A Systems Pharmacology Model of Aging Identifies Optimal Combination Therapies With Secondary Benefits on Weight Loss and Metabolic Health

ABSTRACT

Aging is a systems‐level process linking metabolic dysfunction, inflammation, impaired repair, frailty, and multimorbidity, whereas existing pharmacological strategies usually optimize disease‐specific endpoints such as weight loss or HbA1c rather than aging‐related trajectories. We developed an SBML‐compliant quantitative systems pharmacology (QSP) model in which aging is represented as a dynamic, pharmacologically modifiable endpoint. The model integrates four coupled layers: metabolic/pharmacodynamic responses to GLP‐1 receptor agonism, SGLT2 inhibition, metformin and rapamycin; adverse‐event dynamics; aging states including damage accumulation, repair capacity, frailty and biological age gap; and biomarker outputs including GDF15, cystatin C, leptin, adiponectin and estimated glucose disposal rate. The semaglutide submodel was calibrated against published STEP trial endpoints, and Bayesian hierarchical meta‐analysis, global sensitivity analysis, practical identifiability analysis and internal consistency checks were used to assess model behavior. The calibrated model reproduced semaglutide‐associated weight loss, HbA1c reduction and transient nausea within pre‐specified error benchmarks. Bayesian meta‐analysis confirmed strong metabolic effects for semaglutide, moderate glycaemic effects for SGLT2 inhibitors and metformin, and a near‐zero HbA1c effect for rapamycin. Sensitivity analysis revealed largely orthogonal metabolic and aging parameter spaces. Combination simulations identified two mechanistically distinct optima: GLP‐1 receptor agonist plus SGLT2 inhibitor plus metformin for metabolic improvement, and GLP‐1 receptor agonist plus SGLT2 inhibitor plus rapamycin for aging‐related benefit. Metabolic optimisation and aging optimisation are therefore mechanistically distinct objectives that do not converge on the same drug combination. These predictions are hypothesis‐generating and require external validation against independent longitudinal datasets and clinical safety evaluation before translation to treatment recommendations.