BI 456906: Discovery and preclinical pharmacology of a novel GCGR/GLP-1R dual agonist with robust anti-obesity efficacy
Bibliographic record
Abstract
OBJECTIVE: Obesity and its associated comorbidities represent a global health challenge with a need for well-tolerated, effective, and mechanistically diverse pharmaceutical interventions. Oxyntomodulin is a gut peptide that activates the glucagon receptor (GCGR) and glucagon-like peptide-1 receptor (GLP-1R) and reduces bodyweight by increasing energy expenditure and reducing energy intake in humans. Here we describe the pharmacological profile of the novel glucagon receptor (GCGR)/GLP-1 receptor (GLP-1R) dual agonist BI 456906. METHODS: BI 456906 was characterized using cell-based in vitro assays to determine functional agonism. In vivo pharmacological studies were performed using acute and subchronic dosing regimens to demonstrate target engagement for the GCGR and GLP-1R, and weight lowering efficacy. RESULTS: BI 456906 is a potent, acylated peptide containing a C18 fatty acid as a half-life extending principle to support once-weekly dosing in humans. Pharmacological doses of BI 456906 provided greater bodyweight reductions in mice compared with maximally effective doses of the GLP-1R agonist semaglutide. BI 456906's superior efficacy is the consequence of increased energy expenditure and reduced food intake. Engagement of both receptors in vivo was demonstrated via glucose tolerance, food intake, and gastric emptying tests for the GLP-1R, and liver nicotinamide N-methyltransferase mRNA expression and circulating biomarkers (amino acids, fibroblast growth factor-21) for the GCGR. The dual activity of BI 456906 at the GLP-1R and GCGR was supported using GLP-1R knockout and transgenic reporter mice, and an ex vivo bioactivity assay. CONCLUSIONS: BI 456906 is a potent GCGR/GLP-1R dual agonist with robust anti-obesity efficacy achieved by increasing energy expenditure and decreasing food intake.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".