Portions of the research community are becoming quite proficient at churning out potential drug candidates for specific conditions based on processes that involve a lot more computation and modeling than actual laboratory work. The compound databases these days are huge, containing vast swathes of molecules that are barely explored in the context of medicine. Those researchers interested in very modestly slowing aging through calorie restriction mimetics such as metformin and rapamycin, designated by some as geroprotectors, will be faced with an embarrassment of riches.
This is a strategy I think to be of little worth in comparison to repair-based approaches such as SENS. Still, there will be far too many candidate compounds for the current research community to exhaust any time soon. I imagine that scientists will continue to raise funding and explore much as they are today until that strategy is decisively out-competed by rejuvenation therapies after the SENS model. Repairing the damage that causes aging seems to me an approach that self-evidently must win out in terms of results attained, when considered in comparison to adjusting the operation of metabolism to merely slow down accumulation that damage, given equal quality of implementation on both sides.
Fortunately, a number of damage repair approaches can involve small molecule drug development: clearance of senescent cells, breaking down cross-links, and removal of other metabolic waste such as the constituents making up lipofuscin, for example. All of these lines of development should benefit considerably from highly effective drug candidate identification platforms, just as soon as a few initial candidates are in hand - and that is the case today for senolytics that target senescent cells for destruction. I'm sure we'll be seeing many more of those in the next few years, and a good thing too, as the senolytics discovered to date appear to be fairly specific to tissues or classes of senescent cell. Variety will likely be important in the early years of senolytic therapies.
By 2030, the US Census Bureau projects that one in five people in the US alone will be over the age of 65, a major risk factor for many of the most prevalent, costly, and devastating diseases of today, including cancer, cardiovascular disease, Alzheimer's disease, and Type II diabetes. To offset the burden of this increase, efforts are underway to develop an anti-aging drug or other geroprotective intervention that could extend healthspan, lower disease rates, and maintain productivity in this age group.
Unfortunately, there are many roadblocks to such an intervention. While many aging mechanisms are now catalogued and hundreds of drugs extend lifespan in animal models, approval and testing of new drugs in humans is slow, expensive, and prone to high failure rates. This is particularly true in longevity research and exacerbated by a lack of reliable aging biomarkers other than disease itself. Even if successful, to be used preventatively, anti-aging drugs face extraordinarily high safety and efficacy standards for approval.
One strategy to hasten the process has been the repurposing of existing, FDA-approved drugs that show off-label anti-cancer and anti-aging potential, and at the top of that list are metformin and rapamycin, two drugs that mimic caloric restriction. Taken together, rapamycin and metformin are promising candidates for life and healthspan extension; however, concerns of adverse side effects have hampered their widescale adoption for this purpose. While short term rapamycin use is considered safe, it has been reported to be associated with adverse events. Metformin, while relatively safe, is poorly tolerated in one fourth to one half of patients due to gastrointestinal side effects.
In this work, we initiate an effort to identify safe, natural alternatives to metformin and rapamycin. Our work is done entirely in silico and entails the use of metformin and rapamycin transcriptional and signaling pathway activation signatures to screen for matches amongst natural compounds. We have shown previously that the transcriptional signature of a given drug response, disease state, or other physiological condition, when mapped to the signal pathway activation signature, can be useful for biomarker development and drug screening. In the present study, we apply these methods to screen for nutraceuticals that mimic metformin and/or rapamycin. We reduce a list of over 800 natural compounds to a shortlist of candidate nutraceuticals that show both similarity to the target drugs and low adverse effects profiles.