Pharmacogenetic factors determining the metabolism and safety of aromatic anticonvulsants in the residents of Russia

Introduction. The use of aromatic anticonvulsants (carbamazepine, oxcarbazepine, lamotrigine, and phenytoin) is associated with the risk of severe hypersensitivity reactions, which are partly dependent on HLA-B and HLA-A genotypes (for carbamazepine/oxcarbazepine). Phenytoin and fosphenytoin are metabolized by CYP2C9; therefore, in the setting of genetically determined impaired drug tolerability, the likelihood of adverse events increases. Consideration of the CYP2C9 genotype is important for developing personalized drug dosing schemes and improving treatment outcomes.

Objective. Assessment of the prevalence of major pharmacogenetic variants associated with response to aromatic anticonvulsants, with geographic stratification and identification of at-risk populations warranting preemptive genotyping prior to treatment initiation.

Materials and methods. The study was performed using samples from the Population Frequency Database (GDB) of the Federal Medical and Biological Agency (FMBA) of Russia (n = 120,979, covering 82 RF subjects). Whole-genome sequencing of DNA samples was conducted followed by an analysis of the carrier frequency of HLA-B*15:02, HLA-B*15:11, HLA-A*31:01, and various allelic variants of CYP2C9 with calculation of the enzyme activity score. These metrics were compared across different Russian regions, identifying high-risk biogeographic groups.

Results. The HLA-B*15:02 variant showed a prevalence of less than 1% in all regions of the Russian Federation. A relatively high carrier frequency of HLA-B*15:11 was observed in the Republics of Buryatia and Tyva (1.3%, p = 7.7 × 10^–5 and 3.46%, p = 2.4 × 10^–3, respectively, compared to a population frequency of 0.11%). The elevated frequencies of HLA-A*31:01 were detected in Perm Krai and the Republics of Kalmykia, Buryatia, Tyva, and Sakha (Yakutia) (8.48%, p = 0.042; 8.79%, p = 0.044; 10.3%, p = 3.4 × 10^–10; 20.44%, p = 3.4 × 10^–10; 28.74%, p = 5.4 × 10^–122, respectively, compared to a population frequency of 5.06%). The Republics of Dagestan, Ingushetia, and Kabardino-Balkaria showed a higher prevalence of impaired metabolism phenotype for phenytoin/phosphenytoin (46.4%, p = 5.6 × 10^–36; 44.69%, p = 1.7 × 10^–13; 43.83%, p = 1.9 × 10^–16), primarily due to a high frequency of the CYP2C9*3 allele. The Republics of Tatarstan, Mari El, and Chuvashia were also characterized by a high incidence of alleles associated with impaired metabolism of these drugs (37.06%, p = 0.028; 37.99%, p = 0.031; 41.2%, p = 5.3 × 10^–10), attributable to the presence of the generally rare CYP2C9*29 allele in their genetic structure.

Conclusions. The results obtained enable the formulation of region-specific recommendations for personalizing treatment with aromatic anticonvulsants. For residents of Sakha (Yakutia), Tyva, Buryatia, Kalmykia, and Perm Krai, testing for HLA-A*31:01 carriage is justified. For residents of Tyva and Buryatia, additional testing for HLA-B*15:11 carriage is warranted prior to the prescription of carbamazepine and oxcarbazepine. Before initiating phenytoin therapy, CYP2C9 genotyping is particularly important for the populations of Dagestan, Ingushetia, Kabardino-Balkaria, Tatarstan, Mari El, and Chuvashia. However, this genotyping can be recommended for the entire population of Russia due to the high prevalence of alleles associated with reduced and absent enzyme activity. 

1. Żełabowski K, Wojtyshiak K, Ratka Z, Biedka K, Chlopas-Konowalek A. Lamotrigine Therapy: Relation Between Treatment of Bipolar Affective Disorder and Incidence of Stevens–Johnson Syndrome—A Narrative Review of the Existing Literature. Journal of Clinical Medicine. 2025;14(12):4103. https://doi.org/10.3390/jcm14124103

2. Ahmed AF, Sukasem C, Sabbah MA, Musa NF, Noor DAM, Daud NAA. Genetic Determinants in HLA and Cytochrome P450 Genes in the Risk of Aromatic Antiepileptic-Induced Severe Cutaneous Adverse Reactions. Journal of Personalized Medicine. 2021;11(5):383. https://doi.org/10.3390/jpm11050383

3. Hasegawa A, Abe R. Recent advances in managing and understanding Stevens–Johnson syndrome and toxic epidermal necrolysis. F1000Research. 2020;9:F1000 Faculty Rev-612. https://doi.org/10.12688/f1000research.24748.1

4. Watanabe Y, Hama N. Recent advances in the diagnosis and treatment of Stevens–Johnson syndrome/toxic epidermal necrolysis. Allergology International. 2025;74(3):345–55. https://doi.org/10.1016/j.alit.2025.05.008

5. Mori F, Caffarelli C, Caimmi S, Bottau P, Liotti L, Franceschini F, et al. Drug reaction with eosinophilia and systemic symptoms (DRESS) in children. Acta Biomedica. 2019;90(3-S):66–79.

