Mouse Models of Rheumatoid Arthritis for Studies on Immunopathogenesis and Preclinical Testing of Fc Receptor-Targeting Biologics
Bibliographic record
Abstract
BACKGROUND: Rheumatoid arthritis (RA) is a chronic autoimmune disease that causes inflammation, swelling, and pain in the joints and involves systemic complications. Mouse models of RA have been extensively used to model the pathogenesis of RA and to develop effective therapies. Although many components of the immune system have been studied in these models, the role of crystallizable fragment (Fc) gamma receptors (FcγRs) in RA has been sorely neglected. The aim of this review was to introduce the different mouse models of RA and to describe the different drug development strategies that have been tested in these models to target FcγR function, with the focus being on drugs that have been made from the Fc of immunoglobulin G (IgG). SUMMARY: Evidence suggests that FcγRs play a major role in immune complex-induced inflammation in autoimmune diseases, such as RA. However, there is limited knowledge on the importance of FcγRs in the human disease even though there has been extensive work in mouse models of RA. Numerous mouse models of RA are available, with each model depicting certain aspects of the disease. Induced models of RA have nonspecific immune activation with cartilage-directed autoimmunity, whereas spontaneous models of RA develop without immunization, which results in a more chronic form of arthritis. These models have been used to test FcγR-targeting monoclonal antibodies, intravenous immunoglobulin (IVIg), subcutaneously administered IVIg, and recombinant Fcs for their ability to interact with and modify FcγR function. Recombinant Fcs avidly bind FcγRs and exhibit enhanced therapeutic efficacy in mouse models of RA. Key Message: The therapeutic utility of targeting FcγRs with recombinant Fcs is great and should be explored in human clinical trials for autoimmune diseases, such as RA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".