<i>In Vitro</i>Release Kinetics of Antituberculosis Drugs from Nanoparticles Assessed Using a Modified Dissolution Apparatus
Bibliographic record
Abstract
The aim of this study was to assess the in vitro release kinetics of antituberculosis drug-loaded nanoparticles (NPs) using a "modified" cylindrical apparatus fitted with a regenerated cellulose membrane attached to a standard dissolution apparatus (modifiedcylinder method). The model drugs that were used were rifampicin (RIF) and moxifloxacin hydrochloride (MX). Gelatin and polybutyl cyanoacrylate (PBCA) NPs were evaluated as the nanocarriers, respectively. The dissolution and release kinetics of the drugs from loaded NPs were studied in different media using the modified cylinder method and dialysis bag technique was used as the control technique. The results showed that use of the modified cylinder method resulted in different release profiles associated with unique release mechanisms for the nanocarrier systems investigated. The modified cylinder method also permitted discrimination between forced and normal in vitro release of the model drugs from gelatin NPs in the presence or absence of enzymatic degradation. The use of dialysis bag technique resulted in an inability to differentiate between the mechanisms of drug release from the NPs in these cases. This approach offers an effective tool to investigate in vitro release of RIF and MX from NPs, which further indicate that this technique can be used for performance testing of nanosized carrier systems.
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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) | 0.000 | 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".