Synthesis and Evaluation of Thermoresponsive Renewable Lipid-Based Block Copolymers for Drug Delivery
Bibliographic record
Abstract
Polymeric micelle forming from self-assembly of amphiphilic macromolecules is one of the most potent drug delivery systems. Fatty acids, naturally occurring hydrophobic lipid components, can be considered as potential candidates for the fabrication of block copolymer micelles. However, examples of synthesis of responsive block copolymers using renewable fatty acids are scarce. Herein, we report the synthesis, characterization and testing of block copolymer micelles composed of a renewable fatty-acid-based hydrophobic block and thermoresponsive hydrophilic block for controlled drug delivery. The block copolymers of functionalized fatty acid and poly(N-isopropylacrylamide) (PNIPAM) were prepared via consecutive microwave-assisted reversible addition fragmentation chain transfer (RAFT) polymerization. The block copolymers with variable hydrophobic block length self-assembled in aqueous media and formed spherical nanoparticles of ~30 nm with low critical micelle concentration (CMC). To demonstrate the proof-of-concept, carbamazepine (CBZ) was used as a hydrophobic model drug to evaluate the performance of these micelles as nanocarriers. The in vitro drug release tests were carried out below (25 °C) and above (37 °C) the lower critical solution temperature (LCST) of the block copolymer. The drug release showed obvious temperature-triggered response and an accelerated drug release at 37 °C.
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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) | 0.000 | 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".