6. Calle AM, Aguirre N, Ardila JC, Villa RC. DRESS syndrome: A literature review and treatment algorithm. World Allergy Organization Journal. 2023;16(3):100673. https://doi.org/10.1016/j.waojou.2022.100673

7. López-Rocha E, Blancas L, Rodriguez-Mireles K, Gaspar-Lopez A, O’Farrill-Romanillos P, Amaya-Mejia A, et al. Prevalence of DRESS syndrome. Revista Alergia Mexico. 2014;61(1):14–23.

8. Liang C, An P, Zhang Y, Liu X, Zhang B. Fatal outcome related to drug reaction with eosinophilia and systemic symptoms: a disproportionality analysis of FAERS database and a systematic review of cases. Frontiers in Immunology. 2024;15:1490334. https://doi.org/10.3389/fimmu.2024.1490334

9. Karnes JH, Rettie AE, Somogyi AA, Huddart R, Fohner AE, Formea CM, et al. Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for CYP2C9 and HLA-B Genotypes and Phenytoin Dosing: 2020 Update. Clinical Pharmacology and Therapeutics. 2021;109(2):302–9. https://doi.org/10.1002/cpt.2008

10. Manson LEN, Nijenhuis M, Soree B, de Boer-Veger NJ, Buunk AM, Houwink EJF, et al. Dutch Pharmacogenetics Working Group (DPWG) guideline for the gene-drug interaction of CYP2C9, HLA-A and HLA-B with anti-epileptic drugs. European Journal of Human Genetics. 2024;32(8):903–11. https://doi.org/10.1038/s41431-024-01572-4

11. Phillips EJ, Sukasem C, Whirl-Carrillo M, Muller DJ, Dunnenberger HM, Chantratita W, et al. Clinical Pharmacogenetics Implementation Consortium Guideline for HLA Genotype and Use of Carbamazepine and Oxcarbazepine: 2017 Update. Clinical Pharmacology and Therapeutics. 2018;103(4):574–81. https://doi.org/10.1002/cpt.1004

12. Barbarino JM, Whirl-Carrillo M, Altman RB, Klein TE. PharmGKB: A worldwide resource for pharmacogenomic information. WIREs. 2018;10(4):e1417. https://doi.org/10.1002/wsbm.1417

13. Gusakova M, Dzhumaniiazova I, Zelenova E, Kashtanova D, Ivanov M, Mamchur A, et al. Prevalence of the cancer-associated germline variants in Russian adults and long-living individuals: using the ACMG recommendations and computational interpreters for pathogenicity assessment. Frontiers in Oncology. 2024;14:1420176. https://doi.org/10.3389/fonc.2024.1420176

14. Li H, Handsaker B, Wysoker A, Fennel T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25(16):2078. https://doi.org/10.1093/bioinformatics/btp352

15. Pedersen BS, Quinlan AR. Mosdepth: quick coverage calculation for genomes and exomes. Bioinformatics. 2018;34(5):867–8. https://doi.org/10.1093/bioinformatics/btx699

16. Kim S, Scheffler K, Halpern AL, Bekritsky MA, Noh E, Kallberg M, et al. Strelka2: fast and accurate calling of germline and somatic variants. Nature Methods. 2018;15(8):591–4. https://doi.org/10.1038/s41592-018-0051-x

17. Najafov J, Najafov A. CrossCheck: an open-source web tool for high-throughput screen data analysis. Scientific Reports. 2017;7(1):5855. https://doi.org/10.1038/s41598-017-05960-3

18. Krusche P, Trigg L, Boutros PC, Mason CE, De La Vega FM, Moore BL, et al. Best practices for benchmarking germline small-variant calls in human genomes. Nature Biotechnology. 2019;37(5):555–60. https://doi.org/10.1038/s41587-019-0054-x

19. Finamore JM, Sperling MR, Zhan T, Nei M, Skidmore CT, Mintzer S. Seizure outcome after switching antiepileptic drugs: A matched, prospective study. Epilepsia. 2016;57(8):1294–300. https://doi.org/10.1111/epi.13435

20. Pratt VM, Cavallari LH, Del Tredici AL, Hachad H, Ji Y, Moyer AM, et al. Recommendations for Clinical CYP2C9 Genotyping Allele Selection: A Joint Recommendation of the Association for Molecular Pathology and College of American Pathologists. Journal of Molecular Diagnostics. 2019;21(5):746–55. https://doi.org/10.1016/j.jmoldx.2019.04.003

21. Huddart R, Fohner AE, Whirl-Carrillo M, Wojcik GL, Gignoux CR, Popejoy AB, et al. Standardized Biogeographic Grouping System for Annotating Populations in Pharmacogenetic Research. Clinical Pharmacology and Therapeutics. 2019;105(5):1256–62. https://doi.org/10.1002/cpt.1322

22. Johnson JA, Caudle KE, Gong L, Whirl-Carrillo M, Stein CM, Scott SA, et al. Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for Pharmacogenetics-Guided Warfarin Dosing: 2017 Update. Clinical Pharmacology and Therapeutics. 2017;102(3):397–404. https://doi.org/10.1002/cpt.668

23. Theken KN, Lee CR, Gong L, Caudle KE, Formea CM, Gaedigk A, et al. Clinical Pharmacogenetics Implementation Consortium Guideline (CPIC) for CYP2C9 and Nonsteroidal Anti-Inflammatory Drugs. Clinical Pharmacology and Therapeutics. 2020;108(2):191–200. https://doi.org/10.1002/cpt.1830