A novel temperature-controlled media milling device to produce drug nanocrystals at the laboratory scale
Bibliographic record
Abstract
Poor aqueous solubility of preexisting and emerging drug molecules is a common issue faced in the field of pharmaceutics. To address this, particle size reduction techniques, including drug micro- and nanonisation have been widely employed. Nanocrystals (NCs), drug particles with particle sizes below 1 µm, offer high drug content, improved dissolution, and long-acting capabilities. Media milling is the most used method to prepare NCs using of-the-shelf machinery, both at the laboratory and industrial scales. However, early NCs development, especially when limited amounts of the active are available, require the use of milligram-scale media milling. This study introduces a novel mini-scale milling device (Mini-mill) that incorporates temperature control through a water-cooled jacket. The device was used to produce NCs of three model hydrophobic drugs, itraconazole, ivermectin and curcumin, with lowest particle sizes of 162.5 ± 0.4 nm, 178 ± 2 nm, and 116.7 ± 0.7 nm, respectively. Precise control of milling temperature was achieved at 15, 45, and 75°C, with drug dependent particle size reduction trends, with no adverse effects on the milling materials or polymorphic changes in the NCs, as confirmed by calorimetric analysis. Finally, a scale-up feasibility study was carried out in a lab-scale NanoDisp®, confirming that the novel Mini-mills are a material-efficient tool for early formulation development, with potential for scale-up to commercial mills.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".