Edible oleogels for the oral delivery of lipid soluble molecules: Composition and structural design considerations
Bibliographic record
Abstract
Background Edible oleogels, oils structured by non-triglyceride networks, can be used for the delivery of lipid-soluble molecules due to their composition, functional properties, and structure. Different oleogelators exist, including small molecules that crystallize to form colloidal or fibrillar networks and hydrophobic polymers that self-assemble under specific processing conditions. Several types of edible oleogels have been characterized, but only select systems have been used in oral delivery applications. Scope and approach This review covers the potential for use of edible oleogels for lipophilic molecular delivery. Factors affecting lipolysis relevant to oil gelation will be discussed, as well as the relationship between lipolysis and bioaccessibility. The use of lipid-based delivery systems to increase the bioaccessibility of poorly water-soluble molecules is emphasized, and oleogels are introduced as a delivery material. The review then discusses different methods of oleogelation, and addresses properties of oleogels that may be beneficial for delivery. Key findings and conclusions Oleogel structure, mechanical strength, composition, and gelator type are factors that may affect the rate and extent of lipolysis of the material. These in turn affect the delivery of lipid-soluble molecules from the oleogel. Crystalline oleogels used in oral delivery formulations have been shown to offer increased bioaccessibility, prevention against bioactive recrystallization, and targeted or delayed bioactive release. In addition, the ability to manipulate oleogel physicochemical properties with gelator type, formulation, and processing parameters is beneficial for tailoring the material functionality. Ethylcellulose oleogels are unique food-grade polymer oleogels that could be used for delivery, similar to other crystalline oleogel 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".