.
    Feng S, Gane E, Schwabe C, Zhu M, Triyatni M et al. 2020. A five-in-one first-in-human study to assess safety, tolerability, and pharmacokinetics of RO7049389, an inhibitor of hepatitis B virus capsid assembly, after single and multiple ascending doses in healthy participants. Antimicrob. Agents Chemother. 64:e01323-20
     [Google Scholar]
  56. 56.
    Wiebe ST, Huennemeyer A, Kadus W, Goettel M, Jambrecina A et al. 2021. Midazolam microdosing applied in early clinical development for drug-drug interaction assessment. Br. J. Clin. Pharmacol. 87:178–88
     [Google Scholar]
  57. 57.
    Rohr BS, Foerster KI, Blank A, Burhenne J, Mahmoudi M et al. 2022. Perpetrator characteristics of azole antifungal drugs on three oral factor Xa inhibitors administered as a microdosed cocktail. Clin. Pharmacokinet. 61:97–109
     [Google Scholar]
  58. 58.
    Halama B, Hohmann N, Burhenne J, Weiss J, Mikus G, Haefeli WE. 2013. A nanogram dose of the CYP3A probe substrate midazolam to evaluate drug interactions. Clin. Pharmacol. Ther. 93:564–71
     [Google Scholar]
  59. 59.
    Maeda K, Takano J, Ikeda Y, Fujita T, Oyama Y et al. 2011. Nonlinear pharmacokinetics of oral quinidine and verapamil in healthy subjects: a clinical microdosing study. Clin. Pharmacol. Ther. 90:263–70
     [Google Scholar]
  60. 60.
    van Nuland M, Rosing H, Huitema ADR, Beijnen JH. 2019. Predictive value of microdose pharmacokinetics. Clin. Pharmacokinet. 58:1221–36
     [Google Scholar]
  61. 61.
    Lim YC, Desta Z, Flockhart DA, Skaar TC. 2005. Endoxifen (4-hydroxy-N-desmethyl-tamoxifen) has anti-estrogenic effects in breast cancer cells with potency similar to 4-hydroxy-tamoxifen. Cancer Chemother. Pharmacol. 55:471–78
     [Google Scholar]
  62. 62.
    Mürdter TE, Schroth W, Bacchus-Gerybadze L, Winter S, Heinkele G et al. 2011. Activity levels of tamoxifen metabolites at the estrogen receptor and the impact of genetic polymorphisms of phase I and II enzymes on their concentration levels in plasma. Clin. Pharmacol. Ther. 89:708–17
     [Google Scholar]
  63. 63.
    Schroth W, Winter S, Mürdter T, Schaeffeler E, Eccles D et al. 2017. Improved prediction of endoxifen metabolism by CYP2D6 genotype in breast cancer patients treated with tamoxifen. Front. Pharmacol. 8:582
     [Google Scholar]
  64. 64.
    Saladores P, Mürdter T, Eccles D, Chowbay B, Zgheib NK et al. 2015. Tamoxifen metabolism predicts drug concentrations and outcome in premenopausal patients with early breast cancer. Pharmacogenom. J. 15:84–94
     [Google Scholar]
  65. 65.
    Brauch H, Mürdter TE, Eichelbaum M, Schwab M. 2009. Pharmacogenomics of tamoxifen therapy. Clin. Chem. 55:1770–82
     [Google Scholar]
  66. 66.
    Sutiman N, Lim JSL, Muerdter TE, Singh O, Cheung YB et al. 2016. Pharmacogenetics of UGT1A4, UGT2B7 and UGT2B15 and their influence on tamoxifen disposition in Asian breast cancer patients. Clin. Pharmacokinet. 55:1239–50
     [Google Scholar]
  67. 67.
    Puszkiel A, Arellano C, Vachoux C, Evrard A, Le Morvan V et al. 2021. Model-based quantification of impact of genetic polymorphisms and co-medications on pharmacokinetics of tamoxifen and six metabolites in breast cancer. Clin. Pharmacol. Ther. 109:1244–55
     [Google Scholar]
  68. 68.
