Applications of Dynamic Mechanical Analysis in the Engineering of Amorphous Solid Dispersions
Bibliographic record
Abstract
Abstract Dynamic mechanical analysis (DMA) offers several advantages over prevailing methods in the characterization of amorphous solid dispersions (ASDs) typically used for improving the delivery of poorly water-soluble drugs. This method of analysis, though underutilized in the study of pharmaceutical systems, is particularly attuned to rheological investigations of thermal and mechanical properties of solids such as ASDs. Its ability to determine the viscoelastic properties of systems across a wide range of temperatures and shear conditions provides useful insight for the development and processing of ASDs. The response of materials to an imposed stress, captured by DMA, can help identify proper conditions for preparing homogenous extrudates of the polymer and active pharmaceutical ingredient through hot melt extrusion (HME). As HME continues to gain utility within the pharmaceutical industry, the ability to tailor process conditions will become increasingly important for the efficient design and production of ASD products for poorly water-soluble drugs. Furthermore, DMA can be used to probe molecular mobility and its link to physical stability of ASDs. Establishing the link between molecular mobility and crystallization kinetics is central to predicting the physical stability of ASDs. Therefore, increasing the understanding of material properties through DMA will enable the successful development of more stable amorphous drug products. This review summarizes current characterization tools for ASDs and discusses the potential of utilizing DMA as a robust alternative to traditional methods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".