Emerging biologic therapies in rheumatoid arthritis: cell targets and cytokines
Bibliographic record
Abstract
PURPOSE OF REVIEW: Biologic therapy for rheumatoid arthritis targets specific molecules, both cell-bound and soluble, that mediate and sustain the clinical manifestations of this complex disease. The aim of all the therapeutic strategies is to achieve complete and sustained suppression of inflammation, in the absence of unacceptable short-term and long-term toxicity. Despite the success of the currently available biologic inhibitors of tumor necrosis factor-alpha and interleukin-1, a substantial number of rheumatoid arthritis patients are refractory to these treatments. The purpose of this review is to highlight recent clinical trials of emerging biologic treatments for rheumatoid arthritis. RECENT FINDINGS: T cell co-stimulation has been targeted by the use of cytotoxic T lymphocyte-associated antigen 4-Ig, a genetically engineered fusion protein. In a large controlled clinical trial, this nondepleting approach was shown to achieve impressive clinical responses, without evidence of short-term toxicity. Likewise, rituximab, a B cell-deleting monoclonal antibody, was shown in a controlled clinical trial to have sustained benefit in patients with refractory rheumatoid arthritis. Despite profound B cell depletion with rituximab, there was an acceptable safety profile with this treatment. MRA, a monoclonal antibody that inhibits interleukin-6 by binding to its receptor interleukin-6R, demonstrated clinically significant improvement in rheumatoid arthritis and a particularly impressive reduction in the acute phase response. SUMMARY: The response of rheumatoid arthritis to a wide spectrum of therapeutic strategies attests to the complexity and heterogeneity of the disease and provides further impetus for studies that use these therapies to enhance our understanding of disease pathogenesis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".