Oleosome Delivery Systems: Enhancing Stability and Therapeutic Potential of Natural Products and Xenobiotics
Bibliographic record
Abstract
Oleosomes are submicron oil bodies of a triacylglycerol core enveloped by a phospholipid monolayer and embedded proteins, forming a naturally assembled nanocarrier with exceptional oxidative resilience, interfacial stability, and biocompatibility. Their unique architecture supports solvent-free extraction, self-emulsification, and near-complete encapsulation of highly lipophilic compounds (log P > 4), including curcumin and cannabidiol, with reported efficiencies exceeding 95%. These plant-derived droplets enhance oral bioavailability through lymphatic uptake and enable targeted delivery strategies such as magnetically guided chemotherapy, which has reduced tumor burden by approximately 70% in vivo. The review critically examines recent advances in oleosome research, spanning botanical sourcing, green extraction technologies, interfacial engineering, xenobiotic encapsulation, pharmacokinetics, and therapeutic applications across oncology, dermatology, metabolic disease, and regenerative medicine. Comparative analyses demonstrate that oleosomes rival or surpass synthetic lipid nanocarriers in encapsulation efficiency, oxidative stability, and cost efficiency while offering a sustainable, clean-label alternative. Remaining challenges, including low loading of hydrophilic drugs, allergenicity, and regulatory standardization, are addressed through emerging strategies such as hybrid oleosome-liposome systems, recombinant oleosin engineering, and stimulus-responsive coatings. These advances position oleosomes as a versatile and scalable platform with significant potential for food, cosmetic, and pharmaceutical applications.
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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 | 0.001 |
| 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".