Drug retention after intradiscal administration
Bibliographic record
Abstract
Intradiscal drug delivery is a promising strategy for treating intervertebral disk degeneration (IVDD). Local degenerative processes and intrinsically low fluid exchange are likely to influence drug retention. Understanding their connection will enable the optimization of IVDD therapeutics. Release and retention of an inactive hydrophilic fluorine-19 labeled peptide (19F-P) as model for regenerative peptides was studied in a whole IVD culture model by measuring the 19F-NMR (nuclear magnetic resonance) signal in culture media and IVD tissue extracts. In another set-up, noninvasive near-infrared imaging was used to visualize IR-780, as hydrophobic small molecular drug model, retention upon injection into healthy and degenerative caudal IVDs in a rat model of disk degeneration. Furthermore, IR-780-loaded degradable polyester amide microspheres (PEAM) were injected into healthy and needle pricked degenerative IVDs, subcutaneously, and in knee joints with and without surgically-induced osteoarthritis (OA). Most 19F-P was released from the IVD after 7 days. IR-780 signal intensity declined over a 14-week period after bolus injection, without a difference between healthy and degenerative disks. IR-780 signal declined faster in the skin and knee joints compared to the IVDs. IR-780 delivery by PEAMs enhanced disk retention beyond 16 weeks. Moreover, in degenerated IVDs the IR-780 signal was higher over time than in healthy IVDs while no difference between OA and healthy joints was noted. We conclude that the clearance of peptides and hydrophobic small molecules from the IVD is relatively fast. These results illustrate that development of controlled release formulations should take into account the target anatomical location and local (patho)biology.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".