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Assessment of clinically actionable pharmacogenetic markers to stratify anti-seizure medications

The Pharmacogenomics Journal volume 23, pages 149–160 (2023)Cite this article

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Abstract

Epilepsy treatment is challenging due to heterogeneous syndromes, different seizure types and higher inter-individual variability. Identification of genetic variants predicting drug efficacy, tolerability and risk of adverse-effects for anti-seizure medications (ASMs) is essential. Here, we assessed the clinical actionability of known genetic variants, based on their functional and clinical significance and estimated their diagnostic predictability. We performed a systematic PubMed search to identify articles with pharmacogenomic (PGx) information for forty known ASMs. Functional annotation of the identified genetic variants was performed using different in silico tools, and their clinical significance was assessed using the American College of Medical Genetics (ACMG) guidelines for variant pathogenicity, level of evidence (LOE) from PharmGKB and the United States-Food and drug administration (US- FDA) drug labelling with PGx information. Diagnostic predictability of the replicated genetic variants was evaluated by calculating their accuracy. A total of 270 articles were retrieved with PGx evidence associated with 19 ASMs including 178 variants across 93 genes, classifying 26 genetic variants as benign/ likely benign, fourteen as drug response markers and three as risk factors for drug response. Only seventeen of these were replicated, with accuracy (up to 95%) in predicting PGx outcomes specific to six ASMs. Eight out of seventeen variants have FDA-approved PGx drug labelling for clinical implementation. Therefore, the remaining nine variants promise for potential clinical actionability and can be improvised with additional experimental evidence for clinical utility.

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Fig. 1: Evolution of pharmacogenomics of anti-epileptic drugs in clinical practice.
Fig. 2: This diagram shows all the pharmacogenes of ASM associations with p-value ≤ 0.05 in human autosomes.
Fig. 3: The ROC for risk allele count on pharmacogenetic SNPs used to measure AUC in PWE.

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Data availability

All data generated or analysed during this study are included in this published article [and its supplementary information files].

References

  1. Murray CJ, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C, et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380:2197–223.

    PubMed  Google Scholar 

  2. Striano P, Minassian BA. From genetic testing to precision medicine in epilepsy. Neurotherapeutics. 2020;17:609–15.

    PubMed  PubMed Central  Google Scholar 

  3. Scheffer IE, Berkovic S, Capovilla G, Connolly MB, French J, Guilhoto L, et al. ILAE classification of the epilepsies: position paper of the ILAE commission for classification and terminology. Epilepsia. 2017;58:512–21.

    PubMed  PubMed Central  Google Scholar 

  4. Löscher W, Klotz U, Zimprich F, Schmidt D. The clinical impact of pharmacogenetics on the treatment of epilepsy. Epilepsia. 2009;50:1–23.

    PubMed  Google Scholar 

  5. Wang L, McLeod H, Weinshilboum RM. Genomics and drug response. N Engl J Med. 2011;364:1144–53.

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Spear BB. Pharmacogenetics and antiepileptic drugs. Epilepsia. 2001;42:31–34.

    PubMed  Google Scholar 

  7. Madian AG, Wheeler H, Jones RB, Dolan ME. Relating human genetic variation to variation in drug responses. Trends Genet. 2012;28:487–95.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Ito K, Suzuki H, Horie T, Sugiyama Y. Apical/basolateral surface expression of drug transporters and its role in vectorial drug transport. Pharm Res. 2005;22:1559–77.

    CAS  PubMed  Google Scholar 

  9. Hoffmeyer S, Burk O, von Richter O, Arnold HP, Brockmöller J, Johne A, et al. Functional polymorphisms of the human multidrug-resistance gene: multiple sequence variations and correlation of one allele with P-glycoprotein expression and activity in vivo. Proc Natl Acad Sci. 2000;97:3473–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Keangpraphun T, Towanabut S, Chinvarun Y, Kijsanayotin P. Association of ABCB1 C3435T polymorphism with phenobarbital resistance in Thai patients with epilepsy. J Clin Pharm Ther. 2015;40:315–9.

    CAS  PubMed  Google Scholar 

  11. Seo T, Ishitsu T, Ueda N, Nakada N, Yurube K, Ueda K, et al. ABCB1 polymorphisms influence the response to antiepileptic drugs in Japanese epilepsy patients. Pharmacogenomics. 2006;7:551–61.

    CAS  PubMed  Google Scholar 

  12. Kwan P, Broodie MJ. Potential role of drug transporters in the pathogenesis of medically intractable epilepsy. Epilepsia. 2005;46:224–35.

    CAS  PubMed  Google Scholar 

  13. Rogawski MA, Loscher W. The neurobiology of antiepileptic drugs. Nat Rev Neurosci. 2004;5:553–64.

    CAS  PubMed  Google Scholar 

  14. Guerrini R, Dravet C, Genton P, Belmonte A, Kaminska A, Dulac O. Lamotrigine and seizure aggravation in severe myoclonic epilepsy. Epilepsia. 1998;39:508–12.

    CAS  PubMed  Google Scholar 

  15. Handforth A, Delorey TM, Homanics GE, Olsen RW. Pharmacologic evidence for abnormal thalamocortical functioning in GABA receptor beta3 subunit-deficient mice, a model of Angelman syndrome. Epilepsia. 2005;46:1860–70.

