Design, Synthesis, and Acid‐Responsive Disassembly of Shell‐Sheddable Block Copolymer Labeled with Benzaldehyde Acetal Junction
Bibliographic record
Abstract
Smart nanoassemblies degradable through the cleavage of acid-labile linkages have attracted significant attention because of their biological relevance found in tumor tissues. Despite their high potential to achieve controlled/enhanced drug release, a systematic understanding of structural factors that affect their pH sensitivity remains challenging, particulary in the consruction of effective acid-degradable shell-sheddable nanoassemblies. Herein, the authors report the synthesis and acid-responsive degradation through acid-catalyzed hydrolysis of three acetal and ketal diols and identify benzaldehyde acetal (BzAA) exhibiting optimal hydrolysis profiles in targeted pH ranges to be a suitable candidate for junction acid-labile linkage. The authors explore the synthesis and aqueous micellization of well-defined poly(ethylene glycol)-based block copolymer bearing BzAA linkage covalently attached to a polymethacrylate block for the formation of colloidally-stable nanoassemblies with BzAA groups at core/corona interfaces. Promisingly, the investigation on acid-catalyzed hydrolysis and disassembly shows that the formed nanoassemblies meet the criteria for acid-degradable shell-sheddable nanoassemblies: slow degradation at tumoral pH = 6.5 and rapid disassembly at endo/lysosomal pH = 5.0, while colloidal stability at physiological pH = 7.4. This work guides the design principle of acid-degradable shell-sheddable nanoassemblies bearing BzAA at interfaces, thus offering the promise to address the PEG dilemma and improve endocytosis in tumor-targeting drug delivery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".