The Evolution of TNF-α Blockade for the Treatment of Rheumatoid Arthritis
Bibliographic record
Abstract
Tumor necrosis factor (TNF)-α is a potent trimeric cytokine which plays a fundamental role in the host immuno-inflammatory response, as well as in homeostasis and development. Although critical for canonical immune function, TNF-α has great destructive potential and is implicated in the development of multiple immune-mediated disorders. Within the context of rheumatoid arthritis (RA), TNF-α acts as a primary pathogenic driver by precipitating a pro-inflammatory cytokine cascade and coordinating the attraction and activation of immune cells, all of which culminate in damage to the synovium. The discovery of the paramount role of TNF-α in the pathophysiology of RA motivated studies to understand the effects of TNF blockade in vitro and in vivo. Promising preclinical results provided the impetus for clinical trials, spearheaded in the 1980s and 90s by Marc Feldmann, which revealed significant improvements across RA symptom scores and finally led to FDA approval in 1998. As of 2021, five TNF-α blocking agents have been widely applied clinically, including infliximab (IFX), etanercept (ETN), adalimumab (ADA), golimumab (GLM) and certolizumab pegol (CZP). All of them successfully ameliorated symptoms of RA and the associated tissue damage, especially in patients not responding to traditional treatment methods. Anti-TNFs are most often administered in combination with methotrexate (MTX) as part of Phase II treatment (i.e., second line). Although the general availability of anti-TNFs has dramatically improved patient outcomes, sustained remission is rare and the mechanism of RA remains incompletely understood. Thus, additional basic and translational research is warranted, towards the aim of developing novel RA treatments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".