In Silico Drug Repurposing of FDA-Approved Drugs Highlighting Promacta as a Potential Inhibitor of H7N9 Influenza Virus

Influenza virus infections continue to be a significant and recurrent public health problem. Although vaccine efficacy varies, regular immunisation is the most effective method for suppressing the influenza virus. Antiviral drugs are available for influenza, although two of the four FDA-approved antiviral treatments have resulted in significant drug resistance. Therefore, new treatments are being sought to reduce the burden of flu-related illness. The time-consuming development of treatments for new and re-emerging diseases such as influenza and the high failure rate are increasing concerns. In this context, we used an in silico-based drug repurposing method to repurpose FDA-approved drugs as potential therapies against the H7N9 virus. To find potential inhibitors, a total of 2568 drugs were screened. Promacta, tucatinib, and lurasidone were identified as promising hits in the DrugBank database. According to the calculations of MM-GBSA, tucatinib (−54.11 kcal/mol) and Promacta (−56.20 kcal/mol) occupied the active site of neuraminidase with a higher binding affinity than the standard drug peramivir (−49.09 kcal/mol). Molecular dynamics (MD) simulation studies showed that the C-α atom backbones of the complexes of tucatinib and Promacta neuraminidase were stable throughout the simulation period. According to ADME analysis, the hit compounds have a high gastrointestinal absorption (GI) and do not exhibit properties that allow them to cross the blood–brain barrier (BBB). According to the in silico toxicity prediction, Promacta is not cardiotoxic, while lurasidone and tucatinib show only weak inhibition. Therefore, we propose to test these compounds experimentally against the influenza H7N9 virus. The investigation and validation of these potential H7N9 inhibitors would be beneficial in order to bring these compounds into clinical settings.     

基于微流控技术的细胞水平高通量药物筛选系统的研究进展

药物筛选是新药研发的关键步骤,创新药物的发现需要采用适当的药物作用靶点对大量化合物样品进行筛选.高通量筛选系统能够实现数千个反应同时测试和分析,大大提高了药物筛选的实验规模和效率.其中基于细胞水平的高通量药物筛选系统因为更加接近人体生理条件,成为主要的筛选模式.而目前发展成熟的高通量细胞筛选系统主要基于多孔板,存在细胞...     

Identification of Mtb GlmU Uridyltransferase Domain Inhibitors by Ligand-Based and Structure-Based Drug Design Approaches

Targeting enzymes that play a role in the biosynthesis of the bacterial cell wall has long been a strategy for antibacterial discovery. In particular, the cell wall of Mycobacterium tuberculosis (Mtb) is a complex of three layers, one of which is Peptidoglycan, an essential component providing rigidity and strength. UDP-GlcNAc, a precursor for the synthesis of peptidoglycan, is formed by GlmU, a bi-functional enzyme. Inhibiting GlmU Uridyltransferase activity has been proven to be an effective anti-bacterial, but its similarity with human enzymes has been a deterrent to drug development. To develop Mtb selective hits, the Mtb GlmU substrate binding pocket was compared with structurally similar human enzymes to identify selectivity determining factors. Substrate binding pockets and conformational changes upon substrate binding were analyzed and MD simulations with substrates were performed to quantify crucial interactions to develop critical pharmacophore features. Thereafter, two strategies were applied to propose potent and selective bacterial GlmU Uridyltransferase domain inhibitors: (i) optimization of existing inhibitors, and (ii) identification by virtual screening. The binding modes of hits identified from virtual screening and ligand growing approaches were evaluated further for their ability to retain stable contacts within the pocket during 20 ns MD simulations. Hits that are predicted to be more potent than existing inhibitors and selective against human homologues could be of great interest for rejuvenating drug discovery efforts towards targeting the Mtb cell wall for antibacterial discovery.     

