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Record W2158089224 · doi:10.3109/03639045.2015.1117484

Application of a hot-melt granulation process to enhance fenofibrate solid dose manufacturing

2015· article· en· W2158089224 on OpenAlexaff
R. S. Chaudhary, Edward Amankwaa, Sandeep Kumar, Tom Hu, Mohamed Chan, Pradeep Sanghvi

Bibliographic record

VenueDrug Development and Industrial Pharmacy · 2015
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug Solubulity and Delivery Systems
Canadian institutionsApotex (Canada)
Fundersnot available
KeywordsGranulationCroscarmellose sodiumMaterials scienceDifferential scanning calorimetryEutectic systemScanning electron microscopeComposite materialFenofibrateCompression (physics)Chemical engineeringChromatographyDosage formChemistryMicrostructureThermodynamics

Abstract

fetched live from OpenAlex

Evaluation of hot-melt granulation of fenofibrate and croscarmellose sodium and its cooling time for the molten mass in a ratio of 55:45 was conducted to assess the manufacturing process capability to produce an acceptable granulation which flows well on Korsch PH300 tablet compression machine. The formation of the drug-polymer eutectic mixture was investigated by differential scanning calorimetry, scanning electron microscopy and X-ray powder diffraction. The physical properties of the hot-melt was determined by examining the milled blocks after solidification and milling after cooling periods of 10, 20 and 30 d. The milled material was assessed for the effect of hold time of the blend on the solid dose compression characteristics. The impact of cooling on the processing of the blocks was assessed after 10, 20 and 30 d of cooling. The study suggests that after the hot-melt formed the fenofibrate crystallized independently and a solid solution with croscarmellose sodium was not formed. The age of the blocks determined the hardness of the crystals, changing the processing nature of the granules with respect to compression and powder flow characteristics. The blocks processed after 20 d and beyond produced granules with a characteristic suitable for holding the blend for 14 d in the bin with no impact on flow properties and compressibility of the blend. There was no chipping, capping, sticking or picking observed and a higher compression speed was achieved.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.847
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.165
GPT teacher head0.438
Teacher spread0.273 · 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.

Study designOther design
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

Citations6
Published2015
Admission routes1
Has abstractyes

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