Biocompatible Lipid Nanoparticles as Carriers To Improve Curcumin Efficacy in Ovarian Cancer Treatment
Bibliographic record
Abstract
Curcumin is a natural molecule with proved anticancer efficacy on several human cancer cell lines. However, its clinical application has been limited due to its poor bioavailability. Nanocarrier-based drug delivery approaches could make curcumin dispersible in aqueous media, thus overtaking the limits of its low solubility. The aim of this study was to increase the bioavailability and the antitumoral activity of curcumin, by entrapping it into nanostructured lipid carriers (NLCs). For this purpose here we describe the preparation and characterization of three kinds of curcumin-loaded NLCs. The nanosystems allowed the achievement of a controlled release of curcumin, the amounts of curcumin released after 24 h from Compritol-Captex, Compritol-Miglyol, and Compritol NLCs being, respectively, equal to 33, 28, and 18% w/w on the total entrapped curcumin. Considering the slower curcumin release profile, Compritol NLCs were chosen to perform successive in vitro studies on ovarian cancer cell lines. The results show that curcumin-loaded NLCs maintain anticancer activity, and reduce cell colony survival more effectively than free curcumin. As an example, the ability of A2780S cells to form colonies was decreased after treatment with 5 μM free curcumin by 50% ± 6, whereas, at the same concentration, the delivery of curcumin with NLC significantly (p < 0.05) inhibited colony formation to approximately 88% ± 1, therefore potentiating the activity of curcumin to inhibit A2780S cell growth. The obtained results clearly suggest that the entrapment of curcumin into NLCs increases curcumin efficacy in vitro, indicating the potential use of NLCs as curcumin delivery systems.
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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".