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Systemic Delivery of Peptide Hormones Using Nasal Powders: Strategies and Future Perspectives

2019· article· en· W2952838944 on OpenAlexaff
Lisa Engio, Remigius U. Agu

Bibliographic record

VenueDrug Delivery Letters · 2019
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvanced Drug Delivery Systems
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineNasal administrationHormonePeptide hormoneDrug deliveryGhrelinPharmacologyInternal medicineChemistry

Abstract

fetched live from OpenAlex

Background: Peptide Hormones (PH) are mainly administered as parenteral injections due to their peculiar physicochemical properties, and susceptibility to enzymatic degradation after oral administration. With invasive routes, however, patient safety, acceptability, and compliance become a concern, especially when a patient has a chronic condition that requires repeated injections. The delivery of peptide hormones via the nasal route has gained momentum over the last few decades as a noninvasive alternative to parenteral injections and commercially available nasal liquid products. Objective: The aim of this paper was to review: (1) The benefits and limitations of nasal powder products, (2) Formulation strategies to enhance nasal delivery of peptide hormone drugs, (3) Nasal powder devices, and (4) Future perspectives of therapeutic nasal powders. The drugs examined specifically include calcitonin, desmopressin, ghrelin, glucagon, human growth hormone, insulin, octreotide, and oxytocin. Methods:: Nasal delivery of peptide hormones using powders was reviewed with the following databases: EBSCO, PUBMED, Web of Science, ClinicalTrials.gov, and EU Clinical Trials Register. Results: Nasal powders are a promising drug delivery system that may be safer and more effective than traditional injections and presently marketed nasal liquids for peptide hormone drugs. Conclusion: With sustained interest and growing body of supporting evidence, a range of nasal powders for systemic delivery of these drugs and delivery devices can be expected to enter the market in the future and offer more options to patients

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.887
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.035
GPT teacher head0.336
Teacher spread0.300 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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