Liquid Degradable Poly(trimethylene-carbonate-<i>co</i>-5-hydroxy-trimethylene carbonate): An Injectable Drug Delivery Vehicle for Acid-Sensitive Drugs
Bibliographic record
Abstract
Liquid, injectable hydrophobic polymers have advantages as degradable drug delivery vehicles; however, polymers examined for this purpose to date form acidic degradation products that may damage acid-sensitive drugs. Herein, we report on a new viscous liquid vehicle, poly(trimethylene carbonate- co -5-hydroxy-trimethylene carbonate), which degrades through intramolecular cyclization producing glycerol, carbon dioxide, and water-soluble trimethylene carbonate. Copolymer degradation durations from weeks to months were achieved with the 5-hydroxy-trimethylene carbonate (HTMC) content of the oligomer having the greatest impact on the degradation rate, with oligomers possessing a higher HTMC content degrading fastest. The degradation products were non-cytotoxic towards 3T3 fibroblasts and RAW 264.7 macrophages. These copolymers can be injected manually through standard gauge needles and, importantly, during in vitro degradation, the microenvironmental pH within the oligomers remained near neutral. Complete and sustained release of the acid-sensitive protein vascular endothelial growth factor was achieved, with the protein remaining highly bioactive throughout the release period. These copolymers represent a promising formulation for local and sustained release of acid sensitive drugs.
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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) | 0.000 | 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".