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Microalgae and Cyanobacteria as a Potential Source of Anticancer Compounds
Published in Gokare A. Ravishankar, Ranga Rao Ambati, Handbook of Algal Technologies and Phytochemicals, 2019
Polyketides are a diverse class of compounds that are synthesized through a series of modular enzymes that condense and then modify chains of acetate or propionate units via reduction, dehydration, cyclization and aromatization reactions (Tidgewell et al. 2010). A polyketide isolated from the marine cyanobacterium Trichodemium thiebautii, trichophycin A, was found to exhibit cytotoxic activity against neuro-2a neuroblastoma cell line (EC50=6.5 µM) and human colon cancer cell line HCT-116 (EC50=11.7 µM) (Bertin et al. 2017). The cytotoxicity of the compound could be related to its polyol character. In another study, nuiapolide isolated from Okeaniaplumata was found to display antichemotactic activity against Jurkat cells as well as slowing or blocking the G2/M phase of the cancerous cells (Mori et al. 2015). Another polyketide, polycavernoside D, isolated from Okeania sp, showed moderate activity against the H-460 human lung carcinoma cell line (Navarro et al. 2015). Andrianasolo et al. (2005) isolated another polyketide, swinholide A, from Symploca cf. sp collected from Fiji; and two related glycosylated derivatives, ankaraholides A and B, from Geitlerinema sp. collected from Madagascar. The swinholide-based compounds showed potent inhibition against cancer cell growth and exerted their cytotoxic effect by disrupting actin cytoskeleton. Teruya et al. (2009) tested biselyngbyaside, a macrolide glycoside isolated from Lyngbya sp., and found that it displayed broad-spectrum cytotoxicity in a human tumor cell line panel consisting of 39 cancer cell lines. The compound showed potent antiproliferative activity against the central nervous system cancer SNB-78 (GI50= 0.036 μM) and lung cancer NCI H522 (GI50 = 0.067 μM) cell lines.
Methods in marine natural product drug discovery: what’s new?
Published in Expert Opinion on Drug Discovery, 2023
Jehad Almaliti, William H. Gerwick
There is a rich cross-fertilization potential between computational drug design and the fields of NP discovery based on chemical, metabolomic, proteomic, genomic and biological assay data. As a result, machine learning and other computational methods are becoming more common in the NPs sciences [10]. The drivers for these advances include a greater need to focus on compounds of significance to the desired goals at hand, be they chemical, biological or informatic. For example, sorting through the ‘haystack’ of chemical diversity represented in the extracts, proteomes and genomes of marine organisms to find novel chemical species not previously encountered is a dauting task (i.e. the task of ‘dereplication’). Various informatic programs are now available for this purpose, several of which are based on mass spectrometry data, and include the Global Natural Products Social (GNPS) Molecular Network [11], MS2LDA, and SIRIUS platforms, and a variety of advanced Principal Component Analyses (PCA) [12]. These represent paradigm shifts in NPs research. However, rigorous identification of new NPs still requires characterization by orthogonal techniques, most prominently NMR spectroscopy. Recent efforts have focused on merging various NMR techniques with mass spectrometry and in silico databases. For example, a novel dereplication and structure annotation approach was recently introduced, called Small Molecule Accurate Recognition Technology (SMART), that involved training a deep Convolutional Neural Network (CNN) with over 100,000 1H-13C HSQC spectra from the literature and via calculation. SMART was used to initially characterize a new chimeric swinholide-like macrolide, symplocolide A (4), as well as several derivatives [13].