Anti‐solvent crystallization for the preparation of drug nanocrystals via amphiphilic copolymer ( <scp>AcMH</scp> ‐b‐ <scp>PEG</scp> ) micelles as soft templates
Bibliographic record
Abstract
Abstract Nanometerization of drug particles as a well‐regarded approach can enhance the solubility and bioavailability of water‐insoluble drugs. (R)‐Equol is frequently employed in research for its ability to inhibit the replication of cancer cells, but the application of (R)‐equol is still limited by its water‐insoluble nature. Novel biocompatible polymeric micelles were prepared through the self‐assembly of amphiphilic block copolymers composed of acetylated maltoheptose‐b‐polyethylene glycol (AcMH‐b‐PEG) in water. Using AcMH‐b‐PEG micelles as templates, (R)‐equol nanocrystals with good crystallinity and regular morphology were prepared by a micelle‐mediated anti‐solvent crystallization method. These nanocrystals were identified as an isolated‐site hydrate form of (R)‐equol, stabilized by a robust three‐dimensional hydrogen‐bonding network and distinct from those obtained via conventional methods. In vitro release experiment showed that AcMH‐b‐PEG micelle exhibited a sustained‐release effect on (R)‐equol, with the drug being released slowly and continuously. In mouse plasma, the cumulative release reached 65.70% over 24 h. In particular, it was found that (R)‐equol was more readily released under acidic milieus. This study demonstrated that using amphiphilic micelles as templates for anti‐solvent crystallization represented a potent and robust approach to prepare crystalline nanodrugs.
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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 | 0.000 | 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) | 0.000 | 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".