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Record W4407285176 · doi:10.1093/jcag/gwae059.015

A15 THERAPEUTIC POTENTIAL OF THE GLYCOCAGE TARGETED DELIVERY SYSTEM FOR IMPROVING INFLAMMATORY BOWEL DISEASE TREATMENT

2025· article· en· W4407285176 on OpenAlexaffabout
Wenqiang Ma, Susan C. Menzies, Chen Wang, J Kothandapani, Harry Brumer, Laura M. Sly

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2025
Typearticle
Languageen
FieldChemistry
TopicCarbohydrate Chemistry and Synthesis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInflammatory bowel diseaseMedicineDiseaseInflammatory Bowel DiseasesIntensive care medicineDelivery systemInternal medicinePharmacology

Abstract

fetched live from OpenAlex

Abstract Background Inflammatory bowel disease (IBD), encompassing Crohn’s disease (CD) and ulcerative colitis (UC), is marked by chronic inflammation along the gastrointestinal tract (GIT). IBD cases are rising globally, with Canada having one of the highest prevalance rates. Thus, there is a critical need for more effective treatments. Current IBD therapy includes orally administered small molecule anti-inflammatory drugs, such as corticosteroids and JAK inhibitors. However, their use is limited by the high doses required due to systemic uptake, and consequently, negative off-target effects. To improve delivery of these drugs to the inflamed tissue in the lower GIT for better efficacy, our GlycoCaged prodrug technology links the drug to a plant carbohydrate for targeted release by gut bacterial glycosidases at the disease site. To demonstrate proof-of-principle, we used dexamethasone (DEX), a potent corticosteroid that is not used in IBD treatment due to inherent side effects. We showed that “GlycoCaging’‘ Dex improves its efficacy (10-X lower doses required) and reduces systemic side effects as compared to free DEX in the SHIP-/- model of CD-like ileitis. We hypothesize that Glycocaging IBD drugs will increase drug efficacy and reduce off-target effects by delivering drugs to the site of intestinal inflammation. Aims 1) Investigate the location of prodrug decaging in the GIT using SHIP-/- mouse model. 2) Evaluate GlycoCaged dexamethasone in the T cell transfer model of colitis. Methods To delineate the location of decaging activity in vivo, SHIP+/+ and SHIP-/- mice were orally gavaged with 3 mg/kg of DEX or the molar equivalent of GlycoCaged DEX and collected sections of intestinal tissues for LC-MS/MS analysis at multiple timepoints post oral gavage. To assess the GlycoCage system in another mouse model of IBD, T cell transfer model of colitis is established by injecting (i.p) CD4+CD25-CD45RBhigh T cells (isolated from CD45.1 mice by FACS) into age- and sex-matched Rag-/- mice. A dose titration was performed from 3mg/kg/d for 5 weeks of DEX or the molar equivalent of caged DEX to determine and compare their minimal effective doses. Treatment effects were assessed with histology, flow cytometry and multiplexed cytokine assay. Results Free DEX is absorbed early in the proximal GIT, while the GlycoCage prodrug system protects the DEX to be released starting in the distal small intestine and cecum, closer to the inflamed sites. Caged DEX ameliorated colonic inflammation in the T cell transfer model of colitis at lower doses than free DEX. Conclusions Our results highlight a novel microbiome-cleavable glycoconjugate drug delivery platform that has the potential to improve the therapeutic indices of diverse drugs for treating IBD. In the future, we will evaluate the efficacy of other GlycoCaged drugs. Funding Agencies CAG, CIHRTRIANGLE

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.004
GPT teacher head0.188
Teacher spread0.183 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes2
Has abstractyes

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