Abstract
Cancer remains the most common worldwide problem with the highest impact on global health. It is the second leading cause of death, due to the lack of early diagnosis and high recurrence rate after conventional therapies. Although every year several new therapeutic approaches are proposed the urgent need for more effective therapeutic strategies to improve the survival rate and life expectancy of cancer patients rapidly grows.
A recent promising anticancer strategy is based on multinuclear heterocycles as widely investigated bioactive molecules, considered important synthetic targets for the development of novel therapeutic agents. Many nitrogen heterocycles are known for a long time as natural alkaloids, known to possess the broad and diverse biological activity and medicinal applicability. Nowadays however novel multinuclear drug-like heterocyclic structures are generated by methods of artificial intelligence. Novel approaches are required as more expeditious ways of studying their biological activity, capable of more than explaining their activity, and even prognosticating it.
This study highlights our and other authors’ recent results on the biological activity of multinuclear heterocyclic molecules on cancer cells, explicitly based on their capacity to bind to G-quadruplexes. It further stresses the need for novel G-quadruplex binding compounds, with elucidated biochemical mechanisms of action for biomedical applications, namely in anticancer therapies.
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Acknowledgements
This work has been supported by Grant Number DN 19/11 of 10.12. 2017 from the National Science Fund of Bulgaria. We also acknowledge the provided access to the e-infrastructure of the NCHDC – part of the Bulgarian National Roadmap on RIs, with the financial support by the Grant No D01-387/18.12.2020.
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Bakalova, S.M., Georgieva, M., Kaneti, J. (2022). Modern Approaches to Cancer Treatment. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2022. Lecture Notes in Computer Science(), vol 13346. Springer, Cham. https://doi.org/10.1007/978-3-031-07704-3_18
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DOI: https://doi.org/10.1007/978-3-031-07704-3_18
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