    CAS  PubMed  Google Scholar 

  16. Thakkar AN, Bendkhale SR, Taur SR, Gogtay NJ, Thatte UM. Association of CYP2C9 polymorphisms with phenytoin toxicity in Indian patients. Neurol India. 2012;60:577–80.

    PubMed  Google Scholar 

  17. Mamiya K, Ieiri I, Shimamoto J, Yukawa E, Imai J, Ninomiya H, et al. The effects of genetic polymorphisms of CYP2C9 and CYP2C19 on phenytoin metabolism in Japanese adult patients with epilepsy: studies in stereoselective hydroxylation and population pharmacokinetics. Epilepsia. 1998;39:1317–23.

    CAS  PubMed  Google Scholar 

  18. Glauser TA, Holland K, O’Brien VP, Keddache M, Martin LJ, Clark PO, et al. Pharmacogenetics of antiepileptic drug efficacy in childhood absence epilepsy. Ann Neurol. 2017;81:444–53.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Food and drug administration. Table of Pharmacogenomic Biomarkers in Drug Labeling with Labeling Text. FDA website (2018).

  20. Broodie MJ. Outcomes in newly diagnosed epilepsy in adolescents and adults: insights across a generation in Scotland. Seizure. 2017;44:206–10.

    Google Scholar 

  21. Pohlmann-Eden BWD. The puzzle(s) of pharmacoresistant epilepsy. Epilepsia. 2013;54:1–4.

    PubMed  Google Scholar 

  22. Marson AG, Al-Kharusi AM, Alwaidh M, Appleton R, Baker GA, Chadwick DW, et al. The SANAD study of effectiveness of carbamazepine, gabapentin, lamotrigine, oxcarbazepine, or topiramate for treatment of partial epilepsy: an unblinded randomised controlled trial. Lancet. 2007;369:1000–15.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, et al. Drugbank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006;34:16381955.

    Google Scholar 

  24. Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29:308–11.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Kaur H, Jajodia A, Grover S, Agarwal N, Baghel R, Kukreti R. Pharmacogenomics of neuropsychiatric disorders: analysis of genetic variability in 162 identified neuroreceptors using 1000 Genomes Project data. Pharmacogenomics. 2014;15:1575–87.

    CAS  PubMed  Google Scholar 

  26. Ng PC, Henikoff S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003;31:3812–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al. A method and server for predicting damaging missense mutations. Nat Methods. 2010;7:248–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Rentzsch P, Schubach M, Shendure J, Kircher M. CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores. Genome Med. 2021;13:31.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, Kasowski M, et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 2012;22:1790–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  30. McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, et al. The ensembl variant effect predictor. Genome Biol. 2016;17:122.

    PubMed  PubMed Central  Google Scholar 

  31. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17:405–24.

    PubMed  PubMed Central  Google Scholar 

  32. Whirl-Carrillo M, McDonagh EM, Hebert JM, Gong L, Sangkuhl K, Thorn CF, et al. Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther. 2012;92:414–7.

    CAS  PubMed  Google Scholar 

  33. Whirl-Carrillo M, Huddart R, Gong L, Sangkuhl K, Thorn CF, Whaley R, et al. An evidence-based framework for evaluating pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther. 2021;110:563–72.

    PubMed  PubMed Central  Google Scholar 

  34. Akhondzadeh S. Personalized medicine: a tailor made medicine. Avicenna J Med Biotechnol. 2014;6:191.

    PubMed  PubMed Central  Google Scholar 

  35. Shreffler J, Huecker MR Treasure Island (FL). Diagnostic testing accuracy: sensitivity, specificity, predictive values and likelihood ratios. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2022.

  36. Zhou YMS, Kumondai M, Hiratsuka M, Lauschke VM. An optimized prediction framework to assess the functional impact of pharmacogenetic variants. Pharmacogenomics J. 2019;19:115–26.

    CAS  PubMed  Google Scholar 

  37. Support STATA Technical. Citing Stata software, documentation, and FAQs. StataCorp 2019 Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC.

  38. Sorich M, Coory M. Interpreting the clinical utility of a pharmacogenomic marker based on observational association studies. Pharmacogenomics J. 2014;14:1–5.

    CAS  PubMed  Google Scholar 

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

    Google Scholar 

  40. Consortium, International HAPMAP. The international hapmap project. Nature. 2003;426:789–96.

    Google Scholar 

  41. Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581:434–43.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. L Phan, Y Jin, H Zhang, W Qiang, E Shekhtman, D Shao, D. et al. ALFA: Allele Frequency Aggregator." National Center for Biotechnology Information, US National Library of Medicine, (2020).

  43. Reimers A, Brodtkorb E Second-generation antiepileptic drugs and pregnancy: a guide for clinicians. Expert Rev Neurother. 2012;12:707–17.

  44. Schmidt D, Schachter SC. Drug treatment of epilepsy in adults. BMJ. 2014;28:254.

    Google Scholar 

  45. Sills GJ, Rogaswki M. Mechanisms of action of currently used antiseizure drugs. Neuropharmacology. 2020;168:107966.

    CAS  PubMed  Google Scholar 

  46. Shakir RA Sodium valproate, phenytoin and carbamazepine as sole anticonvulsants. The place of sodium valproate in the treatment of epilepsy. London: Academic Press Inc (London) Ltd and the Royal Society of Medicine (1980): 7‐16.

