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Egyptian Rheumatology and Rehabilitation
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Predictors of tumor necrosis factor inhibitors primary failure in rheumatoid arthritis patients

Egyptian Rheumatology and Rehabilitation volume 51, Article number: 26 (2024) Cite this article

Abstract

Background

Tumor necrosis factor inhibitors (TNFi) have emerged as an efficient therapeutic modality for rheumatoid arthritis (RA). A ratio of patients does not give a response despite therapy. It remains a challenge to predict which patients will respond. Our study aims to investigate early predictors of primary TNFi failure in RA patients. Patients were categorized into two groups based on TNFi therapy (responder/non-responder) and then compared to detect the most significant predictors of treatment failure.

Results

This study included 87 RA patients treated with TNFi for the first time after conventional disease-modifying anti-rheumatic drugs (DMARDs) failed. This study showed that compared to those with successful treatment, patients with overall primary failure were significantly higher in older age, females, smokers, obese, younger age at the onset of the disease, or those with deformity. In addition, the drug failure was significantly related to erythrocyte sedimentation rate (ESR) (100 vs 68 mm/h), C-reactive protein (CRP) (48 vs 12 mg/dl), rheumatoid factor (RF) positivity (29% vs 16%), anti-cyclic citrullinated peptide (anti-CCP) positivity (39% vs 23%), and non-methotrexate (MTX) concomitant use (33% vs 40%).

Conclusion

The increased age, being a smoker, earlier age at onset, presence of a deformity, and positive anti-CCP at baseline were predictors of overall failure. At the same time, concomitant MTX intake increased the success rate by 9.6%.

Highlights

• The increased age, being a smoker, earlier age of disease onset, and the presence of a deformity were predictors of overall TNFi primary failure.

• The overall primary failure of TNFi treatment was significantly related to ESR, CRP, RF positivity, anti-CCP positivity.

• The regression analysis showed that these combined factors predict 70.1% of the TNFi failure rate.

• Concomitant MTX intake increased the success rate by 9.6%.

Background

Rheumatoid arthritis (RA) is a chronic systemic inflammatory autoimmune disease (AID). It is characterized by persistent synovitis and is accompanied by pain, joint damage, disability, and poor quality of life (QoL) [1].

The primary target for RA management is disease remission or low disease activity (LDA). Remission is the state in which there are no longer any noticeable clinical signs of a serious inflammatory illness. Low-disease activity could be considered another satisfactory therapeutic goal, particularly in long-standing disease [2]. According to disease activity score-28 (DAS-28) (ESR), the disease activity cut-offs for remission and LDA are < 2.6 and ≤ 3.2, correspondingly [3].

Disease remission in refractory RA with specific biological medications is one of the optimum goals. Comprised in such agents are tumor necrosis factor-alpha inhibitors (TNFi), often used as the first biological drugs employed for RA management after the failure of conventional synthetic disease-modifying anti-rheumatic drugs (csDMARDs) [4].

To date, TNFi includes five distinctive agents: infliximab (IFX) and its bio-similar (bs-IFX), etanercept (ETA), adalimumab (ADA), golimumab (GOL), and certolizumab pegol (CZP). These five TNFi vary in structures, half-lives, route of administration, dosage intervals, immunogenicity, and pharmacokinetic characteristics [5].

However, the response differs among patients; in other words, not every case gives the same reaction to the same medication. In addition, the response rate of TNFi in RA is erratic and often unpredictable, which makes therapeutic decisions quite complicated [6]. About 35% of cases stop TNFi therapy owing to primary failure, secondary loss of response, or intolerance. In addition, they could induce complications such as infections, malignant tumors, acute infusion and injection hypersensitivity, autoimmunity, and cardiovascular (CV) effects [7, 8]. The therapeutic modalities to manage TNFi failure involve switching to another TNFi, perhaps a different kind of tailored medication with a variety of action mechanisms [5].

