Mucopenetrating and Mucoadhesive Formulations for the Oral Delivery of Satiety-Enhancing Ingredients
Bibliographic record
Abstract
Obesity and linked diseases, such as cardiovascular diseases, have risen among the top health problems in many countries. The existing low-calorie products have been unsuccessful in aiding large masses to maintain a healthy body mass index. Understanding of biological mechanisms behind the body’s satiation processes combined with modern nanotechnology offer new possibilities to control the food intake via enhancing the feeling of satiety artificially. This can be done, in principle, by incorporating biologically active compounds into food beverages, which initiate the specific signaling cascades that lead to the feeling of satiety. Oral delivery of such compounds demands, however, careful formulation, due to the numerous physical and chemical barriers along the way. One such barrier is the intestinal mucus layer. A constant secretion of this viscous glycopeptide gel protects the underlying epithelium but hinders the penetration of orally administered bioactives. Here, a novel formulation for the oral delivery of satiety-enhancing ingredients was designed, by encapsulating nanoparticles capable of penetrating through the mucus layer into a micro-sized alginate-based carrier with mucoadhesive properties. To this end, three different nanoparticles including poly(ethylene glycol) -based micelles, zwitterionic micelles and polymer-lipid hybrid vesicles were assembled and characterized in terms of size, shape, surface charge and cargo-loading capacity. The nanoparticles were further encapsulated into mucoadhesive alginate beads with the aim to increase the residence time in the intestine. The stability of the formulation was evaluated in simulated gastric and intestinal fluids, showing negligible subunit leakage under gastric conditions but a substantial release under the intestinal conditions. Furthermore, in vivo studies revealed the separation of the nanoparticles from the alginate carrier in small intestine of the rats after oral administration. The ability of the free and encapsulated nanoparticles to diffuse in mucus was confirmed in a mucus covered cell culture using confocal laser scanning microscopy. Finally, rosemary extract was chosen as a potential satiety-enhancing ingredient and its ability to stimulate bitter taste receptors was confirmed in vitro. In conclusion, the presented work lays a foundation for the assembly of a novel oral formulation for the oral delivery of satiety-enhancing ingredients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".