Sample records for human disease-drug network
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Human Environmental Disease Network
DEFF Research Database (Denmark)
Taboureau, Olivier; Audouze, Karine
2017-01-01
During the past decades, many epidemiological, toxicological and biological studies have been performed to assess the role of environmental chemicals as potential toxicants for diverse human disorders. However, the relationships between diseases based on chemical exposure have been rarely studied...... by computational biology. We developed a human environmental disease network (EDN) to explore and suggest novel disease-disease and chemical-disease relationships. The presented scored EDN model is built upon the integration on systems biology and chemical toxicology using chemical contaminants information...... and their disease relationships from the reported TDDB database. The resulting human EDN takes into consideration the level of evidence of the toxicant-disease relationships allowing including some degrees of significance in the disease-disease associations. Such network can be used to identify uncharacterized...
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A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases.
Directory of Open Access Journals (Sweden)
Ariel José Berenstein
2016-01-01
Full Text Available Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins and chemical (bioactive compounds data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 105 compounds and several functional relations among 1.67 105 proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by
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A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases.
Berenstein, Ariel José; Magariños, María Paula; Chernomoretz, Ariel; Agüero, Fernán
2016-01-01
Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins) and chemical (bioactive compounds) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 105 compounds and several functional relations among 1.67 105 proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by independent
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Directory of Open Access Journals (Sweden)
Min Oh
Full Text Available The growing number and variety of genetic network datasets increases the feasibility of understanding how drugs and diseases are associated at the molecular level. Properly selected features of the network representations of existing drug-disease associations can be used to infer novel indications of existing drugs. To find new drug-disease associations, we generated an integrative genetic network using combinations of interactions, including protein-protein interactions and gene regulatory network datasets. Within this network, network adjacencies of drug-drug and disease-disease were quantified using a scored path between target sets of them. Furthermore, the common topological module of drugs or diseases was extracted, and thereby the distance between topological drug-module and disease (or disease-module and drug was quantified. These quantified scores were used as features for the prediction of novel drug-disease associations. Our classifiers using Random Forest, Multilayer Perceptron and C4.5 showed a high specificity and sensitivity (AUC score of 0.855, 0.828 and 0.797 respectively in predicting novel drug indications, and displayed a better performance than other methods with limited drug and disease properties. Our predictions and current clinical trials overlap significantly across the different phases of drug development. We also identified and visualized the topological modules of predicted drug indications for certain types of cancers, and for Alzheimer's disease. Within the network, those modules show potential pathways that illustrate the mechanisms of new drug indications, including propranolol as a potential anticancer agent and telmisartan as treatment for Alzheimer's disease.
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Targeting molecular networks for drug research
Directory of Open Access Journals (Sweden)
José Pedro Pinto
2014-06-01
Full Text Available The study of molecular networks has recently moved into the limelight of biomedical research. While it has certainly provided us with plenty of new insights into cellular mechanisms, the challenge now is how to modify or even restructure these networks. This is especially true for human diseases, which can be regarded as manifestations of distorted states of molecular networks. Of the possible interventions for altering networks, the use of drugs is presently the most feasible. In this mini-review, we present and discuss some exemplary approaches of how analysis of molecular interaction networks can contribute to pharmacology (e.g., by identifying new drug targets or prediction of drug side effects, as well as listing pointers to relevant resources and software to guide future research. We also outline recent progress in the use of drugs for in vitro reprogramming of cells, which constitutes an example par excellence for altering molecular interaction networks with drugs.
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Exploring drug-target interaction networks of illicit drugs.
Atreya, Ravi V; Sun, Jingchun; Zhao, Zhongming
2013-01-01
Drug addiction is a complex and chronic mental disease, which places a large burden on the American healthcare system due to its negative effects on patients and their families. Recently, network pharmacology is emerging as a promising approach to drug discovery by integrating network biology and polypharmacology, allowing for a deeper understanding of molecular mechanisms of drug actions at the systems level. This study seeks to apply this approach for investigation of illicit drugs and their targets in order to elucidate their interaction patterns and potential secondary drugs that can aid future research and clinical care. In this study, we extracted 188 illicit substances and their related information from the DrugBank database. The data process revealed 86 illicit drugs targeting a total of 73 unique human genes, which forms an illicit drug-target network. Compared to the full drug-target network from DrugBank, illicit drugs and their target genes tend to cluster together and form four subnetworks, corresponding to four major medication categories: depressants, stimulants, analgesics, and steroids. External analysis of Anatomical Therapeutic Chemical (ATC) second sublevel classifications confirmed that the illicit drugs have neurological functions or act via mechanisms of stimulants, opioids, and steroids. To further explore other drugs potentially having associations with illicit drugs, we constructed an illicit-extended drug-target network by adding the drugs that have the same target(s) as illicit drugs to the illicit drug-target network. After analyzing the degree and betweenness of the network, we identified hubs and bridge nodes, which might play important roles in the development and treatment of drug addiction. Among them, 49 non-illicit drugs might have potential to be used to treat addiction or have addictive effects, including some results that are supported by previous studies. This study presents the first systematic review of the network
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Directory of Open Access Journals (Sweden)
Avijit Podder
2017-06-01
Full Text Available Intercommunication of Dopamine Receptors (DRs with their associate protein partners is crucial to maintain regular brain function in human. Majority of the brain disorders arise due to malfunctioning of such communication process. Hence, contributions of genetic factors, as well as phenotypic indications for various neurological and psychiatric disorders are often attributed as sharing in nature. In our earlier research article entitled “Human Dopamine Receptors Interaction Network (DRIN: a systems biology perspective on topology, stability and functionality of the network” (Podder et al., 2014 [1], we had depicted a holistic interaction map of human Dopamine Receptors. Given emphasis on the topological parameters, we had characterized the functionality along with the vulnerable properties of the network. In support of this, we hereby provide an additional data highlighting the genetic overlapping of various brain disorders in the network. The data indicates the sharing nature of disease genes for various neurological and psychiatric disorders in dopamine receptors connecting protein-protein interactions network. The data also indicates toward an alternative approach to prioritize proteins for overlapping brain disorders as valuable drug targets in the network.
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The emerging paradigm of network medicine in the study of human disease.
Chan, Stephen Y; Loscalzo, Joseph
2012-07-20
The molecular pathways that govern human disease consist of molecular circuits that coalesce into complex, overlapping networks. These network pathways are presumably regulated in a coordinated fashion, but such regulation has been difficult to decipher using only reductionistic principles. The emerging paradigm of "network medicine" proposes to utilize insights garnered from network topology (eg, the static position of molecules in relation to their neighbors) as well as network dynamics (eg, the unique flux of information through the network) to understand better the pathogenic behavior of complex molecular interconnections that traditional methods fail to recognize. As methodologies evolve, network medicine has the potential to capture the molecular complexity of human disease while offering computational methods to discern how such complexity controls disease manifestations, prognosis, and therapy. This review introduces the fundamental concepts of network medicine and explores the feasibility and potential impact of network-based methods for predicting individual manifestations of human disease and designing rational therapies. Wherever possible, we emphasize the application of these principles to cardiovascular disease.
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Exploring drug-target interaction networks of illicit drugs
Atreya, Ravi V; Sun, Jingchun; Zhao, Zhongming
2013-01-01
Background Drug addiction is a complex and chronic mental disease, which places a large burden on the American healthcare system due to its negative effects on patients and their families. Recently, network pharmacology is emerging as a promising approach to drug discovery by integrating network biology and polypharmacology, allowing for a deeper understanding of molecular mechanisms of drug actions at the systems level. This study seeks to apply this approach for investigation of illicit dru...
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Janga, Sarath Chandra; Tzakos, Andreas
2009-12-01
Recent years have seen an explosion in the amount of "omics" data and the integration of several disciplines, which has influenced all areas of life sciences including that of drug discovery. Several lines of evidence now suggest that the traditional notion of "one drug-one protein" for one disease does not hold any more and that treatment for most complex diseases can best be attempted using polypharmacological approaches. In this review, we formalize the definition of a drug-target network by decomposing it into drug, target and disease spaces and provide an overview of our understanding in recent years about its structure and organizational principles. We discuss advances made in developing promiscuous drugs following the paradigm of polypharmacology and reveal their advantages over traditional drugs for targeting diseases such as cancer. We suggest that drug-target networks can be decomposed to be studied at a variety of levels and argue that such network-based approaches have important implications in understanding disease phenotypes and in accelerating drug discovery. We also discuss the potential and scope network pharmacology promises in harnessing the vast amount of data from high-throughput approaches for therapeutic advantage.
