Investigation of Amorphous Solid Dispersions of Poorly Water-soluble Drugs in Poly (2-hydroxyethyl Methacrylate) Hydrogels for Enhanced Solubility and Controlled Release
Bibliographic record
Abstract
The purpose of this study was to investigate the potential of applying amorphous solid dispersions (ASD) in crosslinked PHEMA hydrogels to enhance the dissolution behavior of poorly water-soluble drugs. The first part of the study identifies physicochemical properties affecting the solid state and physical stability of ASD of the model drug indomethacin (IND) in PHEMA hydrogels. The results of the second part show that ASD based on water-insoluble crosslinked PHEMA can maintain a high level of supersaturation over a prolonged duration via a diffusion-controlled feedback mechanism, thus avoiding the initial surge of supersaturation followed by a sharp decline in drug concentration, which is typically encountered with ASD based on water-soluble polymers (e.g., PVP, HPMCAS) under nonsink dissolution conditions. A subsequent study examines the effect of supersaturation generation rate on the resulting kinetic solubility profiles of amorphous pharmaceuticals and delineates the interplay between dissolution and precipitation processes from a mechanistic viewpoint. In the absence of any dissolved polymer to inhibit drug precipitation from the supersaturated state, both our experimental and predicted results confirm that the faster rise of the kinetic solubility profile of an amorphous drug will inevitably lead to an earlier but higher maximum kinetic solubility and a sharper decline in the de-supersaturation phase, and vice versa. The relationship between the achievable maximum supersaturation and the rate of supersaturation generation in the observed kinetic solubility profiles has been described for the first time by our comprehensive mechanistic model taking into account the role of supersaturation in both the nucleation and crystallization processes as well as the associated competitive particle growth and ripening effects. Finally, this theoretical framework was further employed to semi-quantitatively predict the evolution of supersaturation of amorphous pharmaceuticals generated from nonlinear dissolution profiles. The effects of initial degree of supersaturation, dissolution of amorphous drug and that from the IND-PHEMA ASD under nonsink dissolution conditions were subsequently examined in detail. The comparison of dissolution behaviors between amorphous IND and IND-PHEMA ASD demonstrates the advantage of the diffusion-controlled feedback mechanism that makes crosslinked PHEMA a unique and desirable carrier for amorphous drug delivery systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| 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 teacher head, 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".