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

Goryanin I, Damms B, Goryanin I · 2026 · CPT Pharmacometrics Syst Pharmacol · Atlas ID GOR2026

What this study shows

A calibrated systems-pharmacology model of aging finds that optimal drug combinations diverge by objective: GLP-1 receptor agonist + SGLT2 inhibitor + metformin is best for metabolic (weight/HbA1c) improvement, while GLP-1 receptor agonist + SGLT2 inhibitor + rapamycin is predicted best for aging-related (frailty/biological-age-gap) benefit — the two goals do not converge on the same regimen. Authors flag the aging predictions as hypothesis-generating, pending external validation.

Abstract

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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.

Read the full abstract on PubMed →

At a glance

Evidence type	M Molecular — cells, biochemistry, structure Marked M because it is molecular or in-vitro work (model: Quantitative systems pharmacology (QSP) model calibrated to human trial data (semaglutide STEP trials)) rather than a whole-organism health-outcome study. That is often exactly where causal biology gets established -- the code says which system the finding was shown in, and nothing about how good the work is.
Study type	5 - Mechanistic / In Vitro
Model system	Quantitative systems pharmacology (QSP) model calibrated to human trial data (semaglutide STEP trials)
Journal	CPT Pharmacometrics Syst Pharmacol
Year	2026
Peer reviewed	Yes
Record last updated	2026-10-01
Source	DOI 10.1002/psp4.70322 · PMID 42572918

Extracted findings

These fields were extracted from the paper with AI assistance and have not been checked against the source. Read them as a pointer into the paper, not as curated claims; where they disagree with the curated summary above, the curated summary stands.

Intervention	Rapamycin, metformin, SGLT2 inhibitors, GLP-1 receptor agonists (combination therapy, in silico)
Target	mTOR/aging pathway (rapamycin arm) within a multi-drug QSP model
Model	Human (in silico model calibrated to human trial data)
Effect	Predicts GLP-1RA+SGLT2i+rapamycin as the optimal combination for aging-related outcomes (frailty, biological age gap), distinct from the best metabolic-outcome combination

In the Atlas

Cite this paper

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Goryanin, I., Damms, B., & Goryanin, I. (2026). A Systems Pharmacology Model of Aging Identifies Optimal Combination Therapies With Secondary Benefits on Weight Loss and Metabolic Health. CPT Pharmacometrics Syst Pharmacol. https://doi.org/10.1002/psp4.70322

@article{GOR2026,
  author       = {Goryanin, I. and Damms, B. and Goryanin, I.},
  title        = {{A Systems Pharmacology Model of Aging Identifies Optimal Combination Therapies With Secondary Benefits on Weight Loss and Metabolic Health}},
  journal      = {CPT Pharmacometrics Syst Pharmacol},
  year         = {2026},
  doi          = {10.1002/psp4.70322},
  note         = {PMID: 42572918},
}

Cite this Atlas record

The record is the Atlas's own work — the evidence label, the extracted findings and the links. It is cited as part of the dataset, not as the paper.

Barton, O. (2026). Oliver's mTOR Atlas (record GOR2026) [Data set]. https://mtor-atlas.org/study/GOR2026/ · Dataset DOI 10.5281/zenodo.22059963

@misc{atlas_GOR2026,
  author       = {Barton, Oliver},
  title        = {{Oliver's mTOR Atlas}, record GOR2026},
  howpublished = {Data set},
  year         = {2026},
  url          = {https://mtor-atlas.org/study/GOR2026/},
  doi          = {10.5281/zenodo.22059963}
}

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