Nanoparticle-based inhibition of vascular endothelial growth factor receptors alleviates osteoarthritis pain and cartilage damage
Bibliographic record
Abstract
Osteoarthritis (OA) is characterized by cartilage damage, inflammation, and pain. Vascular endothelial growth factor receptors (VEGFRs) have been associated with OA severity, suggesting that inhibitors targeting these receptors alleviate pain (via VEGFR1) or cartilage degeneration (via VEGFR2). We have developed a nanoparticle-based formulation of pazopanib (Votrient), an FDA-approved anticancer drug that targets both VEGFR1 and VEGFR2 (Nano-PAZII). We demonstrate that a single intraarticular injection of Nano-PAZII can effectively reduce joint pain for a prolonged time without substantial side effects in two different preclinical OA rodent models involving either surgical (upon partial medial meniscectomy) or nonsurgical induction (with monoiodoacetate). The injection of Nano-PAZII blocks VEGFR1 and relieves OA pain by suppressing sensory neuronal ingrowth into the knee synovium and neuronal plasticity in the dorsal root ganglia and spinal cord. Simultaneously, the inhibition of VEGFR2 reduces cartilage degeneration. These findings provide a mechanism-based disease-modifying drug strategy that addresses both pain symptoms and cartilage loss in OA.
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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) | 0.000 | 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 | 0.000 |
| 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".