The evolving landscape of gene therapy strategies for the treatment of osteoarthritis
Bibliographic record
Abstract
OBJECTIVES: Significant advances have been made in our understanding of osteoarthritis (OA) pathogenesis; however, no disease-modifying therapies have been identified. This review will summarize the gene therapy landscape, its initial successes for OA, and possible challenges using recent studies and examples of gene therapies in clinical trials. DESIGN: This narrative review has three major sections: 1) vector systems for OA gene therapy, 2) current and emerging targets for OA gene therapy, and 3) considerations and future directions. RESULTS: Gene therapy is the strategy by which nucleic acids are delivered to treat and reverse disease progression. Specificity and prolonged expression of these nucleic acids are achieved by manipulating promoters, genes, and vector systems. Certain vector systems also allow for the development of combinatorial nucleic acid strategies that can be delivered in a single intraarticular injection - an approach likely required to treat the complexity of OA pathogenesis. Several viral and non-viral vector-based gene therapies are in clinical trials for OA, and many more are being evaluated in the preclinical arena. CONCLUSIONS: In a post-coronavirus disease 2019 (COVID-19) era, the future of gene therapy for OA is certainly promising; however, the majority of preclinical validation continues to focus heavily on post-traumatic models and changes in only cartilage and subchondral bone. To ensure successful translation, new candidates in the preclinical arena should be examined against all joint tissues as well as pain using diverse models of injury-, obesity-, and age-induced disease. Lastly, consideration must be given to strategies for repeat administration and the cost of treatment owing to the chronic nature of OA.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".