Crosslinked hydrogels—a promising class of insoluble solid molecular dispersion carriers for enhancing the delivery of poorly soluble drugs
Bibliographic record
Abstract
Water-insoluble materials containing amorphous solid dispersions (ASD) are an emerging category of drug carriers which can effectively improve dissolution kinetics and kinetic solubility of poorly soluble drugs. ASDs based on water-insoluble crosslinked hydrogels have unique features in contrast to those based on conventional water-soluble and water-insoluble carriers. For example, solid molecular dispersions of poorly soluble drugs in poly(2-hydroxyethyl methacrylate) (PHEMA) can maintain a high level of supersaturation over a prolonged period of time via a feedback-controlled diffusion mechanism thus avoiding the initial surge of supersaturation followed by a sharp decline in drug concentration typically encountered with ASDs based on water-soluble polymers. The creation of both immediate- and controlled-release ASD dosage forms is also achievable with the PHEMA based hydrogels. So far, ASD systems based on glassy PHEMA have been shown to be very effective in retarding precipitation of amorphous drugs in the solid state to achieve a robust physical stability. This review summarizes recent research efforts in investigating the potential of developing crosslinked PHEMA hydrogels as a promising alternative to conventional water-soluble ASD carriers, and a related finding that the rate of supersaturation generation does affect the kinetic solubility profiles implications to hydrogel based ASDs.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".