    Magliocco G, Thomas A, Desmeules J, Daali Y. 2019. Phenotyping of human CYP450 enzymes by endobiotics: current knowledge and methodological approaches. Clin. Pharmacokinet. 58:1373–91
     [Google Scholar]
  69. 69.
    de Kesel PMM, Lambert WE, Stove CP. 2016. Alternative sampling strategies for cytochrome P450 phenotyping. Clin. Pharmacokinet. 55:169–84
     [Google Scholar]
  70. 70.
    Donzelli M, Derungs A, Serratore M-G, Noppen C, Nezic L et al. 2014. The Basel cocktail for simultaneous phenotyping of human cytochrome P450 isoforms in plasma, saliva and dried blood spots. Clin. Pharmacokinet. 53:271–82
     [Google Scholar]
  71. 71.
    Bosilkovska M, Samer C, Déglon J, Thomas A, Walder B et al. 2016. Evaluation of mutual drug-drug interaction within Geneva cocktail for cytochrome P450 phenotyping using innovative dried blood sampling method. Basic Clin. Pharmacol. Toxicol. 119:284–90
     [Google Scholar]
  72. 72.
    Opdam FL, Modak AS, Gelderblom H, Guchelaar H-J. 2013. Breath tests to phenotype drug disposition in oncology. Clin. Pharmacokinet. 52:919–26
     [Google Scholar]
  73. 73.
    van Dyk M, Kapetas AJ, Hopkins AM, Rodrigues AD, Vourvahis M et al. 2019. Validation of a 3-h sampling interval to assess variability in cytochrome P450 3A phenotype and the impact of induction and mechanism-based inhibition using midazolam as a probe substrate. Front. Pharmacol. 10:1120
     [Google Scholar]
  74. 74.
    Chen R, Wang H, Shi J, Hu P. 2016. Alternative methods for CYP2D6 phenotyping: comparison of dextromethorphan metabolic ratios from AUC, single point plasma, and urine. Int. J. Clin. Pharmacol. Ther. 54:330–36
     [Google Scholar]
  75. 75.
    Croft M, Keely B, Morris I, Tann L, Lappin G. 2012. Predicting drug candidate victims of drug-drug interactions, using microdosing. Clin. Pharmacokinet. 51:237–46
     [Google Scholar]
  76. 76.
    de Andrés F, Terán S, Bovera M, Fariñas H, Terán E, LLerena A. 2016. Multiplex phenotyping for systems medicine. a one-point optimized practical sampling strategy for simultaneous estimation of CYP1A2, CYP2C9, CYP2C19, and CYP2D6 activities using a cocktail approach. OMICS 20:88–96
     [Google Scholar]
  77. 77.
    Chen R, Zheng X, Hu P. 2017. CYP2D6 phenotyping using urine, plasma, and saliva metabolic ratios to assess the impact of CYP2D6*10 on interindividual variation in a Chinese population. Front. Pharmacol. 8:239
     [Google Scholar]
  78. 78.
    Eide Kvitne K, Hole K, Krogstad V, Wollmann BM, Wegler C et al. 2022. Correlations between 4β-hydroxycholesterol and hepatic and intestinal CYP3A4: protein expression, microsomal ex vivo activity, and in vivo activity in patients with a wide body weight range. Eur. J. Clin. Pharmacol. 78:1289–99
     [Google Scholar]
  79. 79.
    Diczfalusy U, Nylén H, Elander P, Bertilsson L. 2011. 4β-Hydroxycholesterol, an endogenous marker of CYP3A4/5 activity in humans. Br. J. Clin. Pharmacol. 71:183–89
     [Google Scholar]
  80. 80.
    Kasichayanula S, Boulton DW, Luo W-L, Rodrigues AD, Yang Z et al. 2014. Validation of 4β-hydroxycholesterol and evaluation of other endogenous biomarkers for the assessment of CYP3A activity in healthy subjects. Br. J. Clin. Pharmacol. 78:1122–34
     [Google Scholar]
  81. 81.
