Recent advancement of nanosponges in pharmaceutical formulation for drug delivery systems
Bibliographic record
Abstract
Through nanotechnology, nanosponges (NS) have a great impetus to develop ongoing research in drug delivery systems. In several pharmaceutical preparations, concern about the effects and regulation of transporters on drug effects can significantly contribute to our ability to predict drug variations. An ideal drug delivery system will solubilize the active medicament at the target site to decrease or cure the disease stage. NS is solid, porous tiny sponges filled with various drug molecules in their cavities for excellent drug delivery systems that play a significant role in drug delivery to specific target sites. It can improve the aqueous solubility and penurious bioavailability of drugs as they can load water and lipid-soluble drug molecules and reduce their side effects, in various dosage forms for controlled drug delivery such as oral, parenteral, topical, rectal, and inhalational dosage forms. It can also be employed as a biocatalyst carrier in drug delivery by developing drug delivery systems for enzymes, proteins, vaccines, and antibodies. The current review describes the methods of preparation of NS, types of NS, characterization, statistical design for the development of the formulation, their applications, in vitro cytotoxicity studies, recent products, patents filed in this area, and some marketed formulations of NS are all highlighted in this study.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".