Chemical constituents isolated from Hosta ensata and their anti-inflammatory activities

A new phenol derivative, hostaphenol A (1), along with 16 known ones (2-17) were isolated from an ethanolic extract of the whole plants of Hosta ensata F. Maek. Their structures were elucidated by HRMS and NMR data as well as comparison with those reported in literature. The report of the first cyclopeptide and compounds 5, 6, 8, 10, 12-15, and 17 in the Asparagaceae family. Compound 2, as well as compounds 3, 4, 7, 9, 11, and 16 were reported for the first time from the Hosta genus and this plant, respectively. All compounds significantly reduced nitric oxide (NO) production at a concentration of 40 μM with no toxicity in RAW 264.7 cells stimulated by lipopolysaccharide. Among them, compounds 2-5 (40 μM) exerted obvious NO inhibitory activities, and their inhibition rate was exceeded 50%.

Machine learning-based models to predict modes of toxic action of phenols to Tetrahymena pyriformis

The phenols are structurally heterogeneous pollutants and they present a variety of modes of toxic action (MOA), including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles, and soft electrophiles. Because it is often difficult to determine correctly the mechanism of action of a compound, quantitative structure-activity relationship (QSAR) methods, which have proved their interest in toxicity prediction, can be used. In this work, several QSAR models for the prediction of MOA of 221 phenols to the ciliated protozoan Tetrahymena pyriformis, using Chemistry Development Kit descriptors, are reported. Four machine learning techniques (ML), k-nearest neighbours, support vector machine, classification trees, and artificial neural networks, have been used to develop several models with higher accuracies and predictive capabilities for distinguishing between four MOAs. They showed global accuracy values between 95.9% and 97.7% and area under Receiver Operator Curve values between 0.978 and 0.998; additionally, false alarm rate values were below 8.2% for training set. In order to validate our models, cross-validation (10-folds-out) and external test-set were performed with good behaviour in all cases. These models, obtained with ML techniques, were compared with others previously reported by other researchers, and the improvement was significant.