Characterization of LY3324954 a long-acting glucagon-receptor agonist
Bibliographic record
Abstract
Glucagon is a crucial regulator of glucose and lipid metabolism as well as whole-body energy balance. Thus, modulation of glucagon receptor (GCGR) activity in the context of single-molecule multi-receptor co-agonists has become an emerging therapeutic target against obesity and obesity-associated metabolic dysfunction. To better elucidate the role of GCGR-signaling when paired with incretin receptor signaling or on its own, we developed, LY3324954, a GCGR agonist with improved potency and selectivity as compared to the native glucagon peptide. LY3324954 was administered to DIO mice, rats, dogs, and monkeys to evaluate pharmacokinetic (PK) profile. Biweekly treatments were conducted in lean and DIO mice to characterize LY3324954-effects on glucose homeostasis and energy balance. Single dose studies were also conducted in liver Gcgr -deficient mice to establish receptor specificity. LY3324954 also exhibited extended PK profile in DIO mice, rats, dogs, and monkeys. When administered every 72 h, LY3324954 treatment stimulated transient glucose and insulin excursions in lean mice. In diet-induced obese mice, LY3324954 treatment stimulates energy expenditure, weight loss, and a reduction of adiposity in a dose-dependent manner. Benefit to whole-body lipid homeostasis was likewise observed in these mice. Taken together, these studies characterize a long-acting and potent GCGR-agonist and its regulation of glucose and lipid metabolism as well as whole-body energy balance following both acute and chronic treatment in mice. • LY3324954 is a highly potent and selective glucagon receptor (GCGR) agonist with an extended pharmacokinetic profile. • LY3324954 treatment stimulates glucose production from induced pluripotent stem cell (IPSC)-derived hepatocytes. • LY3324954 stimulates glucose and insulin excursions in a GCGR-dependent manner in mice. • LY3324954 reduces fat mass and body weight in diet-induced obese mice via increased energy expenditure.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".