    Penzak SR, Rojas-Fernandez C. 2019. 4β-Hydroxycholesterol as an endogenous biomarker for CYP3A activity: literature review and critical evaluation. J. Clin. Pharmacol. 59:611–24
     [Google Scholar]
  82. 82.
    Shin K-H, Ahn LY, Choi MH, Moon J-Y, Lee J et al. 2016. Urinary 6β-hydroxycortisol/cortisol ratio most highly correlates with midazolam clearance under hepatic CYP3A inhibition and induction in females: a pharmacometabolomics approach. AAPS J. 18:1254–61
     [Google Scholar]
  83. 83.
    Lee J, Yoon SH, Yi S, Kim AH, Kim B et al. 2019. Quantitative prediction of hepatic CYP3A activity using endogenous markers in healthy subjects after administration of CYP3A inhibitors or inducers. Drug. Metab. Pharmacokinet. 34:247–52
     [Google Scholar]
  84. 84.
    Li X-Q, Thelingwani RS, Bertilsson L, Diczfalusy U, Andersson TB, Masimirembwa C. 2021. Evaluation of 1β-hydroxylation of deoxycholic acid as a non-invasive urinary biomarker of CYP3A activity in the assessment of inhibition-based drug-drug interaction in healthy volunteers. J. Pers. Med. 11:457
     [Google Scholar]
  85. 85.
    Faber MS, Jetter A, Fuhr U. 2005. Assessment of CYP1A2 activity in clinical practice: why, how, and when?. Basic Clin. Pharmacol. Toxicol. 97:125–34
     [Google Scholar]
  86. 86.
    Magliocco G, Desmeules J, Samer CF, Thomas A, Daali Y. 2022. Evaluation of CYP1A2 activity. Relationship between the endogenous urinary 6-hydroxymelatonin to melatonin ratio and paraxanthine to caffeine ratio in dried blood spots. Clin. Transl. Sci. 15:1482–91
     [Google Scholar]
  87. 87.
    Lee S, Lee Y, Kim AH, Yoon S, Lee J et al. 2021. Urinary metabolic markers reflect on hepatic, not intestinal, CYP3A activity in healthy subjects. Drug. Metab. Pharmacokinet. 36:100374
     [Google Scholar]
  88. 88.
    Pristup J, Schaeffeler E, Arjune S, Hofmann U, Santamaria-Araujo JA et al. 2022. Molybdenum cofactor catabolism unravels the physiological role of the drug metabolizing enzyme thiopurine S-methyltransferase. Clin. Pharmacol. Ther. 112:808–16
     [Google Scholar]
  89. 89.
    Müller F, Hohl K, Keller S, Schmidt-Gerets S, Deutsch B et al. 2023. N1-methylnicotinamide as biomarker for MATE-mediated renal drug-drug interactions: impact of cimetidine, rifampin, verapamil, and probenecid. Clin. Pharmacol. Ther. 113:1070–79
     [Google Scholar]
  90. 90.
    Müller F, Sharma A, König J, Fromm MF. 2018. Biomarkers for in vivo assessment of transporter function. Pharmacol. Rev. 70:246–77
     [Google Scholar]
  91. 91.
    Derungs A, Donzelli M, Berger B, Noppen C, Krähenbühl S, Haschke M. 2016. Effects of cytochrome P450 inhibition and induction on the phenotyping metrics of the Basel cocktail: a randomized crossover study. Clin. Pharmacokinet. 55:79–91
     [Google Scholar]
  92. 92.
    Leuthold P, Schaeffeler E, Winter S, Büttner F, Hofmann U et al. 2017. Comprehensive metabolomic and lipidomic profiling of human kidney tissue: a platform comparison. J. Proteome Res. 16:933–44
     [Google Scholar]
  93. 93.
    Tay-Sontheimer J, Shireman LM, Beyer RP, Senn T, Witten D et al. 2014. Detection of an endogenous urinary biomarker associated with CYP2D6 activity using global metabolomics. Pharmacogenomics 15:1947–62
     [Google Scholar]
  94. 94.