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Riera-Fernández, Pablo; Munteanu, Cristian R; Dorado, Julian; Martin-Romalde, Raquel; Duardo-Sanchez, Aliuska; González-Diaz, Humberto
2011-12-01
Complex Networks are useful in solving problems in drug research and industry, developing mathematical representations of different systems. These systems move in a wide range from relatively simple graph representations of drug molecular structures to large systems. We can cite for instance, drug-target protein interaction networks, drug policy legislation networks, or drug treatment in large geographical disease spreading networks. In any case, all these networks have essentially the same components: nodes (atoms, drugs, proteins, microorganisms and/or parasites, geographical areas, drug policy legislations, etc.) and edges (chemical bonds, drug-target interactions, drug-parasite treatment, drug use, etc.). Consequently, we can use the same type of numeric parameters called Topological Indices (TIs) to describe the connectivity patterns in all these kinds of Complex Networks despite the nature of the object they represent. The main reason for this success of TIs is the high flexibility of this theory to solve in a fast but rigorous way many apparently unrelated problems in all these disciplines. Another important reason for the success of TIs is that using these parameters as inputs we can find Quantitative Structure-Property Relationships (QSPR) models for different kind of problems in Computer-Aided Drug Design (CADD). Taking into account all the above-mentioned aspects, the present work is aimed at offering a common background to all the manuscripts presented in this special issue. In so doing, we make a review of the most common types of complex networks involving drugs or their targets. In addition, we review both classic TIs that have been used to describe the molecular structure of drugs and/or larger complex networks. Next, we use for the first time a Markov chain model to generalize Galvez TIs to higher order analogues coined here as the Markov-Galvez TIs of order k (MGk). Lastly, we illustrate the calculation of MGk values for different classes of
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The analysis of HIV/AIDS drug-resistant on networks
Liu, Maoxing
2014-01-01
In this paper, we present an Human Immunodeficiency Virus (HIV)/Acquired Immune Deficiency Syndrome (AIDS) drug-resistant model using an ordinary differential equation (ODE) model on scale-free networks. We derive the threshold for the epidemic to be zero in infinite scale-free network. We also prove the stability of disease-free equilibrium (DFE) and persistence of HIV/AIDS infection. The effects of two immunization schemes, including proportional scheme and targeted vaccination, are studied and compared. We find that targeted strategy compare favorably to a proportional condom using has prominent effect to control HIV/AIDS spread on scale-free networks.
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Drug Abuse Warning Network (DAWN-2006)
U.S. Department of Health & Human Services — The Drug Abuse Warning Network (DAWN) is a nationally representative public health surveillance system that has monitored drug related emergency department (ED)...
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Drug Abuse Warning Network (DAWN-2005)
U.S. Department of Health & Human Services — The Drug Abuse Warning Network (DAWN) is a nationally representative public health surveillance system that has monitored drug related emergency department (ED)...
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Drug Abuse Warning Network (DAWN-2007)
U.S. Department of Health & Human Services — The Drug Abuse Warning Network (DAWN) is a nationally representative public health surveillance system that has monitored drug related emergency department (ED)...
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Drug Abuse Warning Network (DAWN-2004)
U.S. Department of Health & Human Services — The Drug Abuse Warning Network (DAWN) is a nationally representative public health surveillance system that has monitored drug related emergency department (ED)...
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Drug Abuse Warning Network (DAWN-2009)
U.S. Department of Health & Human Services — The Drug Abuse Warning Network (DAWN) is a nationally representative public health surveillance system that has monitored drug related emergency department (ED)...
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Drug Abuse Warning Network (DAWN-2010)
U.S. Department of Health & Human Services — The Drug Abuse Warning Network (DAWN) is a nationally representative public health surveillance system that has monitored drug related emergency department (ED)...
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Drug Abuse Warning Network (DAWN-2008)
U.S. Department of Health & Human Services — The Drug Abuse Warning Network (DAWN) is a nationally representative public health surveillance system that has monitored drug related emergency department (ED)...
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Drug Abuse Warning Network (DAWN-2011)
U.S. Department of Health & Human Services — The Drug Abuse Warning Network (DAWN) is a nationally representative public health surveillance system that has monitored drug related emergency department (ED)...
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Human Environmental Disease Network: A computational model to assess toxicology of contaminants.
Taboureau, Olivier; Audouze, Karine
2017-01-01
During the past decades, many epidemiological, toxicological and biological studies have been performed to assess the role of environmental chemicals as potential toxicants associated with diverse human disorders. However, the relationships between diseases based on chemical exposure rarely have been studied by computational biology. We developed a human environmental disease network (EDN) to explore and suggest novel disease-disease and chemical-disease relationships. The presented scored EDN model is built upon the integration of systems biology and chemical toxicology using information on chemical contaminants and their disease relationships reported in the TDDB database. The resulting human EDN takes into consideration the level of evidence of the toxicant-disease relationships, allowing inclusion of some degrees of significance in the disease-disease associations. Such a network can be used to identify uncharacterized connections between diseases. Examples are discussed for type 2 diabetes (T2D). Additionally, this computational model allows confirmation of already known links between chemicals and diseases (e.g., between bisphenol A and behavioral disorders) and also reveals unexpected associations between chemicals and diseases (e.g., between chlordane and olfactory alteration), thus predicting which chemicals may be risk factors to human health. The proposed human EDN model allows exploration of common biological mechanisms of diseases associated with chemical exposure, helping us to gain insight into disease etiology and comorbidity. This computational approach is an alternative to animal testing supporting the 3R concept.
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Identification of human disease genes from interactome network using graphlet interaction.
Directory of Open Access Journals (Sweden)
Xiao-Dong Wang
Full Text Available Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes.
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Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction
Yang, Lun; Wei, Dong-Qing; Qi, Ying-Xin; Jiang, Zong-Lai
2014-01-01
Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes. PMID:24465923
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Large-Scale Analysis of Network Bistability for Human Cancers
Shiraishi, Tetsuya; Matsuyama, Shinako; Kitano, Hiroaki
2010-01-01
Protein–protein interaction and gene regulatory networks are likely to be locked in a state corresponding to a disease by the behavior of one or more bistable circuits exhibiting switch-like behavior. Sets of genes could be over-expressed or repressed when anomalies due to disease appear, and the circuits responsible for this over- or under-expression might persist for as long as the disease state continues. This paper shows how a large-scale analysis of network bistability for various human cancers can identify genes that can potentially serve as drug targets or diagnosis biomarkers. PMID:20628618
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Directory of Open Access Journals (Sweden)
Jian Li
2012-01-01
Full Text Available Current strategies for drug discovery have reached a bottleneck where the paradigm is generally “one gene, one drug, one disease.” However, using holistic and systemic views, network pharmacology may be the next paradigm in drug discovery. Based on network pharmacology, a combinational drug with two or more compounds could offer beneficial synergistic effects for complex diseases. Interestingly, traditional chinese medicine (TCM has been practicing holistic views for over 3,000 years, and its distinguished feature is using herbal formulas to treat diseases based on the unique pattern classification. Though TCM herbal formulas are acknowledged as a great source for drug discovery, no drug discovery strategies compatible with the multidimensional complexities of TCM herbal formulas have been developed. In this paper, we highlighted some novel paradigms in TCM-based network pharmacology and new drug discovery. A multiple compound drug can be discovered by merging herbal formula-based pharmacological networks with TCM pattern-based disease molecular networks. Herbal formulas would be a source for multiple compound drug candidates, and the TCM pattern in the disease would be an indication for a new drug.
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Protein Networks in Alzheimer's Disease
DEFF Research Database (Denmark)
Carlsen, Eva Meier; Rasmussen, Rune
2017-01-01
Overlap of RNA and protein networks reveals glia cells as key players for the development of symptomatic Alzheimer’s disease in humans......Overlap of RNA and protein networks reveals glia cells as key players for the development of symptomatic Alzheimer’s disease in humans...
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Visualization of network target crosstalk optimizes drug synergism in myocardial ischemia.
