Delivery of Hydrophobic Drugs through Self-Assembling Nanostructures
Bibliographic record
Abstract
Many potent therapeutic agents possess a high degree of hydrophobicity which can greatly impede their solubilization in aqueous media and thus hamper their oral or parenteral administration. In order to circumvent this limitation, novel drug delivery systems, such as polymeric micelles and lipid-based nanocapsules, are being developed. In general, these nano-sized carriers contain a hydrophobic core which provides the necessary environment to solubilize poorly water-soluble drugs. In addition, when administered intravenously, they can passively target inflamed or cancerous tissues due to the enhanced permeation and retention (EPR) effect, potentially improving the therapeutic efficacy of the drug while reducing its toxicity. Alternatively, the limited oral bioavailability of hydrophobic agents can be improved by selectively releasing the drug in its molecular form close to the absorption site. Polymeric micelles containing pH-sensitive moieties and loaded with a poorly water-soluble drug can dissociate and release their payload in the intestine. This presentation will focus on injectable polymeric and lipidic vectors for hydrophobic anti-cancer agents and on pH-sensitive polymeric micelles as promoters of the oral bioavailability of poorly water-soluble drugs [1].
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) | 0.000 | 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 | 0.000 | 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".