Systematic Understanding of Mechanisms of a Chinese Herbal Formula in Treatment of Metabolic Syndrome by an Integrated Pharmacology Approach

Metabolic syndrome (MS) is becoming a worldwide health problem. Wendan decoction (WDD)—a famous traditional Chinese medicine formula—has been extensively employed to relieve syndromes related to MS in clinical practice in China. However, its pharmacological mechanisms still remain vague. In this study, a comprehensive approach that integrated chemomics, principal component analysis, molecular docking simulation, and network analysis was established to elucidate the multi-component and multi-target mechanism of action of WDD in treatment of MS. The compounds in WDD were found to possess chemical diversity, complexity and drug-likeness compared to MS drugs. Six nuclear receptors were obtained to have strong binding affinity with 217 compounds of five herbs in WDD. The importance roles of targets and herbs were also identified due to network parameters. Five compounds from Radix Glycyrrhizae Preparata can hit all six targets, which can assist in screening new MS drugs. The pathway network analysis demonstrated that the main pharmacological effects of WDD might lie in maintaining lipid and glucose metabolisms and anticancer activities as well as immunomodulatory and hepatoprotective effects. This study provided a comprehensive system approach for understanding the multi-component, multi-target and multi-pathway mechanisms of WDD during the treatment of MS

Multi-Layer Identification of Highly-Potent ABCA1 Up-Regulators Targeting LXRβ Using Multiple QSAR Modeling, Structural Similarity Analysis, and Molecular Docking

In this study, in silico approaches, including multiple QSAR modeling, structural similarity analysis, and molecular docking, were applied to develop QSAR classification models as a fast screening tool for identifying highly-potent ABCA1 up-regulators targeting LXRβ based on a series of new flavonoids. Initially, four modeling approaches, including linear discriminant analysis, support vector machine, radial basis function neural network, and classification and regression trees, were applied to construct different QSAR classification models. The statistics results indicated that these four kinds of QSAR models were powerful tools for screening highly potent ABCA1 up-regulators. Then, a consensus QSAR model was developed by combining the predictions from these four models. To discover new ABCA1 up-regulators at maximum accuracy, the compounds in the ZINC database that fulfilled the requirement of structural similarity of 0.7 compared to known potent ABCA1 up-regulator were subjected to the consensus QSAR model, which led to the discovery of 50 compounds. Finally, they were docked into the LXRβ binding site to understand their role in up-regulating ABCA1 expression. The excellent binding modes and docking scores of 10 hit compounds suggested they were highly-potent ABCA1 up-regulators targeting LXRβ. Overall, this study provided an effective strategy to discover highly potent ABCA1 up-regulators

Identfication of Potent LXRβ-Selective Agonists without LXRα Activation by In Silico Approaches

Activating Liver X receptors (LXRs) represents a promising therapeutic option for dyslipidemia. However, activating LXRα may cause undesired lipogenic effects. Discovery of highly LXRβ-selective agonists without LXRα activation were indispensable for dyslipidemia. In this study, in silico approaches were applied to develop highly potent LXRβ-selective agonists based on a series of newly reported 3-(4-(2-propylphenoxy)butyl)imidazolidine-2,4-dione-based LXRα/β dual agonists. Initially, Kohonen and stepwise multiple linear regression SW-MLR were performed to construct models for LXRβ agonists and LXRα agonists based on the structural characteristics of LXRα/β dual agonists, respectively. The obtained LXRβ agonist model gave a good predictive ability (R2train = 0.837, R2test = 0.843, Q2LOO = 0.715), and the LXRα agonist model produced even better predictive ability (R2train = 0.968, R2test = 0.914, Q2LOO = 0.895). Also, the two QSAR models were independent and can well distinguish LXRβ and LXRα activity. Then, compounds in the ZINC database met the lower limit of structural similarity of 0.7, compared to the 3-(4-(2-propylphenoxy)butyl)imidazolidine-2,4-dione scaffold subjected to our QSAR models, which resulted in the discovery of ZINC55084484 with an LXRβ prediction value of pEC50 equal to 7.343 and LXRα prediction value of pEC50 equal to −1.901. Consequently, nine newly designed compounds were proposed as highly LXRβ-selective agonists based on ZINC55084484 and molecular docking, of which LXRβ prediction values almost exceeded 8 and LXRα prediction values were below 0

Novel room-temperature perylene liquid crystals: synthesis of 1,7-dibrominated cholesterol–perylene bisimides with different ester-bridging chains and their mesomorphic properties

<p>Three novel 1,7-dibrominated cholesterol–perylene liquid crystals <b>6a, 6b</b> and <b>6c</b> with different ester-bridging chains were designed and synthesised in yields of 30–40%. Their structures were characterised by FT-IR, ¹H NMR and HR-MS spectra. Their mesomorphic behaviours were studied by differential scanning calorimetry (DSC), polarising optical microscopy (POM) and X-ray diffraction (XRD). Compounds <b>6a, 6b</b> and <b>6c</b> exhibit hexagonal columnar liquid crystalline phase at room temperature. Their mesomorphic temperature ranges are as wide as 140–162°C. Their fluorescence spectra suggested that they possess good fluorescence properties in solution. The soft ester-bridging chains are more favourable for room-temperature mesophase and high fluorescence than the rigid ester-bridging chain.</p