    Behrle AC, Douglas J, Leeder JS, van Haandel L. 2022. Isolation and identification of 3,4-seco-solanidine-3,4-dioic acid (SSDA) as a urinary biomarker of cytochrome P450 2D6 (CYP2D6) activity. Drug. Metab. Dispos. 50:1342–51
     [Google Scholar]
  95. 95.
    Magliocco G, Desmeules J, Matthey A, Quirós-Guerrero LM, Bararpour N et al. 2021. Metabolomics reveals biomarkers in human urine and plasma to predict cytochrome P450 2D6 (CYP2D6) activity. Br. J. Pharmacol. 178:4708–25
     [Google Scholar]
  96. 96.
    Cheng J, Chen C, Kristopher KW, Manna SK, Scerba M et al. 2013. Identification of 2-piperidone as a biomarker of CYP2E1 activity through metabolomic phenotyping. Toxicol. Sci. 135:37–47
     [Google Scholar]
  97. 97.
    Pu X, Gao Y, Li R, Li W, Tian Y et al. 2019. Biomarker discovery for cytochrome P450 1A2 activity assessment in rats, based on metabolomics. Metabolites 9:77
     [Google Scholar]
  98. 98.
    Ignatiadis M, Sledge GW, Jeffrey SS. 2021. Liquid biopsy enters the clinic—implementation issues and future challenges. Nat. Rev. Clin. Oncol. 18:297–312
     [Google Scholar]
  99. 99.
    Alix-Panabières C, Pantel K 2021. Liquid biopsy: from discovery to clinical application. Cancer Discov 11:858–73
     [Google Scholar]
  100. 100.
    Raposo G, Stoorvogel W. 2013. Extracellular vesicles: exosomes, microvesicles, and friends. J. Cell. Biol. 200:373–83
     [Google Scholar]
  101. 101.
    van Niel G, D'Angelo G, Raposo G 2018. Shedding light on the cell biology of extracellular vesicles. Nat. Rev. Mol. Cell Biol. 19:213–28
     [Google Scholar]
  102. 102.
    Habtemariam HD, Guchelaar H-J. 2022. The potential application of extracellular vesicles from liquid biopsies for determination of pharmacogene expression. Pharmaceuticals 15:252
     [Google Scholar]
  103. 103.
    Yu D, Li Y, Wang M, Gu J, Xu W et al. 2022. Exosomes as a new frontier of cancer liquid biopsy. Mol. Cancer 21:56
     [Google Scholar]
  104. 104.
    Soung YH, Ford S, Zhang V, Chung J. 2017. Exosomes in cancer diagnostics. Cancers 9:8
     [Google Scholar]
  105. 105.
    Melo SA, Luecke LB, Kahlert C, Fernandez AF, Gammon ST et al. 2015. Glypican-1 identifies cancer exosomes and detects early pancreatic cancer. Nature 523:177–82
     [Google Scholar]
  106. 106.
    Rowland A, Ruanglertboon W, van Dyk M, Wijayakumara D, Wood LS et al. 2019. Plasma extracellular nanovesicle (exosome)-derived biomarkers for drug metabolism pathways: a novel approach to characterize variability in drug exposure. Br. J. Clin. Pharmacol. 85:216–26
     [Google Scholar]
  107. 107.
    Tremmel R, Herrmann K, Engst W, Meinl W, Klein K et al. 2017. Methyleugenol DNA adducts in human liver are associated with SULT1A1 copy number variations and expression levels. Arch. Toxicol. 91:3329–39
     [Google Scholar]
  108. 108.
    Furukawa M, Nishimura M, Ogino D, Chiba R, Ikai I et al. 2004. Cytochrome p450 gene expression levels in peripheral blood mononuclear cells in comparison with the liver. Cancer Sci 95:520–29
     [Google Scholar]
  109. 109.
    Haas CE, Brazeau D, Cloen D, Booker BM, Frerichs V et al. 2005. Cytochrome P450 mRNA expression in peripheral blood lymphocytes as a predictor of enzyme induction. Eur. J. Clin. Pharmacol. 61:583–93
     [Google Scholar]
  110. 110.