  47. Gruber CM, Brock JT, Dyken MD Comparison of the effectiveness of phenobarbital, mephobarbital, primidone, dipheylhydantoin, ethotoin, metharbital, and methylphenylhydantion in motor seizures. Clinical Pharmacology & Therapeutics (1962); 23‐8.

  48. Löscher W, Klitgaard H, Twyman RE, Schmidt D. New avenues for anti-epileptic drug discovery and development. Nat Rev Drug Discov. 2013;12:757–76.

    PubMed  Google Scholar 

  49. Smith PE. UK Oxcarbazepine advisory board. clinical recommendations for oxcarbazepine. Seizure. 2001;10:87–91.

    PubMed  Google Scholar 

  50. Panebianco M, Bresnahan R, Ramaratnam S, Marson AG. Lamotrigine add-on therapy for drug-resistant focal epilepsy. Cochrane Database Syst Rev. 2020;3:CD001909.

    PubMed  Google Scholar 

  51. Rahman M, Nguyen H Valproic Acid. [Updated 2022 Jul 4]. In: StatPearls [Internet] Treasure Island (FL): StatPearls Publishing; 2023 Jan- (2023).

  52. Abou-Khalil B. Levetiracetam in the treatment of epilepsy. Neuropsychiatr Dis Treat. 2008;4:507–23.

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Schmidt D. Efficacy of new antiepileptic drugs. Epilepsy Curr. 2011;11:9–11.

    PubMed  PubMed Central  Google Scholar 

  54. Hakimi T. Efficacy and tolerability of anti-seizure drugs. Ther Adv Neurol Disord. 2021;14:1–21.

    Google Scholar 

  55. Treiman DM. Management of refractory complex partial seizures: current state of the art. Neuropsychiatr Dis Treat. 2010;6:297–308.

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Kumar S, Sarangi SC, Tripathi M, Gupta YK. Evaluation of adverse drug reaction profile of antiepileptic drugs in persons with epilepsy: a cross-sectional study. Epilepsy Behav. 2020;105:106947.

    PubMed  Google Scholar 

  57. Du W, Tutag V, Lieh-Lai M. An algorithm to detect adverse drug reactions in the neonatal intensive care unit: a new approach. J Clin Pharmacol. 2013;53:87–95.

    PubMed  Google Scholar 

  58. Lo C, Nguyen S, Yang C, Witt L, Wen A, Liao TV, et al. Pharmacogenomics in Asian subpopulations and impacts on commonly prescribed medications. Clin Transl Sci. 2020;13:861–70.

    PubMed  PubMed Central  Google Scholar 

  59. Chung WH, Chang WC, Lee YS, Wu YY, Yang CH, Ho HC, et al. Genetic variants associated with phenytoin-related severe cutaneous adverse reactions. Jama. 2014;312:525–34.

    PubMed  Google Scholar 

  60. Ozeki T, Mushiroda T, Yowang A, Takahashi A, Kubo M, Shirakata Y, 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. 2011;20:1034–41.

    CAS  PubMed  Google Scholar 

  61. Jang HW, Kim SW, Cho YJ, Heo K, Lee BI, Lee SK, et al. GWAS identifies two susceptibility loci for lamotrigine-induced skin rash in patients with epilepsy. Epilepsy Res. 2015;115:88–94.

    CAS  PubMed  Google Scholar 

  62. Wolking S, Moreau C, McCormack M, Krause R, Krenn M, Berkovic S, et al. Assessing the role of rare genetic variants in drug-resistant, non-lesional focal epilepsy. Ann Clin Transl Neurol. 2021;8:1376–87.

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Athanasiu L, Smorr LL, Tesli M, Røssberg JI, Sønderby IE, Spigset O, et al. Genome-wide association study identifies common variants associated with pharmacokinetics of psychotropic drugs. J Psychopharmacol. 2015;29:884–91.

    CAS  PubMed  Google Scholar 

  64. Heavin SB, McCormack M, Wolking S, Slattery L, Walley N, Avbersek A, et al. Genomic and clinical predictors of lacosamide response in refractory epilepsies. Epilepsia Open. 2019;4:563–71.

    PubMed  PubMed Central  Google Scholar 

  65. Ho AM, Coombes BJ, Nguyen TTL, Liu D, McElroy SL, Singh B, et al. Mood-stabilizing antiepileptic treatment response in bipolar disorder: a genome-wide association study. Clin Pharmacol Ther. 2020;108:1233–42.

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Wolking S, Schulz H, Nies AT, McCormack M, Schaeffeler E, Auce P, et al. Pharmacoresponse in genetic generalized epilepsy: a genome-wide association study. Pharmacogenomics. 2020;21:325–35.

    CAS  PubMed  Google Scholar 

  67. Wolking S, Campbell C, Stapleton C, McCormack M, Delanty N, Depondt C, et al. Role of common genetic variants for drug-resistance to specific anti-seizure medications. Front Pharmacol. 2021;12:688386.