Studies that addressed the primary response to TNFi therapy and the predictors of its failure in RA patients are scarce [9,10,11]. In addition, reviewing the available current literature reveals a lack of recent and up-to-date Egyptian studies in this area. Therefore, this study was done to fill this gap for better disease treatment outcomes and to forecast the patients who will react to a specific method.

The current study aimed to investigate early predictors of primary TNFi failure in RA patients. This could help to enhance the risk–benefit ratio (RBR) and cost-effectiveness in individual cases and the overall therapeutic success.

Subjects and methods

This was a retrospective, record-based, descriptive study with an analytic component. The study was conducted at the Rheumatology and Rehabilitation Department, xxxxxxxxxx.

Inclusion criteria

It included all RA classified according to the 2010 rheumatoid arthritis classification criteria [12] biological naive patients who received anti-TNF as primary biological therapy.

Exclusion criteria

  1. 1.

    Patients received biological treatment other than TNFi.

  2. 2.

    patients developed severe side effects that limited continuous use of TNFi.

  3. 3.

    Patients had an acute or active infection.

  4. 4.

    Patients had congested heart failure or other contraindications to TNFi.

  5. 5.

    Biosimilars were excluded due to limited experience.

Method

The data listed below were gathered:

  1. 1.

    Demographic data: age, sex, body mass index (BMI), tobacco smoking

  2. 2.

    Co-morbidities: such as diabetes, hypertension, CV diseases, lung diseases, and renal dysfunction.

    Disease characteristics: age of diagnosis, disease duration, time from diagnosis to the initiation of therapy with biological drugs, extra-articular manifestations, such as rheumatoid nodules, pulmonary affection, ocular manifestations, and vasculitis. Besides, previous and concomitant treatments.

    Failure to respond is defined as failure to achieve remission or LDA according to DAS28-ESR, while treatment response is the fulfillment of remission or LDA according to the DAS28-ESR.

  3. 3.

    Laboratory analysis (done at baseline and 3 months later), including complete blood count (CBC), rheumatoid factor (RF), anti-citrullinated peptide antibodies (Anti-CCP), C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR).

  4. 4.

    The disease activity score-28 (DAS-28) was measured at baseline and three months following starting the first biological disease-modifying anti-rhematic drugs (bDMARD). The DAS28 is a composite score derived from the following four variables [13]: (A) determination of swollen joint count (SJC) (out of 28), (B) determination of tender joint count (TJC) (out of 28), (C) measurement of the ESR, and (D) determination of the patient global assessment (PGA).

Based on this score, three months after starting the therapy, patients were divided into two groups: responder and non-responder to their first TNFi therapy. This included IFX, ETA, ADA, and GOL.

Ethical consideration

The Medical Research Ethics Committee gave their approval to the research protocol, (code number: MS.21.10.1698). Consent is not applicable because the study was retrospective and was based on patients’ data collection which was anonymized. Personal privacy and confidentiality were upheld.

Statistical analysis

Using SPSS (Version 21) for Windows, the gathered data was coded, processed, and examined. program. A descriptive statistical analysis of all patients was performed. Patients were categorized into two groups based on TNFi therapy (responder/non-responder). The baseline demographics, as well as clinical and disease characteristics, were compared between the two groups to identify overall possible predisposing factors and predictors of TNFi failure in these cases.

These groups were compared using Pearson’s chi-square, Fisher’s exact tests, Student’s t-tests, Monte Carlo, or Mann–Whitney U tests according to the data distribution. Using the “failure to response” (failure to achieve remission or LDA according to DAS28 ESR) as the dependent variable, multiple univariate logistic regression was conducted to recognize which features were accompanied by such outcomes and to be considered in the multivariate analyses. For modeling the response to each TNFi, a binary logistic regression model was used. With parameter (B) Beta regression coefficient. A p value of 5% or less was taken to be statistically significant.

Results

This is a record-based study which included 87 RA patients who were managed with TNFi for the first time after the failure of csDMARDS.

