Aging and other diseases, such as cancer, type 2 diabetes, osteoarthritis, and viral infection, all involve cellular senescence as a stress response. Targeted removal of senescent cells is gaining popularity, although few senolytics are known since their molecular targets need to be better understood. Here, scientists describe finding three senolytics with relatively inexpensive machine learning algorithms that were educated entirely on previously published data. In human cell lines undergoing different types of senescence, they confirmed the senolytic action of ginkgetin, periplocin, and oleandrin using computational screening of multiple chemical libraries. The chemicals are as effective as well-established analytics, demonstrating that oleandrin is more effective than current gold standards against its target. The method reduced drug screening expenses by a factor of several hundred, and it shows that AI can make the most of limited and varied drug screening data. This opens the door to novel, data-driven methods for drug discovery’s early stages.

Although senolytics have shown considerable promise in relieving symptoms of numerous diseases in mice, their elimination has also been related to several negative outcomes, including the impairment of processes like wound healing and liver function. Despite promising findings, only two drugs have shown efficacy in clinical studies for their senolytic action.

Some good analytics have been developed in the past. However, they are generally harmful to healthy cells. Now, researchers at Scotland’s University of Edinburgh have developed a novel approach to identify chemical compounds that can remove these faulty cells without harming healthy ones.

They constructed a machine-learning model to identify compounds with senolytic qualities and taught it to do so. Chemicals from two existing chemical libraries, which include a wide range of FDA-approved or clinical-stage chemicals, were merged with data used to train the model from various sources, such as academic articles and commercial patents. To avoid biasing the machine-learning system, the dataset includes 2,523 substances with both senolytic and non-senolytic characteristics. After applying the algorithm to a database of over 4,000 compounds, 21 promising candidates were found.

Three compounds, ginkgetin, periplocin, and oleandrin, were shown during testing to eliminate senescent cells without affecting healthy cells, making them good candidates. The results showed that oleandrin was the most effective of the three. All three are common components of herbal remedies.

The oleander plant (Nerium oleander) is the source of oleandrin, a substance with comparable effects to the cardiac medication digoxin, which is used to treat heart failure and certain irregular heart rhythms (arrhythmias). Anticancer, anti-inflammatory, anti-HIV, antibacterial, and antioxidant effects have all been observed in oleandrin. The therapeutic window for oleandrin in humans is small, as it is highly toxic over therapeutic levels. Therefore, selling or using it as a food additive or pharmaceutical is illegal.

Like oleandrin, Linkedin has been proven to have beneficial effects against cancer, inflammation, microbes, and the nervous system in the form of antioxidant and neuroprotective characteristics. The Ginkgo (Ginkgo biloba) tree is the oldest living tree species, and its leaves and seeds have been used for herbal medicine in China for thousands of years. This tree is the source of Linkedin. The tree’s dried leaves are used to create an extract of Ginkgo biloba that is sold without a prescription. It is a top-selling herbal supplement in the United States and Europe.

According to the study authors, their results show that the chemicals are as effective as, if not more so than, the senolytics identified in earlier studies. They claim that their machine-learning-based approach was so effective that it cut down on the number of compounds required to be screened by a factor of over 200.

The team believes their AI-based strategy is a major step forward in discovering effective treatments for serious diseases. Several novel features in this technique set it apart from standard AI use in the pharmaceutical industry. 

First, it doesn’t require additional funds to be spent on in-house experimental characterization of training compounds because it uses only published data for model training.

Second, senolysis is a rare molecular property, and there are few senolytics reported in the literature, so the machine learning models were trained on a much smaller dataset than is typically considered in the field. The method’s effectiveness indicates that machine learning can make the most of literature data, even though such material is often more diverse and limited in scope than one may anticipate. 

Third, phenotypic indicators of pharmacological activity were used in target-agnostic model training. Many conditions impose a significant economic and societal burden but for which few or no targets are known; for these conditions, phenotypic drug discovery presents an opportunity to expand the number of chemical starting points that can be advanced through the discovery pipeline.

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