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Synthesis, cytotoxic activity evaluation and HQSAR study of novel isosteviol derivatives as potential anticancer agents
A series of novel isosteviol derivatives bearing amino alcohol and thiourea fragments have been stereo-selectively synthesized and screened for their in vitro cytotoxic activities against three human cancer cell lines (HCT-116, HGC-27 and JEKO-1). The results demonstrated that these compounds exhibi...
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Published in: | European journal of medicinal chemistry 2016-06, Vol.115, p.26-40 |
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Main Authors: | , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | A series of novel isosteviol derivatives bearing amino alcohol and thiourea fragments have been stereo-selectively synthesized and screened for their in vitro cytotoxic activities against three human cancer cell lines (HCT-116, HGC-27 and JEKO-1). The results demonstrated that these compounds exhibited prominent cytotoxicities. Especially, the compound Iw displayed the most potent anticancer activities against HCT-116 cell with IC50 value of 1.450 μM. On the basis of this bioassay results, these derivatives were further investigated by the hologram quantitative structure–activity relationship (HQSAR) technique. The optimal HQSAR model with q2 = 0.663, r2 = 0.895, SEE = 0.179 was generated using A/B/H/Ch as fragment distinction parameters and 4–7 as fragment size. This model was employed to predict the cytotoxic activities of test set compounds, and the predicted values were in good agreement with the experimental results. The contribution maps derived from the optimal model explained the individual atomic contribution to the total activity of single molecule.
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•A series of novel isosteviol-fused amino alcohols and thioureas were synthesized.•Their cytotoxicities were assessed against HCT-116, HGC-27 and JEKO-1 cell lines.•Compound Iw is promising anticancer agent against HCT-116 (IC50 = 1.450 μM).•HQSAR studies revealed good predictive model. |
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ISSN: | 0223-5234 1768-3254 |
DOI: | 10.1016/j.ejmech.2016.03.009 |