    Kumar S, Sinha N, Gerth KA, Rahman MA, Yallapu MM, Midde NM. 2017. Specific packaging and circulation of cytochromes P450, especially 2E1 isozyme, in human plasma exosomes and their implications in cellular communications. Biochem. Biophys. Res. Commun. 491:675–80
     [Google Scholar]
  111. 111.
    Achour B, Al-Majdoub ZM, Grybos-Gajniak A, Lea K, Kilford P et al. 2021. Liquid biopsy enables quantification of the abundance and interindividual variability of hepatic enzymes and transporters. Clin. Pharmacol. Ther. 109:222–32
     [Google Scholar]
  112. 112.
    Bosilkovska M, Magliocco G, Desmeules J, Samer C, Daali Y. 2019. Interaction between fexofenadine and CYP phenotyping probe drugs in Geneva cocktail. J. Pers. Med. 9:45
     [Google Scholar]
  113. 113.
    Achour B, Gosselin P, Terrier J, Gloor Y, Al-Majdoub ZM et al. 2022. Liquid biopsy for patient characterization in cardiovascular disease: verification against markers of cytochrome P450 and P-glycoprotein activities. Clin. Pharmacol. Ther. 111:1268–77
     [Google Scholar]
  114. 114.
    Richter T, Mürdter TE, Heinkele G, Pleiss J, Tatzel S et al. 2004. Potent mechanism-based inhibition of human CYP2B6 by clopidogrel and ticlopidine. J. Pharmacol. Exp. Ther. 308:189–97
     [Google Scholar]
  115. 115.
    Rodrigues AD, van Dyk M, Sorich MJ, Fahmy A, Useckaite Z et al. 2021. Exploring the use of serum-derived small extracellular vesicles as liquid biopsy to study the induction of hepatic cytochromes P450 and organic anion transporting polypeptides. Clin. Pharmacol. Ther. 110:248–58
     [Google Scholar]
  116. 116.
    Rodrigues AD, Wood LS, Vourvahis M, Rowland A. 2022. Leveraging human plasma-derived small extracellular vesicles as liquid biopsy to study the induction of cytochrome P450 3A4 by modafinil. Clin. Pharmacol. Ther. 111:425–34
     [Google Scholar]
  117. 117.
    Wolbold R, Klein K, Burk O, Nüssler AK, Neuhaus P et al. 2003. Sex is a major determinant of CYP3A4 expression in human liver. Hepatology 38:978–88
     [Google Scholar]
  118. 118.
    Suárez B, Solé C, Márquez M, Nanetti F, Lawrie CH. 2022. Circulating microRNAs as cancer biomarkers in liquid biopsies. Adv. Exp. Med. Biol. 1385:23–73
     [Google Scholar]
  119. 119.
    Drula R, Ott LF, Berindan-Neagoe I, Pantel K, Calin GA. 2020. MicroRNAs from liquid biopsy derived extracellular vesicles: recent advances in detection and characterization methods. Cancers 12:2009
     [Google Scholar]
  120. 120.
    Rieger JK, Klein K, Winter S, Zanger UM. 2013. Expression variability of absorption, distribution, metabolism, excretion-related microRNAs in human liver: influence of nongenetic factors and association with gene expression. Drug. Metab. Dispos. 41:1752–62
     [Google Scholar]
  121. 121.
    Kugler N, Klein K, Zanger UM. 2020. MiR-155 and other microRNAs downregulate drug metabolizing cytochromes P450 in inflammation. Biochem. Pharmacol. 171:113725
     [Google Scholar]
  122. 122.
    Zhang H-F, Zhu L-L, Yang X-B, Gao N, Fang Y et al. 2021. Variation in the expression of cytochrome P450-related miRNAs and transcriptional factors in human livers: correlation with cytochrome P450 gene phenotypes. Toxicol. Appl. Pharmacol. 412:115389
     [Google Scholar]
  123. 123.