Directory of Open Access Journals (Sweden)
Xiaojing Wan
Full Text Available Numerous drugs and compounds have been validated as protecting against myocardial ischemia (MI, a leading cause of heart failure; however, synergistic possibilities among them have not been systematically explored. Thus, there appears to be significant room for optimization in the field of drug combination therapy for MI. Here, we propose an easy approach for the identification and optimization of MI-related synergistic drug combinations via visualization of the crosstalk between networks of drug targets corresponding to different drugs (each drug has a unique network of targets. As an example, in the present study, 28 target crosstalk networks (TCNs of random pairwise combinations of 8 MI-related drugs (curcumin, capsaicin, celecoxib, raloxifene, silibinin, sulforaphane, tacrolimus, and tamoxifen were established to illustrate the proposed method. The TCNs revealed a high likelihood of synergy between curcumin and the other drugs, which was confirmed by in vitro experiments. Further drug combination optimization showed a synergistic protective effect of curcumin, celecoxib, and sililinin in combination against H₂O₂-induced ischemic injury of cardiomyocytes at a relatively low concentration of 500 nM. This result is in agreement with the earlier finding of a denser and modular functional crosstalk between their networks of targets in the regulation of cell apoptosis. Our study offers a simple approach to rapidly search for and optimize potent synergistic drug combinations, which can be used for identifying better MI therapeutic strategies. Some new light was also shed on the characteristic features of drug synergy, suggesting that it is possible to apply this method to other complex human diseases.
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Visualization of network target crosstalk optimizes drug synergism in myocardial ischemia.
Wan, Xiaojing; Meng, Jia; Dai, Yingnan; Zhang, Yina; Yan, Shuang
2014-01-01
Numerous drugs and compounds have been validated as protecting against myocardial ischemia (MI), a leading cause of heart failure; however, synergistic possibilities among them have not been systematically explored. Thus, there appears to be significant room for optimization in the field of drug combination therapy for MI. Here, we propose an easy approach for the identification and optimization of MI-related synergistic drug combinations via visualization of the crosstalk between networks of drug targets corresponding to different drugs (each drug has a unique network of targets). As an example, in the present study, 28 target crosstalk networks (TCNs) of random pairwise combinations of 8 MI-related drugs (curcumin, capsaicin, celecoxib, raloxifene, silibinin, sulforaphane, tacrolimus, and tamoxifen) were established to illustrate the proposed method. The TCNs revealed a high likelihood of synergy between curcumin and the other drugs, which was confirmed by in vitro experiments. Further drug combination optimization showed a synergistic protective effect of curcumin, celecoxib, and sililinin in combination against H₂O₂-induced ischemic injury of cardiomyocytes at a relatively low concentration of 500 nM. This result is in agreement with the earlier finding of a denser and modular functional crosstalk between their networks of targets in the regulation of cell apoptosis. Our study offers a simple approach to rapidly search for and optimize potent synergistic drug combinations, which can be used for identifying better MI therapeutic strategies. Some new light was also shed on the characteristic features of drug synergy, suggesting that it is possible to apply this method to other complex human diseases.
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Pro-cognitive drug effects modulate functional brain network organization
Giessing, Carsten; Thiel, Christiane M.
2012-01-01
Previous studies document that cholinergic and noradrenergic drugs improve attention, memory and cognitive control in healthy subjects and patients with neuropsychiatric disorders. In humans neural mechanisms of cholinergic and noradrenergic modulation have mainly been analyzed by investigating drug-induced changes of task-related neural activity measured with functional magnetic resonance imaging (fMRI). Endogenous neural activity has often been neglected. Further, although drugs affect the coupling between neurons, only a few human studies have explicitly addressed how drugs modulate the functional connectome, i.e., the functional neural interactions within the brain. These studies have mainly focused on synchronization or correlation of brain activations. Recently, there are some drug studies using graph theory and other new mathematical approaches to model the brain as a complex network of interconnected processing nodes. Using such measures it is possible to detect not only focal, but also subtle, widely distributed drug effects on functional network topology. Most important, graph theoretical measures also quantify whether drug-induced changes in topology or network organization facilitate or hinder information processing. Several studies could show that functional brain integration is highly correlated with behavioral performance suggesting that cholinergic and noradrenergic drugs which improve measures of cognitive performance should increase functional network integration. The purpose of this paper is to show that graph theory provides a mathematical tool to develop theory-driven biomarkers of pro-cognitive drug effects, and also to discuss how these approaches can contribute to the understanding of the role of cholinergic and noradrenergic modulation in the human brain. Finally we discuss the “global workspace” theory as a theoretical framework of pro-cognitive drug effects and argue that pro-cognitive effects of cholinergic and noradrenergic drugs
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Network Analysis of Human Genes Influencing Susceptibility to Mycobacterial Infections
Lipner, Ettie M.; Garcia, Benjamin J.; Strong, Michael
2016-01-01
Tuberculosis and nontuberculous mycobacterial infections constitute a high burden of pulmonary disease in humans, resulting in over 1.5 million deaths per year. Building on the premise that genetic factors influence the instance, progression, and defense of infectious disease, we undertook a systems biology approach to investigate relationships among genetic factors that may play a role in increased susceptibility or control of mycobacterial infections. We combined literature and database mining with network analysis and pathway enrichment analysis to examine genes, pathways, and networks, involved in the human response to Mycobacterium tuberculosis and nontuberculous mycobacterial infections. This approach allowed us to examine functional relationships among reported genes, and to identify novel genes and enriched pathways that may play a role in mycobacterial susceptibility or control. Our findings suggest that the primary pathways and genes influencing mycobacterial infection control involve an interplay between innate and adaptive immune proteins and pathways. Signaling pathways involved in autoimmune disease were significantly enriched as revealed in our networks. Mycobacterial disease susceptibility networks were also examined within the context of gene-chemical relationships, in order to identify putative drugs and nutrients with potential beneficial immunomodulatory or anti-mycobacterial effects. PMID:26751573
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Interconnectivity of human cellular metabolism and disease prevalence
Lee, Deok-Sun
2010-12-01
Fluctuations of metabolic reaction fluxes may cause abnormal concentrations of toxic or essential metabolites, possibly leading to metabolic diseases. The mutual binding of enzymatic proteins and ones involving common metabolites enforces distinct coupled reactions, by which local perturbations may spread through the cellular network. Such network effects at the molecular interaction level in human cellular metabolism can reappear in the patterns of disease occurrence. Here we construct the enzyme-reaction network and the metabolite-reaction network, capturing the flux coupling of metabolic reactions caused by the interacting enzymes and the shared metabolites, respectively. Diseases potentially caused by the failure of individual metabolic reactions can be identified by using the known disease-gene association, which allows us to derive the probability of an inactivated reaction causing diseases from the disease records at the population level. We find that the greater the number of proteins that catalyze a reaction, the higher the mean prevalence of its associated diseases. Moreover, the number of connected reactions and the mean size of the avalanches in the networks constructed are also shown to be positively correlated with the disease prevalence. These findings illuminate the impact of the cellular network topology on disease development, suggesting that the global organization of the molecular interaction network should be understood to assist in disease diagnosis, treatment, and drug discovery.
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Prescription Drug Plan Formulary, Pharmacy Network, and P...
U.S. Department of Health & Human Services — These public use files contain formulary, pharmacy network, and pricing data for Medicare Prescription Drug Plans and Medicare Advantage Prescription Drug Plans...
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Interconnectivity of human cellular metabolism and disease prevalence
International Nuclear Information System (INIS)
Lee, Deok-Sun
2010-01-01
Fluctuations of metabolic reaction fluxes may cause abnormal concentrations of toxic or essential metabolites, possibly leading to metabolic diseases. The mutual binding of enzymatic proteins and ones involving common metabolites enforces distinct coupled reactions, by which local perturbations may spread through the cellular network. Such network effects at the molecular interaction level in human cellular metabolism can reappear in the patterns of disease occurrence. Here we construct the enzyme-reaction network and the metabolite-reaction network, capturing the flux coupling of metabolic reactions caused by the interacting enzymes and the shared metabolites, respectively. Diseases potentially caused by the failure of individual metabolic reactions can be identified by using the known disease–gene association, which allows us to derive the probability of an inactivated reaction causing diseases from the disease records at the population level. We find that the greater the number of proteins that catalyze a reaction, the higher the mean prevalence of its associated diseases. Moreover, the number of connected reactions and the mean size of the avalanches in the networks constructed are also shown to be positively correlated with the disease prevalence. These findings illuminate the impact of the cellular network topology on disease development, suggesting that the global organization of the molecular interaction network should be understood to assist in disease diagnosis, treatment, and drug discovery
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Csermely, Peter; Korcsmáros, Tamás; Kiss, Huba J.M.; London, Gábor; Nussinov, Ruth
2013-01-01
Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only gives a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The “central hit strategy” selectively targets central node/edges of the flexible networks of infectious agents or cancer cells to kill them. The “network influence strategy” works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach. PMID:23384594
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Network-based prediction and knowledge mining of disease genes.
