Dual-responsive core-crosslinked polyphosphoester-based nanoparticles for pH/redox-triggered anticancer drug delivery
Bibliographic record
Abstract
"Intelligent" crosslinked nanoparticles (NPs) provide great advantages in enhancing drug bioavailability and reducing side effects in anticancer therapeutics. In this study, a novel biodegradable polyphosphoester-based functional copolymer prodrug PTX-(PBYP-g-MPA)-b-PEEP was prepared to construct pH/redox dual-responsive core-crosslinked nanoparticles (DOX/CCL NPs), in which paclitaxel (PTX) was conjugated to the polyphosphoester to form an amphiphilic prodrug and doxorubicin (DOX) was encapsulated inside the prodrug NPs. At first, PTX was used as an initiator to polymerize 2-(but-3-yn-1-yloxy)-2-oxo-1,3,2-dioxaphospholane (BYP) and 2-ethoxy-2-oxo-1,3,2-dioxaphospholane (EOP) by one-pot sequential ring-opening polymerization, yielding a biodegradable polymeric prodrug PTX-PBYP-b-PEEP. Subsequently, a radical-mediated thiol-yne "click" reaction was performed between the alkynyl groups on the PBYP segment and the thiol group of 3-mercaptopropionic acid (MPA) to form a functional carboxyl group at the side chain. The potential positively charged DOX·HCl can be physically encapsulated via electrostatic interaction with the carboxyl group and hydrophobic interaction. Afterwards, the DOX/CCL NPs with cleavable disulfide (S-S) linkages can be formed by partial crosslinking through amidation between the pendant carboxyl groups and cystamine. These NPs possess multifunctional characteristics used for in vitro drug release. Notably, a redox-responsive crosslinker, cystamine dihydrochloride, and synergetic non-covalent interactions not only stabilize the nanoparticles, achieve high DOX-loading capacity of drug loading content (DLC, 14.6%) and drug loading efficiency (DLE, 73.1%), but also endow the DOX/CCL NPs with controlled drug release capacity, which is due to the cleavage of S-S bonds in the presence of 10 mM glutathione (GSH) and weakened electrostatic interaction caused by the protonation of carboxyl groups at a lower pH (5.0). Moreover, these pH/redox dual-responsive DOX/CCL NPs can be steadily internalized by HeLa cells, exhibiting high-efficiency cellular proliferation inhibition. This study presents a promising strategy for controlled intracellular drug release in cancer therapy.
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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".