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Record W4360604402 · doi:10.1016/j.heliyon.2023.e14796

The development of an in-situ biopolymer-based floating gel for the oral delivery of metformin hydrochloride

2023· article· en· W4360604402 on OpenAlexfundno aff
Ruedeekorn Wiwattanapatapee, Kalayamas Klabklay, Nontawat Raksajit, Worrawee Siripruekpong, Nattawut Leelakanok, Arpa Petchsomrit

Bibliographic record

VenueHeliyon · 2023
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsnot available
FundersFaculty of Pharmacy and Pharmaceutical Sciences, University of AlbertaPrince of Songkla UniversityBurapha University
KeywordsMetforminBioavailabilityBiopolymerPectinDiabetes mellitusPharmacologyDrug deliveryMedicineHydrochlorideMetformin HydrochlorideSodiumChemistryPolymerFood scienceEndocrinologyBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Diabetes remains a global public health threat because of its increasing prevalence and mortality, especially in people under the age of 25. Metformin hydrochloride (HCl), as recommended by American Diabetes Association in 2022, is the first-line therapy for type 2 diabetes in adults. Metformin has low oral bioavailability due to poor permeability. Therefore, by developing metformin HCl oral in situ gel, sustained delivery of metformin can be achieved, thus enhancing the absorption of the drug. Sodium alginate and pectin were used for formulating the system. Different adjuvant polymers, including HPMC K4M, HPMC K100 LV, PEG 4000, and SCMC were used as released-pattern-modifying agents. All formulations could afloat in 0.1 N HCl at the pH of 1.2 within a minute and stay afloat for over 8 h. The optimized formulation could be made from either sodium alginate (2%) and HPMC K4M (0.5%) or pectin (2%) and HPMC K4M (2%). The optimized formulations gradually released metformin HCl with a cumulative release of 80% within 8 h. We successfully developed floating in situ gels that can release metformin HCl sustainedly.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score0.196

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 head0.034
GPT teacher head0.289
Teacher spread0.254 · 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

Citations17
Published2023
Admission routes1
Has abstractyes

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