As regards the characteristics of studied RA patients as shown in Table 1, among the studied 87 RA patients, the mean age was 47.8 ± 13.1 years, 77% of them were females, 35.6% were obese, and the diagnosis was delayed for one or more years for about 60% of them. Among these patients, the median age (interquartile range) (IQR) was 31 (25–44) years at disease onset, 32 (25–44) years at diagnosis, and 8 (5–15) years from diagnosis to the first biological treatment.

Table 1 The characteristics of the studied rheumatoid arthritis (RA) patients
Full size table

Drug-related response in Table 2 showed that the overall primary failure (OPF) of the biological treatment in the studied patients was 49.4%. In addition, Table 2 showed that, according to the type of biological treatment, the primary failure was 35.3% for IFX, 58.3% for ETA, 50% for ADA, and 50% for GOL. Methotrexate (MTX) was given to 83.9% of patients, with a good response of 54.8%. At the same time, concomitant MTX intake increased the success rate by 9.6%.

Table 2 The overall and drug-related response among the studied patients
Full size table

Table 3 revealed, in comparison with patients with successful responses to biological treatment, patients with OPF were significantly older (51.7 ± 12.9 years vs 43.9 ± 12.1 years, p = 0.005), females than males (88.4% vs 11.6%, p = 0.013), smokers (44.2% vs. 4.5%, p < 0.001), obese (48.8% vs. 22.7%, p = 0.01), and aged younger at the onset of the disease (29.9 ± 10.4 vs. 34.9 ± 11.2, p = 0.038). There was no significant difference between the two groups as regards parent consanguinity, family history of RA, age at diagnosis, delayed diagnosis, or duration after diagnosis and before the biological treatment.

Table 3 The relationship between the overall primary failure (OPF) of biological treatment and characteristics of the studied RA patients
Full size table

Table 4 showed that deformity was statistically higher among patients with primary failure than those with successful treatment (38.5% vs. 6.8%, p = 0.0007). There was no statistically significant difference between the two subgroups regarding arthritis pattern, number of painful joints, and other studied parameters. Moreover, non-concomitant MTX use was associated with biological therapy failure.

Table 4 The relationship between the overall primary failure (OPF) of biological treatment and clinical presentation of the studied RA patients
Full size table

Regarding laboratory findings in Table 5, the OPF of biological treatment was significantly related to ESR, CRP, RF positivity, and anti-CCP positivity. In contrast, there was no statistically significant difference between the OPF of biological treatment and other lab parameters.

Table 5 The relation between the overall primary failure (OPF) of the biological treatment and the laboratory findings of the studied RA patients
Full size table

Concerning the concomitant intake of steroids, as demonstrated in Table 6, there was no statistically significant difference between patients with successful responses to biological treatment and patients with OPF.

Table 6 The relation between overall primary failure (OPF) of the biological treatment and steroid intake in the studied RA patients
Full size table

As we can find in Table 7, the increased age, being smoker, earlier age at onset, presence of deformity, and positive anti-CCP at baseline were statistically significant predictors of overall failure (odds ratios are1.11, 32.3, 0.855, 8.5, and 6.48 respectively) in the studied cases, with the overall percentage predicted 70.1% by the combination of the previous factors.

Table 7 Predictors of the overall failure among studied cases
Full size table

Discussion

RA is a chronic inflammatory autoimmune disease that has a debilitating nature and a great effect on one’s health. TNFi is one of the various lines of RA treatment usually used after failure of csDMARD with the aim of remission or LDA.

In the current study, the OPF of the biological treatment in the studied patients was 49.4%. The primary failure was 35.3% for IFX, 58.3% for ETA, 50% for ADA, and 50% for GOL.

In Wijbrandts and Tak., 2017 [14] study, it was reported that the failure rate of TNFi was 30–40% in patients who formerly failed csDMARD therapy, including MTX. In addition, there was no specific factor that explains or predicts response to TNFi. Generally, about 40% of RA cases do not give a response to the first biologic or gradually lose responsiveness [5, 15].