    PubMed  PubMed Central  Google Scholar 

  68. Berghuis B, Stapleton C, Sonsma ACM, Hulst J, de Haan GJ, Lindhout D, et al. A genome-wide association study of sodium levels and drug metabolism in an epilepsy cohort treated with carbamazepine and oxcarbazepine. Epilepsia Open. 2019;4:102–9.

    PubMed  PubMed Central  Google Scholar 

  69. Campbell C, McCormack M, Patel S, Stapleton C, Bobbili D, Krause R, et al. A pharmacogenomic assessment of psychiatric adverse drug reactions to levetiracetam. Epilepsia. 2022;63:1563–70.

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Mullan KA, Anderson A, Shi YW, Ding JH, Ng CC, Chen Z, et al. Potential role of regulatory DNA variants in modifying the risk of severe cutaneous reactions induced by aromatic anti-seizure medications. Epilepsia. 2022;63:936–49.

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Perucca P, Anderson A, Jazayeri D, Hitchcock A, Graham J, Todaro M, et al. Antiepileptic drug teratogenicity and De Novo genetic variation load. Ann Neurol. 2020;87:897–906.

    CAS  PubMed  Google Scholar 

  72. McCormack M, Urban TJ, Shianna KV, Walley N, Pandolfo M, Depondt C, et al. Genome-wide mapping for clinically relevant predictors of lamotrigine- and phenytoin-induced hypersensitivity reactions. Pharmacogenomics. 2012;13:399–405.

    CAS  PubMed  Google Scholar 

  73. Nicoletti P, Barrett S, McEvoy L, Daly AK, Aithal G, Lucena MI, et al. Shared genetic risk factors across carbamazepine-induced hypersensitivity reactions. Clin Pharmacol Ther. 2019;106:1028–36.

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Robinson J, Halliwell JA, Hayhurst JH, Flicek P, Parham P, Marsh SGE. The IPD and IMGT/HLA database: allele variant databases. Nucleic Acids Res. 2015;43:D423–431.

    CAS  PubMed  Google Scholar 

  75. Park Y, Kim H, Choi JY, Yun S, Min BJ, Seo ME, et al. Star Allele-Based haplotyping versus gene-wise variant burden scoring for predicting 6-Mercaptopurine intolerance in paediatric acute lymphoblastic leukaemia patients. Front Pharmcol. 2019;10:654.

    CAS  Google Scholar 

  76. Gonzalez-Galarza FF, Christmas S, Middleton D, Jones AR. Allele frequency net: a database and online repository for immune gene frequencies in worldwide populations. Nucleic Acids Res. 2011;39:913.

    Google Scholar 

  77. McCarthy JJH, Hilfiker R. The use of single-nucleotide polymorphism maps in pharmacogenomics. Nat Biotechnol. 2000;18:505–8.

    CAS  PubMed  Google Scholar 

  78. Khoury MJ, Little J. Human genome epidemiology reviews: the beginning of something HuGE. Am J Epidemiol. 2000;151:2–3.

    CAS  PubMed  Google Scholar 

  79. Guin D, Rani J, Singh P, Grover S, Bora S, Talwar P, et al. Global text mining and development of pharmacogenomic knowledge resource for precision medicine. Front Pharmacol. 2019;2019:839.

    Google Scholar 

  80. Balestrini S, Sisodiya SM. Pharmacogenomics in epilepsy. Neurosci Lett. 2018;667:27–39.

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Bloch KM, Sills GJ, Pirmohamed M, Alfirevic A. Pharmacogenetics of antiepileptic drug-induced hypersensitivity. Pharmacogenomics. 2014;15:857–68.

    CAS  PubMed  Google Scholar 

  82. Grover S, Kukreti R. HLA alleles and hypersensitivity to carbamazepine: an updated systematic review with meta-analysis. Pharmacogenet Genom. 2014;24:94–112.

    CAS  Google Scholar 

  83. Phung TH, Cong Duong KN, Junio Gloria MA, Nguyen TK. The association between HLA-B*15:02 and phenytoin-induced severe cutaneous adverse reactions: a meta-analysis. Pharmacogenomics. 2022;23:49–59.

    CAS  PubMed  Google Scholar 

  84. Deng Y, Li S, Zhang L, Jin H, Zou X. Association between HLA alleles and lamotrigine-induced cutaneous adverse drug reactions in Asian populations: a meta-analysis. Seizure. 2018;60:163–71.

    PubMed  Google Scholar 

  85. Nevitt SJ, Sudell M, Cividini S, Marson AG, Tudur Smith C. Antiepileptic drug monotherapy for epilepsy: a network meta-analysis of individual participant data. Cochrane Database Syst Rev. 2022;4:Cd011412.

    PubMed  Google Scholar 

  86. Biswas M, Ershadian M, Shobana J, Nguyen AH, Sukasem C. Associations of HLA genetic variants with carbamazepine-induced cutaneous adverse drug reactions: an updated meta-analysis. Clin Transl Sci. 2022;15:1887–905.

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Sukasem C, Sririttha S, Chaichan C, Nakkrut T, Satapornpong P, Jaruthamsophon K, et al. Spectrum of cutaneous adverse reactions to aromatic antiepileptic drugs and human leukocyte antigen genotypes in Thai patients and meta-analysis. Pharmacogenom J. 2021;21:682–90.

