Ionic co-aggregates (ICAs) based oral drug delivery: Solubilization and permeability improvement
Bibliographic record
Abstract
Due to the overwhelming percentage of poorly water-soluble drugs, pharmaceutical industry is in urgent need of efficient approaches for solubilization and permeability improvement. Salts consisting of lipophilic fatty acid anions and hydrophilic choline cations are found to be surface active and able to form ionic co-aggregates (ICAs) in water. Choline oleate-based ICAs significantly enhance oral absorption of paclitaxel (PTX) as compared with cremophor EL-based micelles (MCs). Aggregation-caused quenching probes enable tracking of intact ICAs in in vivo transport and cellular interaction. Prolonged intestinal retention of ICAs than MCs implies stronger solubilizing capability in vivo. Ex vivo imaging of major organs and intestinal tracts suggests transepithelial transport of intact ICAs. Cellular studies support the enhanced absorption of PTX and transmembrane transport of intact ICAs. In conclusion, ICAs, consisting of lipophilic ions and hydrophilic counter-ions, are of great potential in delivery of poorly water-soluble drugs by enhancing solubility and permeability.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".