Anti-inflammatory drug nanocrystals: state of art and regulatory perspective
Bibliographic record
Abstract
Anti-inflammatory drugs have been prescribed extensively for a wide range of diseases. Combined with over-the-counter use, approximately 30 billion doses of non-steroidal inflammatory drugs (NSAIDs) are consumed annually in the USA. The global market of glucocorticoids (GCs) is forecast to reach US$ 8.6 billion by 2025. Severe adverse effects have been reported for NSAIDs, GCs, and COX-2 selective NSAIDs (COXIBs). Furthermore, the overwhelming majority of these drug substances are BCS class II, which limits their bioavailability due to poor water solubility. Drug nanocrystals, a carrier-free nanosystem, can increase saturation solubility, dissolution rate, and the mucoadhesiveness of these drugs. The enhancement of these properties was highlighted in our findings. These features improve the efficacy and safety of anti-inflammatory drugs. In this review, we show that drug nanocrystals are an attractive strategy that contributes to an important shift in the development of innovative products for different routes of administration. The possibility of targeting can minimize the adverse effects and improve the efficacy in the management of inflammatory conditions. We comprehensively review the critical quality attributes (CQAs) in the anti-inflammatory drug nanocrystals preparation, which are fundamental to developing a successful marketable product. Despite the advantages, maintaining properties such as average particle size, surface properties, and physicochemical stability of these preparations during shelf life poses challenges to be overcome.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".