Elderly cells in our bodies stop functioning but live on, after a fashion, causing inflammation and emitting waste and other damaging chemicals into the bloodstream.

These troublemakers are called “senescent” cells. Normally, our immune system sweeps them out but, as we age, senescent cells begin to pile up. Cellular senescence has been named as a key cause of the physical degeneration associated with aging, as well as diseases from Alzheimer’s to Parkinson’s.

Chemicals that scour senescent cells out of the body are called “senolytics” and now we have three new and better ones, thanks to a machine learning algorithm developed at Scotland’s University of Edinburgh.

A few promising senolytics have already been found but, typically, they also damage healthy cells while they’re escorting out the dysfunctional ones.

The Scottish team created a “machine learning” computer program, which means that it digitally tests ideas at random, tosses those that obviously don’t work, and continues refining and retesting those that show promise.

All of this can be done at computer speeds, reducing to days or even hours what would take humans months or years. 

The Scots fed their algorithm details of 2,523 compounds, some of which have senolytic value and some that don’t. The program then was instructed to find senolytic candidates that won’t harm healthy cells. With those instructions, the algorithm screened more than 4,000 chemicals. 

In the end, it identified three.

Oleandrin, an extract of the oleander plant, was found to be the most effective. In studies, it’s been shown to fight inflammation, cancer, HIV, and harmful viruses and bacteria, among other benefits. It’s also highly toxic outside of a narrow dosage range. As a result, it hasn’t been approved as a drug or a dietary supplement.

Ginkgetin also conveys many of the same benefits as well as protecting brain cells against damage and disease. It’s derived from the ginkgo tree, as is ginkgo biloba, made from the tree’s leaves and sold over the counter as a general tonic and brain booster. 

The third the algorithm flagged is periplocin, which is taken from the Chinese silk vine’s root bark. In studies, it has been found to improve heart health, kill cancer cells, and prevent the growth of unwanted cells.

Using machine learning cut the time needed to find these compounds by 200-fold compared to the usual human-powered studies, the team said.

TRENDPOST: These new compounds can now be pursued as the basis of commercial anti-aging drugs by the pharma industry.

As we have often pointed out, natural remedies are increasingly being shown to be as effective against illness as synthetic drugs in many cases, and usually without strange and dangerous side effects.

The combination of nature’s ingredients, machine learning, and artificial intelligence will speed the development of nature-based treatments exponentially.

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