Poly(ethylene glycol)-poly(ε-caprolactone)-based micelles for solubilization and tumor-targeted delivery of silibinin
Bibliographic record
Abstract
Introduction: Silibinin is a naturally occurring compound with known positive impacts on prevention and treatment of many types of human illnesses in general and cancer in particular. Silibinin is poorly water soluble which results in its insufficient bioavailability and lack of therapeutic efficacy in cancer. Here, we proposed to examine the potential of micelles composed of poly(ethylene glycol) (PEG) as the hydrophilic block and poly(ε-caprolactone) (PCL), poly(α-benzylcarboxylate-ε-caprolactone) (PBCL), or poly(lactide)-(PBCL) (PLA-PBCL) as hydrophobic blocks for enhancing the water solubility of silibinin and its targeted delivery to tumor. Methods: Co-solvent evaporation method was used to incorporate silibinin into PEG-PCL based micelles. Drug release profiles were assessed using dialysis bag method. MTT assay also was used to analyze functional activity of drug delivery in B16 melanoma cells. Results: Silibinin encapsulated micelles were shown to be less than 60 nm in size. Among different structures under study, the one with PEG-PBCL could incorporate silibinin with the highest encapsulation efficiency being 95.5%, on average. PEG-PBCL micelles could solubilize 1 mg silibinin in 1 mL water while the soluble amount of silibinin was found to be 0.092 mg/mL in the absence of polymeric micelles. PEG-PBCL micelles provided the sustained release of silibinin indicated with less than 30% release of silibinin within 24 hours. Silibinin encapsulated in PEG-PBCL micelles resulted in growth inhibitory effect in B16 cancer cells which was significantly higher than what observed with free drug. Conclusion: Our findings showed that PEG-PBCL micellar nanocarriers can be a useful vehicle for solubilization and targeted delivery of silibinin.
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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".