Arguing for More Computational Modeling to Aid the Transition to Clinical Trials that Target Aging

At some point in the future, clinical trials for therapies that target mechanisms of aging must start to assess the outcome on aging, rather than the present situation in which regulators force potentially broad rejuvenation therapies – such as senolytics – to address only one specific age-related condition at a time. The authors of this paper argue that this will be a challenging transition for present regulatory and research institutions, and that a great deal more use of computational modelling of aging and the effects of interventions will be needed to smooth the way. I agree that the regulatory system is a barrier and a roadblock to the paths that should be taken; I’m not sure that I agree with the specific recommendations made in this paper. Greater effective use of computational modelling should, in principle, allow cost reductions across the board in the development of therapies, but I don’t know that this really changes the nature of the problem beyond reducing the expense of efforts made to solve it.

The conventional paradigm “one disease, one drug” should be updated to achieve the vision of targeting aging as a common component of human diseases. The current deterministic genetic paradigm of diagnosing and treating each separate age-related disease fails to fit with the broader anti-aging strategies aimed to address the closely related concepts of healthspan, resilience, and lifespan, which should be therapeutically managed in the absence of discrete, targetable genetic drivers of aging progression. Perhaps more importantly, current frameworks cannot capture the stochastic aspects that drive the shared trade-offs of the emerging strategies for organismal healthspan and rejuvenation, namely tissue-repair/wound-healing impairment and tumorigenesis.

Successful clinical trials with new families of candidate interventions targeting the biologic machinery of aging per se would be groundbreaking; delaying, preventing (or even reversing) the aging process would result in tremendous cost savings for healthcare systems while increasing the productive contributions that could be made by the older members of our societies. By modeling and predicting the behavior of interventions that target the aging hallmarks in both long-term and acute settings, defined by extension of healthspan/lifespan and enhanced resilience to acute stressors (i.e., reduced frailty), respectively, robust and standardized approaches such as stochastic biomathematical platforms would have the ability to sidestep most of the current challenges in aging-targeting clinical trials, to accelerate the achievement of optimum health and life quality in aging populations.


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