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Record W2151475276 · doi:10.18433/j33k5n

Design and evaluation of a novel floating osmotic pump system

2009· article· en· W2151475276 on OpenAlexvenueno aff
Zhihong Zhang, Bo Peng, Xinggang Yang, Chao Wang, Guangmei Sun, Weisan Pan

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2009
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug Solubulity and Delivery Systems
Canadian institutionsnot available
Fundersnot available
KeywordsIn vivoDipyridamoleChemistryBeagleDissolution testingChromatographyPharmacologyOral administrationDrugMedicineBiologyInternal medicineBiopharmaceutics Classification System

Abstract

fetched live from OpenAlex

PURPOSE: Find a novel delivery system for oral administration of drugs that have absorption window in the upper part of gastrointestinal (GI) track. METHODS: Dipyridamole was chosen as the model drug. A novel system, which combined the osmotic pump controlled release system and the floating system, was designed; matrix tablets (MT) were prepared for compares. The effects of pH, temperature and hydrodynamic conditions on drug release and the floating behavior of floating osmotic pump system (FOP) were investigated. In vivo evaluation was performed by a three-crossover study in six Beagle dogs relative to the conventional tablet (CT). Cumulative percent input in vivo was compared with that of in vitro release profiles. RESULTS: Floating behavior of FOP, drug releases from FOP and MT were sensitive to pH of dissolution media but not sensitive to temperature; the release of dipyridamole from MT was influenced by stirring rate while drug release from FOP was not. AUC of FOP was larger than MT and CT. The linear correlations between fraction absorbed in vivo and fraction dissolved in vitro was established for FOP-a true zero-order release formula, whereas only a nonlinear correlation was obtained for MT. CONCLUSIONS: FOP could be a novel way for the oral administration for drugs like dipyridamole.

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)0.018
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.766
Threshold uncertainty score0.906

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.396
GPT teacher head0.519
Teacher spread0.123 · 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 designSimulation or modeling
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

Citations12
Published2009
Admission routes1
Has abstractyes

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