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In silico clinical trials for anti-aging therapies
Javier A Menendez1,2, Elisabet Cuyàs1,2, Núria Folguera-Blasco3
1ProCURE (Program Against Cancer Therapeutic Resistance),Metabolism and Cancer Group, Catalan Institute of Oncology, Girona, Spain.
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.
Insights
Strategies to combat aging, including blood factors, diet, senolytics, and reprogramming, may synergize but risk trade-offs. Computational modeling can predict individual responses to interventions like ketogenic diets and mTOR inhibition for personalized aging therapies.
Area of Science:
- Gerontology and computational biology.
- Focus on aging hallmarks and intervention strategies.
Background:
- Aging interventions target systemic factors, metabolism, senescence, and reprogramming.
- Combining strategies requires understanding trade-offs like impaired healing and increased tumorigenesis.
- Individual variability significantly influences the efficacy and risks of aging interventions.
Purpose of the Study:
- To explore the potential synergism and trade-offs of combining aging intervention strategies.
- To investigate how computational models can predict intervention efficacy based on individual epigenetic variations.
- To propose the development of in silico platforms for aging research and personalized medicine.
Main Methods:
- Review of therapeutic strategies targeting aging hallmarks.
- Computational simulation using proxies to model intervention efficacy.
- Analysis of epigenetic heterogeneity's impact on ketogenic diet and mTOR inhibition outcomes.
Main Results:
- Computational proxies can predict the influence of epigenetic variations on intervention outcomes.
- Ketogenic diet and mTOR inhibition efficacy is modulated by baseline epigenetic differences.
- Potential for personalized anti-aging strategies based on individual profiles.
Conclusions:
- In silico biomathematical modeling and simulation are crucial for accelerating aging clinical trials.
- Personalized approaches and robust biomarkers for healthy aging can be developed using these computational tools.
- Understanding individual variability is key to optimizing the benefit/risk ratio of aging interventions.
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