<scp>SemaGBA</scp> : A System Dynamics Model of the Semaglutide‐Responsive Gut‐Brain Axis A Model of How the Brain and Semaglutide Regulate Appetite and Weight

AIMS: Semaglutide is a GLP-1 receptor agonist for the treatment of type 2 diabetes and obesity. Its clinical effects are well established, but the underlying mechanisms remain unclear. This study aims to use computational modelling to generate hypotheses about semaglutide's long-term metabolic (body weight, net energy intake, blood glucose, insulin, insulin sensitivity, glucotoxicity, leptin, leptin sensitivity, lipotoxicity, GLP-1 and βcell function) and neural (AgRP, POMC, and dopamine neural activity) effects. MATERIALS AND METHODS: The SemaGBA computational model was developed in Julia using a system dynamics approach, integrating 14 metabolic and neural variables. First, a version without neural variables was constructed and validated against clinical data for blood glucose and weight loss. Subsequently, the model was extended with neural variables. To represent distinct population groups, baseline variable profiles were defined, capturing the typical physiological characteristics for people with type 2 diabetes, obesity, and a healthy condition. Simulations were performed for semaglutide treatment in groups with prediabetes induced by chronic overeating, type 2 diabetes, and obesity over periods ranging from 30 weeks to 5 years. RESULTS: The reduced model accurately reproduced clinical outcomes. It predicts glucose reductions of 38.0 mg/dL (data: 41.0 mg/dL) and weight loss of 3.2 kg (data: 3.8 kg) for diabetes (0.5 mg semaglutide), and 15.1% weight loss (data: 14.9%-17.1%) for obesity (2.4 mg semaglutide). Simulations showed semaglutide's interconnected mechanisms, including reduced lipotoxicity and glucotoxicity, enhanced β-cell function, and glucose-dependent insulin secretion. Intervention during prediabetes prevented progression to diabetes by preserving β-cell function and maintaining glucose in the pre-diabetic range. Neural variables illustrated the potential contribution of AgRP, POMC, and dopamine neuron activity to reduced net energy intake. CONCLUSION: The SemaGBA model demonstrates how semaglutide achieves glucose control and weight loss through integrated metabolic and neural pathways. Validation is limited by data availability, but the framework provides hypotheses for future research into semaglutide's neural effects.