pH-responsive polymer core-shell nanospheres for drug delivery
Bibliographic record
Abstract
Stimuli-responsive polymer nanoparticles are playing an increasingly more important role in drug delivery applications. However, limited knowledge has been accumulated about processes which use stimuli-responsive polymer nanospheres (matrix nanoparticles whose entire mass is solid) to carry and deliver hydrophobic therapeutics in aqueous solution. In this research, pyrene was selected as a model hydrophobic drug and a pyrene-loaded core-shell structured nanosphere named poly(DEAEMA)-poly(PEGMA) was designed as a drug carrier where DEAEMA and PEGMA represent 2-(diethylamino)ethyl methacrylate and poly(ethylene glycol) methacrylate, respectively. The pyrene-loaded core-shell nanospheres were prepared via an in situ two-step semibatch emulsion polymerization method. The particle size of the core-shell nanosphere can be well controlled through adjusting the level of surfactant used in the polymerization where an average particle diameter of below 100 nm was readily achieved. The surfactant was removed via a dialysis operation after polymerization. Egg lecithin vesicles (liposome) were prepared to mimic the membrane of a cell and to receive the released pyrene from the nanosphere carriers. The in vitro release profiles of pyrene toward different pH liposome vesicles were recorded as a function of time at 37 °C. It was found that release of pyrene from the core-shell polymer matrix can be triggered by a change in the environmental pH. In particular the pyrene-loaded nanospheres are capable of responding to a narrow window of pH change from pH = 5, 6, to 7 and can achieve a significant pyrene release of above 80% within 90 h. The rate of release increased with a decrease in pH. A first-order kinetic model was proposed to describe the rate of release with respect to the concentration of pyrene in the polymer matrix. The first-order rate constant of release k was thus determined as 0.049 h−1 for pH = 5; 0.043 h−1 for pH = 6; and 0.035 h−1 for pH = 7 at 37 °C. The release of pyrene was considered to follow a diffusion-controlled mechanism. The synthesis and encapsulation process developed herein provides a new approach to prepare smart nanoparticles for efficient delivery of hydrophobic drugs. © 2013 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2013, 51, 4440–4450
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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".