Nanoengineering Branched Star Polymer‐Based Formulations: Scope, Strategies, and Advances
Bibliographic record
Abstract
Soft nanoparticles continue to offer a promising platform for the encapsulation and controlled delivery of poorly water-soluble drugs and help enhance their bioavailability at targeted sites. Linear amphiphilic block copolymers are the most extensively investigated in formulating delivery vehicles. However, more recently, there has been increasing interest in utilizing branched macromolecules for nanomedicine, as these have been shown to lower critical micelle concentrations, form particles of smaller dimensions, facilitate the inclusion of varied compositions and function-based entities, as well as provide prolonged and sustained release of cargo. In this review, it is aimed to discuss some of the key variables that are studied in tailoring branched architecture-based assemblies, and their influence on drug loading and delivery. By understanding structure-property relationships in these formulations, one can better design branched star polymers with suitable characteristics for efficient therapeutic interventions. The role played by polymer composition, chain architecture, crosslinking, stereocomplexation, compatibility between polymers and drugs, drug/polymer concentrations, and self-assembly methods in their performance as nanocarriers is highlighted.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".