    CAS  Google Scholar 

  88. McCormack M, Gui H, Ingason A, Speed D, Wright GEB, Zhang EJ, et al. Genetic variation in CFH predicts phenytoin-induced maculopapular exanthema in European-descent patients. Neurology. 2018;90:e332–e341.

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Chang WC, Hung SI, Carleton BC, Chung WH. An update on CYP2C9 polymorphisms and phenytoin metabolism: implications for adverse effects. Expert Opin Drug Metab Toxicol. 2020;16:723–34.

    CAS  PubMed  Google Scholar 

  90. Kanjanasilp J, Sawangjit R, Phanthaisong S, Borihanthanawuth W. A meta-analysis of effects of CYP2C9 and CYP2C19 polymorphisms on phenytoin pharmacokinetic parameters. Pharmacogenomics. 2021;22:629–40.

    CAS  PubMed  Google Scholar 

  91. Liao K, Liu Y, Ai CZ, Yu X, Li W. The association between CYP2C9/2C19 polymorphisms and phenytoin maintenance doses in Asian epileptic patients: a systematic review and meta-analysis. Int J Clin Pharmacol Ther. 2018;56:337–46.

    CAS  PubMed  Google Scholar 

  92. Fan YX, Zhang Z, Meng JR, Yin SJ, Wang P, Zhou T, et al. Association of ABCB1 polymorphisms with carbamazepine metabolism and resistance in epilepsy: a meta-analysis. Epilepsy Res. 2021;177:106785.

    CAS  PubMed  Google Scholar 

  93. Zhao GX, Zhang Z, Cai WK, Shen ML, Wang P, He GH. Associations between CYP3A4, CYP3A5 and SCN1A polymorphisms and carbamazepine metabolism in epilepsy: a meta-analysis. Epilepsy Res. 2021;173:106615.

    CAS  PubMed  Google Scholar 

  94. Glauser T, Ben-Menachem E, Bourgeois B, Cnaan A, Guerreiro C, Kälviäinen R, et al. Updated ILAE evidence review of antiepileptic drug efficacy and effectiveness as initial monotherapy for epileptic seizures and syndromes. Epilepsia. 2013;54:551–63.

    CAS  PubMed  Google Scholar 

  95. Marigorta UM, Rodríguez JA, Gibson G, Navarro A. Replicability and prediction: lessons and challenges from GWAS. Trends Genet. 2018;34:504–17.

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Greene CS, Penrod NM, Williams SM, Moore JH. Failure to replicate a genetic association may provide important clues about genetic architecture. PLoS One. 2009;4:e5639.

    PubMed  PubMed Central  Google Scholar 

  97. Zhai S, Zhang H, Mehrotra DV, Shen J. Pharmacogenomics polygenic risk score for drug response prediction using PRS-PGx methods. Nat Commun. 2022;13:5278.

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Meyer U. Pharmacogenetics—five decades of therapeutic lessons from genetic diversity. Nat Rev Genet. 2004;5:669–76.

    CAS  PubMed  Google Scholar 

  99. Ventola CL. Pharmacogenomics in clinical practice: reality and expectations. P T. 2011;36:412–50.

    PubMed  PubMed Central  Google Scholar 

  100. Relling MV, Gardner EE, Sandborn WJ, Schmiegelow K, Pui CH, Yee SW, et al. Clinical Pharmacogenetics Implementation Consortium. Clinical Pharmacogenetics Implementation Consortium guidelines for thiopurine methyltransferase genotype and thiopurine dosing. Clin Pharm Ther. 2011;89:387–91.

    CAS  Google Scholar 

  101. Parker D, Sanders EJ, Burghardt KJ. Pharmacogenetics of antiepileptic drugs: a brief review. Ment Health Clin. 2016;6:28–34.

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Cavalleri GL, McCormack M, Alhusaini S, Chaila E, Delanty N. Pharmacogenomics and epilepsy: the road ahead. Pharmacogenomics. 2011;12:1429–47.

    PubMed  Google Scholar 

  103. Wolfe D, Dudek S, Ritchie MD, Pendergrass SA. Visualizing genomic information across chromosomes with PhenoGram. BioData Min. 2013;6:18.

    PubMed  PubMed Central  Google Scholar 

  104. Yukawa E, Mamiya K. Effect of CYP2C19 genetic polymorphism on pharmacokinetics of phenytoin and phenobarbital in Japanese epileptic patients using non-linear mixed effects model approach. J Clin Pharm Ther. 2006;31:275–82.

    CAS  PubMed  Google Scholar 

  105. Rosemary J, Surendiran A, Rajan S, Shashindran CH, Adithan C. Influence of the CYP2C9 AND CYP2C19 polymorphisms on phenytoin hydroxylation in healthy individuals from south India. Indian. Indian J Med Res. 2006;123:665–70.

    CAS  PubMed  Google Scholar 

  106. Hung CC, Lin CJ, Chen CC, Chang CJ, Liou HH. Dosage recommendation of phenytoin for patients with epilepsy with different CYP2C9/CYP2C19 polymorphisms. Ther Drug Monit. 2004;26:534–40.

    CAS  PubMed  Google Scholar 

  107. Soga Y, Nishimura F, Ohtsuka Y, Araki H, Iwamoto Y, Naruishi H, et al. CYP2C polymorphisms, phenytoin metabolism and gingival overgrowth in epileptic subjects. Life Sci. 2004;74:827–34.

