Properties, Engineering and Applications of Lipid-Based Nanoparticle Drug-Delivery Systems: Current Research and Advances
Bibliographic record
Abstract
Lipid-based drug-delivery systems have evolved from micro- to nano-scale, enhancing the efficacy and therapeutic applications of these delivery systems. Production of lipid-based pharmaceutical nanoparticles is categorized into top-down (fragmentation of particulate material to reduce its average total dimensions) and bottom-up (amalgamation of molecules through chemical interactions creating particles of greater size) production methods. Selection of the appropriate method depends on the physiochemical properties of individual entities within the nanoparticles. The production method also influences the type of nanoparticle formulations being produced. Liposomal formulations and solid-core micelles are the most widely utilized lipid-based nanoparticles, with surface modifications improving their therapeutic outcomes through the production of long-circulating, tissue-targeted and/or pH-sensitive nanoparticles. More recently, solid lipid nanoparticles have been engineered to reduce toxicity toward mammalian cells, while multifunctional lipid-based nanoparticles (i.e., hybrid lipid nanoparticles) have been formulated to simultaneously perform therapeutic and diagnostic functions. This article will discuss novel lipid-based drug-delivery systems, outlining the properties and applications of lipid-based nanoparticles alongside their methods of production. In addition, a comparison between generations of the lipid-based nano-formulations is examined, providing insight into the current directions of lipid-based nanoparticle drug-delivery systems.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".