Coated glucose microbeads stimulate enteric hormone release and improve glucose tolerance in Phase 1 and 2 clinical trials
Bibliographic record
Abstract
AIMS: Incretin agonists are used to treat obesity and metabolic dysfunction. Instead of systemically delivering high levels of hormone receptor agonists that can lead to adverse effects, we tested and optimized oral microbead formulations that activate endogenous enteroendocrine signalling systems via distal nutrient-sensing cells. MATERIALS AND METHODS: We report two randomized Phase 1 studies (NCT05713773 and NCT05737927) measuring acute pharmacokinetic/pharmacodynamic responses following consumption of microbeads that deliver glucose to the distal small intestine: these studies compared coating variations and glucose dosing. The primary endpoint was plasma glucagon-like peptide 1 (GLP-1) levels; we also measured GLP-2, PYY, glicentin, oxyntomodulin, glucose-dependent insulinotropic peptide, C-peptide, and insulin as exploratory endpoints. In a subsequent randomized Phase 2a trial (NCT05803772), prediabetic subjects consumed a lead formulation or placebo once daily for 6 weeks each in a two-period, two-sequence crossover design. Oral glucose tolerance was measured at baseline and following treatment in each sequence, with the primary endpoint being the change in the area under the curve. RESULTS: Our microbead formulation successfully targeted the distal small intestine and elicited a robust plurihormonal enteroendocrine response; our Phase 2a data show that the lead formulation improved glucose tolerance in pre-diabetic patients, comparable to results using GLP-1 mimetics. Adverse events were infrequent and modest. CONCLUSIONS: Targeted glucose release activates endogenous enteroendocrine signalling networks, improves a clinically relevant metabolic endpoint, and has minimal adverse effects. The approach to target native enteroendocrine signalling has disruptive potential for the treatment of metabolic disorders, including obesity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".