    CAS  PubMed  Google Scholar 

  108. Imai J, Ieiri I, Mamiya K, Miyahara S, Furuumi H, Nanba E, et al. Polymorphism of the cytochrome P450 (CYP) 2C9 gene in Japanese epileptic patients: genetic analysis of the CYP2C9 locus. Pharmacogenetics. 2000;10:85–9.

    CAS  PubMed  Google Scholar 

  109. van der Weide J, Steijns LS, van Weelden MJ, de Haan K. The effect of genetic polymorphism of cytochrome P450 CYP2C9 on phenytoin dose requirement. Pharmacogenetics. 2001;11:287–91.

    PubMed  Google Scholar 

  110. Su SC, Chen CB, Chang WC, Wang CW, Fan WL, Lu LY, et al. HLA Alleles and CYP2C9*3 as predictors of phenytoin hypersensitivity in East Asians. Clin Pharmacol Ther. 2019;105:476–85.

    CAS  PubMed  Google Scholar 

  111. Hikino K, Ozeki T, Koido M, Terao C, Kamatani Y, Mizukawa Y, et al. HLA-B*51:01 and CYP2C9*3 are risk factors for phenytoin-induced eruption in the Japanese population: analysis of data from the biobank Japan Project. Clin Pharmacol Ther. 2020;107:1170–8.

    CAS  PubMed  Google Scholar 

  112. Axapharm Ltd. phenytoin gerot. Desitin. Drug labelling https://www.axapharm.ch/produkt/phenytoin-gerot/

  113. Munisamy M, Al-Gahtany M, Tripathi M, Subbiah V. Impact of MTHFR (C677T) gene polymorphism on antiepileptic drug monotherapy in North Indian epileptic population. Ann Saudi Med. 2015;35:51–7.

    PubMed  PubMed Central  Google Scholar 

  114. Yoo JH, Hong SB. A common mutation in the methylenetetrahydrofolate reductase gene is a determinant of hyperhomocysteinemia in epileptic patients receiving anticonvulsants. Metab: Clin Exp. 1999;48:1047–51.

    CAS  PubMed  Google Scholar 

  115. Allabi AC, Gala JL, Horsmans Y. CYP2C9, CYP2C19, ABCB1 (MDR1) genetic polymorphisms and phenytoin metabolism in a Black Beninese population. Pharmacogenetics Genom. 2005;15:779–86.

    CAS  Google Scholar 

  116. Ebid AH, Ahmed MM, Mohammed SA. Therapeutic drug monitoring and clinical outcomes in epileptic Egyptian patients: a gene polymorphism perspective study. Ther Drug Monit. 2007;29:305–12.

    PubMed  Google Scholar 

  117. Lakhan R, Kumari R, Misra UK, Kalita J, Pradhan S, Mittal B. Differential role of sodium channels SCN1A and SCN2A gene polymorphisms with epilepsy and multiple drug resistance in the north Indian population. Br J Clin Pharmacol. 2009;68:214–20.

    CAS  PubMed  PubMed Central  Google Scholar 

  118. Kumari R, Lakhan R, Garg RK, Kalita J, Misra UK, Mittal B. Pharmacogenomic association study on the role of drug metabolizing, drug transporters and drug target gene polymorphisms in drug-resistant epilepsy in a north Indian population. Indian J Hum Genet. 2011;17:S32–40.

    CAS  PubMed  PubMed Central  Google Scholar 

  119. Caudle KE, Rettie AE, Whirl-Carrillo M, Smith LH, Mintzer S, Lee MT, et al. Clinical pharmacogenetics implementation consortium guidelines for CYP2C9 and HLA-B genotypes and phenytoin dosing. Clin Pharmacol Ther. 2014;96:542–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  120. Ramírez E, Bellón T, Tong HY, Borobia AM, de Abajo FJ, Lerma V, et al. Significant HLA class I type associations with aromatic antiepileptic drug (AED)-induced SJS/TEN are different from those found for the same AED-induced DRESS in the Spanish population. Pharmcol Res. 2017;115:168–78.

    Google Scholar 

  121. Man CB, Kwan P, Baum L, Yu E, Lau KM, Cheng AS, et al. Association between HLA-B*1502 allele and antiepileptic drug-induced cutaneous reactions in Han Chinese. Epilepsia. 2007;48:1015–8.

    CAS  PubMed  Google Scholar 

  122. Health Care Service Corporation. Annotation of HCSC Label for phenytoin and HLA-B. https://www.pharmgkb.org/labelAnnotation/PA166127701

  123. Lakhan R, Misra UK, Kalita J, Pradhan S, Gogtay NJ, Singh MK, et al. No association of ABCB1 polymorphisms with drug-refractory epilepsy in a north Indian population. Epilepsy Behav. 2009;14:78–82.

    CAS  PubMed  Google Scholar 

  124. Grover S, Bala K, Sharma S, Gourie-Devi M, Baghel R, Kaur H, et al. Absence of a general association between ABCB1 genetic variants and response to antiepileptic drugs in epilepsy patients. Biochimie. 2010;92:1207–12.

