Computational model review predicts dual optima for combination therapy in ageing

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medRxiv Published April 24, 2026 Medically reviewed April 24, 2026 Study authors: Goryanin, I.; Damms, B.; Goryanin, I. DOI ↗ By Dr. Julia Lee, PhD · Oncology, Genomics & Drug Development

Key Takeaway

Consider that metabolic and ageing benefits from combination therapy may require distinct optimization strategies.

This review examines a computational model investigating combination therapy with semaglutide, sodium glucose cotransporter 2 inhibitors, metformin, and rapamycin for ageing and metabolic disorders. The model reproduced semaglutide efficacy and tolerability dynamics, found rapamycin's glycaemic effect to be minimal, and identified rapamycin as the dominant driver of repair-related ageing outcomes. Notably, the model predicted two distinct optima for combination therapy: one favoring metabolic improvement and one favoring ageing-related benefit.

The authors highlight that metabolic and ageing optimization are mechanistically distinct objectives, and that weight loss and glycaemic improvement alone may be insufficient surrogates for health span benefit. These limitations underscore the complexity of targeting ageing with multi-drug regimens.

As a computational model review, these findings are hypothesis-generating and require clinical validation. No patient-level data, adverse events, or practice recommendations are provided. Clinicians should interpret the results cautiously and await further evidence.

Study Details

EvidenceLevel 5

PublishedApr 2026

View Original Abstract ↓

Background: Ageing is a systems level biological process underlying the onset and progression of multiple chronic disorders. Rather than arising from a single pathway, age related decline reflects interacting disturbances in metabolic regulation, inflammation, nutrient sensing, cellular stress responses, and tissue repair. Although GLP1 receptor agonists, sodium glucose cotransporter2 inhibitors, metformin, and rapamycin are usually evaluated against disease-specific endpoints. Objective: To develop an SBML compliant quantitative systems pharmacology model in which ageing is the primary pharmacological endpoint and to evaluate which combination therapy provides the greatest benefit for both metabolic and ageing related outcomes. Methods: We developed model comprising four layers: a metabolic/pharmacodynamic layer describing weight loss, HbA1c reduction, and nausea with tolerance; a drug layer capturing class-specific effects of GLP1 agonists, sodium glucose cotransporter2 inhibitors, metformin, and rapamycin; an ageing layer representing damage accumulation, repair capacity, frailty, and biological age gap; and a biomarker layer generating trajectories and estimated glucose disposal rate. Calibration was staged across semaglutide clinical endpoints. Bayesian hierarchical meta analysis, global sensitivity analysis, and practical identifiability analysis were used to assess robustness and interpretability. Results: The model reproduced semaglutide efficacy and tolerability dynamics and supported distinct drug-class profiles across metabolic and ageing axes. Rapamycin showed minimal glycaemic effect but emerged as a dominant driver of repair related ageing outcomes. Combination simulations predicted two distinct optima: one favouring metabolic improvement and one favouring ageing related benefit. Conclusion: The model supports the view that metabolic and ageing optimization are mechanistically distinct objectives and that weight loss and glycaemic improvement alone may be insufficient surrogates for health span benefit.