Carson, Matthew B; Lu, Hui
2015-01-01
could be used to identify likely disease associations. We analyzed the human protein interaction network and its relationship to disease and found that both the number of interactions with other proteins and the disease relationship of neighboring proteins helped to determine whether a protein had a relationship to disease. Our classifier predicted many proteins with no annotated disease association to be disease-related, which indicated that these proteins have network characteristics that are similar to disease-related proteins and may therefore have disease associations not previously identified. By performing a post-processing step after the prediction, we were able to identify evidence in literature supporting this possibility. This method could provide a useful filter for experimentalists searching for new candidate protein targets for drug repositioning and could also be extended to include other network and data types in order to refine these predictions.
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A Novel Human Body Area Network for Brain Diseases Analysis.
Lin, Kai; Xu, Tianlang
2016-10-01
Development of wireless sensor and mobile communication technology provide an unprecedented opportunity for realizing smart and interactive healthcare systems. Designing such systems aims to remotely monitor the health and diagnose the diseases for users. In this paper, we design a novel human body area network for brain diseases analysis, which is named BABDA. Considering the brain is one of the most complex organs in the human body, the BABDA system provides four function modules to ensure the high quality of the analysis result, which includes initial data collection, data correction, data transmission and comprehensive data analysis. The performance evaluation conducted in a realistic environment with several criteria shows the availability and practicability of the BABDA system.
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Barneh, Farnaz; Jafari, Mohieddin; Mirzaie, Mehdi
2016-11-01
Network pharmacology elucidates the relationship between drugs and targets. As the identified targets for each drug increases, the corresponding drug-target network (DTN) evolves from solely reflection of the pharmaceutical industry trend to a portrait of polypharmacology. The aim of this study was to evaluate the potentials of DrugBank database in advancing systems pharmacology. We constructed and analyzed DTN from drugs and targets associations in the DrugBank 4.0 database. Our results showed that in bipartite DTN, increased ratio of identified targets for drugs augmented density and connectivity of drugs and targets and decreased modular structure. To clear up the details in the network structure, the DTNs were projected into two networks namely, drug similarity network (DSN) and target similarity network (TSN). In DSN, various classes of Food and Drug Administration-approved drugs with distinct therapeutic categories were linked together based on shared targets. Projected TSN also showed complexity because of promiscuity of the drugs. By including investigational drugs that are currently being tested in clinical trials, the networks manifested more connectivity and pictured the upcoming pharmacological space in the future years. Diverse biological processes and protein-protein interactions were manipulated by new drugs, which can extend possible target combinations. We conclude that network-based organization of DrugBank 4.0 data not only reveals the potential for repurposing of existing drugs, also allows generating novel predictions about drugs off-targets, drug-drug interactions and their side effects. Our results also encourage further effort for high-throughput identification of targets to build networks that can be integrated into disease networks. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.
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Padhi, Radhakant; Bhardhwaj, Jayender R
2009-06-01
An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.
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Dog and human inflammatory bowel disease rely on overlapping yet distinct dysbiosis networks.
Vázquez-Baeza, Yoshiki; Hyde, Embriette R; Suchodolski, Jan S; Knight, Rob
2016-10-03
Inflammatory bowel disease (IBD) is an autoimmune condition that is difficult to diagnose, and animal models of this disease have questionable human relevance 1 . Here, we show that the dysbiosis network underlying IBD in dogs differs from that in humans, with some bacteria such as Fusobacterium switching roles between the two species (as Bacteroides fragilis switches roles between humans and mice) 2 . For example, a dysbiosis index trained on humans fails when applied to dogs, but a dog-specific dysbiosis index achieves high correlations with the overall dog microbial community diversity patterns. In addition, a random forest classifier trained on dog-specific samples achieves high discriminatory power, even when using stool samples rather than the mucosal biopsies required for high discriminatory power in humans 2 . These relationships were not detected in previously published dog IBD data sets due to their limited sample size and statistical power 3 . Taken together, these results reveal the need to train host-specific dysbiosis networks and point the way towards a generalized understanding of IBD across different mammalian models.
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Hook, Vivian; Bandeira, Nuno
2015-12-01
Neuropeptides regulate intercellular signaling as neurotransmitters of the central and peripheral nervous systems, and as peptide hormones in the endocrine system. Diverse neuropeptides of distinct primary sequences of various lengths, often with post-translational modifications, coordinate and integrate regulation of physiological functions. Mass spectrometry-based analysis of the diverse neuropeptide structures in neuropeptidomics research is necessary to define the full complement of neuropeptide signaling molecules. Human neuropeptidomics has notable importance in defining normal and dysfunctional neuropeptide signaling in human health and disease. Neuropeptidomics has great potential for expansion in translational research opportunities for defining neuropeptide mechanisms of human diseases, providing novel neuropeptide drug targets for drug discovery, and monitoring neuropeptides as biomarkers of drug responses. In consideration of the high impact of human neuropeptidomics for health, an observed gap in this discipline is the few published articles in human neuropeptidomics compared with, for example, human proteomics and related mass spectrometry disciplines. Focus on human neuropeptidomics will advance new knowledge of the complex neuropeptide signaling networks participating in the fine control of neuroendocrine systems. This commentary review article discusses several human neuropeptidomics accomplishments that illustrate the rapidly expanding diversity of neuropeptides generated by protease processing of pro-neuropeptide precursors occurring within the secretory vesicle proteome. Of particular interest is the finding that human-specific cathepsin V participates in producing enkephalin and likely other neuropeptides, indicating unique proteolytic mechanisms for generating human neuropeptides. The field of human neuropeptidomics has great promise to solve new mechanisms in disease conditions, leading to new drug targets and therapeutic agents for human
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Gene expression variability in human hepatic drug metabolizing enzymes and transporters.
Directory of Open Access Journals (Sweden)
Lun Yang
Full Text Available Interindividual variability in the expression of drug-metabolizing enzymes and transporters (DMETs in human liver may contribute to interindividual differences in drug efficacy and adverse reactions. Published studies that analyzed variability in the expression of DMET genes were limited by sample sizes and the number of genes profiled. We systematically analyzed the expression of 374 DMETs from a microarray data set consisting of gene expression profiles derived from 427 human liver samples. The standard deviation of interindividual expression for DMET genes was much higher than that for non-DMET genes. The 20 DMET genes with the largest variability in the expression provided examples of the interindividual variation. Gene expression data were also analyzed using network analysis methods, which delineates the similarities of biological functionalities and regulation mechanisms for these highly variable DMET genes. Expression variability of human hepatic DMET genes may affect drug-gene interactions and disease susceptibility, with concomitant clinical implications.
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Humanized mouse models: Application to human diseases.
Ito, Ryoji; Takahashi, Takeshi; Ito, Mamoru
2018-05-01
Humanized mice are superior to rodents for preclinical evaluation of the efficacy and safety of drug candidates using human cells or tissues. During the past decade, humanized mouse technology has been greatly advanced by the establishment of novel platforms of genetically modified immunodeficient mice. Several human diseases can be recapitulated using humanized mice due to the improved engraftment and differentiation capacity of human cells or tissues. In this review, we discuss current advanced humanized mouse models that recapitulate human diseases including cancer, allergy, and graft-versus-host disease. © 2017 Wiley Periodicals, Inc.
