Controlled Release of Doxorubicin from Doxorubicin/<i>γ</i>‐Polyglutamic Acid Ionic Complex
Bibliographic record
Abstract
Formation of drug/polymer complexes through ionic interactions has proven to be very effective for the controlled release of drugs. The stability of such drug/polymer ionic complexes can be greatly influenced by solution pH and ionic strength. The aim of the current work was to evaluate the potential ofγ‐polyglutamic acid (γ‐PGA) as a carrier for the anticancer drug, Doxorubicin (DOX). We investigated the formation of ionic complexes betweenγ‐PGA and DOX using scanning electron microscopy, spectroscopy, thermal analysis, and X‐ray diffraction. Our studies demonstrate that DOX specifically interacts withγ‐PGA forming random colloidal aggregates and results in almost 100% complexation efficiency. In vitro drug release studies illustrated that these complexes were relatively stable at neutral pH but dissociates slowly under acidic pH environments, facilitating a pH‐triggered release of DOX from the complex. Hydrolytic degradation ofγ‐PGA and DOX/γ‐PGA complex was also evaluated in physiological buffer. In conclusion, these studies clearly showed the feasibility ofγ‐PGA to associate cationic drug such as DOX and that is may serve as a new drug carrier for the controlled release of DOX in malignant tissues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".