Challenges and Future Prospects of Nanoemulsion as a Drug Delivery System
Bibliographic record
Abstract
Nanoemulsion has the potential to overcome several disadvantages in drug formulation. Loading poor water-soluble drugs in the appropriate nanoemulsions enhances their wettability and/or solubility. Consequently, this improves their pharmacokinetics and pharmacodynamics by different routes of administration. Associated with the optimum nanodroplets size or even combined with key components, the droplets act as a reservoir of drugs, enabling nanoemulsion to be multifunctional platform to treat diverse diseases. A number of important advantages, which comprise nanoemulsion attributes, such as efficient drug release with appropriate rate, prolonged efficacy, drug uptake control, low side effects and drug protection properties from enzymatic or oxidative processes, have been reported in last decade. The high flexibility of nanoemulsion includes also a variety of manufacturing process options and a combination of widely assorted components such as surfactants, liquid lipids or even drug-conjugates. These features provide alternatives for designing innovative nanoemulsions aiming at high-value applications. This review presents the challenges and prospects of different nanoemulsion types and its application. The drug interaction with the components of the formulation, as well as the drug mechanistic interaction with the biological environment of different routes of administration are also presented.
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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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".