In silico clinical trials for anti-aging therapies

Abstract

Therapeutic strategies targeting the hallmarks of aging can be broadly grouped into four categories, namely systemic (blood) factors, metabolic manipulation (diet regimens and dietary restriction mimetics), suppression of cellular senescence (senolytics), and cellular reprogramming, which likely have common characteristics and mechanisms of action. In evaluating the potential synergism of combining such strategies, however, we should consider the possibility of constraining trade-off phenotypes such as impairment in wound healing and immune response, tissue dysfunction and tumorigenesis. Moreover, we are rapidly learning that the benefit/risk ratio of aging-targeted interventions largely depends on intra- and inter-individual variations of susceptibility to the healthspan-, resilience-, and/or lifespan-promoting effects of the interventions. Here, we exemplify how computationally-generated proxies of the efficacy of a given lifespan/healthspan-promoting approach can predict the impact of baseline epigenetic heterogeneity on the positive outcomes of ketogenic diet and mTOR inhibition as single or combined anti-aging strategies. We therefore propose that stochastic biomathematical modeling and computational simulation platforms should be developed as in silico strategies to accelerate the performance of clinical trials targeting human aging, and to provide personalized approaches and robust biomarkers of healthy aging at the individual-to-population levels.