By Applying Artificial Intelligence Machine Learning, The Researchers Managed To Identify Three Anti-Aging Substances That Can Fight With Life Extension. This Approach Can Be An Effective Way To Identify New Drugs And Treat Complex Diseases.

Cell division is necessary for the growth of our body and the regeneration of tissues. Cellular senescence describes a phenomenon in which cells do not divide permanently but remain in the body, causing tissue damage and aging in the body’s organs and systems.

Normally, old cells are cleared from the body by our immune system. But as we age, our immune system is less effective in cleaning these cells, and their number increases.

An increase in senescent cells has been linked to diseases such as cancer and Alzheimer’s and signs of aging such as deteriorating vision and reduced mobility. Due to the harmful effects on the body, there are efforts to create effective senolytic compounds. These compounds remove old cells from the body.

Previous studies have identified some effective analytics, but most compounds are toxic to healthy cells. A study by researchers at the University of Edinburgh in Scotland has used a pioneering method to search for chemicals that can safely and effectively destroy these faulty cells.

The researchers developed and trained a machine learning model to recognize key features of chemicals with senolytic properties. The training data for this model was obtained from multiple sources, including academic articles, some pharmaceuticals, and existing chemical compounds that contain a wide range of FDA-approved compounds.

The complete data set included 2,523 compounds and had compounds with senolytic and non-senolytic properties so that the machine-learning algorithm would not be biased. Then, this algorithm was used to screen more than 4 thousand chemicals, among which 21 effective candidates were identified.

By testing these effective compounds, the researchers found that three chemicals named Ginkgetin, Periplusin, and Olandrin remove old cells without harming healthy cells. Of the three, oleandrin was the most effective.

All three products are natural and found in traditional herbal medicines.

Oleander is extracted from Nerium oleander and has properties similar to digoxin, which treats heart failure and some abnormal heart rhythms (arrhythmia). Studies have shown that oleander has anti-cancer, anti-inflammatory, anti-HIV, antimicrobial and antioxidant properties.

Beyond therapeutic levels, oleandrin is highly toxic, which precludes its clinical use in humans. As such, this chemical has not been approved by regulatory agencies as a prescription drug or dietary supplement.

Several types of research have shown that Linkedin, like oleandrin, has anti-cancer, anti-inflammatory, antimicrobial, antioxidant, and neuroprotective properties. Ginectin is extracted from the Ginkgo tree (Ginkgo biloba), the oldest living tree species whose leaves and seeds have been used in Chinese herbal medicine for thousands of years.

A highly concentrated extract of ginkgo biloba, made from the tree’s dried leaves, is available over the counter. This supplement is among the best-selling herbal supplements across the US and Europe.

Periplocin is isolated from the root bark of the Chinese silk vine (Periploca sepium). Studies have shown that this substance can improve heart function, block cell growth, and cause cell death of cancer cells.

The researchers say their findings show that these compounds have a comparable or higher potency than senolytics described in previous studies. More importantly, they say their machine learning-based method was highly efficient, reducing the number of compounds that needed to be screened by more than 200-fold.

The researchers say their AI-based approach is a breakthrough in identifying new drugs, especially for treating complex diseases.

“This study shows that artificial intelligence can be incredibly effective in identifying new drugs, especially in the early stages of drug discovery and for diseases with complex biology or little-known molecular targets,” said Diego Ovarzon, lead author of the study.

Also, according to researchers, this approach is more cost-effective than standard drug screening methods, such as clinical trials.