    CAS  PubMed  Google Scholar 

  125. Daci A, Beretta G, Vllasaliu D, Shala A, Govori V, Norata GD, et al. Polymorphic variants of SCN1A and EPHX1 influence plasma Carbamazepine concentration, metabolism and pharmacoresistance in a population of Kosovar Albanian Epileptic patients. PLoS One. 2015;10:e0142408.

    PubMed  PubMed Central  Google Scholar 

  126. Bertok S, Dolžan V, Goričar K, Podkrajšek KT, Battelino T, Rener-Primec Z. The association of SCN1A p.Thr1067Ala polymorphism with epilepsy risk and the response to antiepileptic drugs in Slovenian children and adolescents with epilepsy. Seizure. 2017;51:9–13.

    PubMed  Google Scholar 

  127. Ma CL, Wu XY, Zheng J, Wu ZY, Hong Z, Zhong MK. Association of SCN1A, SCN2A and ABCC2 gene polymorphisms with the response to antiepileptic drugs in Chinese Han patients with epilepsy. Pharmacogenomics. 2014;15:1323–36.

    CAS  PubMed  Google Scholar 

  128. Haerian BS, Baum L, Kwan P, Tan HJ, Raymond AA, Mohamed Z. SCN1A, SCN2A and SCN3A gene polymorphisms and responsiveness to antiepileptic drugs: a multicenter cohort study and meta-analysis. Pharmacogenomics. 2013;14:1153–66.

    CAS  PubMed  Google Scholar 

  129. Clinical Pharmacogenetics Implementation Consortium (CPIC). Genes-Drugs. 2023. https://cpicpgx.org/genes-drugs/

  130. Genin E, Chen DP, Hung SI, Sekula P, Schumacher M, Chang PY, et al. HLA-A*31:01 and different types of carbamazepine-induced severe cutaneous adverse reactions: an international study and meta-analysis. Pharmacogenomics J. 2014;14:281–288.

    CAS  PubMed  Google Scholar 

  131. Nguyen DV, Chu HC, Nguyen DV, Phan MH, Craig T, Baumgart K, et al. HLA-B*1502 and carbamazepine-induced severe cutaneous adverse drug reactions in Vietnamese. Asia Pac Allergy. 2015;5:68–77.

    PubMed  PubMed Central  Google Scholar 

  132. Yuliwulandari R, Kristin E, Prayuni K, Sachrowardi Q, Suyatna FD, Menaldi SL, et al. Association of the HLA-B alleles with carbamazepine-induced Stevens-Johnson syndrome/toxic epidermal necrolysis in the Javanese and Sundanese population of Indonesia: the important role of the HLA-B75 serotype. Pharmacogenomics. 2017;18:1643–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  133. Khor AH, Lim KS, Tan CT, Wong SM, Ng CC. HLA-B*15:02 association with carbamazepine-induced Stevens-Johnson syndrome and toxic epidermal necrolysis in an Indian population: a pooled-data analysis and meta-analysis. Epilepsia. 2014;55:e120–124.

    CAS  PubMed  Google Scholar 

  134. Tan JC, Murrell DF, Hersch MI. Genetic screening for human leukocyte antigen alleles prior to carbamazepine treatment. J Clin Neurosci. 2015;22:1992–3.

    PubMed  Google Scholar 

  135. Ikeda H, Takahashi Y, Yamazaki E, Fujiwara T, Kaniwa N, Saito Y, et al. HLA class I markers in Japanese patients with carbamazepine-induced cutaneous adverse reactions. Epilepsia. 2010;51:297–300.

    PubMed  Google Scholar 

  136. Health Care Service Corporation. Annotation of HCSC Label for carbamazepine and HLA-A, HLA-B. https://www.pharmgkb.org/chemical/PA448785/prescribingInfo#guideline-annotations

  137. Schweiz AG Novartis Pharmaceutical Corp. Tegretol. https://www.novartis.com/node/35406

  138. Kim SH, Lee KW, Song WJ, Kim SH, Jee YK, Lee SM, et al. Carbamazepine-induced severe cutaneous adverse reactions and HLA genotypes in Koreans. Epilepsy Res. 2011;97:190–7.

    CAS  PubMed  Google Scholar 

  139. Hsiao YH, Hui RC, Wu T, Chang WC, Hsih MS, Yang CH, et al. Genotype-phenotype association between HLA and carbamazepine-induced hypersensitivity reactions: strength and clinical correlations. J Dermatolo Sci. 2014;73:101–9.

    CAS  Google Scholar 

  140. Mushiroda T, Takahashi Y, Onuma T, Yamamoto Y, Kamei T, Hoshida T, et al. Association of HLA-A*31:01 screening With the Incidence of Carbamazepine-induced cutaneous adverse reactions in a Japanese population. JAMA Neurol. 2018;75:842–9.

    PubMed  PubMed Central  Google Scholar 

  141. Kim EY, Kim MY, Park CS, Choi JH, Ghim JL, Kim HS, et al. Antiepileptic drug-induced severe cutaneous adverse reactions and HLA alleles: a report of five cases with lymphocyte activation test. Transl Clin Pharmacol. 2019;27:64–68.

