Thermodynamic and Structural Characterization of a Mechanochemically Synthesized Pyrazinamide–Acetylsalicylic–Acid Eutectic Mixture
Bibliographic record
Abstract
Background/Objectives: This study aims to develop a sustainable and environmentally friendly drug delivery system by synthesizing a novel drug–drug eutectic mixture (DDEM) of acetylsalicylic acid (ASA) and pyrazinamide (PZA) using a green and efficient mechanochemical approach. Methods: The DDEM was characterized using various techniques, including differential scanning calorimetry (DSC), thermogravimetry and differential thermal analysis (TG-DTA), powder X-ray diffraction (PXRD), Fourier transform infrared spectroscopy (FT-IR), and Raman spectroscopy. Binary phase diagrams and Tammann’s triangle analysis determined the eutectic point. Density functional theory (DFT) calculations were performed on the starting compounds. The new system was evaluated for aqueous solubility, dissolution, and hygroscopicity. Results: A V-shaped binary phase diagram indicated the formation of a DDEM with a 2:1 molar ratio of ASA to PZA. A positive mixing enthalpy suggested a quasi-eutectic structure. The solubility of ASA and PZA increased by 61.5% and 85.8%, respectively, in the DDEM compared to the pure drugs. Conclusions: These findings highlight the potential of DDEMs to enhance drug properties and delivery. The synergistic interaction between ASA and PZA in the eutectic mixture may further improve therapeutic efficacy, warranting further investigation.
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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 | 0.000 |
| 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 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".