“Old Drugs, New Tricks” – Local controlled drug release systems for treatment of degenerative joint disease
Bibliographic record
Abstract
Osteoarthritis (OA) and chronic low back pain (CLBP) caused by intervertebral disc (IVD) degeneration are joint diseases that have become major causes for loss of quality of life worldwide. Despite the unmet need, effective treatments other than invasive, and often ineffective, surgery are lacking. Systemic administration of drugs entails suboptimal local drug exposure in the articular joint and IVD. This review provides an overview of the potency of biomaterial-based drug delivery systems as novel treatment modality, with a focus on the biological effects of drug release systems that have reached translation at the level of in vivo models and relevant ex vivo models. These studies have shown encouraging results of biomaterial-based local delivery of several types of drugs, mostly inhibitors of inflammatory cytokines or other degenerative factors. Prevention of inflammation and degeneration and pain relief was achieved, although mainly in small animal models, with interventions applied at an early disease stage. Less convincing data were obtained with the delivery of regenerative factors. Multidisciplinary efforts towards tackling the discord between in vitro and in vivo release, combined with adaptations in the regulatory landscape may be needed to enhance safe and expeditious introduction of more and more effective controlled release-based treatments with the OA and CLBP patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".