    PubMed  PubMed Central  Google Scholar 

  142. Kulkantrakorn K, Tassaneeyakul W, Tiamkao S, Jantararoungtong T, Prabmechai N, Vannaprasaht S, et al. HLA-B*1502 strongly predicts carbamazepine-induced Stevens-Johnson syndrome and toxic epidermal necrolysis in Thai patients with neuropathic pain. Pain Pract. 2012;12:202–8.

    PubMed  Google Scholar 

  143. Chen Z, Liew D, Kwan P. Effects of a HLA-B*15:02 screening policy on antiepileptic drug use and severe skin reactions. Neurology. 2014;83:2077–84.

    CAS  PubMed  Google Scholar 

  144. Cheung YK, Cheng SH, Chan EJ, Lo SV, Ng MH, Kwan P. HLA-B alleles associated with severe cutaneous reactions to antiepileptic drugs in Han Chinese. Epilepsia. 2013;54:1307–14.

    CAS  PubMed  Google Scholar 

  145. He XJ, Jian LY, He XL, Wu Y, Xu YY, Sun XJ, et al. Association between the HLA-B*15:02 allele and carbamazepine-induced Stevens-Johnson syndrome/toxic epidermal necrolysis in Han individuals of north-eastern China. Pharmacol Rep. 2013;65:1256–62.

    CAS  PubMed  Google Scholar 

  146. Puranik YG, Birnbaum AK, Marino SE, Ahmed G, Cloyd JC, Remmel RP, et al. Association of carbamazepine major metabolism and transport pathway gene polymorphisms and pharmacokinetics in patients with epilepsy. Pharmacogenomics. 2013;14:35–45.

    CAS  PubMed  Google Scholar 

  147. Leckband SG, Kelsoe JR, Dunnenberger HM, George AL Jr., Tran E, Berger R, et al. Clinical pharmacogenetics implementation consortium guidelines for HLA-B genotype and carbamazepine dosing. Clin Pharmacol Ther. 2013;94:324–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  148. Phillips EJ, Sukasem C, Whirl-Carrillo M, Müller DJ, Dunnenberger HM, Chantratita W, et al. Clinical pharmacogenetics implementation consortium guideline for HLA genotype and use of Carbamazepine and Oxcarbazepine: 2017 update. Clin Pharmacol Ther. 2018;103:574–81.

    CAS  PubMed  Google Scholar 

  149. Büyüköztürk S, Kekik Ç, Gökyiğit AZ, Tezer Filik FI, Karakaya G, Saygi S, et al. Cutaneous drug reactions to antiepileptic drugs and relation with HLA alleles in the Turkish population. Eur Ann Allergy Clin Immunol. 2018;50:36–41.

    PubMed  Google Scholar 

  150. Wang P, Lin XQ, Cai WK, Xu GL, Zhou MD, Yang M, et al. Effect of UGT2B7 genotypes on plasma concentration of valproic acid: a meta-analysis. Eur J Clin Pharmacol. 2018;74:433–42.

    CAS  PubMed  Google Scholar 

  151. Chung JY, Cho JY, Yu KS, Kim JR, Lim KS, Sohn DR, et al. Pharmacokinetic and pharmacodynamic interaction of lorazepam and valproic acid in relation to UGT2B7 genetic polymorphism in healthy subjects. Clin Pharmacol Ther. 2008;83:595–600.

    CAS  PubMed  Google Scholar 

  152. Basic S, Hajnsek S, Bozina N, Filipcic I, Sporis D, Mislov D, et al. The influence of C3435T polymorphism of ABCB1 gene on penetration of phenobarbital across the blood-brain barrier in patients with generalized epilepsy. Seizure. 2008;17:524–30.

    PubMed  Google Scholar 

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Acknowledgements

The author thank Priyanka Singh, Sarita Thakran and Saroj Yadav for their valuable contribution in the literature screening and diagnostic predictability calculations. The authors would also like to acknowledge Dr. Souvik Maiti, Director, CSIR-IGIB, for his unconditional support in resource management and scientific vision. Financial support from Council of Scientific and Industrial Research (CSIR) is duly acknowledged. DG acknowledge CSIR for fellowship.

Funding

Financial support for this project has been provided by the Council of Scientific and Industrial Research (CSIR) funded project OLP1120 and OLP2301.

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Authors and Affiliations

  1. Genomics and Molecular Medicine Unit, Council of Scientific and Industrial Research (CSIR)—Institute of Genomics and Integrative Biology (IGIB), New Delhi, 110007, India

    Debleena Guin & Ritushree Kukreti

  2. Department of Biotechnology, Delhi Technological University, Shahbad Daulatpur, Delhi, 110042, India

    Debleena Guin & Yasha Hasija

  3. Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, 201002, India

    Ritushree Kukreti

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RK originally conceived and designed the experiments of this article. RK materialised the study design and structured the manuscript. DG and RK screened the literature and managed the data. YH and, RK scrutinised the data and manuscript. DG wrote the manuscript. All authors interpreted data, reviewed successive draughts and approved the final version of the article. RK and YH supervised the overall study.

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Correspondence to Ritushree Kukreti.

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Guin, D., Hasija, Y. & Kukreti, R. Assessment of clinically actionable pharmacogenetic markers to stratify anti-seizure medications. Pharmacogenomics J 23, 149–160 (2023). https://doi.org/10.1038/s41397-023-00313-y

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