Preparation of p<scp>H</scp>‐<scp>R</scp>esponsive Polymer Core–<scp>S</scp>hell Nanospheres for Delivery of Hydrophobic Antineoplastic Drug Ellipticine
Bibliographic record
Abstract
Antineoplastic drug ellipticine and its derivatives are used in human cancer therapy. However, their clinical applications have been limited by its great hydrophobicity and severe side effects. An efficient delivery system is therefore very desirable. In this research, an ellipticine-loaded core-shell structured nanosphere namely poly(DEAEMA)-poly(PEGMA) is designed as a drug carrier and prepared via a two-step semibatch emulsion polymerization method where DEAEMA and PEGMA represent 2-(diethylamino)ethyl methacrylate and poly(ethylene glycol) methacrylate, respectively. The in-vitro release profiles of ellipticine towards the different pH liposome vesicles are recorded as a function of time at 37 °C. It is found that release of ellipticine from the core-shell polymer matrix is a pH-responsive and controlled release process. The three pH's of 3, 4, and 5 trigger a significant ellipticine release of 88% after 98 h, 83% after 98 h, and 79% after 122 h, respectively. The release mechanism of ellipticine from the core-shell polymer matrix under acidic conditions is explored. The synthesis and encapsulation process developed herein provides a new perspective for the development of appropriate delivery systems to deliver the ellipticine and its analogues, as well as other types of hydrophobic drugs to a given target cell or tumor tissue.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".