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Choudhary, Neha
2018-01-01
Piper longum (P. longum, also called as long pepper) is one of the common culinary herbs that has been extensively used as a crucial constituent in various indigenous medicines, specifically in traditional Indian medicinal system known as Ayurveda. For exploring the comprehensive effect of its constituents in humans at proteomic and metabolic levels, we have reviewed all of its known phytochemicals and enquired about their regulatory potential against various protein targets by developing high-confidence tripartite networks consisting of phytochemical—protein target—disease association. We have also (i) studied immunomodulatory potency of this herb; (ii) developed subnetwork of human PPI regulated by its phytochemicals and could successfully associate its specific modules playing important role in diseases, and (iii) reported several novel drug targets. P10636 (microtubule-associated protein tau, that is involved in diseases like dementia etc.) was found to be the commonly screened target by about seventy percent of these phytochemicals. We report 20 drug-like phytochemicals in this herb, out of which 7 are found to be the potential regulators of 5 FDA approved drug targets. Multi-targeting capacity of 3 phytochemicals involved in neuroactive ligand receptor interaction pathway was further explored via molecular docking experiments. To investigate the molecular mechanism of P. longum’s action against neurological disorders, we have developed a computational framework that can be easily extended to explore its healing potential against other diseases and can also be applied to scrutinize other indigenous herbs for drug-design studies. PMID:29320554
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Jang, Jiho; Yoo, Jeong-Eun; Lee, Jeong-Ah; Lee, Dongjin R; Kim, Ji Young; Huh, Yong Jun; Kim, Dae-Sung; Park, Chul-Yong; Hwang, Dong-Youn; Kim, Han-Soo; Kang, Hoon-Chul; Kim, Dong-Wook
2012-03-31
The generation of disease-specific induced pluripotent stem cell (iPSC) lines from patients with incurable diseases is a promising approach for studying disease mechanisms and drug screening. Such innovation enables to obtain autologous cell sources in regenerative medicine. Herein, we report the generation and characterization of iPSCs from fibroblasts of patients with sporadic or familial diseases, including Parkinson's disease (PD), Alzheimer's disease (AD), juvenile-onset, type I diabetes mellitus (JDM), and Duchenne type muscular dystrophy (DMD), as well as from normal human fibroblasts (WT). As an example to modeling disease using disease-specific iPSCs, we also discuss the previously established childhood cerebral adrenoleukodystrophy (CCALD)- and adrenomyeloneuropathy (AMN)-iPSCs by our group. Through DNA fingerprinting analysis, the origins of generated disease-specific iPSC lines were identified. Each iPSC line exhibited an intense alkaline phosphatase activity, expression of pluripotent markers, and the potential to differentiate into all three embryonic germ layers: the ectoderm, endoderm, and mesoderm. Expression of endogenous pluripotent markers and downregulation of retrovirus-delivered transgenes [OCT4 (POU5F1), SOX2, KLF4, and c-MYC] were observed in the generated iPSCs. Collectively, our results demonstrated that disease-specific iPSC lines characteristically resembled hESC lines. Furthermore, we were able to differentiate PD-iPSCs, one of the disease-specific-iPSC lines we generated, into dopaminergic (DA) neurons, the cell type mostly affected by PD. These PD-specific DA neurons along with other examples of cell models derived from disease-specific iPSCs would provide a powerful platform for examining the pathophysiology of relevant diseases at the cellular and molecular levels and for developing new drugs and therapeutic regimens.
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Directory of Open Access Journals (Sweden)
Emre Guney
Full Text Available Complex biological systems usually pose a trade-off between robustness and fragility where a small number of perturbations can substantially disrupt the system. Although biological systems are robust against changes in many external and internal conditions, even a single mutation can perturb the system substantially, giving rise to a pathophenotype. Recent advances in identifying and analyzing the sequential variations beneath human disorders help to comprehend a systemic view of the mechanisms underlying various disease phenotypes. Network-based disease-gene prioritization methods rank the relevance of genes in a disease under the hypothesis that genes whose proteins interact with each other tend to exhibit similar phenotypes. In this study, we have tested the robustness of several network-based disease-gene prioritization methods with respect to the perturbations of the system using various disease phenotypes from the Online Mendelian Inheritance in Man database. These perturbations have been introduced either in the protein-protein interaction network or in the set of known disease-gene associations. As the network-based disease-gene prioritization methods are based on the connectivity between known disease-gene associations, we have further used these methods to categorize the pathophenotypes with respect to the recoverability of hidden disease-genes. Our results have suggested that, in general, disease-genes are connected through multiple paths in the human interactome. Moreover, even when these paths are disturbed, network-based prioritization can reveal hidden disease-gene associations in some pathophenotypes such as breast cancer, cardiomyopathy, diabetes, leukemia, parkinson disease and obesity to a greater extend compared to the rest of the pathophenotypes tested in this study. Gene Ontology (GO analysis highlighted the role of functional diversity for such diseases.
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Drug-selected human lung cancer stem cells: cytokine network, tumorigenic and metastatic properties.
Directory of Open Access Journals (Sweden)
Vera Levina
2008-08-01
Full Text Available Cancer stem cells (CSCs are thought to be responsible for tumor regeneration after chemotherapy, although direct confirmation of this remains forthcoming. We therefore investigated whether drug treatment could enrich and maintain CSCs and whether the high tumorogenic and metastatic abilities of CSCs were based on their marked ability to produce growth and angiogenic factors and express their cognate receptors to stimulate tumor cell proliferation and stroma formation.Treatment of lung tumor cells with doxorubicin, cisplatin, or etoposide resulted in the selection of drug surviving cells (DSCs. These cells expressed CD133, CD117, SSEA-3, TRA1-81, Oct-4, and nuclear beta-catenin and lost expression of the differentiation markers cytokeratins 8/18 (CK 8/18. DSCs were able to grow as tumor spheres, maintain self-renewal capacity, and differentiate. Differentiated progenitors lost expression of CD133, gained CK 8/18 and acquired drug sensitivity. In the presence of drugs, differentiation of DSCs was abrogated allowing propagation of cells with CSC-like characteristics. Lung DSCs demonstrated high tumorogenic and metastatic potential following inoculation into SCID mice, which supported their classification as CSCs. Luminex analysis of human and murine cytokines in sonicated lysates of parental- and CSC-derived tumors revealed that CSC-derived tumors contained two- to three-fold higher levels of human angiogenic and growth factors (VEGF, bFGF, IL-6, IL-8, HGF, PDGF-BB, G-CSF, and SCGF-beta. CSCs also showed elevated levels of expression of human VEGFR2, FGFR2, CXCR1, 2 and 4 receptors. Moreover, human CSCs growing in SCID mice stimulated murine stroma to produce elevated levels of angiogenic and growth factors.These findings suggest that chemotherapy can lead to propagation of CSCs and prevention of their differentiation. The high tumorigenic and metastatic potentials of CSCs are associated with efficient cytokine network production that may represent
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Targeting cysteine proteases in trypanosomatid disease drug discovery.
Ferreira, Leonardo G; Andricopulo, Adriano D
2017-12-01
Chagas disease and human African trypanosomiasis are endemic conditions in Latin America and Africa, respectively, for which no effective and safe therapy is available. Efforts in drug discovery have focused on several enzymes from these protozoans, among which cysteine proteases have been validated as molecular targets for pharmacological intervention. These enzymes are expressed during the entire life cycle of trypanosomatid parasites and are essential to many biological processes, including infectivity to the human host. As a result of advances in the knowledge of the structural aspects of cysteine proteases and their role in disease physiopathology, inhibition of these enzymes by small molecules has been demonstrated to be a worthwhile approach to trypanosomatid drug research. This review provides an update on drug discovery strategies targeting the cysteine peptidases cruzain from Trypanosoma cruzi and rhodesain and cathepsin B from Trypanosoma brucei. Given that current chemotherapy for Chagas disease and human African trypanosomiasis has several drawbacks, cysteine proteases will continue to be actively pursued as valuable molecular targets in trypanosomatid disease drug discovery efforts. Copyright © 2017. Published by Elsevier Inc.
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Cheng, Feixiong; Zhao, Junfei; Fooksa, Michaela; Zhao, Zhongming
2016-07-01
Development of computational approaches and tools to effectively integrate multidomain data is urgently needed for the development of newly targeted cancer therapeutics. We proposed an integrative network-based infrastructure to identify new druggable targets and anticancer indications for existing drugs through targeting significantly mutated genes (SMGs) discovered in the human cancer genomes. The underlying assumption is that a drug would have a high potential for anticancer indication if its up-/down-regulated genes from the Connectivity Map tended to be SMGs or their neighbors in the human protein interaction network. We assembled and curated 693 SMGs in 29 cancer types and found 121 proteins currently targeted by known anticancer or noncancer (repurposed) drugs. We found that the approved or experimental cancer drugs could potentially target these SMGs in 33.3% of the mutated cancer samples, and this number increased to 68.0% by drug repositioning through surveying exome-sequencing data in approximately 5000 normal-tumor pairs from The Cancer Genome Atlas. Furthermore, we identified 284 potential new indications connecting 28 cancer types and 48 existing drugs (adjusted P < .05), with a 66.7% success rate validated by literature data. Several existing drugs (e.g., niclosamide, valproic acid, captopril, and resveratrol) were predicted to have potential indications for multiple cancer types. Finally, we used integrative analysis to showcase a potential mechanism-of-action for resveratrol in breast and lung cancer treatment whereby it targets several SMGs (ARNTL, ASPM, CTTN, EIF4G1, FOXP1, and STIP1). In summary, we demonstrated that our integrative network-based infrastructure is a promising strategy to identify potential druggable targets and uncover new indications for existing drugs to speed up molecularly targeted cancer therapeutics. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All
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Drug Abuse Warning Network US (DAWN-NS-1994)
U.S. Department of Health & Human Services — The Drug Abuse Warning Network (DAWN) survey is designed to capture data on emergency department (ED) episodes that are induced by or related to the use of an...
