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Showing 3311 to 3314 of 5186 entries
Published on September 14, 2018
Drug Repurposing of Metabolic Agents in Malignant Glioma.
Abstract: Gliomas are highly invasive brain tumors with short patient survival. One major pathogenic factor is aberrant tumor metabolism, which may be targeted with different specific and unspecific agents. Drug repurposing is of increasing interest in glioma research. Drugs interfering with the patient's metabolism may also influence glioma metabolism. In this review, we outline definitions and methods for drug repurposing. Furthermore, we give insights into important candidates for a metabolic drug repurposing, namely metformin, statins, non-steroidal anti-inflammatory drugs, disulfiram and lonidamine. Advantages and pitfalls of drug repurposing will finally be discussed.
Published on September 13, 2018
Sertraline, chlorprothixene, and chlorpromazine characteristically interact with the REST-binding site of the corepressor mSin3, showing medulloblastoma cell growth inhibitory activities.
Abstract: Dysregulation of repressor-element 1 silencing transcription factor REST/NRSF is related to several neuropathies, including medulloblastoma, glioblastoma, Huntington's disease, and neuropathic pain. Inhibitors of the interaction between the N-terminal repressor domain of REST/NRSF and the PAH1 domain of its corepressor mSin3 may ameliorate such neuropathies. In-silico screening based on the complex structure of REST/NRSF and mSin3 PAH1 yielded 52 active compounds, including approved neuropathic drugs. We investigated their binding affinity to PAH1 by NMR, and their inhibitory activity toward medulloblastoma cell growth. Interestingly, three antidepressant and antipsychotic medicines, sertraline, chlorprothixene, and chlorpromazine, were found to strongly bind to PAH1. Multivariate analysis based on NMR chemical shift changes in PAH1 residues induced by ligand binding was used to identify compound characteristics associated with cell growth inhibition. Active compounds showed a new chemo-type for inhibitors of the REST/NRSF-mSin3 interaction, raising the possibility of new therapies for neuropathies caused by dysregulation of REST/NRSF.
Published on September 12, 2018
A database of tissue-specific rhythmically expressed human genes has potential applications in circadian medicine.
Abstract: The discovery that half of the mammalian protein-coding genome is regulated by the circadian clock has clear implications for medicine. Recent studies demonstrated that the circadian clock influences therapeutic outcomes in human heart disease and cancer. However, biological time is rarely given clinical consideration. A key barrier is the absence of information on tissue-specific molecular rhythms in the human body. We have applied the cyclic ordering by periodic structure (CYCLOPS) algorithm, designed to reconstruct sample temporal order in the absence of time-of-day information, to the gene expression collection of 13 tissues from 632 human donors. We identified rhythms in gene expression across the body; nearly half of protein-coding genes were shown to be cycling in at least 1 of the 13 tissues analyzed. One thousand of these cycling genes encode proteins that either transport or metabolize drugs or are themselves drug targets. These results provide a useful resource for studying the role of circadian rhythms in medicine and support the idea that biological time might play a role in determining drug response.
Published on September 10, 2018
Proteomics, Post-translational Modifications, and Integrative Analyses Reveal Molecular Heterogeneity within Medulloblastoma Subgroups.
Abstract: There is a pressing need to identify therapeutic targets in tumors with low mutation rates such as the malignant pediatric brain tumor medulloblastoma. To address this challenge, we quantitatively profiled global proteomes and phospho-proteomes of 45 medulloblastoma samples. Integrated analyses revealed that tumors with similar RNA expression vary extensively at the post-transcriptional and post-translational levels. We identified distinct pathways associated with two subsets of SHH tumors, and found post-translational modifications of MYC that are associated with poor outcomes in group 3 tumors. We found kinases associated with subtypes and showed that inhibiting PRKDC sensitizes MYC-driven cells to radiation. Our study shows that proteomics enables a more comprehensive, functional readout, providing a foundation for future therapeutic strategies.
Published on September 1, 2018
Identification by virtual screening and functional characterisation of novel positive and negative allosteric modulators of the alpha7 nicotinic acetylcholine receptor.
