Canine IL4-10 fusion protein provides disease modifying activity in a canine model of OA; an exploratory study
Bibliographic record
Abstract
OBJECTIVE: An ideal disease modifying osteoarthritis drug (DMOAD) has chondroprotective, anti-inflammatory, and analgesic effects. This study describes the production and characterization of a canine IL4-10 fusion protein (IL4-10 FP) and evaluates its in vivo DMOAD activity in a canine model of osteoarthritis (OA). DESIGN: The canine Groove model was used as an in vivo model of degenerative knee OA. Six weeks after OA induction dogs were intra-articularly injected weekly, for ten weeks, with either IL4-10 FP or phosphate buffered saline (PBS). In addition to the use of human IL4-10 FP, canine IL4-10 FP was developed and characterized in vitro, and tested in vivo. Force plate analysis (FPA) was performed to analyze joint loading as a proxy measure for pain. After ten weeks dogs were euthanized and cartilage and synovial tissue samples were analyzed by histochemistry (OARSI scores) and biochemistry (cartilage proteoglycan turnover). RESULTS: Repetitive intra-articular injections with human IL4-10 FP led to antibody formation, that blocked its functional activity. Therefore, a canine IL4-10 FP was developed, which completely inhibited LPS-induced TNFα production by canine blood cells, and increased proteoglycan synthesis of canine cartilage in vitro (p = 0.043). In vivo, canine IL4-10 FP restored the, by OA impaired, joint loading (p = 0.002) and increased cartilage proteoglycan content (p = 0.029). CONCLUSIONS: This first study on the potential DMOAD activity upon prolonged repeated treatment with IL4-10 FP demonstrates that a species-specific variant has anti-inflammatory and chondroprotective effects in vitro and chondroprotective and analgesic effects in vivo. These data warrant further research on the DMOAD potential of the IL4-10 FP.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".