Analysis of Plant–Plant Interactions Reveals the Presence of Potent Antileukemic Compounds

A method to identify anticancer compounds in plants was proposed based on the hypothesis that these compounds are primarily present in plants to provide them with an ecological advantage over neighboring plants and other competitors. According to this view, identifying plants that contain compounds that inhibit or interfere with the development of other plant species may facilitate the discovery of novel anticancer agents. The method was developed and tested using Magnolia grandiflora, Gynoxys verrucosa, Picradeniopsis oppositifolia, and Hedyosmum racemosum, which are plant species known to possess compounds with cytotoxic activities. Plant extracts were screened for growth inhibitory activity, and then a thin-layer chromatography bioautography assay was conducted. This located the major antileukemic compounds 1, 2, 4, and 5 in the extracts. Once the active compounds were located, they were extracted and purified, and their structures were determined. The growth inhibitory activity of the purified compounds showed a significant correlation with their antileukemic activity. The proposed approach is rapid, inexpensive, and can easily be implemented in areas of the world with high biodiversity but with less access to advanced facilities and biological assays.     

Novel Inhibitors of 2′-O-Methyltransferase of the SARS-CoV-2 Coronavirus

The COVID-19 pandemic is still affecting many people worldwide and causing a heavy burden to global health. To eliminate the disease, SARS-CoV-2, the virus responsible for the pandemic, can be targeted in several ways. One of them is to inhibit the 2′-O-methyltransferase (nsp16) enzyme that is crucial for effective translation of viral RNA and virus replication. For methylation of substrates, nsp16 utilizes S-adenosyl methionine (SAM). Binding of a small molecule in the protein site where SAM binds can disrupt the synthesis of viral proteins and, as a result, the replication of the virus. Here, we performed high-throughput docking into the SAM-binding site of nsp16 for almost 40 thousand structures, prepared for compounds from three libraries: Enamine Coronavirus Library, Enamine Nucleoside Mimetics Library, and Chemdiv Nucleoside Analogue Library. For the top scoring ligands, semi-empirical quantum-chemical calculations were performed, to better estimate protein–ligand binding enthalpy. Relying upon the calculated binding energies and predicted docking poses, we selected 21 compounds for experimental testing.     

Effect of deuteron density distribution on the deduction of screening potential from the D(d,p)T reaction in Be metals

The D(d,p)T reaction in Be metal environments has been measured to investigate the electron screening effect in metals in an energy region of from 5.5 keV to 10 keV in a center of mass system(CMS)at a temperature of 121 K.The depth distribution of deuteron density in Be metals has an impact on the observed reaction yields.A model of deuteron density distribution in metal has been proposed to obtain the original yields.A screening energy of(116±46)eV has been obtained with the assumed deuteron density distribution model.     

stk: A python toolkit for supramolecular assembly

A tool for the automated assembly, molecular optimization and property calculation of supramolecular materials is presented. stk is a modular, extensible and open‐source Python library that provides a simple Python API and integration with third party computational codes. stk currently supports the construction of linear polymers, small linear oligomers, organic cages in multiple topologies and covalent organic frameworks (COFs) in multiple framework topologies, but is designed to be easy to extend to new, unrelated, supramolecules or new topologies. Extension to metal–organic frameworks (MOFs), metallocycles or supramolecules, such as catenanes, would be straightforward. Through integration with third party codes, stk offers the user the opportunity to explore the potential energy landscape of the assembled supramolecule and then calculate the supramolecule's structural features and properties. stk provides support for high‐throughput screening of large batches of supramolecules at a time. The source code of the program can be found at https://github.com/supramolecular-toolkit/stk . © 2018 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc.     

用于考察外源添加剂对卷烟常规烟气指标影响的样品制备因子筛选

为筛选用于考察外源添加剂对卷烟常规烟气指标影响的样品制备关键控制因子,采用L_8(2~7)正交试验设计进行香兰素及乙基麦芽酚的添加实验.卷烟常规烟气指标变化数值通过极差分析、方差分析,结果显示:参比卷烟种类是影响卷烟烟气总粒相物、焦油、水分的高度显著因子,就试验采用的1R5F和3R4F参比卷烟而言,添加剂在高焦油3R4F卷烟中对常规烟气指标的影响比其在低焦油1R5F卷烟中的影响大;添加剂种类与浓度的交互作用不仅对卷烟烟气总粒相物有高度显著影响,而且对总粒相物中的水分也有显著影响;因此,规范样品制备,可减少误差、保证后续外源添加剂加入对卷烟常规烟气指标影响评价结果和推断的可靠性及科学性.