Triterpenoids with antiproliferative activities from the twigs and leaves of Melaleuca linariifolia.

One new pentacyclic triterpenoid, urs-12,16-dien-3-one (1), together with twelve known pentacyclic triterpenoids (2‒13), were isolated from the twigs and leaves of Melaleuca linariifolia. Their structures were characterized by their 1D- and 2 D-NMR spectra analysis and mass spectra studies. Furthermore, all isolated compounds were tested the inhibitory effect on proliferation of six human cancer cell lines in vitro, including NCI-H441, NCI-H460, A549, SKOV3, hela, and caki-1 cells. Among them, compounds 3, 5, 7, 9, 12, and 13 exhibited moderate antiproliferative activities with IC50 values ranging from 3.85 to 33.31 µM.

ADME Properties Evaluation in Drug Discovery: Prediction of Caco-2 Cell Permeability Using a Combination of NSGA-II and Boosting.

The Caco-2 cell monolayer model is a popular surrogate in predicting the in vitro human intestinal permeability of a drug due to its morphological and functional similarity with human enterocytes. A quantitative structure-property relationship (QSPR) study was carried out to predict Caco-2 cell permeability of a large data set consisting of 1272 compounds. Four different methods including multivariate linear regression (MLR), partial least-squares (PLS), support vector machine (SVM) regression and Boosting were employed to build prediction models with 30 molecular descriptors selected by nondominated sorting genetic algorithm-II (NSGA-II). The best Boosting model was obtained finally with R(2) = 0.97, RMSEF = 0.12, Q(2) = 0.83, RMSECV = 0.31 for the training set and RT(2) = 0.81, RMSET = 0.31 for the test set. A series of validation methods were used to assess the robustness and predictive ability of our model according to the OECD principles and then define its applicability domain. Compared with the reported QSAR/QSPR models about Caco-2 cell permeability, our model exhibits certain advantage in database size and prediction accuracy to some extent. Finally, we found that the polar volume, the hydrogen bond donor, the surface area and some other descriptors can influence the Caco-2 permeability to some extent. These results suggest that the proposed model is a good tool for predicting the permeability of drug candidates and to perform virtual screening in the early stage of drug development.

Multi-Target Screening and Experimental Validation of Natural Products from Selaginella Plants against Alzheimer's Disease.

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder which is considered to be the most common cause of dementia. It has a greater impact not only on the learning and memory disturbances but also on social and economy. Currently, there are mainly single-target drugs for AD treatment but the complexity and multiple etiologies of AD make them difficult to obtain desirable therapeutic effects. Therefore, the choice of multi-target drugs will be a potential effective strategy inAD treatment. To find multi-target active ingredients for AD treatment from Selaginella plants, we firstly explored the behaviors effects on AD mice of total extracts (TE) from Selaginella doederleinii on by Morris water maze test and found that TE has a remarkable improvement on learning and memory function for AD mice. And then, multi-target SAR models associated with AD-related proteins were built based on Random Forest (RF) and different descriptors to preliminarily screen potential active ingredients from Selaginella. Considering the prediction outputs and the quantity of existing compounds in our laboratory, 13 compounds were chosen to carry out the in vitro enzyme inhibitory experiments and 4 compounds with BACE1/MAO-B dual inhibitory activity were determined. Finally, the molecular docking was applied to verify the prediction results and enzyme inhibitory experiments. Based on these study and validation processes, we explored a new strategy to improve the efficiency of active ingredients screening based on trace amount of natural product and numbers of targets and found some multi-target compounds with biological activity for the development of novel drugs for AD treatment.