Denopamine, a selective beta1-receptor agonist and a new coronary vasodilator

Inhibitory efficiency of potential drugs against SARS-CoV-2 by blocking human angiotensin converting enzyme-2: Virtual screening and molecular dynamics study

Till date millions of people are infected by SARS-CoV-2 throughout the world, while no potential therapeutics or vaccines are available to combat this deadly virus. Blocking of human angiotensin-converting enzyme 2 (ACE-2) receptor, the binding site of SARS-CoV-2 spike protein, an effective strategy to discover a drug for COVID-19. Herein we have selected 24 anti-bacterial and anti-viral drugs and made a comprehensive analysis by screened them virtually against ACE-2 receptor to find the best blocker by molecular docking and molecular dynamics studies. Analysis of results revealed that, Cefpiramide (CPM) showed the highest binding affinity of −9.1 kcal/mol. Furthermore, MD study for 10 ns and evaluation of parameters like RMSD, RMSF, radius of gyration, solvent accessible surface area analysis confirmed that CPM effectively binds and blocks ACE-2 receptor efficiently.

Drug Side-Effect Prediction Via Random Walk on the Signed Heterogeneous Drug Network

Drug side-effects have become a major public health concern as they are the underlying cause of over a million serious injuries and deaths each year. Therefore, it is of critical importance to detect side-effects as early as possible. Existing computational methods mainly utilize the drug chemical profile and the drug biological profile to predict the side-effects of a drug. In the utilized drug biological profile information, they only focus on drug–target interactions and neglect the modes of action of drugs on target proteins. In this paper, we develop a new method for predicting potential side-effects of drugs based on more comprehensive drug information in which the modes of action of drugs on target proteins are integrated. Drug information of multiple types is modeled as a signed heterogeneous information network. We propose a signed heterogeneous information network embedding framework for learning drug embeddings and predicting side-effects of drugs. We use two bias random walk procedures to obtain drug sequences and train a Skip-gram model to learn drug embeddings. We experimentally demonstrate the performance of the proposed method by comparison with state-of-the-art methods. Furthermore, the results of a case study support our hypothesis that modes of action of drugs on target proteins are meaningful in side-effect prediction.

N-coumaroyldopamine and N-caffeoyldopamine increase cAMP via beta 2-adrenoceptors in myelocytic U937 cells

N-caffeoyldopamine is a phytochemical found in various plants, including cocoa (Theobroma cacao L.). N-caffeoyldopamine and its natural analogs (N-cinnamoyldopamine, N-coumaroyldopamine, N-feruloyldopamine, and N-sinapoyldopamine) were synthesized and investigated to determine their potency as beta-adrenoceptor agonists, because they have chemical structural moieties found in beta-adrenoceptor agonists. Among the compounds tested in this study, N-coumaroyldopamine and N-caffeoyldopamine were the two most potent compounds, able to increase cAMP at the concentrations < 0.05 microM in U937 cells. The decreasing order of potency was N-coumaroyldopamine > N-caffeoyldopamine > N-feruloyldopamine > N-sinapoyldopamine > N-cinnamoyldopamine. Using beta2-specific antagonists (butoxamine and ICI 118551), N-coumaroyldopamine and N-caffeoyldopamine were found to increase cAMP via beta2-adrenoceptors in U937 cells. In producing cAMP in U937 cells, N-coumaroyldopamine and N-caffeoyldopamine were as potent as several well-known beta2-adrenoceptor agonists (salbutamol, procaterol, and fenoterol). These results indicate that N-coumaroyldopamine and N-caffeoyldopamine are potent compounds able to increase cAMP via beta2-adrenoceptors in U937 cells, and may have potential effects on human health.