In this study, compared to patients with successful responses to biological treatment, patients with primary failure were significantly older patients, females, smokers, obese, aged younger at the onset of the disease, and had a deformity.

A systematic review and meta-analysis reported that old age (more than 55 years), females, obesity, and smoking were the main variables accompanied by poor response to TNFi. This can be explained by the fact that older patients are at a higher risk of having a prolonged duration of disease and usually have co-morbidities at baseline that induce early biological agents’ discontinuation [16]. It was also found that the innate and adaptive immune systems are impacted by aging. As we age, the innate immune system becomes less focused on its activity; this participates in increased chronic inflammation and co-morbidities [17]. Also, the adaptive immune system develops flaws and changes in phenotype with age, participating in the breakdown of immunological tolerance that results in an increased prevalence of AIDS [16]. In contrast, different researchers recorded no effects of age on response to TNFi [18, 19].

Along the same line, many researchers recorded that, with the same treatment, females had a worse prognosis of RA compared to males [20,21,22].

In addition, obesity was reported as an indicator of poor remission in patients receiving TNFi. The adipose tissue releases pro-inflammatory cytokines, which include TNF-α and interleukin-6 (IL-6). The high fat mass and levels of such cytokines could interfere with the medicinal responses [23]. In addition, being a current or ex-smoker reduces the response to TNFi. Chronic cigarette smoking triggers different morphological, physiological, and enzymatic changes. These changes impair inflammatory responses [24]. It acts on cellular and humoral immunity, causing systemic proinflammatory state [25].

The present study showed no difference between both groups as regards parent consanguinity, family history of RA, age at diagnosis, delayed diagnosis, duration after diagnosis and before the biological treatment, arthritis pattern, extra-articular manifestations, or disease complications.

Another study [16] concluded that disease duration, high TJC, and high SJC score at the diagnosis time were not significantly accompanied by a lower remission rate. In contrast, other studies reported that disease duration and disability were accompanied by lower remission rates [26, 27].

In this study, the OPF of biological treatment was significantly related to ESR, CRP, RF positivity, anti-CCP positivity, and non-MTX concomitant use. Similarly, a study [16] concluded that a lower remission rate accompanied by increased ESR and positive anti-CCP at the diagnosis time. On the other hand, regarding the relationship between both RF and anti-CCP at baseline and response to TNFi treatment, many studies reported contradictory results. Two studies [28, 29] reported that RF and anti-CCP at baseline significantly correlate with better response to TNFi. In contrast, other studies [30, 31] said the presence of RF or anti-CCP antibodies was accompanied by a decreased response to TNFi drugs.

In recent years, there has been no biomarker identified to predict response to TNFi in RA cases. RF was not significantly accompanied by poor response to TNFi; on the other hand, increased ESR was demonstrated to be a significantly poor predictor of remission, and cases with positive anti-CCP showed a high remission rate as a response to TNFi as concluded in a recent study [16]. Other research recorded no correlation between RF or anti-CCP positivity and the response to the therapy [32,33,34].

The anti-CCP positivity was accompanied by better responses to abatacept and adalimumab [35]. The anti-CCP binds to the Fc receptors, shown by immunological cells of the myeloid lineage, stimulating the complement system [36]. Most TNFi work on suppressing T cells, B cells, and their products of antibodies and cytokines; this partly clarifies their comparative efficiency in cases with positive anti-CCP [37].

In the current study, MTX was given to 83.9% of patients, with a good response of 54.8%. This agreed with a study [26] which reported that concurrent MTX therapy with TNFi therapies improves TNFi efficacy. Furthermore, a meta-analysis revealed that concurrent MTX therapy with TNFi improved the clinical response compared to biologic monotherapy [38]. Moreover, the combination of MTX to biological agents reduces the production of anti-drug antibodies and improves drug persistence [39].

The simultaneous MTX therapy improves the efficiency of low-dose IFX. However, the advantages of greater dosages of IFX are not clear [40]. Disease duration did not appear to interfere with the positive impact of IFX on radiological progression in RA patients [41]. In contrast, a recent study [16] concluded that prior or concurrent utilization of MTX was not significantly accompanied by a lower remission rate.

