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In 2005 I attended a high-level symposium that sought a solution for an obstacle that was delaying the discovery of cures for human illnesses.
The obstacle is “information overload.” Stated simply, there is too much data being published for any single person to read, analyze, and connect it with over ten million existing biomedical papers.
The presenters at the 2005 symposium declared there to be enough published data to cure lethal diseases, but no efficient way to tie it together in a meaningful way.
I immediately understood what these computer experts were seeking. The reason was that we at Life Extension® had been manually reviewing thousands of published medical studies for decades, and finding ways to circumvent diseases that physicians often diagnosed as “terminal.”
For example, prior to this symposium we developed an individual protocol that cured a patient whose oncologists said only had months to live.
Two years after we cured this patient’s cancer, a treatment-related side effect manifested. Once again, the doctors said the patient was “terminal.” We went back to the medical library and used the published literature to identify two existing drugs (pentoxifylline and cabergoline) that cured the radiation necrosis that had developed in this patient’s brain.
Our staff spent hundreds of hours reviewing thousands of published studies to save this one patient (who has remained alive for the past 17 years).
What if instead of manually trying to connect every scientific paper that relates to a disease, an artificial intelligence (AI) system read every new biomedical study and then connected those findings with the 10 million existing published studies?
From our nearly 40 years of experience, we believe this kind of AI technology will generate cures for many diseases via creative use of existing therapies.
I’m pleased to announce that Life Extension has partnered with a group called Insilico Medicine to develop nutrient formulas (using proprietary AI) to target and neutralize specific pathologies that contribute to degenerative aging.
Each year, about 500,000 new papers are published that relate to medicine. While this represents an information renaissance, it also creates a serious dilemma.
What human could read even a fraction of these new studies and then tie them together with more than 10 million existing biomedical published papers?
An example of challenges in translating a scientific discovery into medical practice can be seen with the drug metformin.
Metformin was first described in the scientific literature in 1922.1
Its sugar-lowering action was demonstrated in rabbits in 1929.2 Metformin was then forgotten, as doctors focused on other diabetic treatments such as insulin.
Interest in metformin resumed at the end of the 1940s. After many published reports, it was approved as an antidiabetic drug in England in 1958.3,4
Metformin became available to American diabetic patients in March 1995,5 which is the identical time when Life Extension Magazine® added it to a list of drugs to slow aging.
We’ve since published dozens of articles advocating that non-diabetics consider using metformin to prevent and treat cancer, to prevent type II diabetes, and to slow aging processes by activating an enzyme called AMPK.6-19
If you enter “metformin and cancer” into the National Library of Medicine database, you will find thousands of published studies talking about its potential use as an adjuvant cancer preventive and treatment.
So from its time of discovery, it took 75 years for metformin to be approved by the FDA as an antidiabetic drug, with many more years of human research needed to fully validate its ability to protect against other illnesses.
How many other drugs already exist that, like metformin, have lifesaving mechanisms beyond their “approved” use? We’ve identified dozens since our inception, including low-dose aspirin to protect against cancer and occlusive arterial disease.20-29
Taking it a step further, how many other compounds might be combined with metformin and aspirin to make them work better?30-32
An exclusive report from biomedical conferences on aging held in 2016 appears in this month’s issue.
Urgent Need for Deep-Learning AI
Readers of this magazine cannot wait decades for serendipitous discoveries to transform into routine medical practice.
The solution to finding lifesaving nuggets lies with deep-learning artificial intelligence that works around the clock reviewing every published scientific paper using precise analytical tools that enable it to develop effective therapeutic protocols for human review and implementation.
We know this works because we’ve been doing it manually for nearly 40 years.
A pioneering group called Insilico Medicine has developed a way to dramatically speed up our ability to identify nutrients and drugs that not only attack human aging, but also can be used to treat illnesses ike heart disease, cancer, diabetes, and others.
The First AI Project
In last month’s special issue, I described a drug called dasatinib that demonstrated remarkable age-reversal properties in a landmark study published in August 2015.33
Dasatinib works by selectively removing senescent cells from older bodies. The 2015 study used older mice and found the following benefits when dasatinib was given for just a short period:
- Frailty symptoms alleviated
- Cardiac/arterial function improved
- Osteoporosis reduced
- Exercise endurance increased
- Healthy lifespans extended
As we age and our immune function declines, we accumulate dysfunctional senile cells that:
- Impede organ function33-39
- Create chronic inflammation40-42
- Increase cancer risk with age43-46
- Shorten healthy lifespan47,48
There is no value in retaining dysfunctional senile cells. You want them purged from your body, yet your senescent immune system fails to rid you of them as it did in youth.
We at Life Extension (and others including researchers at Mayo Clinic) want to rapidly initiate a study to see if dasatinib works in elderly people as it did in older mice.
While we wait, over 5,000 Americans perish each day from degenerative diseases,49 many that involve the accumulation of senescent cells in vital tissues. This is where Insilico Medicine comes to the rescue.
Using AI to Develop a Natural “Senolytic”
A “senolytic” compound destroys senescent cells.
Dasatinib is a drug that has demonstrated powerful senolytic properties in the animal model with remarkable rejuvenation benefits.33
We expect a human study on dasatinib to commence soon that should provide quick results since the dosage period may only be three weeks.
In the meantime, it is critical that we do something to safely purge our bodies of senescent cells while delaying the conversion of healthy cells to a senescent state so we don’t prematurely lose our health.
So we asked the scientists at Insilico Medicine to use their proprietary software to analyze what gene expression and cell signaling pathways can be “triggered” to induce senescent cells to self-destruct. Additionally, to prevent our bodies from accumulating more senescent cells over time, Insilico Medicine also looked for pathways that combat cellular stress, a leading cause of senescence.
Insilico Medicine programmed in our specifications and then looked at the “senolytic” effect of hundreds of different plant extracts. Over a multi-month period, Insilico Medicine was able to identify the plant extracts most likely to provide the greatest benefits.
In this month’s issue, you will learn about Insilico Medicine’s remarkable AI technology and a novel botanical formula they developed in collaboration with our scientists.
Via Tonya Scholz
