ArticlePharmaceutical research2022
Identification of a Multi-Component Formulation for Intestinal Delivery of a GLP-1/Glucagon Co-agonist Peptide.
Article in Pharmaceutical research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Navigating the complexity of oral peptide delivery: challenges and strategies to enhance oral bioavailability.Frontiers in drug delivery · 2026Review
- Oral Treatment of Obesity by GLP-1 and Its Analogs.Pharmaceutics · 2025Review
- Controlling Gastric Delivery of a GIP/GLP1 Peptide in Monkeys by Mucoadhesive SNAC Tablets.Pharmaceutical research · 2025Article
- Challenges in Permeability Assessment for Oral Drug Product Development.Pharmaceutics · 2023Review
- Enteric coating of tablets containing an amorphous solid dispersion of an enteric polymer and a weakly basic drug: A strategy to enhance in vitro release.International journal of pharmaceutics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeOral delivery of therapeutic peptides has been challenging due to multiple physiological factors and physicochemical properties of peptides. We report a systematic approach to identify formulation compositions combining a permeation enhancer and a peptidase inhibitor that minimize proteolytic degradation and increase absorption of a peptide across the small intestine.
methodsAn acylated glucagon-like peptide-1/glucagon co-agonist peptide (4.5 kDa) was selected as a model peptide. Proteolytic stability of the peptide was investigated in rat and pig SIF. Effective PEs and multiple component formulations were identified in rats. Relative bioavailability of the peptide was determined in minipigs via intraduodenal administration (ID) of enteric capsules.
resultsThe peptide degraded rapidly in the rat and pig SIF. Citric acid, SBTI, and SBTCI inhibited the enzymatic degradation. The peptide self-associated into trimers in solution, however, addition of PEs monomerized the peptide. C10 was the most effective PE among tested PEs (DPC, LC, rhamnolipid, C12-maltosides, and SNAC) to improve intestinal absorption of the peptide in the rat IJ-closed loop model. A combination of C10 and SBTI or SBTCI increased the peptide exposure 5-tenfold compared to the exposure with the PE alone in the rat IJ-cannulated model, and achieved 1.06 ± 0.76% bioavailability in minipigs relative to subcutaneous via ID administration using enteric capsules.
conclusionWe identified SBTI and C10 as an effective peptidase inhibitor and PE for intestinal absorption of the peptide. The combination of SBTI and C10 addressed the peptide physiochemical properties and provides a formulation strategy to achieve intestinal delivery of this peptide.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.