Oral delivery of solid lipid nanoparticles: underlining the physicochemical characteristics and physiological condition affecting the lipolysis rate
Bibliographic record
Abstract
INTRODUCTION: Lipid-based nano-drug delivery systems (LBNDDSs) have gained widespread attention in oral drug delivery due to their tunable and versatile properties such as biocompatibility and biodegradability, which makes them promising delivery systems for a variety of therapeutics. Currently, different types of LBNDDSs including liposomes, micelles, nanoemulsions, and solid lipid nanoparticles (SLNs) are developed for drug delivery applications. SLNs can be used as a controlled drug delivery system for oral delivery applications. However, its lipidic context makes that susceptible to lipolysis. The lipolysis rate of SLNs is affected by many factors that raise many questions for developing a more efficient delivery system. AREAS COVERED: In the present work, we highlighted different factors affecting the digestion rate/level of SLNs in the gastrointestinal tract. This paper can be most useful for those researchers who are keen to develop a properly controlled drug delivery system based on SLNs for oral delivery applications. EXPERT OPINION: SLNs can be used as a controlled drug delivery system for oral delivery applications. However, its lipidic context makes that susceptible to lipolysis. The lipolysis rate of SLNs is affected by many factors that raise many questions for developing a more efficient delivery system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".