No abstract

Human epidermal growth factor receptor 2, HER2, is a commonly over-expressed tyrosine kinase receptor found in many types of carcinoma. Despite that there are several HER2 inhibitors, namely Iressa, Tarceva and Tykerb, currently in clinical trials, all can cause several side effects. In this study, both structure-based and ligand-based drug design were employed to design novel HER2 inhibitors from traditional Chinese medicine (TCM). The HER2 structure model was built in homology modeling based on known receptors of the same family. Docking and de novo evolution experiments were performed to identify candidates and to build derivatives. A training set of 32 compounds with inhibitory activities to HER2 was used to formulate the pharmacophore hypotheses that were subsequently used to examine candidates obtained from the docking study. Hydrogen bond interactions, salt-bridge formations and pi-stacking were observed between the ligands and Phe731, Lys753, Asp863 and Asp808 of HER2 protein. Combining results from both docking and pharmacophore mapping analysis, CLC015-5, CLC604-11 and CLC604-18 were well accepted and consistent in both approaches and were considered as the most potential HER2 inhibitors.

Peroxisome proliferator-activated receptors alpha, delta and gamma are a collection of ligand-activated transcription factors crucial in lipid and glucose homeostasis. The involvement of these receptors in lipid metabolism makes them perfect therapeutic target for treating obesity and stroke. In this study, 'sum of activity' model was employed to design multi-target agonists. We used a new strategy to design agonists that fit both alpha and delta but not gamma, to avoid side effect. The CoMFA and CoMSIA models were used to explore the pharmacophore features by constructing three individual models: (a) alpha-model, (b) delta-model and (c) gamma-model, and two sum models: (d) alpha, delta- model, and (e) alpha, delta and gamma-model. The CoMFA model yielded a significant cross validation value, q(2), of 0.729 and non-cross validation value, r(2), of 0.933 in the alpha, delta-model. The CoMSIA studies yielded the best predictive models with q(2) of 0.622 in A+S and with r(2) of 0.911 in the alpha, delta-model. Finally, we proposed that distinct features shown in models (a), (b), (d) but not (c) and (e) should be accounted in designing weight-controlling drugs.

Phosphodiesterase superfamily is the key regulator of 3',5'-cyclic guanosine monophosphate (cGMP) decomposition in human body. Phosphodiesterase-5 (PDE-5) inhibitors, sildenafil, vardenafil and tadalafil, are well known oral treatment for males with erectile dysfunction. To investigate the inhibitory effects of traditional Chinese medicine (TCM) compounds to PDE-5, we performed both ligand-based and structure-based studies on this topic. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) studies were conducted to construct three dimensional quantitative structure-activity relationship (3D-QSAR) models of series of known PDE-5 inhibitors. The predictive models had cross-validated, q(2), and non cross-validated coefficient, r(2), values of 0.791 and 0.948 for CoMFA and 0.724 and 0.908 for CoMSIA. These two 3D-QSAR models were used to predict activity of TCM compounds. Docking simulations were performed to further analyze the binding mode of training set and TCM compounds. A putative binding model was proposed based on CoMFA and CoMSIA contour maps and docking simulations; formation of pi-stacking, water bridge and specific hydrogen bonding were deemed important interactions between ligands and PDE-5. Of our TCM compounds, engeletin, satisfied our binding model, and hence, emerged as PDE-5 inhibitor candidate. Using this study as an example, we demonstrated that docking should be conducted for qualitative purposes, such as identifying protein characteristics, rather than for quantitative analyses that rank compound efficacy based on results of scoring functions. Prediction of compound activity should be reserved for QSAR analyses, and scoring functions and docking scores should be used for preliminary screening of TCM database (http://tcm.cmu.edu.tw/index.php).