Although this study included all the available records of the patients who received TNFi, the sample size was small owing to financial limitations and neglected recording of some cases. Therefore, further studies with larger sample sizes are recommended.

Despite these limitations, this study has strengths; our prediction model used routine investigations, history taking, and clinical examination, which can be easily done without too much cost. Also, there is a lack of recent and up-to-date Egyptian studies in this area.

Conclusion

This record-based study demonstrated that increased age, being a smoker, earlier age of disease onset, presence of deformity, and positive anti-CCP at baseline were predictors of overall failure in the studied cases. Meanwhile, concomitant MTX intake increases the success rate by 9.6%.

Availability of data and materials

All data is available upon request.

Abbreviations

ADA:

Adalimumab

AID:

Autoimmune disease

Anti-CCP:

Anti-cyclic citrullinated peptide

bDMARDS:

Biologic disease-modifying anti-rhematic drugs

BMI:

Body mass index

bs-IFX:

Biosimilar infliximab

CBC:

Complete blood count

CRP:

C-reactive protein

csDMARDS:

Conventional synthetic disease-modifying anti-rheumatic drugs

CV:

Cardiovascular

CZP:

Certolizumab pegol

DAS28:

28-Joint disease activity score

DMARDS:

Disease-modifying anti-rheumatic drugs

ESR:

Erythrocyte sedimentation rate

ETN:

Etanercept

GOL:

Golimumab

IFX:

Infliximab

IL:

Interleukin

IQR:

Interquartile range

LDA:

Low disease activity

MTX:

Methotrexate

OPF:

Overall primary failure

PGA:

Patient global assessment

QoL:

Quality of life

RA:

Rheumatoid arthritis

RBR:

Risk-benefit ratio

RF:

Rheumatoid factor

SJC:

Swollen joint count

TJC:

Tender joint count

TNFα:

Tumor necrosis factor-alpha

TNFi:

Tumor necrosis factor inhibitor

References

  1. Harrold LR, Reed GW, Solomon DH, Curtis JR, Liu M, Greenberg JD et al (2009) (2016): Comparative effectiveness of abatacept versus tocilizumab in rheumatoid arthritis patients with prior TNFi exposure in the US Corrona registry. Arthritis Res Ther 18(1):280

    Article  Google Scholar 

  2. Smolen JS, Breedveld FC, Burmester GR, Bykerk V, Dougados M, Emery P et al (2016) Treating rheumatoid arthritis to target: 2014 update of the recommendations of an international task force. Ann Rheum Dis 75(1):3–15

    Article  PubMed  Google Scholar 

  3. Anderson J, Caplan L, Yazdany J, Robbins ML, Neogi T, Michaud K et al (2012) Rheumatoid arthritis disease activity measures: rheumatoid arthritis disease activity measures: American College of Rheumatology recommendations for use in clinical practice. Arthritis Care Res (Hoboken) 64(5):640–647

    Article  PubMed  Google Scholar 

  4. Smolen JS, Landewé R, Bijlsma J, Burmester G, Chatzidionysiou K, Dougados M et al (2017) EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease modifying anti-rheumatic drugs: 2016 update. Ann Rheum Dis 76:960–977

    Article  PubMed  Google Scholar 

  5. Rubbert-Roth A, Szabó MZ, Kedves M, Nagy G, Atzeni F, Sarzi-Puttini P (2019) Failure of anti-TNF treatment in patients with rheumatoid arthritis: The pros and cons of the early use of alternative biological agents. Autoimmune Rev 18(12):102398 (PMID: 31639514)

    Article  Google Scholar 

  6. Reams JA, Berger A, Denio A (2020) Efficacy predictors of a second tumour necrosis factor inhibitor in the treatment of rheumatoid arthritis. Medicine (Baltimore) 99(35):e21827

