Telodendrimers: Promising Architectural Polymers for Drug Delivery
Bibliographic record
Abstract
Architectural complexity has played a key role in enhancing the efficacy of nanocarriers for a variety of applications, including those in the biomedical field. With the continued evolution in designing macromolecules-based nanoparticles for drug delivery, the combination approach of using important features of linear polymers with dendrimers has offered an advantageous and viable platform. Such nanostructures, which are commonly referred to as telodendrimers, are hybrids of linear polymers covalently linked with different dendrimer generations and backbones. There is considerable variety in selection from widely studied linear polymers and dendrimers, which can help tune the overall composition of the resulting hybrid structures. This review highlights the advances in articulating syntheses of these macromolecules, and the contributions these are making in facilitating therapeutic administration. Limited progress has been made in the design and synthesis of these hybrid macromolecules, and it is through an understanding of their physicochemical properties and aqueous self-assembly that one can expect to fully exploit their potential in drug delivery.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".