Characterization and in vitro anticancer study of PEGylated liposome dually loaded with ferulic acid and doxorubicin
Bibliographic record
Abstract
Doxorubicin is an anthracycline antibiotic widely used in cancer therapy. However, its cytotoxic properties affect both cancerous and healthy cells. Combining doxorubicin with antioxidants such as ferulic acid reduces its side effects, while simultaneously enhancing therapeutic effectiveness. The low bioavailability of these drugs demonstrate that drug delivery carriers are required to enable the target site to be accessed. The doxorubicin and ferulic acid-loaded liposome composed of HSPC, Cholesterol, and DSPE-mPEG2000 (55:40:5 molar ratio) was prepared by thin film hydration method. The findings indicate that the encapsulation of ferulic acid had an impact on liposome characteristics, i.e., increasing the particle size of Lipo-DOX from 134.5 ± 4.8 nm to 154.1 ± 5.2 nm for Lipo DOX-FA, increasing the zeta potential of Lipo-DOX from − 16.04 ± 2.59 to 0.2 ± 0.0 mV for Lipo DOX-FA, and reducing the entrapment efficiency percentage of Lipo-DOX from 88.30 ± 1.89% to 85.99 ± 3.02% for Lipo DOX-FA. The infrared spectra of Lipo DOX-FA exhibited shifted absorption bands, indicating the interaction between the carboxyl group of ferulic acid and the choline polar head of phospholipid. Moreover, changes to the DSC thermogram were observed following the incorporation of ferulic acid into the liposome, while the Lipo DOX-FA exhibited a relatively rapid drug release compared to Lipo DOX suggesting a slightly shorter period necessary to attain both therapeutic efficacy and the maintenance of a stable drug encapsulation in the systemic circulation. An in vitro study of LLC and HeLa cells showed that the IC50 values of Lipo DOX-FA were 0.70 µg/mL and 1.56 µg/mL, while the CC50 value in normal HEK cells was 6.50 µg/mL. This study suggested that while co-loading FA into Lipo DOX reduced the IC50 value, indicating enhanced cytotoxicity in cancer cells, it had no effect on DOX liposome cytotoxicity in normal HEK cells.
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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".