McColl · The Journal of physiology 2026 · computational kinetic modeling study · n=?

Multifactorial nature of anabolic resistance in ageing skeletal muscle: A systems modelling study.

Cited 0 times in the scientific literature.

Level 5 - mechanism / opinion, no new human data

In silico mechanistic systems kinetic modeling study based on published literature parameters without new human clinical data.

PubMed 42464764 · doi:10.1113/JP290799 · record verified 2026-08-27

What was done

Researchers developed and applied a mechanistic, multiscale kinetic computational model of leucine-mediated intracellular signaling and protein metabolism in human skeletal muscle. Parameter values were calibrated using previously published experimental data. The authors conducted global sensitivity analyses to determine drivers of muscle protein synthesis (MPS) and net protein balance, simulated virtual populations responding to amino acid feeding to categorize anabolic-sensitive versus anabolic-resistant phenotypes, and simulated single versus multi-target therapeutic interventions.

What was found

The abstract reports no empirical numeric measurements, effect sizes, or confidence intervals. Sensitivity analyses indicated that intracellular signaling cascades controlling MPS dominate net protein balance. In simulations, no single isolated dysregulation replicated age-related blunting of MPS; anabolic resistance emerged only when multiple impairments operated simultaneously. Full restoration of simulated anabolic sensitivity required coordinated, multitarget interventions.

Why it matters

This work provides a theoretical mechanistic explanation for why single-agent interventions often fail to reverse sarcopenic anabolic resistance, supporting the rationale for multimodal therapeutic and nutritional strategies.

Limits

The study is entirely an in silico simulation based on secondary parameter estimates rather than newly collected human trial data. Model validity depends strictly on the assumptions and kinetic formulations used, and the abstract provides no specific quantitative validation metrics or sample characteristics from the underlying literature.

Cited by