Pharmacogenetic model for predicting therapeutic response to methotrexate in patients with rheumatoid arthritis

Background. Approximately 30 % of rheumatoid arthritis (RA) patients exhibit inadequate response to methotrexate (MTX), with associated adverse effects limiting treatment efficacy, necessitating tools for predicting therapeutic outcomes [1]. The absence of robust pharmacogenetic models hinders personalized RA management.

Objective. This study aimed to develop a pharmacogenetic model to predict the risk of non-response to MTX in RA patients based on polymorphisms in genes encoding key proteins involved in MTX metabolism.

Methods. A prospective cohort study enrolled 281 RA patients meeting the European Alliance of Associations for Rheumatology criteria, receiving MTX as the initial disease-modifying antirheumatic drug. After 6 months, therapeutic response was assessed using the Disease Activity Score-28 (DAS28), identifying 170 responders and 111 non-responders. Genotyping was performed for polymorphisms in SLC19A1 (rs1051266), ABCB1 (rs1128503, rs2032582), GGH (rs3758149), FPGS (rs4451422, rs1544105), MTHFR (rs1801131, rs1801133), ATIC (rs2372536), ADA (rs244076), AMPD1 (rs17602729), ITPA (rs1127354).

Predictive models were developed using multifactor dimensionality reduction (MDR) and information analysis (Shannon entropy).

Results. The final model, incorporating five single nucleotide polymorphisms “ATIC rs2372536 + MTHFR rs1801133 + ADA rs244076 + MTHFR rs1801131 + SLC19A1 rs1051266”, achieved a sensitivity of 80.2 %, specificity of 69.4 % (OR 9.18 [95 % CI 5.19; 16.22]), and high cross-validation consistency (10/10).

Conclusion. This five-gene model demonstrates robust diagnostic performance for predicting MTX non-response in RA, with practical implementation via an “if-then” decision rule.

1. Sherbini AA, Gwinnutt JM, Hyrich KL; RAMS Co-Investigators; Verstappen SMM. Rates and predictors of methotrexate-related adverse events in patients with early rheumatoid arthritis: results from a nationwide UK study. Rheumatology (Oxford). 2022 Oct 6;61(10):3930-3938. doi: 10.1093/rheumatology/keab917.

2. Aletaha D, Neogi T, Silman AJ, et al. 2010 rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Ann Rheum Dis. 2010 Sep;69(9):1580-8. doi: 10.1136/ard.2010.138461.

3. Nasonov EL, Lila AM, Dubinina TV, et al. Advances in rheumatology at the beginning of the 21st century. Rheumatology Science and Practice. 2022;60(1):5-20. (In Russ.). doi:10.47360/1995-4484-2022-5-20.

4. Ministry of Health of the Russian Federation. Clinical guidelines: Rheumatoid arthritis. Moscow: Ministry of Health of the Russian Federation; 2024. (In Russ.). Доступно по: https://www.consultant.ru/document/cons_doc_LAW_495885/. Ссылка активна на 19.06.2025.

5. Fraenkel L, Bathon JM, England BR, et al. 2021 American College of Rheumatology Guideline for the Treatment of Rheumatoid Arthritis. Arthritis Care Res (Hoboken). 2021 Jul;73(7):924-939. doi: 10.1002/acr.24596.

6. Duong SQ, Crowson CS, Athreya A, Atkinson EJ, Davis JM 3rd, Warrington KJ, Matteson EL, Weinshilboum R, Wang L, Myasoedova E. Clinical predictors of response to methotrexate in patients with rheumatoid arthritis: a machine learning approach using clinical trial data. Arthritis Res Ther. 2022 Jul 1;24(1):162. doi: 10.1186/s13075-022-02851-5.

7. Sergeant JC, Hyrich KL, Anderson J, et al. Prediction of primary non-response to methotrexate therapy using demographic, clinical and psychosocial variables: results from the UK Rheumatoid Arthritis Medication Study (RAMS). Arthritis Res Ther. 2018 Jul 13;20(1):147. doi: 10.1186/s13075-018-1645-5.

8. Floris A, Perra D, Cangemi I, et al. Current smoking predicts inadequate response to methotrexate monotherapy in rheumatoid arthritis patients naïve to DMARDs: Results from a retrospective cohort study. Medicine (Baltimore). 2021 Apr 30;100(17):e25481. doi: 10.1097/MD.0000000000025481.

9. de Rotte MCFJ, Pluijm SMF, de Jong PHP, et al. Development and validation of a prognostic multivariable model to predict insufficient clinical response to methotrexate in rheumatoid arthritis. PLoS One. 2018 Dec 10;13(12):e0208534. doi: 10.1371/journal.pone.0208534.

10. Jeiziner C, Allemann SS, Hersberger KE, et al. Is Pharmacogenetic Panel Testing Applicable to Low-Dose Methotrexate in Rheumatoid Arthritis? A Case Report. Pharmgenomics Pers Med. 2022 May 9;15:465-475. doi: 10.2147/PGPM.S354011.

11. Patent RF dlya programmy EVM № 2024691240/03.12.24. Devald IV, Lila AM, Hromova EB. Prognoz effektivnosti metotreksata pri revmatoidnom artrite po re-zul'tatam genotipirovaniya pacienta (In Russ.). Доступно по https://www.elibrary.ru/item.asp?id=76408958. Ссылка активна на 29.06.2025.

12. Sychev DA. The transition to personalized medicine is the priority of Russia's scientific and technological development. Farmakogenetika i farmakogenomika = Pharmacogenetics and pharmacogenomics. 2017;(1):3-4. (In Russ.).

13. Devald IV, Khodus EA, Myslivtsova KY, et al. Effect of ATIC, ADA, ITPA, and AMPD1 gene polymorphisms on the efficacy of methotrexate in rheumatoid arthritis. Farmakogenetika i farmakogenomika = Pharmacogenetics and pharmacogenomics. 2024;(1):4-13. (In Russ.). doi: 10.37489/2588-0527-2024-1-4-13. EDN: KCZHLK.

14. Devald IV, Hodus EA, Nokhrin DY, et al. Evaluation of the influence of polymorphisms of the transporter genes (RFC1, MDR1) and GGH on the efficacy of methotrexate in rheumatoid arthritis. Sovremennaya Revmatologiya=Modern Rheumatology Journal. 2023;17(4):28-34. (In Russ.). doi: 10.14412/1996-7012-2023-4-28-34.

15. Devald IV, Khodus EA, Khromova EB, et al. Association of RFC-1 and MDR1 Transporter Gene Polymorphisms with Therapeutic Response to Methotrexate in Rheumatoid Arthritis Patients. Bulletin of Chelyabinsk State University. Education and Healthcare. 2022;19(3):5-14. (In Russ.). doi: 10.24411/2409-4102-2022-10301.