GSK3787-Loaded Poly(Ester Amide) Particles for Intra-Articular Drug Delivery
Bibliographic record
Abstract
Osteoarthritis (OA) is a debilitating joint disorder affecting more than 240 million people. There is no disease modifying therapeutic, and drugs that are used to alleviate OA symptoms result in side effects. Recent research indicates that inhibition of peroxisome proliferator-activated receptor δ (PPARδ) in cartilage may attenuate the development or progression of OA. PPARδ antagonists such as GSK3787 exist, but would benefit from delivery to joints to avoid side effects. Described here is the loading of GSK3787 into poly(ester amide) (PEA) particles. The particles contained 8 wt.% drug and had mean diameters of about 600 nm. Differential scanning calorimetry indicated the drug was in crystalline domains in the particles. Atomic force microscopy was used to measure the Young's moduli of individual particles as 2.8 MPa. In vitro drug release studies showed 11% GSK3787 was released over 30 days. Studies in immature murine articular cartilage (IMAC) cells indicated low toxicity from the drug, empty particles, and drug-loaded particles and that the particles were not taken up by the cells. Ex vivo studies on murine joints showed that the particles could be injected into the joint space and resided there for at least 7 days. Overall, these results indicate that GSK3787-loaded PEA particles warrant further investigation as a delivery system for potential OA therapy.
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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".