    Article  CAS  PubMed  Google Scholar 

  7. Atzeni F, Benucci M, Sallì S, Bongiovanni S, Boccassini L, Sarzi-Puttini P (2013) Different effects of biological drugs in rheumatoid arthritis. Autoimmune Rev 12:575–579

    Article  CAS  Google Scholar 

  8. Atzeni F, Gianturco L, Talotta R, Varisco V, Ditto MC, Turiel M et al (2015) Investigating the potential side effects of anti-TNF therapy for rheumatoid arthritis: cause for concern 7(4):353–361

    CAS  Google Scholar 

  9. Monti S, Klersy C, Gorla R, Sarzi-Puttini P, Atzeni F, Pellerito R et al (2017) Factors influencing the choice of first- and second-line biologic therapy for the treatment of rheumatoid arthritis: real-life data from the Italian LORHEN Registry. Clin Rheumatol 36(4):753–761

    Article  PubMed  Google Scholar 

  10. Santos-Moreno P, Sánchez G, Castro C (2019) Rheumatoid factor as predictor of response to treatment with anti-TNF alpha drugs in patients with rheumatoid arthritis: Results of a cohort study. Medicine (Baltimore) 98(5):e1418

    Article  Google Scholar 

  11. Sebastiani M, Manfredi A, Iannone F, Gremese E, Bortoluzzi A, Favalli E et al (2020) Factors predicting early failure of etanercept in rheumatoid arthritis: an analysis from the Gruppo Italiano di Studio sulla Early Arthritis (Italian Group for the Study of Early Arthritis) Registry. Arch Rheumatol 35(2):163–169

    Article  PubMed  PubMed Central  Google Scholar 

  12. Aletaha D, Neogi T, Silman AJ, Funovits J, Felson DT, Bingham CO 3rd et al (2010) 2010 Rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Arthritis Rheum 69(9):1580–1588

    Google Scholar 

  13. Prevoo ML, Hof MA, Kuper HH, van Leeuwen MA, van de Putte LB, van riel PL (1995) Modified disease activity scores that include twenty-eight joint counts. Development and validation in a prospective longitudinal study of patients with rheumatoid arthritis. Arthritis Rheum. 38(1):44–8

    Article  CAS  PubMed  Google Scholar 

  14. Wijbrandts CA, TakPP (2017) Prediction of Response to Targeted Treatment in Rheumatoid Arthritis. Mayo Clin Proc 92(7):1129–1143

    Article  CAS  PubMed  Google Scholar 

  15. Mehta P, Manson JJ (2020) What is the clinical relevance of TNF inhibitor immunogenicity in the management of patients with rheumatoid arthritis? Front Immunol 11:589

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Khader Y, Beran A, Ghazaleh S, Lee-Smith W, Altorok N (2022) Predictors of remission in rheumatoid arthritis patients treated with TNFi: a systematic review and meta-analysis. Clin Rheumatol 41(12):3615–3627

    Article  PubMed  PubMed Central  Google Scholar 

  17. Serhal L, Lwin MN, Holroyd C, Edwards CJ (2020) Rheumatoid arthritis in the elderly: characteristics and treatment considerations. Autoimmun Rev. 19(6):102528 (ISSN 1568-9972)

    Article  PubMed  Google Scholar 

  18. Hyrich KL, Watson KD, Silman AJ, Symmons DP (2006) British Society for Rheumatology TNFi Register Predictors of response to anti-TNF-alpha therapy among patients with rheumatoid arthritis: results from the British Society for Rheumatology TNFi Register. Rheumatology (Oxford) 45(12):1558–65

    Article  CAS  PubMed  Google Scholar 

  19. Mueller RB, Kaegi T, Finckh A, Haile SR, Schulze-Koops H, von Kempis J et al (2014) Is radiographic progression of late-onset rheumatoid arthritis different from young-onset rheumatoid arthritis? Results from the Swiss prospective observational cohort. Rheumatology (Oxford) 53:671–677

