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To explore new compounds with antitumour activity, fifteen phenolic nor-tripterpenes isolated from Celastraceae species, Maytenus jelskii, Maytenus cuzcoina, and Celastrus vulcanicola, have been studied. Their chemical structures were elucidated through spectroscopic and spectrometric techniques, resulting in the identification of three novel chemical compounds. Evaluation on human tumour cell lines (A549 and SW1573, non-small cell lung; HBL-100 and T-47D, breast; HeLa, cervix, and WiDr, colon) revealed that three compounds, named 6-oxo-pristimerol, demethyl-zeylasteral, and zeylasteral, exhibited significant activity (GI50 ranging from 0.45 to 8.6 µM) on at least five of the cell lines tested. Continuous live cell imaging identified apoptosis as the mode of action of selective cell killing in HeLa cells. Furthermore, their effect on a drug-sensitive Saccharomyces cerevisiae strain has been investigated to deepen on their mechanism of action. In dose-response growth curves, zeylasteral and 7α-hydroxy-blepharodol were markedly active. Additionally, halo assays were conducted to assess the involvement of oxidative stress and/or mitochondrial function in the anticancer profile, ruling out these modes of action for the active compounds. Finally, we also delve into the structure-activity relationship, providing insights into how the molecular structure of these compounds influences their biological activity. This comprehensive analysis enhances our understanding of the therapeutic potential of this triterpene type and underscores its relevance for further research in this field. [ABSTRACT FROM AUTHOR]

Natural products represent an excellent source of unprecedented anticancer compounds. However, the identification of the mechanism of action remains a major challenge. Several techniques and methodologies have been considered, but with limited success. In this work, we explored the combination of live cell imaging and machine learning techniques as a promising tool to depict in a fast and affordable test the mode of action of natural compounds with antiproliferative activity. To develop the model, we selected the non-small cell lung cancer cell line SW1573, which was exposed to the known antimitotic drugs paclitaxel, colchicine and vinblastine. The novelty of our methodology focuses on two main features with the highest relevance, (a) meaningful phenotypic metrics, and (b) fast Fourier transform (FFT) of the time series of the phenotypic parameters into their corresponding amplitudes and phases. The resulting algorithm was able to cluster the microtubule disruptors, and meanwhile showed a negative correlation between paclitaxel and the other treatments. The FFT approach was able to group the samples as efficiently as checking by eye. This methodology could easily scale to group a large amount of data without visual supervision.

Natural products represent an excellent source of unprecedented anticancer compounds. However, the identification of the mechanism of action remains a major challenge. Several techniques and methodologies have been considered, but with limited success. In this work, we explored the combination of live cell imaging and machine learning techniques as a promising tool to depict in a fast and affordable test the mode of action of natural compounds with antiproliferative activity. To develop the model, we selected the non-small cell lung cancer cell line SW1573, which was exposed to the known antimitotic drugs paclitaxel, colchicine and vinblastine. The novelty of our methodology focuses on two main features with the highest relevance, (a) meaningful phenotypic metrics, and (b) fast Fourier transform (FFT) of the time series of the phenotypic parameters into their corresponding amplitudes and phases. The resulting algorithm was able to cluster the microtubule disruptors, and meanwhile showed a negative correlation between paclitaxel and the other treatments. The FFT approach was able to group the samples as efficiently as checking by eye. This methodology could easily scale to group a large amount of data without visual supervision. [ABSTRACT FROM AUTHOR]

[Display omitted] Isatin derivatives have attracted a lot of interest for their potential in the development of new anticancer drugs. A library of 38 isatin derivatives, created through an Ugi four-component reaction, underwent an initial screening in a panel of six human solid tumor cell lines. The four most active derivatives were then selected for further testing. These compounds showed selectivity towards the non-small cell lung cancer (NSCLC) cell line SW1573, whilst NSCLC A549 cells were barely affected. The combination of phenotypic assays, including wound healing, clonogenic and continuous live cell imaging provided a deeper understanding of the compounds' mode of action. In particular, the latter demonstrated that isatin derivatives were able to induce necroptosis in SW1573 cells. The kinetics of cell death showed that necroptosis appeared after 2.5 h of exposure, which could be delayed to 7 h when co-treated with necrostatin-1. Interaction between the isatin derivatives and the KRAS G12C protein variant was discarded after in silico studies. Further studies are warranted to identify the cellular target responsible for the observed selectivity among cell lines. [ABSTRACT FROM AUTHOR]