Dry Powder Coating using Planetary Centrifugal Mixer
Bibliographic record
Abstract
PURPOSE: Extemporaneous compounding is an important part of pharmacy practice, and should be standardized and sophisticated to ensure the quality of the compounded preparations. Recently, we applied a planetary centrifugal mixer (PCM) to powder blending, which has attracted interest for its small scale and lack of contamination. In this study, we aimed to reveal the feasibility of dry powder coating through ordered mixing of fine particles using PCM. METHODS: Cohesive lactose powders (Pharmatose450M) were dry coated with magnesium stearate (MgSt) using from 0.1 to 5%(w/w) content. The operational variables tested were operation time (1-30 min), operation speed (400-1000 rpm), vessel size (24-100 mL), and charging rate in the vessel (20-40%). The processed powders were evaluated for their surface morphology, flowability, and wettability. Furthermore, fine ibuprofen particles were coated with various lubricants, and then the dissolution profiles were examined. The crystallinity of ibuprofen was assessed using FT-IR and PXRD. RESULTS: Lactose powders were successfully coated with MgSt using PCM. When the level of MgSt was over 1%, the surface of the lactose powders was thoroughly covered. Angles of repose were 51° and 41° for unprocessed and processed powders with 1% MgSt, respectively. The contact angle of the water drop on the 1% MgSt sample leached to be 132°, changing to a hydrophobic surface. Investigations under various operational conditions revealed that higher improvement was observed upon higher speed and longer time, and a smaller charging rate in the vessel. Vessel size had no impact. Moreover, improved dissolution of ibuprofen coated with both hydrophilic and hydrophobic lubricants was observed owing to good dispersing behavior. Besides, no alteration of crystallinity was detected. CONCLUSIONS: PCM is an effective tool for dry powder coating with low impact stress. The presented method will contribute a great deal to making crushed tablets a functional powder.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".