Molecular Dynamics and Physical Stability of Coamorphous Ezetimib and Indapamide Mixtures
Bibliographic record
Abstract
Low physical stability is the main reason limiting the widespread use of amorphous pharmaceuticals. One approach to overcome this problem is to mix these drugs with various excipients. In this study coamorphous drug-drug compositions of different molar ratios of ezetimib and indapamid (i.e., EZB 10:1 IDP, EZB 5:1 IDP, EZB 2:1 IDP, EZB 1:1 IDP and EZB 1:2 IDP) were prepared and investigated using differential scanning calorimetry (DSC), broadband dielectric spectroscopy (BDS), and X-ray diffraction (XRD). Our studies have shown that the easily recrystallizing ezetimib drug can be significantly stabilized in its amorphous form by using even a small amount of indapamid (8.8 wt %). DSC experiments indicate that the glass transition temperature (Tg) of the tested mixtures changes with the drug concentration in accordance with the Gordon-Taylor equation. We also investigated the effect of indapamid on the molecular dynamics of the ezetimib. As a result it was found that, with increasing indapamid content, the molecular mobility of the binary drug-drug system is slowed down. Finally, using the XRD technique we examined the long-term physical stability of the investigated binary systems stored at room temperature. These measurements prove that low-molecular-weight compounds are able to significantly improve the physical stability of amorphous APIs.
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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) | 0.000 | 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 | 0.000 |
| 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".