    Article  PubMed  Google Scholar 

  20. Forslind K, Hafstrom I, Ahlmen M, Svensson B et al (2007) Sex: a major predictor of remission in early rheumatoid arthritis? Ann Rheum Dis 66:46–52

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Likuni N, Sato E, Hoshi M, Inoue E, Taniguchi A, Hara M et al (2009) The influence of sex on patients with rheumatoid arthritis in a large observational cohort. J Rheumatol 36:508–511

    Article  Google Scholar 

  22. Jawaheer D, Maranian P, Park G, Lahiff M, Amjadi SS, Paulus HE et al (2010) Disease progression and treatment responses in a prospective DMARD-naive seropositive early rheumatoid arthritis cohort: does gender matter? J Rheumatol 37:2475–2485

    Article  PubMed  PubMed Central  Google Scholar 

  23. Francisco V, Pino J, Gonzalez-Gay MA, Mera A, Lago F, Gómez R et al (2018) Adipokines and inflammation: is it a question of weight? Br J Pharmacol 175:1569–1579

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Harel-Meir M, Sherer Y, Shoenfeld Y (2007) Tobacco smoking and autoimmune rheumatic diseases. Nat Clin PractRheumatol 3:707–715

    Article  CAS  Google Scholar 

  25. Sopori M (2002) Effects of cigarette smoke on the immune system. Nat Rev Immunol 2:372–377

    Article  CAS  PubMed  Google Scholar 

  26. Anderson JJ, Wells G, Verhoeven AC, Felson DT (2000) Factors predicting response to treatment in rheumatoid arthritis: the importance of disease duration. Arthritis Rheum 43:22–29

    Article  CAS  PubMed  Google Scholar 

  27. Baumgartner SW, Fleischmann RM, Moreland LW, Schiff MH, Markenson J, Whitmore JB (2004) Etanercept (Enbrel) in patients with rheumatoid arthritis with recent onset versus established disease: improvement in disability. J Rheumatol 31:1532–1537

    CAS  PubMed  Google Scholar 

  28. Braun-Moscovici Y, Markovits D, Zinder O, Schapira D, Rozin A, Ehrenburg M et al (2006) Anti-cyclic citrullinated protein antibodies as a predictor of response to anti-tumor necrosis factor-alpha therapy in patients with rheumatoid arthritis. J Rheumatol 33(3):497–500

    CAS  PubMed  Google Scholar 

  29. Klaasen R, Cantaert T, Wijbrandts CA, Teitsma C, Gerlag DM, Out TA et al (2011) The value of rheumatoid factor and anti-citrullinated protein antibodies as predictors of response to infliximab in rheumatoid arthritis: an exploratory study. Rheumatology (Oxford) 50(8):1487–1493

    Article  CAS  PubMed  Google Scholar 

  30. Potter C, Hyrich KL, Tracey A, Lunt M, Plant D, Symmons DP et al (2011) Association of rheumatoid factor and anti-cyclic citrullinated peptide positivity, but not carriage of shared epitope or PTPN22 susceptibility variants, with anti-tumor necrosis factor response in rheumatoid arthritis. Ann Rheum Dis 70(8):1519

    Google Scholar 

  31. Mancarella L, Bobbio-Pallavicini F, Ceccarelli F, Falappone PC, Ferrante A, Malesci D et al (2007) Good clinical response, remission, and predictors of remission in rheumatoid arthritis patients treated with tumor necrosis factor-alpha blockers: the GISEA study. J Rheumatol 34(9):1947

    Google Scholar 

  32. Burmester GR, Feist E, Kellner H, Braun J, Iking-Konert C, Rubbert-Roth A (2011) Effectiveness and safety of the interleukin 6-receptor antagonist tocilizumab after 4 and 24 weeks in patients with active rheumatoid arthritis: the first phase IIIb real-life study (TAMARA). Ann Rheum Dis 70:755–759

    Article  CAS  PubMed  Google Scholar 

  33. Pers YM, Fortunet C, Constant E, Lambert J, Godfrin-Valnet M, De Jong A et al (2014) Predictors of response and remission in a large cohort of rheumatoid arthritis patients treated with tocilizumab in clinical practice. Rheumatology (Oxford) 53:76–84