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Drug Abuse Warning Network US (DAWN-NS-1997)
U.S. Department of Health & Human Services — The Drug Abuse Warning Network (DAWN) survey is designed to capture data on emergency department (ED) episodes that are induced by or related to the use of an...
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The effect of network biology on drug toxicology
DEFF Research Database (Denmark)
Gautier, Laurent; Taboureau, Olivier; Audouze, Karine Marie Laure
2013-01-01
Introduction: The high failure rate of drug candidates due to toxicity, during clinical trials, is a critical issue in drug discovery. Network biology has become a promising approach, in this regard, using the increasingly large amount of biological and chemical data available and combining...... it with bioinformatics. With this approach, the assessment of chemical safety can be done across multiple scales of complexity from molecular to cellular and system levels in human health. Network biology can be used at several levels of complexity. Areas covered: This review describes the strengths and limitations...... of network biology. The authors specifically assess this approach across different biological scales when it is applied to toxicity. Expert opinion: There has been much progress made with the amount of data that is generated by various omics technologies. With this large amount of useful data, network...
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EDITORIAL Drugs for Neglected Diseases Initiative
African Journals Online (AJOL)
Dr.Kofi-Tsekpo
disease, and malaria have a devastating impact on humanity, yet R&D for new drugs for these diseases has been progressively marginalised because they are not considered a lucrative investment. DNDi, a needs-driven initiative, keeps the needs of patients suffering from neglected diseases paramount in its search for.
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Directory of Open Access Journals (Sweden)
Navin Gupta
2017-08-01
Full Text Available The multitude of research clarifying critical factors in embryonic organ development has been instrumental in human stem cell research. Mammalian organogenesis serves as the archetype for directed differentiation protocols, subdividing the process into a series of distinct intermediate stages that can be chemically induced and monitored for the expression of stage-specific markers. Significant advances over the past few years include established directed differentiation protocols of human embryonic stem cells and human induced pluripotent stem cells (hiPSC into human kidney organoids in vitro. Human kidney tissue in vitro simulates the in vivo response when subjected to nephrotoxins, providing a novel screening platform during drug discovery to facilitate identification of lead candidates, reduce developmental expenditures, and reduce future rates of drug-induced acute kidney injury. Patient-derived hiPSC, which bear naturally occurring DNA mutations, may allow for modelling of human genetic diseases to enable determination of pathological mechanisms and screening for novel therapeutics. In addition, recent advances in genome editing with clustered regularly interspaced short palindromic repeats (CRISPR/Cas9 enable the generation of specific mutations to study genetic disease, with non-mutated lines serving as an ideal isogenic control. The growing population of patients with end-stage kidney disease is a worldwide healthcare problem, with high morbidity and mortality rates, that warrants the discovery of novel forms of renal replacement therapy. Coupling the outlined advances in hiPSC research with innovative bioengineering techniques, such as decellularised kidney and three-dimensional printed scaffolds, may contribute to the development of bioengineered transplantable human kidney tissue as a means of renal replacement therapy.
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From gene networks to drugs: systems pharmacology approaches for AUD.
Ferguson, Laura B; Harris, R Adron; Mayfield, Roy Dayne
2018-06-01
The alcohol research field has amassed an impressive number of gene expression datasets spanning key brain areas for addiction, species (humans as well as multiple animal models), and stages in the addiction cycle (binge/intoxication, withdrawal/negative effect, and preoccupation/anticipation). These data have improved our understanding of the molecular adaptations that eventually lead to dysregulation of brain function and the chronic, relapsing disorder of addiction. Identification of new medications to treat alcohol use disorder (AUD) will likely benefit from the integration of genetic, genomic, and behavioral information included in these important datasets. Systems pharmacology considers drug effects as the outcome of the complex network of interactions a drug has rather than a single drug-molecule interaction. Computational strategies based on this principle that integrate gene expression signatures of pharmaceuticals and disease states have shown promise for identifying treatments that ameliorate disease symptoms (called in silico gene mapping or connectivity mapping). In this review, we suggest that gene expression profiling for in silico mapping is critical to improve drug repurposing and discovery for AUD and other psychiatric illnesses. We highlight studies that successfully apply gene mapping computational approaches to identify or repurpose pharmaceutical treatments for psychiatric illnesses. Furthermore, we address important challenges that must be overcome to maximize the potential of these strategies to translate to the clinic and improve healthcare outcomes.
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Fitzgerald, G A; Yang, G; Paschos, G K; Liang, X; Skarke, C
2015-09-01
Molecular clockworks knit together diverse biological networks and compelling evidence from model systems infers their importance in metabolism, immunological and cardiovascular function. Despite this and the diurnal variation in many aspects of human physiology and the phenotypic expression of disease, our understanding of the role and importance of clock function and dysfunction in humans is modest. There are tantalizing hints of connection across the translational divide and some correlative evidence of gene variation and human disease but most of what we know derives from forced desynchrony protocols in controlled environments. We now have the ability to monitor quantitatively ex vivo or in vivo the genome, metabolome, proteome and microbiome of humans in the wild. Combining this capability, with the power of mobile telephony and the evolution of remote sensing, affords a new opportunity for deep phenotyping, including the characterization of diurnal behaviour and the assessment of the impact of the clock on approved drug function. © 2015 John Wiley & Sons Ltd.
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Gulbahce, Natali; Yan, Han; Vidal, Marc; Barabasi, Albert-Laszlo
2010-03-01
Viral infections induce multiple perturbations that spread along the links of the biological networks of the host cells. Understanding the impact of these cascading perturbations requires an exhaustive knowledge of the cellular machinery as well as a systems biology approach that reveals how individual components of the cellular system function together. Here we describe an integrative method that provides a new approach to studying virus-human interactions and its correlations with diseases. Our method involves the combined utilization of protein - protein interactions, protein -- DNA interactions, metabolomics and gene - disease associations to build a ``viraldiseasome''. By solely using high-throughput data, we map well-known viral associated diseases and predict new candidate viral diseases. We use microarray data of virus-infected tissues and patient medical history data to further test the implications of the viral diseasome. We apply this method to Epstein-Barr virus and Human Papillomavirus and shed light into molecular development of viral diseases and disease pathways.
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Luo, Yunan; Zhao, Xinbin; Zhou, Jingtian; Yang, Jinglin; Zhang, Yanqing; Kuang, Wenhua; Peng, Jian; Chen, Ligong; Zeng, Jianyang
2017-09-18
The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. In this work, we develop a computational pipeline, called DTINet, to predict novel drug-target interactions from a constructed heterogeneous network, which integrates diverse drug-related information. DTINet focuses on learning a low-dimensional vector representation of features, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then makes prediction based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for drug-target interaction prediction. Moreover, we experimentally validate the novel interactions between three drugs and the cyclooxygenase proteins predicted by DTINet, and demonstrate the new potential applications of these identified cyclooxygenase inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug-target interactions and repurpose existing drugs.Network-based data integration for drug-target prediction is a promising avenue for drug repositioning, but performance is wanting. Here, the authors introduce DTINet, whose performance is enhanced in the face of noisy, incomplete and high-dimensional biological data by learning low-dimensional vector representations.
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Directory of Open Access Journals (Sweden)
Kai Wang
2009-12-01
Full Text Available Co-expression networks are routinely used to study human diseases like obesity and diabetes. Systematic comparison of these networks between species has the potential to elucidate common mechanisms that are conserved between human and rodent species, as well as those that are species-specific characterizing evolutionary plasticity. We developed a semi-parametric meta-analysis approach for combining gene-gene co-expression relationships across expression profile datasets from multiple species. The simulation results showed that the semi-parametric method is robust against noise. When applied to human, mouse, and rat liver co-expression networks, our method out-performed existing methods in identifying gene pairs with coherent biological functions. We identified a network conserved across species that highlighted cell-cell signaling, cell-adhesion and sterol biosynthesis as main biological processes represented in genome-wide association study candidate gene sets for blood lipid levels. We further developed a heterogeneity statistic to test for network differences among multiple datasets, and demonstrated that genes with species-specific interactions tend to be under positive selection throughout evolution. Finally, we identified a human-specific sub-network regulated by RXRG, which has been validated to play a different role in hyperlipidemia and Type 2 diabetes between human and mouse. Taken together, our approach represents a novel step forward in integrating gene co-expression networks from multiple large scale datasets to leverage not only common information but also differences that are dataset-specific.
