Prevalence of TNF-α Blocker Immunogenicity in Psoriatic Arthritis
Bibliographic record
Abstract
OBJECTIVE: The longterm use of tumor necrosis factor (TNF)-α blockers is limited by the formation of neutralizing antibodies. To the best of our knowledge, immunogenicity in psoriatic arthritis (PsA) has not been investigated in depth. Our objective was to evaluate the prevalence and significance of TNF-α blocker immunogenicity in PsA. METHODS: Consecutive patients with PsA treated with either infliximab (IFX), adalimumab (ADA), or etanercept (ETN) > 3 months participated in our cross-sectional study. Their demographic and clinical characteristics, skin and joint disease activity, and records of use of methotrexate (MTX) and other medications were collected. Drug levels (ELISA) and antidrug antibodies (ADAb; Bridging ELISA) were evaluated before the next injection or infusion. RESULTS: A total of 93 patients with PsA were recruited (48 receiving ADA, 24 IFX, and 21 ETN), with a mean age of 53 years (range 21-83 yrs), composed of 53% women. One-fourth of the patients were concomitantly treated with MTX. Altogether, 77% of the patients demonstrated therapeutic drug levels. High levels of ADAb were found in 29% of patients taking ADA, 21% taking IFX, and 0% taking ETN. ADAb significantly correlated with lower drug levels, higher 28-joint Disease Activity Scores, and higher global assessments. MTX use correlated significantly with a lower prevalence of ADAb. CONCLUSION: Significant levels of ADAb were present in up to 29% of patients with PsA treated with ADA or IFX. ADAb clearly correlated with low therapeutic drug levels and higher disease activity variables. The use of MTX significantly decreased ADAb prevalence, and its use should be strongly considered in combination with TNF-α blocker antibodies in patients with PsA.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".