Research Perspective Volume 11, Issue 16 pp 6591—6601
In silico clinical trials for anti-aging therapies
- 1 ProCURE (Program Against Cancer Therapeutic Resistance), Metabolism and Cancer Group, Catalan Institute of Oncology, Girona, Spain
- 2 Girona Biomedical Research Institute (IDIBGI), Girona, Spain
- 3 Quantitative Cell Biology Lab, The Francis Crick Institute, London, United Kingdom
- 4 Unit of Clinical Research, Catalan Institute of Oncology, Girona, Spain
- 5 Unitat de Recerca Biomèdica (URB-CRB), Hospital Universitari de Sant Joan, Institut d'Investigació Sanitària Pere Virgili, Reus, Spain
- 6 ICREA, Barcelona, Spain
- 7 Centre de Recerca Matemàtica (CRM), Barcelona, Spain
- 8 Departament de Matemàtiques, Universitat Autònoma de Barcelona, Barcelona, Spain
- 9 Barcelona Graduate School of Mathematics (BGSMath), Barcelona, Spain
Received: February 21, 2019 Accepted: August 9, 2019 Published: August 24, 2019https://doi.org/10.18632/aging.102180
How to Cite
Copyright © 2019 Menendez et al. This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY) 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.