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Senolytic performance of oleandrin and periplocin. A Cell survival assay measuring the senolytic effect in OIS. Panels show representative crystal violet staining of tissue culture dishes of confluent senescent IMR90 ER:RAS cells and control IMR90 ER:STOP cells cultured with 100 nM 4OHT and treated with 10 nM oleandrin, ouabain, and periplocin and DMSO as vehicle control for 72 h. b Cell survival by quantification of crystal violet staining of the experiment shown in A, as described in the “Methods” section. Data shown as individual data points, and bars and error bars representing the mean ± SEM of 12 independent experiments. Statistical analysis was performed using one-way ANOVA (Tukey’s test) for multiple comparisons. vs Cell survival assay measuring senolytic effect in replicative senescence. Graphs representing cell survival by quantification of crystal violet staining of confluent cultures of IMR90 cells at passage 27 (replicative senescence) and IMR90 cells at passage 13 (control) treated with 10 nM oleandrin, ouabain and periplocin, and DMSO as vehicle control for 72 h (linked to Supplementary Fig. 9b). Data shown as individual data points, and bars and error bars representing the mean ± SEM of 12 independent experiments. Statistical analysis was performed using one-way ANOVA (Tukey’s test) for multiple comparisons. d Caspase 3/7 activity assay in control IMR90 ER:STOP and senescent IMR90 ER:RAS cells cultured in medium 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 numbers. Data representative of one of two independent experiments. Scale bars represent 100 μm. ns not significant, *p < 0.05, **p< 0.01, ***p < 0.001, ****p< 0.0001. Source data is provided in the form of a source data file. Credit: Nature Communication(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 AI trained to uncover senolytic medicinal chemistry in familiar compounds.
In the article “Discovery of senolytics using machine learning,” Posted in Nature Communicationresearchers detail efforts to search through more than 4,300 scientifically described compounds for the right chemical composition to treat cellular senescence.
Cellular senescence is what happens when a cell stops multiplying. This may be due to age, where a cell may determine on its own that there are too many accumulated mutations to replicate safely, or because the cell has been damaged. Senescence plays an essential role in limiting tumor progression by preventing the proliferation of damaged or mutated cells.
The body can tolerate having a few non-participating senescent cells in a tissue. The accumulation of high levels of senescent cells with age is associated with various forms of disease: osteoarthritis, lung disease, Alzheimer’s disease, dementia and cancer.
THE screening method used 58 previously identified senolytics and 2,465 compounds known to have no senolytic effect to form IA. After sufficient training, AI has been introduced to libraries containing 4,340 FDA-approved or clinical-stage compounds.
Three senolytic compounds identified in the study are found in medicinal plants. Are you sure human cells revealed that all three, ginkgetin, periplocin and oleandrin, were able to eliminate senescent cells without damaging healthy cells.
One of the three stood out from the others, oleandrin, as the most promising. Oleandrin is a compound in the class of cardiac glycosides that are often used as a heart medication to increase cardiac flow during heart failure and in the treatment of hypotension and arrhythmias.
The main 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 poison arrows used by Maasai warriors in Kenya.
Oleandrin showed greater senolytic performance than ouabain, operating at a low nanomolar range, inhibiting its canonical target and activating its senolytic pathway with higher efficiency. Oleandrin did not affect the viability of normal cells at the concentrations tested, all indicating promising senolytic potential.
Cardiotoxicity is always a problem with oleandrin. The researchers suggest that using the damaged tissue directly at the site as a localized senolytic therapy could make it an effective treatment while reducing off-site risks, and clinical tests are currently evaluating such local administration of senolytics for osteoarthritis.
In a parallel effort, the researchers show that ex-vivo senolytic perfusion of transplanted human livers preserves tissue architecture and regenerative capacity during cold storage.
The study used existing data to uncover previously unknown insights. While the current research results offer an exciting new compound to test for overcoming cellular senescence, it is also another example of how AI can benefit researchers.
More information:
Vanessa Smer-Barreto et al, Discovery of senolytics by machine learning, Nature Communication(2023). DOI: 10.1038/s41467-023-39120-1
© 2023 Science X Network
Quote: Researchers follow AI path to safer senolytic compounds (2023, Jun 16) Retrieved June 16, 2023 from https://phys.org/news/2023-06-ai-path-safer-senolytic- compounds.html
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