Revolutionizing Drug Discovery: Machine Learning Models Identify Potential Anti-Aging Compounds, Paving the Way for Treatments of Complex Diseases of the Future

Aging and other diseases such as cancer, type 2 diabetes, osteoarthritis, and viral infections all involve cellular senescence as a stress response. Targeted elimination of senescent cells is gaining popularity, but senolytic drugs are largely unknown due to the need for a deeper understanding of the molecular targets of senescent cell destruction. Here the scientists describe their discovery of three senescent cell analyzes using relatively inexpensive machine learning algorithms, fully trained on previously published data. They confirmed the senolytic effects of gingethin, periplosin, and oleandrin in human cell lines undergoing various types of senescence using computational screening of multiple chemical libraries. These chemicals are as effective as established analyzes, showing that oleandrin is more effective than the current gold standard for its goals. This method reduced drug screening costs by a factor of several hundred, showing that AI can make the most of limited and diverse drug screening data. This opens the door to new data-driven methods in the early stages of drug discovery.

Although senolytic drugs have shown considerable promise in alleviating symptoms of many diseases in mice, removal of senolytic drugs has several implications, such as impairment of processes such as wound healing and liver function. It is also associated with negative outcomes. Despite promising findings, only two drugs have shown efficacy in clinical studies for their senolytic activity.

Some excellent analyzes have been developed in the past. However, they are generally toxic to healthy cells. Now, researchers at the University of Edinburgh in Scotland have developed a new approach to identify compounds that can eliminate these defective cells without harming healthy cells.

They built a machine learning model to identify compounds with senolytic properties and learned to do so. Chemicals from two existing chemical libraries, including a wide range of FDA-approved and clinical-stage chemicals, were integrated with data used to train models from various sources, including academic publications and commercial patents. To avoid biasing the machine learning system, the dataset contains 2,523 substances with both senolytic and non-senolytic properties. Applying this algorithm to a database of over 4,000 compounds, he found 21 promising candidates.

Ginggetin, Periprosin, and Oleandrin, three compounds that were shown during testing to eliminate senescent cells without affecting healthy cells, made them strong candidates. Results showed that oleandrin was the most effective of his three. All three are common ingredients in herbal medicines.

The oleander plant (Nerium oleander) is a source of oleandrin, a substance with similar effects to the heart drug digoxin, 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. Due to its supratherapeutic toxicity, oleandrin has a narrow therapeutic window in humans. Therefore, it is illegal to sell or use it as a food additive or medicine.

Like oleandrin, lynxin has proven beneficial effects against cancer, inflammation, microbes, and the nervous system in the form of antioxidant and neuroprotective properties. The ginkgo tree is the oldest surviving tree species, and its leaves and seeds have been used in Chinese medicine for thousands of years. This tree is his Linkedin source. The dried leaves of this tree are used to create a ginkgo biloba extract that is sold without a prescription. #1 Selling Herbal Supplement in the US and Europe.

According to the study authors, the results show that these chemicals are as effective as or more effective than the senolytic drugs identified in previous studies. They claim that the machine-learning-based approach was so effective that he was able to reduce the number of compounds needed for screening by more than 200-fold.

The research team believes AI-based strategies are a major step forward in finding effective treatments for serious diseases. The technology has several new features that set it apart from standard AI uses in the pharmaceutical industry.

• First, we only use publicly available data for model training, so we don’t need to spend additional money on in-house experimental characterization of training compounds.
• Second, because senescent cell degradation is a rare molecular property and few senescent cell degradations have been reported in the literature, machine learning models can be used with much smaller datasets than is usually thought in the field. trained. The effectiveness of this method demonstrates that machine learning can make the most of literature data. Even though such material is often more diverse and limited in scope than expected.
• Third, phenotypic indicators of pharmacological activity were used to train target-independent models. Many situations impose a significant economic and social burden, the targets of which are little or never known. For these indications, phenotypic drug discovery offers an opportunity to expand the number of chemical starting points that can be advanced through the discovery pipeline.

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