    Article  CAS  PubMed  Google Scholar 

  34. Narvaez J, Magallares B, Diaz Torne C, Hernández MV, Reina D, Corominas H et al (2016) Predictive factors for induction of remission in patients with active rheumatoid arthritis treated with tocilizumab in clinical practice. Semin Arthritis Rheum 45:386–390

    Article  CAS  PubMed  Google Scholar 

  35. Sokolove J, Schiff M, Fleischmann R, Weinblatt ME, Connolly SE, Connolly SE et al (2016) Impact of baseline anti-cyclic citrullinated peptide-2 antibody concentration on efficacy outcomes following treatment with subcutaneous abatacept or adalimumab: 2-year results from the AMPLE trial. Ann Rheum Dis 75:709–714

    Article  CAS  PubMed  Google Scholar 

  36. Pongratz G, Fleck M (2012) Anti-citrullinated protein antibodies and mechanism of action of common disease modifying drugs– insights in pathomechanisms of autoimmunity. Curr Pharm Des 18:4526–4536

    Article  CAS  PubMed  Google Scholar 

  37. McInnes IB, Schett G (2017) Pathogenetic insights from the treatment of rheumatoid arthritis. Lancet 389:2328–2337

    Article  CAS  PubMed  Google Scholar 

  38. Donahue KE, Schulman ER, Gartlehner G, Jonas BL, Coker-Schwimmer E, Patel SV et al (2019) Comparative effectiveness of combining MTX with biologic drug therapy versus either MTX or Biologics alone for early rheumatoid arthritis in adults: a systematic review and network meta-analysis. J Gen Intern Med 34:2232–2245

    Article  PubMed  PubMed Central  Google Scholar 

  39. Schaeverbeke T, Truchetet ME, Kostine M, Barnetche T, Bannwarth B, Richez C (2016) Immunogenicity of biologic agents in rheumatoid arthritis patients: lessons for clinical practice. Rheumatology 55(2):210–220

    Article  CAS  PubMed  Google Scholar 

  40. Maini RN, Breedveld FC, Kalden JR, Smolen JS, Davis D, Macfarlane JD et al (1998) Therapeutic efficacy of multiple intravenous infusions of anti-tumor necrosis factor alpha monoclonal antibody combined with low-dose weekly methotrexate in rheumatoid arthritis. Arthritis Rheum 41:1552–1563

    Article  CAS  PubMed  Google Scholar 

  41. Breedveld FC, Emery P, Keystone E, Patel K, Furst DE, Kalden JR et al (2004) Infliximab in active early rheumatoid arthritis. Ann Rheum Dis 63:149–155

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the participants for their cooperation.

Funding

This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Rheumatology and Rehabilitation, Mansoura University Hospitals, Mansoura University Faculty of Medicine, Mansoura, Egypt

    Amira Mohamed Khafagi & Doaa Mosad Mosa

  2. Department of Rheumatology and Rehabilitation, Mansoura University, Faculty of Medicine, Mansoura, Egypt

    Salah Hawaas & Eman Abdelrazek Hafez

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Contributions

All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by A.K, D.M, S.H, and E.A. The first draft of the manuscript was written by A.K, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript

Corresponding author

Correspondence to Doaa Mosad Mosa.

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Ethics approval and consent to participate

The Institutional Research Board (IRB), Faculty of Medicine, Mansoura University, Egypt (R.21.03.1276) approved this study.

Consent to participate

Consent is not applicable because the study was retrospective and was based on patients’ data collection which was anonymized

Competing interests

The authors declare that they have no competing interests.

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Khafagi, A.M., Mosa, D.M., Hawaas, S. et al. Predictors of tumor necrosis factor inhibitors primary failure in rheumatoid arthritis patients. Egypt Rheumatol Rehabil 51, 26 (2024). https://doi.org/10.1186/s43166-024-00260-x

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