Bile Acid-Based Drug Delivery Systems for Enhanced Doxorubicin Encapsulation: Comparing Hydrophobic and Ionic Interactions in Drug Loading and Release
Bibliographic record
Abstract
Doxorubicin (Dox) is a drug of choice in the design of drug delivery systems directed toward breast cancers, but is often limited by loading and control over its release from polymer micelles. Bile acid-based block copolymers present certain advantages over traditional polymer-based systems for drug delivery purposes, since they can enable a higher drug loading via the formation of a reservoir through their aggregation process. In this study, hydrophobic and electrostatic interactions are compared for their influence on Dox loading inside cholic acid based block copolymers. Poly(allyl glycidyl ether) (PAGE) and poly(ethylene glycol) (PEG) were grafted from the cholic acid (CA) core yielding a star-shaped block copolymer with 4 arms (CA-(PAGE- b -PEG) 4 ) and then loaded with Dox via a nanoprecipitation technique. A high Dox loading of 14 wt % was achieved via electrostatic as opposed to hydrophobic interactions with or without oleic acid as a cosurfactant. The electrostatic interactions confer a pH responsiveness to the system. 50% of the loaded Dox was released at pH 5 in comparison to 12% at pH 7.4. The nanoparticles with Dox loaded via hydrophobic interactions did not show such a pH responsiveness. The systems with Dox loaded via electrostatic interactions showed the lowest IC 50 and highest cellular internalization, indicating the pre-eminence of this interaction in Dox loading. The blank formulations are biocompatible and did not show cytotoxicity up to 0.17 mg/mL. The new functionalized star block copolymers based on cholic acid show great potential as drug delivery carriers.
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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 | 0.001 |
| 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".