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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]

In this work, we propose a general methodology to assess the bioactive potential (BP) of extracts in the quest of vegetable-based drugs. To exemplify the method, we studied the anticancer potential (AP) of four endemic species of genus Hypericum (Hypericum canariense L, Hypericum glandulosum Aiton, Hypericum grandifolium Choisy and Hypericum reflexum L.f) from the Canary Islands. Microextracts were obtained from the aerial parts of these species and were tested against six human tumor cell lines, A549 (non-small-cell lung), HBL-100 (breast), HeLa (cervix), SW1573 (non-small-cell lung), T-47D (breast) and WiDr (colon). The methanol–water microextracts were evaluated further for cell migration, autophagy and cell death. The most promising bioactive polar microextracts were analyzed by UHPLC–DAD–MS. The extraction yield, the bioactivity evaluation and the chemical profiling by LC–MS suggested that H. grandifolium was the species with the highest AP. Label-free live-cell imaging studies on HeLa cells exposed to the methanol–water microextract of H. grandifolium enabled observing cell death and several apoptotic hallmarks. Overall, this study allows us to select Hypericum grandifolium Choisy as a source of new chemical entities with a potential interest for cancer treatment. [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. [ABSTRACT FROM AUTHOR]