Release and activity of anti‐TNFα therapeutics from injectable chitosan preparations for local drug delivery
Bibliographic record
Abstract
BACKGROUND: Tumor necrosis factor alpha (TNFalpha) is a cytokine that regulates immune and inflammatory overactivation in various pathological states. Protein therapeutics may antagonize this cytokine, but may also have systemic toxicities. Small molecule natural products are also efficacious, but can suffer from poor oral bioavailability. A drug delivery vehicle is needed to sustain release of active therapeutics and address localized inflammation. MATERIALS: Chitosan is a biocompatible aminopolysaccharide that undergoes thermally-initiated gelation in cosolutions with glycerophosphate (GP), and may entrap and sustain release of additive therapeutics. Gelation time and temperature of chitosan/GP were evaluated by turbidity (OD(350)), as was the kinetic effect of bovine serum albumin (BSA) entrapment. We investigated in vitro release of BSA and various anti-TNF agents (curcumin, sTNFRII, anti-TNF antibody) and confirmed in vitro activity of the released drugs using an established bioassay. RESULTS: Turbidity results show that chitosan/GP thermogel achieves gelation at 37 degrees C within 10 min, even with significant protein loading. Sustained BSA release occurred with 50% retained at 7 days. All anti-TNF therapeutics exhibited sustained release, with 10% of sTNFRII and anti-TNF antibody remaining after 7 days and 10% of curcumin remaining after 20 days. After release, each compound antagonized TNFalpha-cytotoxicity in murine fibrosarcoma cells. CONCLUSIONS: This study demonstrates that thermogelling chitosan/GP entraps and sustains release of a broad range of anti-TNF agents. Such delivery of disease-modifying therapy could establish a drug depot to treat local inflammation. The breadth of molecular sizes demonstrates significant versatility, and slow release could protect against toxicities of systemic delivery.
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 | 0.000 | 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".