in vitro- and in vivo Evaluation of Methotrexate-Loaded Hydrogel Nanoparticles Intended to Treat Primary CNS Lymphoma via Intranasal Administration
Bibliographic record
Abstract
PURPOSE: Although it passes through blood-brain barrier (BBB) very poorly, methotrexate (MTX) is an important therapeutic in the treatment of many central nervous system malignancies. Accordingly, intranasal (IN) administration accompanied with a muco-adhesive chitosan-based nanoformulation is expected to overcome this problem. METHODS: Nanogel containing MTX was prepared through an ionic gelation method and then characterized in terms of particle size, morphology, zeta potential, drug loading and drug release behavior. The drug release results were fitted on eight mathematical models to choose the model best describing the phenomenon. Then the nano-formulation and free drug solution in deionized water as control were administered in the nasal cavity for rats and after 15, 30, 60 and 240 minutes their brain and plasma were analyzed for MTX quantity. RESULTS: The nano-formulation demonstrated an average particle size near 100 nm with a zeta potential of 18.65±1.77 mv. Loading efficiency and loading capacity were calculated to be 65.46±7.66 and 3.02±0.34 respectively. The Weibull model was found to be best describing the release phenomenon as a combination of swelling and Fickian diffusion. Moreover in in vivo studies, drug targeting efficiency and direct transport percentage for nanogel (test) and free drug solution (control) were 424.88% and 76.46% and 34842.15% and 99.71% respectively. Conclusion: According to in vivo studies, nanogel produced significantly higher concentration of MTX in the brain but not in the plasma when compared to the free drug solution. Besides, in comparison to intravenous administration of the same nanogel it was indicated that intranasal administration significantly increases the brain concentration of MTX.
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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) | 0.001 | 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".