Evaluating the Therapeutic Efficacy of Iopanoic Acid in a DMM-Induced Osteoarthritis Mouse Model and Osteochondral Lesioned Human Explants
Bibliographic record
Abstract
Objective To evaluate the therapeutic potential of iopanoic acid (IOP), a thyroid hormone pathway inhibitor, in preserving cartilage and bone integrity in osteoarthritis (OA), using in vivo and ex vivo tissue models. Design In the DMM mouse model, IOP was administered through intra-articular (i.a.) injection, either alone or combined with a thermosensitive hydrogel to enable sustained release. Histological analyses included damage, osteophyte, and synovitis scoring. Immunohistochemistry was performed for Col2, Mmp13, and CCDC80 to evaluate anabolic, catabolic, and hypertrophic markers. Micro-CT assessed subchondral bone changes. In the ex vivo studies, IOP was applied to lesioned human osteochondral OA explants. Matrix degradation and repair were evaluated by sulfated glycosaminoglycan (sGAG) release, Mankin histology scores, and RT-qPCR for cartilage matrix genes. Results Administration of IOP significantly reduced cartilage degeneration in DMM mice, characterized by increased Col2, and decreased Mmp13 and CCDC80 expression. Notably, IOP also prevented pathological subchondral bone thickening. In human explants, IOP treatment led to a significant reduction in sGAG release compared to untreated explants on day 6 of the IOP treatment. Moreover, Mankin scores were significantly improved in IOP-treated compared to untreated explants, indicating reduced cartilage degradation. Conclusion IOP demonstrates strong chondroprotective effects, reducing cartilage degradation and promoting repair in OA models. Its combination with a thermosensitive hydrogel amplifies therapeutic potential, offering a promising strategy for OA treatment. Next steps are to optimize delivery and validate early molecular effects.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".