Researchers follow AI path to safer senolytic compounds

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Senolytic performance of oleandrin and periplocin. A Cell survival assay measuring senolytic effect in OIS. Panels show representative crystal violet staining of tissue culture plates of confluent IMR90 ER:RAS and IMR90 ER:STOP control senescent cells grown with 100nM 4OHT and treated with 10nM oleandrin, ouabain and periplocin and DMSO as vehicle control for 72 hours. b Cell survival by quantification of the crystal violet stain of the experiment shown in A, as described in the Methods section. Data represented as single data points and bars and error bars representing the meanSEM of 12 independent experiments. Statistical analysis was performed using a one-way ANOVA (Tukeys test) for multiple comparisons. c Cell survival assay measuring senolytic effect in replicative senescence. Graphs representing cell survival by quantification of crystal violet staining of confluent cultures of passage 27 IMR90 cells (replicative senescence) and passage 13 IMR90 cells (control) treated with 10nM oleandrin, ouabain and periplocin and DMSO as vehicle control for 72 hours (relative to Supplementary Fig. 9b). Data represented as single data points and bars and error bars representing the meanSEM of 12 independent experiments. Statistical analysis was performed using a one-way ANOVA (Tukeys test) for multiple comparisons. d Analysis of caspase 3/7 activity in IMR90 control ER:STOP and IMR90 ER:RAS senescent cells grown in media containing 100 nM 4OHT and treated for 35 h with 10 nM oleandrin, ouabain and periplocin and DMSO as vehicle control. Panels show representative fluorescent images of caspase 3/7 positive cells (lower panels) and brightfield images (upper panels) of the same field for cell scoring. The percentage of green fluorescent cells per condition is indicated in the panel figures. Representative data from one of two independent experiments. Scale bars represent 100 m. ns not significant, *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001. Source data is provided as source data files. Credit: Nature communications (2023). DOI: 10.1038/s41467-023-39120-1

Researchers from the University of Edinburgh, Scotland, and the University of Cantabria, Spain, have teamed up to develop an artificial intelligence trained to discover senolytic medicinal chemistry in familiar compounds.

In the document “Discovery of senolytics using machine learning”, published in Nature communicationsThe researchers detail efforts to search more than 4,300 scientifically described compounds for the right chemical makeup to address cellular senescence.

Cellular senescence is what happens when a cell stops multiplying. This may be due to age, where a cell can self-determine that there are too many mutations accumulated to replicate safely or because the cell has been damaged. Senescence plays an essential role in limiting tumor progression by preventing damaged or mutated cells from proliferating.

The body can tolerate having some non-participating senescent cells in a tissue. The accumulation of high levels of senescent cells with age is associated with various forms of diseases such as osteoarthritis, lung disease, Alzheimer’s, dementia and cancer.

The screening method used 58 previously identified senolytics and 2,465 compounds known to have no senolytic effects to train the AI. After sufficient training, AI was introduced to libraries containing 4,340 FDA-approved or clinical-stage compounds.

Three senolytic compounds identified in the study are found in herbal medicines. Tests on human cells revealed that all three, ginkgetin, periplocin and oleandrin, were able to remove senescent cells without harming healthy cells.

One of the three stood out from the others, oleandrin, as the most promising. Oleandrin is a compound of the class of cardiac glycosides that are often used as heart drugs to increase cardiac output during heart failure and in the treatment of hypotension and arrhythmias.

The major cardiac glycoside compound currently in use is ouabain, which is known to have relatively high toxicity and is a common cause of poisoning. In fact, ouabain is a crucial component of the poison arrows used by Maasai warriors in Kenya.

Oleandrin showed greater senolytic performance than ouabain, functioning at a low nanomolar range, inhibiting its canonical target and activating its senolytic pathway more effectively. Oleandrin did not affect the viability of normal cells at the concentrations tested, indicating promising senolytic potential.

Cardiotoxicity is still an issue with oleandrin. The researchers suggest that being used directly to the damaged tissue site as a localized senolytic therapy could make it an impact treatment by reducing off-site risks, and clinical trials are currently evaluating such local administration of senolytics for osteoarthritis.

In a parallel effort, the researchers show that ex vivo senolytic perfusion of human transplanted livers preserves tissue architecture and regenerative capacity during cold storage.

The study used existing data to uncover previously unknown knowledge. While the current research findings offer an exciting new compound to test for overcoming cellular senescence, it is also another example of the beneficial contribution AI can make in the hands of researchers.

More information:
Vanessa Smer-Barreto et al, Discovery of senolytics using machine learning, Nature communications (2023). DOI: 10.1038/s41467-023-39120-1

About the magazine:
Nature communications

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