Self‐Assembling Peptide as a Potential Carrier for Hydrophobic Anticancer Drug Ellipticine: Complexation, Release and In Vitro Delivery
Bibliographic record
Abstract
Abstract The self‐assembling peptide EAK16‐II is capable of stabilizing hydrophobic compounds to form microcrystal suspensions in aqueous solution. Here, the ability of this peptide to stabilize the hydrophobic anticancer agent ellipticine is investigated. The formation of peptide‐ellipticine suspensions is monitored with time until equilibrium is reached. The equilibration time is found to be dependent on the peptide concentration. When the peptide concentration is close to its critical aggregation concentration, the equilibration time is minimal at 5 h. With different combinations of EAK16‐II and ellipticine concentrations, two molecular states (protonated or cyrstalline) of ellipticine could be stabilized. These different states of ellipticine significantly affect the release kinetics of ellipticine from the peptide‐ellipticine complex into the egg phosphatidylcholine vesicles, which are used to mimic cell membranes. The transfer rate of protonated ellipticine from the complex to the vesicles is much faster than that of crystalline ellipticine. This observation may also be related to the size of the resulting complexes as revealed from the scanning electron micrographs. In addition, the complexes with protonated ellipticine are found to have a better anticancer activity against two cancer cell lines, A549 and MCF‐7. This work forms the basis for studies of the peptide‐ellipticine suspensions in vitro and in vivo leading to future development of self‐assembling peptide‐based delivery of hydrophobic anticancer drugs.
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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".