Biologic Agents in the Treatment of Rheumatoid Arthritis
Bibliographic record
Abstract
Advances in the understanding of the pathogenesis of rheumatoid arthritis (RA) as well as improved biotechnology has enabled selective targeting of the pathogenic elements of disease. Targeting cell recruitment through adhesion molecules has been shown to be successful in pre-clinical murine models. Results of studies of an anti-ICAM-1 monoclonal antibody and anti-sense oligonucleotides have been encouraging. An alternate approach to inhibiting recruitment has been the targeting of chemoattractant molecules ie. chemokines. Important advances have been made in cytokine directed therapy targeting TNFalpha and IL-1. TNF antagonists (anti-TNF monoclonal antibody/soluble TNF receptor Fc fusion protein) have resulted in rapid and substantial improvement in signs and symptoms of disease as well as disease modification, shown by slowing of radiological progression. IL-1 receptor antagonist protein appears to have a significant effect on radiological progression despite a modest effect on symptoms and signs. Studies using anti-inflammatory cytokines such as IL-10 are in progress. A more recent therapeutic approach has been to target T-cell activation by interfering with co-stimulatory complexes such as CD40L/CD40 and CD28/CD80 and CD86. Both pre-clinical and preliminary clinical studies in human subjects support the concept. Another approach involving T-cell receptor peptide vaccination with VB peptides over-utilized in RA synovium has shown to be beneficial. Targeting the cytokines driving T-cells in the RA synovium ie. IL-12 & IL-15 has also proven beneficial in animal studies. Recent attention has also been directed toward the invading synovial fibroblast using Fas-FasL mediated apoptosis. Pre-clinical studies in which angiogenesis and osteoclast activation are targeted have been encouraging. In conclusion, the proof of principle has been established that selective targeting of pathogenic elements of disease results in substantial improvement in signs and symptoms as well as disease progression. Improved efficacy is expected with more aggressive targeting of the pathogenic elements.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".