Stimuli‐responsive zein‐based nanoparticles as a potential carrier for ellipticine: Synthesis, release, and in vitro delivery
Bibliographic record
Abstract
In the present research, we have investigated a drug delivery system based on the pH‐responsive behaviors of zein colloidal nanoparticles coated with sodium caseinate (SC) and poly ethylene imine (PEI). These systematically designed nanoparticles were used as nanocarriers for encapsulation of ellipticine (EPT), as an anticancer drug. SC and PEI coatings were applied through electrostatic adsorption, leading to the increased size and improved polydispersity index of nanoparticles as well as sustained release of drug. Physicochemical characteristics such as hydrodynamic diameter, size distribution, zeta potential and morphology of nanoparticles prepared using different formulations and conditions were also determined. Based on the results, EPT was encapsulated into the prepared nanoparticles with a high drug loading capacity (5.06%) and encapsulation efficiency (94.8%) under optimal conditions. in vitro experiments demonstrated that the release of EPT from zein‐based nanoparticles was pH sensitive. When the pH level decreased from 7.4 to 5.5, the rate of drug release was considerably enhanced. The mechanism of pH‐responsive complexation in the drug encapsulation and release processes was extensively investigated. The pH‐dependent electrostatic interactions and drug state were hypothesized to affect the release profiles. Compared to the EPT‐loaded zein/PEI nanoparticles, the EPT‐loaded zein/SC nanoparticles exhibited a better drug sustained‐release profile, with a smaller initial burst release and longer release period. According to the results of in vitro cytotoxicity experiments, drug‐free nanoparticles were associated with a negligible cytotoxicity, whereas the EPT‐loaded nanoparticles displayed a high toxicity for the cancer cell line, A549. Our findings indicate that these pH‐sensitive protein‐based nanoparticles can be used as novel nanotherapeutic tools and potential antineoplastic drug carriers for cancer chemotherapy with controlled release.
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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".