Abstract: Several previous studies have demonstrated that the activity of neurotransmitters acting on ligand-gated ion channels such as the nicotinic acetylcholine receptor (nAChR) can be altered by compounds binding to allosteric modulatory sites. In the case of alpha7 nAChRs, both positive and negative allosteric modulators (PAMs and NAMs) have been identified and have attracted considerable interest. A recent study, employing revised structural models of the transmembrane domain of the alpha7 nAChR in closed and open conformations, has provided support for an inter-subunit transmembrane allosteric binding site (Newcombe et al 2017). In the present study, we have performed virtual screening of the DrugBank database using pharmacophore queries that were based on the predicted binding mode of PAMs to alpha7 nAChR structural models. A total of 81 compounds were identified in the DrugBank database, of which the 25 highest-ranked hits corresponded to one of four previously-identified therapeutic compound groups (carbonic anhydrase inhibitors, cyclin-dependent kinase inhibitors, diuretics targeting the Na(+)-K(+)-Cl(-) cotransporter, and fluoroquinolone antibiotics targeting DNA gyrase). The top-ranked compound from each of these four groups (DB04763, DB08122, furosemide and pefloxacin, respectively) was tested for its effects on human alpha7 nAChR expressed in Xenopus oocytes using two-electrode voltage-clamp electrophysiology. These studies, conducted with wild-type, mutant and chimeric receptors, resulted in all four compounds exerting allosteric modulatory effects. While DB04763, DB08122 and pefloxacin were antagonists, furosemide potentiated ACh responses. Our findings, supported by docking studies, are consistent with these compounds acting as PAMs and NAMs of the alpha7 nAChR via interaction with a transmembrane site.
Published on September 1, 2018
Prediction of Novel Drugs and Diseases for Hepatocellular Carcinoma Based on Multi-Source Simulated Annealing Based Random Walk.
Abstract: Computational techniques for foreseeing drug-disease associations by means of incorporating gene expression as well as biological network give high intuitions to the composite associations amongst targets, drugs, disease genes in addition to the diseases at a system level. Hepatocellular Carcinoma (HCC) is a malevolent tumor containing a greater rate of sickness as well as mortality. In the present work, an Integrative framework is presented with the aim of resolving this problem, for identifying new Drugs for HCC dependent upon Multi-Source Random Walk (PD-MRW), in which score the complete drugs by means of building the drug-drug similarity network. On the other hand, the collection of clinical phenotypes as well as drug side effects in combination with patient-specific genetic info. As a result, the formation of disease-drug networks that denotes the prescriptions, which are allotted to treat those diseases that are not concentrated by means of PD-MRW model. With the aim of overcoming this issue, this research offers an integrative framework for foreseeing new drugs as well as diseases for HCC dependent upon Multi-Source Simulated Annealing based Random Walk (PDD-MSSARW). Primarily, build a Gene-Gene Weighted Interaction Network (GWIN), dependent upon the gene expression as well as protein interaction network. After that, construct a drug-drug similarity network, dependent upon multi-source random walk in GWIN, disease-drug similarity network with the help of Similarity Weighted Bipartite Graph Network (SWBGN) that is build up in which the nodes are drugs as well as association among one node to another node that explains the disease diagnoses. Lastly, dependent upon the known drugs for HCC, score the entire drugs in the similarity networks. The sturdiness of the likelihoods, their overlap with those stated in Comparative Toxicogenomics Database (CTD) as well as kinds of literature, and their enhanced KEGG pathway illustrate PDD-MSSARW method be capable of efficiently find out novel drug signs.
Published on September 1, 2018
A systematic assessment of the availability and clinical drug information coverage of machine-readable clinical drug data sources for building knowledge translation products.
