Formulation, Optimization, and Invitro Characterization of Lipid-Based Nanoparticles for Effective Delivery to The Liver
Bibliographic record
Abstract
Chronic liver disorders are the major causes of illness and mortality worldwide.Patients with chronic liver diseases have a greater chance of developing cirrhosis, hepatocellular carcinoma, progressive liver fibrosis, and subsequently liver failure.Currently there are no effective treatments available for patients with the various kinds of liver diseases.The use of nanotechnology is considered a rapidly growing field of interest for the safe and targeted delivery of insufficiently water-insoluble hepatoprotective drugs.Therefore, the nanoparticle combination improves bioavailability and plasma stability of drugs with poor aqueous solubility.Thus, this study aims at developing chemically and physically stable Fenretinide loaded solid lipid nanoparticles (FEN-SLNs) for successful delivery to the liver.The nanoencapsulation of FEN in Gelucire-based, surfactant-free SLNs was developed.SLNs were characterized in terms of physicochemical properties, surface morphology, drug loading, release behavior as well as in vivo biodistribution study.The results showed that adopting hot homogenization method for preparation of FEN loaded solid lipid nanoparticles using Gelucire 50/13 and Precirol provided chemically and physically stable FEN-SLNs.Further, the optimized FEN-SLNs has particle size 298.3 ± 2.54 and PDI 0.3 with negative zeta potential -15.2 ± 3.61 mV, and Entrapment efficiency exceeding 92%.Furthermore, in vitro release experiment ensured sustained release of FEN over > 24 h with no signs of degradation.In addition, TEM photomicrographs showed spherical particles.Noteworthy, the in vivo biodistribution results showed that fluorescently labeled SLNs retained in the liver for 8h with diminished migration to the other organs unlike the free dye.In conclusion the study highlights the effective encapsulation of FEN and effective delivery to the liver.
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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) | 0.000 | 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".