Effect of Secukinumab and Tumor Necrosis Factor Inhibitors on Humoral Response to BNT162b2 mRNA Vaccine in Patients With Spondyloarthritis Compared to Immunocompetent Controls
Bibliographic record
Abstract
Objective To assess the humoral response to the BNT162b2 mRNA vaccine among patients with spondyloarthritis (SpA) receiving secukinumab (SEC) compared to those receiving tumor necrosis factor inhibitors (TNFi) and immunocompetent controls. Methods Consecutive patients with psoriatic arthritis or axial SpA receiving SEC (n = 37) or TNFi (monotherapy, n = 109; + methotrexate [MTX], n = 16), immunocompetent controls (n = 122), and patients with rheumatoid arthritis (RA) receiving TNFi therapy (controls, n = 50) were vaccinated with 2 or 3 doses of the BNT162b2 vaccine. We evaluated humoral response, adverse events, and disease activity, and monitored for breakthrough coronavirus disease 2019 (COVID-19) postvaccination. Results The 2-dose vaccine regimen induced a comparable seropositive response in all study groups. S1/S2 antibody titers (in binding antibody units/mL; mean [SD]) were higher in the SEC group vs the TNFi + MTX–SpA and TNFi-RA groups (192.5 [68.4] vs 104.6 [46.9],P< 0.001, and 143.1 [81.9],P= 0.004). After 6 months, 96.3%, 96.6%, and 80.9% of the SEC, immunocompetent, and TNFi monotherapy-SpA groups (P= 0.10), respectively; 66.7% of the TNFi + MTX–SpA group (P= 0.03); and 63% of the TNFi-RA group (P= 0.004) remained seropositive. S1/S2 antibody titer decline was steeper in the TNFi groups than the SEC group. After the third dose, 100% of the SpA and immunocompetent and 88.9% of the TNFi-RA (P= 0.25) groups were seropositive. Rate of breakthrough COVID-19 infection was higher in the TNFi groups than in the SEC group (36-37.5% vs 10.8%). No significant between-group differences were observed for postvaccination disease activity and adverse events. Conclusion SEC did not interfere with the immunogenic response to BNT162b2 vaccine in patients with SpA; however, TNFi therapy was associated with lower S1/S2-antibody titers, faster decline, and higher rate of breakthrough infections.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".