Effect of drug physico-chemical properties on the efficiency of top-down process and characterization of nanosuspension
Bibliographic record
Abstract
INTRODUCTION: The top-down approach is frequently used for drug nanocrystal production. A large number of review papers have referred to the top-down approach in terms of process parameters such as stabilizer selection. However, a very important factor, that is, the influence of drug properties, has been not addressed so far. AREAS COVERED: This review will first discuss different nanocrystal technologies in brief. The focus will be on reviewing the different drug properties such as solid state and particle morphology on the efficiency of particle size reduction during top-down processes. Furthermore, the drug properties in the final nanosuspensions are critical for drug dissolution velocity. Therefore, another focus is the characterization of drugs in obtained nanosuspension. EXPERT OPINION: Drug physical properties play an important role in the production efficiency. The combinative technologies using modified drugs could significantly improve the performances of top-down processes. However, further understanding of the drug millability and homogenization will still be needed. In addition, a carefully established characterization system for nansuspension is essential.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 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".