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Effect of Different Meltable Binders on the Disintegration and Dissolution Behavior of Zolmitriptan Oromucosal Fast Melt Tablets

2017· article· en· W2588359579 on OpenAlexvenueno aff
Doaa Ahmed El-Setouhy, Emad B. Basalious, Nevine Shawky Abdelmalak

Bibliographic record

VenueJournal of Pharmacy and Nutrition Sciences · 2017
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug Solubulity and Delivery Systems
Canadian institutionsnot available
Fundersnot available
KeywordsZolmitriptanFriabilityGranulationBioavailabilityCroscarmellose sodiumBaicalinPoloxamerOnset of actionPharmacologyPolyethylene glycolFirst pass effectDissolutionAbsorption (acoustics)ChemistryChromatographyMaterials scienceDosage formMedicineHigh-performance liquid chromatographyPolymerOrganic chemistry

Abstract

fetched live from OpenAlex

Objective: Fast melt tablets and sublingual route have been widely used for providing quick onset of action with the avoidance of first pass metabolism. The objective of this work was to compare the effect of different meltable binders namely; polyethylene glycol (PEG) 4000, pluronic F127 and pluronic F68 on the performance of fast release tablets of the model drug zolmitriptan prepared using the melt granulation technique regarding disintegration time (DT) and dissolution rate (DR) as criteria for rapid absorption and hence quick onset of action. Zolmitriptan is a potent antimigraine drug. Current oral zolmitriptan tablets suffer from slow onset of action, poor bioavailability and large inter-subject variability. Methods: 33 factorial design was adopted. The effect of binder type, binder concentration and croscarmellose sodium (disintegrant) concentration were studied on DT and DR. Results: The three factors were found to significantly affect the DR and the inverse square root of DT and significant interactions were elucidated. Conclusion: Although satisfactory results were obtained regarding DR, modifications using different excipients and or preparation methods should be considered to comply with pharmacopoeia requirement for DT.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.281
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.116
GPT teacher head0.450
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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