Microwave-Induced In Situ Amorphization: A New Strategy for Tackling the Stability Issue of Amorphous Solid Dispersions
Bibliographic record
Abstract
The thermodynamically unstable nature of amorphous drugs has led to a persistent stability issue of amorphous solid dispersions (ASDs). Lately, microwave-induced in situ amorphization has been proposed as a promising solution to this problem, where the originally loaded crystalline drug is in situ amorphized within the final dosage form using a household microwave oven prior to oral administration. In addition to circumventing issues with physical stability, it can also simplify the problematic downstream processing of ASDs. In this review paper, we address the significance of exploring and developing this novel technology with an emphasis on systemically reviewing the currently available literature in this pharmaceutical arena and highlighting the underlying mechanisms involved in inducing in situ amorphization. Specifically, in order to achieve a high drug amorphicity, formulations should be composed of drugs with high solubility in polymers, as well as polymers with high hygroscopicity and good post-plasticized flexibility of chains. Furthermore, high microwave energy input is considered to be a desirable factor. Lastly, this review discusses challenges in the development of this technology including chemical stability, selection criteria for excipients and the dissolution performance of the microwave-induced 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 | 0.001 | 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) | 0.002 | 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".