siRNA conjugate with high albumin affinity and degradation resistance for delivery and treatment of arthritis in mice and guinea pigs
Bibliographic record
Abstract
Osteoarthritis and rheumatoid arthritis are debilitating joint diseases marked by pain, inflammation and cartilage destruction. Current osteoarthritis treatments only relieve symptoms, while rheumatoid arthritis therapies can cause immune suppression and provide variable efficacy. Here we developed an optimized small interfering RNA targeting matrix metalloproteinase 13 for preferential delivery to arthritic joints. Chemical modifications in a stabilizing 'zipper' pattern improved RNA resistance to degradation, and two independent linkers with 18 ethylene glycol repeats connecting to tandem C18 lipids enhanced albumin binding and targeted delivery to inflamed joints following intravenous administration. In preclinical models of post-traumatic osteoarthritis and rheumatoid arthritis, a single intravenous injection of the albumin-binding small interfering RNA achieved long-term joint retention, sustained gene silencing and reduced matrix metalloproteinase 13 activity over 30 days, resulting in decreased cartilage erosion and improved clinical outcomes, including reduced joint swelling and pressure sensitivity. This approach demonstrated superior efficacy over corticosteroids and small-molecule MMP inhibitors, highlighting the therapeutic promise of albumin 'hitchhiking' for targeted, systemic delivery of gene-silencing therapeutics to treat osteoarthritis and rheumatoid arthritis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".