Evaluation of Brain Targeting and Mucosal Integrity of Nasally Administrated Nanostructured Carriers of a CNS Active Drug, Clonazepam
Bibliographic record
Abstract
PURPOSE: The aim of the study was to target clonazepam, a CNS active drug, to the brain through the non-invasive intranasal (in) route using of nanocarriers with proven safety METHOD: in clonazepam nanocarriers were prepared by mixing isopropyl myristate, Tween 80, Cremophor EL or lecithin, polyethylene glycol 200, propylene glycol or ethanol in different ratios with water. in-vitro characterization of the nanocarriers was done by various methods including: polarized light microscopy, particle size determination, viscosity measurements and drug release studies. in-vivo study comparing intranasal and intravenous administration was performed. The drug targeting efficiency (DTE %) and direct nose to brain transport percentage (DTP %) were calculated and nasal integrity assessment was carried out. RESULTS: The obtained formulae had particle size below 100 nm favoring rapid direct nose to brain transport and the time for 100% drug release (T100%) depended on systems composition. Plasma Tmax of clonazepam nanostructured carriers varied from 10-30 min., while their brain Tmax did not exceed 10 min, in comparison with 30 min for iv solution. Although there was no significant difference (p>0.05) between the plasma AUC0-∞ of the different tested nanocarriers and intravenous one, the increase in brain AUC 0 -∞ of different nasal formulations in comparison to that of iv administration (3.6 -7.2 fold) confirms direct nose to brain transport via olfactory region. Furthermore, DTE and DTP% confirmed brain targeting of clonazepam following intranasal administration. CONCLUSION: The results confirmed that intranasal nanocarriers were proved to be safe alternative for iv clonazepam delivery with rapid nose to brain transport.
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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) | 0.000 | 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".