Dissolution Enhancement of Gliclazide Using pH Change Approach in Presence of Twelve Stabilizers with Various Physico-Chemical Properties
Bibliographic record
Abstract
PURPOSE: The micronization using milling process to enhance dissolution rate is extremely inefficient due to a high energy input, and disruptions in the crystal lattice which can cause physical or chemical instability. Therefore, the aim of the present study is to use in situ micronization process through pH change method to produce micron-size gliclazide particles for fast dissolution hence better bioavailability. METHODS: Gliclazide was recrystallized in presence of 12 different stabilizers and the effects of each stabilizer on micromeritic behaviors, morphology of microcrystals, dissolution rate and solid state of recrystallized drug particles were investigated. RESULTS: The results showed that recrystallized samples showed faster dissolution rate than untreated gliclazide particles and the fastest dissolution rate was observed for the samples recrystallized in presence of PEG 1500. Some of the recrystallized drug samples in presence of stabilizers dissolved 100% within the first 5 min showing at least 10 times greater dissolution rate than the dissolution rate of untreated gliclazide powders. Micromeritic studies showed that in situ micronization technique via pH change method is able to produce smaller particle size with a high surface area. The results also showed that the type of stabilizer had significant impact on morphology of recrystallized drug particles. The untreated gliclazide is rod or rectangular shape, whereas the crystals produced in presence of stabilizers, depending on the type of stabilizer, were very fine particles with irregular, cubic, rectangular, granular and spherical/modular shape. The results showed that crystallization of gliclazide in presence of stabilizers reduced the crystallinity of the samples as confirmed by XRPD and DSC results. CONCLUSION: In situ micronization of gliclazide through pH change method can successfully be used to produce micron-sized drug particles to enhance dissolution rate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".