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2013-04-11
... Availability. SUMMARY: The Food and Drug Administration (FDA) is announcing the selection of disease areas to... selection criteria, which were published in the September 24, 2012, Federal Register notice: Disease areas... DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration [Docket No. FDA-2012-N-0967...
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Mostafavi, Sara; Gaiteri, Chris; Sullivan, Sarah E; White, Charles C; Tasaki, Shinya; Xu, Jishu; Taga, Mariko; Klein, Hans-Ulrich; Patrick, Ellis; Komashko, Vitalina; McCabe, Cristin; Smith, Robert; Bradshaw, Elizabeth M; Root, David E; Regev, Aviv; Yu, Lei; Chibnik, Lori B; Schneider, Julie A; Young-Pearse, Tracy L; Bennett, David A; De Jager, Philip L
2018-06-01
There is a need for new therapeutic targets with which to prevent Alzheimer's disease (AD), a major contributor to aging-related cognitive decline. Here we report the construction and validation of a molecular network of the aging human frontal cortex. Using RNA sequence data from 478 individuals, we first build a molecular network using modules of coexpressed genes and then relate these modules to AD and its neuropathologic and cognitive endophenotypes. We confirm these associations in two independent AD datasets. We also illustrate the use of the network in prioritizing amyloid- and cognition-associated genes for in vitro validation in human neurons and astrocytes. These analyses based on unique cohorts enable us to resolve the role of distinct cortical modules that have a direct effect on the accumulation of AD pathology from those that have a direct effect on cognitive decline, exemplifying a network approach to complex diseases.
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Using biological networks to improve our understanding of infectious diseases
Directory of Open Access Journals (Sweden)
Nicola J. Mulder
2014-08-01
Full Text Available Infectious diseases are the leading cause of death, particularly in developing countries. Although many drugs are available for treating the most common infectious diseases, in many cases the mechanism of action of these drugs or even their targets in the pathogen remain unknown. In addition, the key factors or processes in pathogens that facilitate infection and disease progression are often not well understood. Since proteins do not work in isolation, understanding biological systems requires a better understanding of the interconnectivity between proteins in different pathways and processes, which includes both physical and other functional interactions. Such biological networks can be generated within organisms or between organisms sharing a common environment using experimental data and computational predictions. Though different data sources provide different levels of accuracy, confidence in interactions can be measured using interaction scores. Connections between interacting proteins in biological networks can be represented as graphs and edges, and thus studied using existing algorithms and tools from graph theory. There are many different applications of biological networks, and here we discuss three such applications, specifically applied to the infectious disease tuberculosis, with its causative agent Mycobacterium tuberculosis and host, Homo sapiens. The applications include the use of the networks for function prediction, comparison of networks for evolutionary studies, and the generation and use of host–pathogen interaction networks.
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Verkhivker, Gennady M
2016-01-01
The human protein kinome presents one of the largest protein families that orchestrate functional processes in complex cellular networks, and when perturbed, can cause various cancers. The abundance and diversity of genetic, structural, and biochemical data underlies the complexity of mechanisms by which targeted and personalized drugs can combat mutational profiles in protein kinases. Coupled with the evolution of system biology approaches, genomic and proteomic technologies are rapidly identifying and charactering novel resistance mechanisms with the goal to inform rationale design of personalized kinase drugs. Integration of experimental and computational approaches can help to bring these data into a unified conceptual framework and develop robust models for predicting the clinical drug resistance. In the current study, we employ a battery of synergistic computational approaches that integrate genetic, evolutionary, biochemical, and structural data to characterize the effect of cancer mutations in protein kinases. We provide a detailed structural classification and analysis of genetic signatures associated with oncogenic mutations. By integrating genetic and structural data, we employ network modeling to dissect mechanisms of kinase drug sensitivities to oncogenic EGFR mutations. Using biophysical simulations and analysis of protein structure networks, we show that conformational-specific drug binding of Lapatinib may elicit resistant mutations in the EGFR kinase that are linked with the ligand-mediated changes in the residue interaction networks and global network properties of key residues that are responsible for structural stability of specific functional states. A strong network dependency on high centrality residues in the conformation-specific Lapatinib-EGFR complex may explain vulnerability of drug binding to a broad spectrum of mutations and the emergence of drug resistance. Our study offers a systems-based perspective on drug design by unravelling
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A hybrid network-based method for the detection of disease-related genes
Cui, Ying; Cai, Meng; Dai, Yang; Stanley, H. Eugene
2018-02-01
Detecting disease-related genes is crucial in disease diagnosis and drug design. The accepted view is that neighbors of a disease-causing gene in a molecular network tend to cause the same or similar diseases, and network-based methods have been recently developed to identify novel hereditary disease-genes in available biomedical networks. Despite the steady increase in the discovery of disease-associated genes, there is still a large fraction of disease genes that remains under the tip of the iceberg. In this paper we exploit the topological properties of the protein-protein interaction (PPI) network to detect disease-related genes. We compute, analyze, and compare the topological properties of disease genes with non-disease genes in PPI networks. We also design an improved random forest classifier based on these network topological features, and a cross-validation test confirms that our method performs better than previous similar studies.
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Drug targets in the cytokine universe for autoimmune disease.
Liu, Xuebin; Fang, Lei; Guo, Taylor B; Mei, Hongkang; Zhang, Jingwu Z
2013-03-01
In autoimmune disease, a network of diverse cytokines is produced in association with disease susceptibility to constitute the 'cytokine milieu' that drives chronic inflammation. It remains elusive how cytokines interact in such a complex network to sustain inflammation in autoimmune disease. This has presented huge challenges for successful drug discovery because it has been difficult to predict how individual cytokine-targeted therapy would work. Here, we combine the principles of Chinese Taoism philosophy and modern bioinformatics tools to dissect multiple layers of arbitrary cytokine interactions into discernible interfaces and connectivity maps to predict movements in the cytokine network. The key principles presented here have important implications in our understanding of cytokine interactions and development of effective cytokine-targeted therapies for autoimmune disorders. Copyright © 2012 Elsevier Ltd. All rights reserved.
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2014-01-01
Background Abnormal states in human liver metabolism are major causes of human liver diseases ranging from hepatitis to hepatic tumor. The accumulation in relevant data makes it feasible to derive a large-scale human liver metabolic network (HLMN) and to discover important biological principles or drug-targets based on network analysis. Some studies have shown that interesting biological phenomenon and drug-targets could be discovered by applying structural controllability analysis (which is a newly prevailed concept in networks) to biological networks. The exploration on the connections between structural controllability theory and the HLMN could be used to uncover valuable information on the human liver metabolism from a fresh perspective. Results We applied structural controllability analysis to the HLMN and detected driver metabolites. The driver metabolites tend to have strong ability to influence the states of other metabolites and weak susceptibility to be influenced by the states of others. In addition, the metabolites were classified into three classes: critical, high-frequency and low-frequency driver metabolites. Among the identified 36 critical driver metabolites, 27 metabolites were found to be essential; the high-frequency driver metabolites tend to participate in different metabolic pathways, which are important in regulating the whole metabolic systems. Moreover, we explored some other possible connections between the structural controllability theory and the HLMN, and find that transport reactions and the environment play important roles in the human liver metabolism. Conclusion There are interesting connections between the structural controllability theory and the human liver metabolism: driver metabolites have essential biological functions; the crucial role of extracellular metabolites and transport reactions in controlling the HLMN highlights the importance of the environment in the health of human liver metabolism. PMID:24885538
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Prescriptive Oriented Drug Analysis of Multiple Sclerosis Disease by LC-UV in Whole Human Blood.
