Highly water-soluble dapsone nanocrystals: Towards innovative preparations for an undermined drug
Bibliographic record
Abstract
Dapsone (DAP)is a dual-function drug substance; however, its limited water solubility may impair its bioavailability. Drug nanocrystals are an alternative to overcome this limitation. Herein, a DAP nanosuspension was prepared using adesign space approach aiming to investigate the influence of raw material properties and process parameters on the critical quality attributes of the drugnanocrystals. Optimized nanocrystals with 206.3 ± 6.7 nm using povacoat™ as stabilizer were made. The nanoparticles were characterized by dynamic light scattering, laser diffraction, scanning electron microscopy, differential scanning calorimetry, X-ray powder diffraction, and saturation solubility. Compared to the raw material, the nanocrystals were 250-times smaller. Meanwhile, its crystalline state remained basically unchanged even after milling and drying. The nanosuspension successfully maintained its physical stability inlong-termandaccelerated stability studiesover, 4 and 3 months. Furthermore, toxicity studiesshowed low a toxicity at a20 mg/kg. As expected for nanocrystals, the size reduction improvedsaturation solubility3.78 times in water. An attempt to scale up from lab to pilot scale resulted nanocrystals of potential commercial quality. In conclusion, the present study describes the development of dapsone nanocrystals for treating infectious and inflammatory diseases. The nanocrystal formuation can be scaled up for commercial use.
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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".