Glutathione-Triggered Disassembly of Dual Disulfide Located Degradable Nanocarriers of Polylactide-Based Block Copolymers for Rapid Drug Release
Bibliographic record
Abstract
Reduction-responsive degradation based on disulfide-thiol chemistry is highly desirable in the development of self-assembled block copolymer nanocarriers for multifunctional polymer-based drug delivery applications. Most conventional approaches involve the incorporation of disulfide linkages at a single location. Herein, we report a new dual disulfide located degradable polylactide (PLA)-based block copolymer (DL-ssABP) synthesized by a combination of ring opening polymerization, facile coupling reactions, and controlled radical polymerization. The amphiphilic design of the DL-ssABP enables the formation of self-assembled micelles having disulfides positioned both in the hydrophobic PLA core and at the core/corona interface. The reductive response to glutathione as a cellular trigger results in the cleavage of the disulfide linkage at the interface shedding hydrophilic coronas as well as the disulfides in the PLA core causing disintegration of PLA cores. Such dual disulfide degradation process leads to a synergistically enhanced release of encapsulated anticancer drugs in cellular environments. These results, combined with flow cytometry and confocal laser scanning microscopy (CLSM) as well as cell viability measurements, suggest that DL-ssABP offers versatility in tumor-targeting controlled/enhanced drug delivery applications.
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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".