The utility of pathway selective estrogen receptor ligands that inhibit nuclear factor-κB transcriptional activity in models of rheumatoid arthritis
Bibliographic record
Abstract
Rheumatoid arthritis (RA) is a chronic inflammatory disease that produces synovial proliferation and joint erosions. The pathologic lesions of RA are driven through the production of inflammatory mediators in the synovium mediated, in part, by the transcription factor NF-kappaB. We have identified a non-steroidal estrogen receptor ligand, WAY-169916, that selectively inhibits NF-kappaB transcriptional activity but is devoid of conventional estrogenic activity. The activity of WAY-169916 was monitored in two models of arthritis, the HLA-B27 transgenic rat and the Lewis rat adjuvant-induced model, after daily oral administration. In both models, a near complete reversal in hindpaw scores was observed as well as marked improvements in the histological scores. In the Lewis rat adjuvant model, WAY-169916 markedly suppresses the adjuvant induction of three serum acute phase proteins: haptoglobin, alpha1-acid glycoprotein (alpha1-AGP), and C-reactive protein (CRP). Gene expression experiments also demonstrate a global suppression of adjuvant-induced gene expression in the spleen, liver, and popliteal lymph nodes. Finally, WAY-169916 was effective in suppressing tumor necrosis factor-alpha-mediated inflammatory gene expression in fibroblast-like synoviocytes isolated from patients with RA. Together, these data suggest the utility of WAY-169916, and other compounds in its class, in treating RA through global suppression of inflammation via selective blockade of NF-kappaB transcriptional activity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".