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Record W2967943501 · doi:10.25082/jpbr.2019.02.005

Developments in encapsulation of insulin: Is oral delivery now possible?

2019· article· en· W2967943501 on OpenAlexaff
Anubhav Pratap‐Singh, Yigong Guo, Anika Singh, Wen Xie, Patrick Jiang

Bibliographic record

VenueJournal of Pharmaceutical and Biopharmaceutical Research · 2019
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvanced Drug Delivery Systems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInsulinBioavailabilityTransdermalLiposomeInsulin deliveryPharmacologyChemistryMedicineDiabetes mellitusInternal medicineBiochemistryEndocrinologyType 1 diabetes

Abstract

fetched live from OpenAlex

This review presents the possibilities of oral delivery of insulin. Insulin, being readily destroyed/ transformed by the proteolytic enzymes and first-pass effects in the digestive system, has mainly been administered through injection, such as intravenous injection and transdermal injection. With developments in the material sciences, appropriate encapsulation methodologies have been developed that could be employed to protect insulin from the digestive effects of the human GI system, and thereby have opened a gateway of research exploring the oral route of insulin delivery. One approach is to incorporate insulin into an emulsion with an appropriate oil-phase, which protects the insulin from degradation. Coating with natural or synthetic polymeric materials, or with lipids, followed by size-reduction to 100-1000 nm is applied as another common approaches of insulin encapsulation. Other approaches like liposomes, nanogels, etc. are also being explored. This review gives a summary of methods of preparation as well as in vitro and in vivo bioavailability of insulin through these methods. It is observed that the oral bioavailability of insulin intake has increased from about 0.1% to about 20% for encapsulated insulin.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
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 score0.387
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.281
GPT teacher head0.540
Teacher spread0.259 · 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 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

Citations16
Published2019
Admission routes1
Has abstractyes

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