    Gill P, Bhattacharyya S, McCullough S, Letzig L, Mishra PJ et al. 2017. MicroRNA regulation of CYP 1A2, CYP3A4 and CYP2E1 expression in acetaminophen toxicity. Sci. Rep. 7:12331
     [Google Scholar]
  124. 124.
    Lippert J, Burghaus R, Kuepfer L, Ploeger B, Schaller S et al. 2016. Modeling and simulation of in vivo drug effects. Handb. Exp. Pharmacol. 232:313–29
     [Google Scholar]
  125. 125.
    Kuepfer L, Kerb R, Henney AM. 2014. Clinical translation in the virtual liver network. CPT Pharmacometrics Syst. Pharmacol. 3:e127
     [Google Scholar]
  126. 126.
    Türk D, Fuhr LM, Marok FZ, Rüdesheim S, Kühn A et al. 2021. Novel models for the prediction of drug-gene interactions. Expert. Opin. Drug. Metab. Toxicol. 17:1293–310
     [Google Scholar]
  127. 127.
    Jones HM, Chen Y, Gibson C, Heimbach T, Parrott N et al. 2015. Physiologically based pharmacokinetic modeling in drug discovery and development: a pharmaceutical industry perspective. Clin. Pharmacol. Ther. 97:247–62
     [Google Scholar]
  128. 128.
    Adiwidjaja J, Boddy AV, McLachlan AJ. 2018. A strategy to refine the phenotyping approach and its implementation to predict drug clearance: a physiologically based pharmacokinetic simulation study. CPT Pharmacometrics Syst. Pharmacol. 7:798–808
     [Google Scholar]
  129. 129.
    Wojtyniak J-G, Selzer D, Schwab M, Lehr T. 2021. Physiologically based precision dosing approach for drug-drug-gene interactions: a simvastatin network analysis. Clin. Pharmacol. Ther. 109:201–11
     [Google Scholar]
  130. 130.
    Rüdesheim S, Selzer D, Fuhr U, Schwab M, Lehr T. 2022. Physiologically-based pharmacokinetic modeling of dextromethorphan to investigate interindividual variability within CYP2D6 activity score groups. CPT Pharmacometrics Syst. Pharmacol. 11:494–511
     [Google Scholar]
  131. 131.
    Grzegorzewski J, Brandhorst J, König M. 2022. Physiologically based pharmacokinetic (PBPK) modeling of the role of CYP2D6 polymorphism for metabolic phenotyping with dextromethorphan. Front. Pharmacol. 13:1029073
     [Google Scholar]
  132. 132.
    Darwich AS, Polasek TM, Aronson JK, Ogungbenro K, Wright DFB et al. 2021. Model-informed precision dosing: background, requirements, validation, implementation, and forward trajectory of individualizing drug therapy. Annu. Rev. Pharmacol. Toxicol. 61:225–45
     [Google Scholar]
  133. 133.
    Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. 2018. Artificial intelligence in radiology. Nat. Rev. Cancer 18:500–10
     [Google Scholar]
  134. 134.
    Liu X, Faes L, Kale AU, Wagner SK, Fu DJ et al. 2019. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit. Health 1:e271–97
     [Google Scholar]
  135. 135.
    Ietswaart R, Arat S, Chen AX, Farahmand S, Kim B et al. 2020. Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology. EBioMedicine 57:102837
     [Google Scholar]
  136. 136.
    Zhou Y, Tremmel R, Schaeffeler E, Schwab M, Lauschke VM. 2022. Challenges and opportunities associated with rare-variant pharmacogenomics. Trends Pharmacol. Sci. 43:852–65
     [Google Scholar]
  137. 137.
    Wei W-Q, Zhao J, Roden DM, Peterson JF. 2021. Machine learning challenges in pharmacogenomic research. Clin. Pharmacol. Ther. 110:552–54
     [Google Scholar]
  138. 138.
    Picard M, Scott-Boyer M-P, Bodein A, Périn O, Droit A. 2021. Integration strategies of multi-omics data for machine learning analysis. Comput. Struct. Biotechnol. J. 19:3735–46
     [Google Scholar]
  139. 139.
