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

Résumé fourni par la source

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.

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