Polymeric Lipid Hybrid Nanoparticles: Properties and Therapeutic Applications
Bibliographic record
Abstract
Some of the criteria for selection of a viable nanocarrier formulation currently being explored are the development of a site-specific and bioavailable formulation. Although the literature reports a variety of techniques for fabrication of nanocarrier systems, their stability and scale-up issues are a concern for their prominence in the pharmaceutical industry. The other widely recognized drawbacks of nanoparticulates, i.e., polymeric nanoparticles and lipid vesicular nanoparticles (liposomes), are low circulatory half-lives due to reticuloendothelial system (RES) uptake and leaky architecture leading to burst kinetics. Polymeric lipid hybrid nanoparticles (PLHNs) or lipomers are the recent advancement in nanodrug delivery systems composed of a polymeric core and lipid shell which imparts physicochemical stability and biocompatibility to the nanoparticles. The lipomers are a blend of positive attributes of both liposomes and polymeric nanoparticles wherein their individual innate flaws are negated. An extensive study of PLHN was engineered using single/two or multiple methods carried out for encapsulation efficiency, physicochemical properties, and stability. The influence of shape and composition of PLHN has also shown promising results in terms of reticuloendothelial uptake. These PLHNs have shown to hold a promising place in designing drug delivery systems for the treatment of cancer and infectious diseases as well as for theranostic purposes. The present review article encompasses various types of PLHNs, their physicochemical characteristics, and their applications as future perspectives in strategizing drug delivery to their desired sites of action.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".