    Ko J, Baldassano SN, Loh P-L, Kording K, Litt B, Issadore D. 2018. Machine learning to detect signatures of disease in liquid biopsies—a user's guide. Lab Chip 18:395–405
     [Google Scholar]
  140. 140.
    Penson A, Camacho N, Zheng Y, Varghese AM, Al-Ahmadie H et al. 2020. Development of genome-derived tumor type prediction to inform clinical cancer care. JAMA Oncol 6:84–91
     [Google Scholar]
  141. 141.
    McInnes G, Dalton R, Sangkuhl K, Whirl-Carrillo M, Lee S-B et al. 2020. Transfer learning enables prediction of CYP2D6 haplotype function. PLOS Comput. Biol. 16:e1008399
     [Google Scholar]
  142. 142.
    van der Lee M, Guchelaar H-J, Swen JJ. 2021. Substrate specificity of CYP2D6 genetic variants. Pharmacogenomics 22:1081–89
     [Google Scholar]
  143. 143.
    Athreya AP, Neavin D, Carrillo-Roa T, Skime M, Biernacka J et al. 2019. Pharmacogenomics-driven prediction of antidepressant treatment outcomes: a machine-learning approach with multi-trial replication. Clin. Pharmacol. Ther. 106:855–65
     [Google Scholar]
  144. 144.
    Athreya AP, Brückl T, Binder EB, Rush AJ, Biernacka J et al. 2021. Prediction of short-term antidepressant response using probabilistic graphical models with replication across multiple drugs and treatment settings. Neuropsychopharmacology 46:1272–82
     [Google Scholar]
  145. 145.
    Epah J, Gülec I, Winter S, Dörr J, Geisen C et al. 2022. From unit to dose: a machine learning approach for precise prediction of hemoglobin and iron content in individual packed red blood cell units. Adv. Sci. 9:e2204077
     [Google Scholar]
  146. 146.
    Klaassen T, Jetter A, Tomalik-Scharte D, Kasel D, Kirchheiner J et al. 2008. Assessment of urinary mephenytoin metrics to phenotype for CYP2C19 and CYP2B6 activity. Eur. J. Clin. Pharmacol. 64:387–98
     [Google Scholar]
  147. 147.
    Krauss M, Hofmann U, Schafmayer C, Igel S, Schlender J et al. 2017. Translational learning from clinical studies predicts drug pharmacokinetics across patient populations. NPJ Syst. Biol. Appl. 3:11
     [Google Scholar]
  148. 148.
    Fendt R, Hofmann U, Schneider ARP, Schaeffeler E, Burghaus R et al. 2021. Data-driven personalization of a physiologically based pharmacokinetic model for caffeine: a systematic assessment. CPT Pharmacometrics Syst. Pharmacol. 10:782–93
     [Google Scholar]
  149. 149.
    Shibasaki H, Hosoda K, Goto M, Suzuki A, Yokokawa A et al. 2013. Intraindividual and interindividual variabilities in endogenous cortisol 6β-hydroxylation clearance as an index for in vivo CYP3A phenotyping in humans. Drug. Metab. Dispos 41:475–79
     [Google Scholar]
  150. 150.
    Hu Z-Y, Zhao Y-S, Wu Di, Cheng Z-N 2009. Endogenous cortisol 6β-hydroxylation clearance is not an accurate probe for overall cytochrome P450 3A phenotyping in humans. Clin. Chim. Acta 408:92–97
     [Google Scholar]
/content/journals/10.1146/annurev-pharmtox-032023-121106
Loading
Circulating Biomarkers Instead of Genotyping to Establish Metabolizer Phenotypes
Annual Review of Pharmacology and Toxicology 64, 65 (2024); https://doi.org/10.1146/annurev-pharmtox-032023-121106
/content/journals/10.1146/annurev-pharmtox-032023-121106
/content/journals/10.1146/annurev-pharmtox-032023-121106
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