Abstract: Objective: To identify and describe clinical drug data sources that have the potential to serve as a repository of information for developing drug knowledge translation products. Methods: Two reviewers independently screened citations from PubMed and Embase, websites from the web search engine Google, and references from selected journals. Publicly licensed or non-proprietary data sources containing clinical drug information accessible in a machine-readable format were eligible. Data sources were assessed for their coverage across 18 pre-specified domains and 74 elements of clinical drug information. Results: Of the 3369 unique citations or webpages screened, 44 drug information data sources were identified. Of these, 22 data sources met the study inclusion criteria. There was a mean of 4.5 (SD = 5.19) domains covered by each source and a mean of 10.9 (SD = 18) elements covered by each source. None of the data sources covered all domains and eight elements were not addressed by any source. All of the data sources identified by the study are government or academic databases. Conclusion: Our study demonstrated the availability of machine-readable clinical drug data that could help facilitate the creation of novel drug knowledge translation products. However, we identified clinical content gaps in the available non-proprietary drug information sources. Further evaluation of the quality of each data source would be necessary prior to incorporating these sources into any knowledge translation products intended for clinical use.
Published on August 31, 2018
Machine Learning for Drug-Target Interaction Prediction.
Abstract: Identifying drug-target interactions will greatly narrow down the scope of search of candidate medications, and thus can serve as the vital first step in drug discovery. Considering that in vitro experiments are extremely costly and time-consuming, high efficiency computational prediction methods could serve as promising strategies for drug-target interaction (DTI) prediction. In this review, our goal is to focus on machine learning approaches and provide a comprehensive overview. First, we summarize a brief list of databases frequently used in drug discovery. Next, we adopt a hierarchical classification scheme and introduce several representative methods of each category, especially the recent state-of-the-art methods. In addition, we compare the advantages and limitations of methods in each category. Lastly, we discuss the remaining challenges and future outlook of machine learning in DTI prediction. This article may provide a reference and tutorial insights on machine learning-based DTI prediction for future researchers.
Published in August 2018
Identification of drug target candidates of the swine pathogen Actinobacillus pleuropneumoniae by construction of protein-protein interaction network.
Abstract: Porcine pleuropneumonia caused by Actinobacillus pleuropneumoniae has led to severe economic losses in the pig industry worldwide. A. pleuropneumoniae displays various levels of antimicrobial resistance, leading to the dire need to identify new drug targets. Protein-protein interaction (PPI) network can aid the identification of drug targets by discovering essential proteins during the life of bacteria. The aim of this study is to identify drug target candidates of A. pleuropneumoniae from essential proteins in PPI network. The homologous protein mapping method (HPM) was utilized to construct A. pleuropneumoniae PPI network. Afterwards, the subnetwork centered with H-NS was selected to verify the PPI network using bacterial two-hybrid assays. Drug target candidates were identified from the hub proteins by analyzing the topology of the network using interaction degree and homologous comparison with the pig proteome. An A. pleuropneumoniae PPI network containing 2737 non-redundant interaction pairs among 533 proteins was constructed. These proteins were distributed in 21 COG functional categories and 28 KEGG metabolic pathways. The A. pleuropneumoniae PPI network was scale free and the similar topological tendencies were found when compared with other bacteria PPI network. Furthermore, 56.3% of the H-NS subnetwork interactions were validated. 57 highly connected proteins (hub proteins) were identified from the A. pleuropneumoniae PPI network. Finally, 9 potential drug targets were identified from the hub proteins, with no homologs in swine. This study provides drug target candidates, which are promising for further investigations to explore lead compounds against A. pleuropneumoniae.
Published in August 2018
PregOMICS-Leveraging systems biology and bioinformatics for drug repurposing in maternal-child health.
Abstract: Obstetric diseases remain underserved and understudied. Drug repurposing-utilization of a drug whose use is accepted in one condition for a different condition-could represent a rapid and low-cost way to identify new therapies that are known to be safe. In diseases of pregnancy, the known safety profile is a strong additional incentive. We describe the techniques and steps used in the use of 'omics data for drug repurposing. We illustrate these techniques using case studies of published drug repurposing projects. We provide a set of available databases with low barriers to entry which investigators can use to perform their own projects. The promise of 'omics techniques is unbiased screening, either of all drug targets or of all patients using particular drugs to find which are likely to alter disease risk or progression. However, we caution that reproducibility across the underlying studies, and thus the drugs suggested for repurposing, can be poor. We suggest that improved nosology, for example correlating patient clinical conditions with placental pathology, could yield more robust associations. We conclude that 'omics-driven drug repurposing represents a potential fruitful path to discover new, safe treatments of obstetric diseases.
Showing 3311 to 3314 of 5186 entries