Evolution of Supersaturation of Amorphous Pharmaceuticals: The Effect of Rate of Supersaturation Generation
Bibliographic record
Abstract
The combination of a rapidly dissolving and supersaturating "spring" with a precipitation retarding "parachute" has often been pursued as an effective formulation strategy for amorphous solid dispersions (ASDs) to enhance the rate and extent of oral absorption. However, the interplay between these two rate processes in achieving and maintaining supersaturation remains inadequately understood, and the effect of rate of supersaturation buildup on the overall time evolution of supersaturation during the dissolution of amorphous solids has not been explored. The objective of this study is to investigate the effect of supersaturation generation rate on the resulting kinetic solubility profiles of amorphous pharmaceuticals and to delineate the evolution of supersaturation from a mechanistic viewpoint. Experimental concentration-time curves under varying rates of supersaturation generation and recrystallization for model drugs, indomethacin (IND), naproxen (NAP) and piroxicam (PIR), were generated from infusing dissolved drug (e.g., in ethanol) into the dissolution medium and compared with that predicted from a comprehensive mechanistic model based on the classical nucleation theory taking into account both the particle growth and ripening processes. In the absence of any dissolved polymer to inhibit drug precipitation, both our experimental and predicted results show that the maximum achievable supersaturation (i.e., kinetic solubility) of the amorphous solids increases, the time to reach maximum decreases, and the rate of concentration decline in the de-supersaturation phase increases, with increasing rate of supersaturation generation (i.e., dissolution rate). Our mechanistic model also predicts the existence of an optimal supersaturation rate which maximizes the area under the curve (AUC) of the kinetic solubility concentration-time profile, which agrees well with experimental data. In the presence of a dissolved polymer from ASD dissolution, these observed trends also hold true except the de-supersaturation phase is more extended due to the crystallization inhibition effect. Since the observed kinetic solubility of nonequilibrium amorphous solids depends on the rate of supersaturation generation, our results also highlight the underlying difficulty in determining a reproducible solubility advantage for amorphous solids.
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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 | 0.001 |
| 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 | 0.001 |
| 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".