Suneetha, A; Rajeswari, Raja K
2016-02-01
As a polytherapy treatment, multiple sclerosis disease demands prescriptions with more than one drug. Polytherapy is sometimes rational for drug combinations chosen to minimize adverse effects. Estimation of drugs that are concomitantly administered in polytherapy is acceptable as it shortens the analytical timepoints and also the usage of biological matrices. In clinical phase trials, the withdrawal of biofluids is a critical issue for each analysis. Estimating all the coadminsitered drugs in a single shot will be more effective and economical for pharmaceuticals. A single, simple, rapid and sensitive high-performance liquid chromatography assay method has been developed with UV detection and fully validated for the quantification of 14 drugs (at random combinations) used in the treatment of multiple sclerosis disease. The set of combinations was based on prescriptions to patients. Separations were achieved on an X-Terra MS C18 (100 × 3.9 mm, 5 µm) column. The analytes were extracted from 50 µL aliquots of whole human blood with protein precipitation using acetonitrile. All the drugs were sufficiently stable during storage for 24 h at room temperature and for 23 days at 2-8°C. The percentage recoveries of all drugs were between 90 and 115%, with RSD values <10.6%. This method has been shown to be reproducible and sensitive and can be applied to clinical samples from pharmacokinetic studies and also a useful tool in studying the drug interaction studies. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
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Directory of Open Access Journals (Sweden)
David J. Wiley
2014-06-01
Full Text Available Genetic interaction networks that underlie most human diseases are highly complex and poorly defined. Better-defined networks will allow identification of a greater number of therapeutic targets. Here we introduce our Yeast Augmented Network Analysis (YANA approach and test it with the X-linked spinal muscular atrophy (SMA disease gene UBA1. First, we express UBA1 and a mutant variant in fission yeast and use high-throughput methods to identify fission yeast genetic modifiers of UBA1. Second, we analyze available protein-protein interaction network databases in both fission yeast and human to construct UBA1 genetic networks. Third, from these networks we identified potential therapeutic targets for SMA. Finally, we validate one of these targets in a vertebrate (zebrafish SMA model. This study demonstrates the power of combining synthetic and chemical genetics with a simple model system to identify human disease gene networks that can be exploited for treating human diseases.
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DenguePredict: An Integrated Drug Repositioning Approach towards Drug Discovery for Dengue
Wang, QuanQiu; Xu, Rong
2015-01-01
Dengue is a viral disease of expanding global incidence without cures. Here we present a drug repositioning system (DenguePredict) leveraging upon a unique drug treatment database and vast amounts of disease- and drug-related data. We first constructed a large-scale genetic disease network with enriched dengue genetics data curated from biomedical literature. We applied a network-based ranking algorithm to find dengue-related diseases from the disease network. We then developed a novel algori...
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FocusHeuristics - expression-data-driven network optimization and disease gene prediction.
Ernst, Mathias; Du, Yang; Warsow, Gregor; Hamed, Mohamed; Endlich, Nicole; Endlich, Karlhans; Murua Escobar, Hugo; Sklarz, Lisa-Madeleine; Sender, Sina; Junghanß, Christian; Möller, Steffen; Fuellen, Georg; Struckmann, Stephan
2017-02-16
To identify genes contributing to disease phenotypes remains a challenge for bioinformatics. Static knowledge on biological networks is often combined with the dynamics observed in gene expression levels over disease development, to find markers for diagnostics and therapy, and also putative disease-modulatory drug targets and drugs. The basis of current methods ranges from a focus on expression-levels (Limma) to concentrating on network characteristics (PageRank, HITS/Authority Score), and both (DeMAND, Local Radiality). We present an integrative approach (the FocusHeuristics) that is thoroughly evaluated based on public expression data and molecular disease characteristics provided by DisGeNet. The FocusHeuristics combines three scores, i.e. the log fold change and another two, based on the sum and difference of log fold changes of genes/proteins linked in a network. A gene is kept when one of the scores to which it contributes is above a threshold. Our FocusHeuristics is both, a predictor for gene-disease-association and a bioinformatics method to reduce biological networks to their disease-relevant parts, by highlighting the dynamics observed in expression data. The FocusHeuristics is slightly, but significantly better than other methods by its more successful identification of disease-associated genes measured by AUC, and it delivers mechanistic explanations for its choice of genes.
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Drug-domain interaction networks in myocardial infarction.
Wang, Haiying; Zheng, Huiru; Azuaje, Francisco; Zhao, Xing-Ming
2013-09-01
It has been well recognized that the pace of the development of new drugs and therapeutic interventions lags far behind biological knowledge discovery. Network-based approaches have emerged as a promising alternative to accelerate the discovery of new safe and effective drugs. Based on the integration of several biological resources including two recently published datasets i.e., Drug-target interactions in myocardial infarction (My-DTome) and drug-domain interaction network, this paper reports the association between drugs and protein domains in the context of myocardial infarction (MI). A MI drug-domain interaction network, My-DDome, was firstly constructed, followed by topological analysis and functional characterization of the network. The results show that My-DDome has a very clear modular structure, where drugs interacting with the same domain(s) within each module tend to have similar therapeutic effects. Moreover it has been found that drugs acting on blood and blood forming organs (ATC code B) and sensory organs (ATC code S) are significantly enriched in My-DDome (p drugs, their known targets, and seemingly unrelated proteins can be revealed.
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Can the silkworm (Bombyx mori) be used as a human disease model?
Tabunoki, Hiroko; Bono, Hidemasa; Ito, Katsuhiko; Yokoyama, Takeshi
2016-02-01
Bombyx mori (silkworm) is the most famous lepidopteran in Japan. B. mori has long been used in the silk industry and also as a model insect for agricultural research. In recent years, B. mori has attracted interest in its potential for use in pathological analysis of model animals. For example, the human macular carotenoid transporter was discovered using information of B. mori carotenoid transporter derived from yellow-cocoon strain. The B. mori carotenoid transport system is useful in human studies. To develop a human disease model, we characterized the human homologs of B. mori, and by constructing KAIKO functional annotation pipeline, and to analyze gene expression profile of a unique B. mori mutant strain using microarray analysis. As a result, we identified a novel molecular network involved in Parkinson's disease. Here we describe the potential use of a spontaneous mutant silkworm strain as a human disease model. We also summarize recent progress in the application of genomic information for annotation of human homologs in B. mori. The B. mori mutant will provide a clue to pathological mechanisms, and the findings will be helpful for the development of therapies and for medical drug discovery.
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Network Medicine for Alzheimer's Disease and Traditional Chinese Medicine.
Jarrell, Juliet T; Gao, Li; Cohen, David S; Huang, Xudong
2018-05-11
Alzheimer’s Disease (AD) is a neurodegenerative condition that currently has no known cure. The principles of the expanding field of network medicine (NM) have recently been applied to AD research. The main principle of NM proposes that diseases are much more complicated than one mutation in one gene, and incorporate different genes, connections between genes, and pathways that may include multiple diseases to create full scale disease networks. AD research findings as a result of the application of NM principles have suggested that functional network connectivity, myelination, myeloid cells, and genes and pathways may play an integral role in AD progression, and may be integral to the search for a cure. Different aspects of the AD pathology could be potential targets for drug therapy to slow down or stop the disease from advancing, but more research is needed to reach definitive conclusions. Additionally, the holistic approaches of network pharmacology in traditional Chinese medicine (TCM) research may be viable options for the AD treatment, and may lead to an effective cure for AD in the future.
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Huang, Liang-Chin; Wu, Xiaogang; Chen, Jake Y
2013-01-01
The prediction of adverse drug reactions (ADRs) has become increasingly important, due to the rising concern on serious ADRs that can cause drugs to fail to reach or stay in the market. We proposed a framework for predicting ADR profiles by integrating protein-protein interaction (PPI) networks with drug structures. We compared ADR prediction performances over 18 ADR categories through four feature groups-only drug targets, drug targets with PPI networks, drug structures, and drug targets with PPI networks plus drug structures. The results showed that the integration of PPI networks and drug structures can significantly improve the ADR prediction performance. The median AUC values for the four groups were 0.59, 0.61, 0.65, and 0.70. We used the protein features in the best two models, "Cardiac disorders" (median-AUC: 0.82) and "Psychiatric disorders" (median-AUC: 0.76), to build ADR-specific PPI networks with literature supports. For validation, we examined 30 drugs withdrawn from the U.S. market to see if our approach can predict their ADR profiles and explain why they were withdrawn. Except for three drugs having ADRs in the categories we did not predict, 25 out of 27 withdrawn drugs (92.6%) having severe ADRs were successfully predicted by our approach. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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Serpe, Loredana; Canaparo, Roberto; Daperno, Marco; Sostegni, Raffaello; Martinasso, Germana; Muntoni, Elisabetta; Ippolito, Laura; Vivenza, Nicoletta; Pera, Angelo; Eandi, Mario; Gasco, Maria Rosa; Zara, Gian Paolo
2010-03-18
Standard treatment for inflammatory bowel diseases (IBD) necessitates frequent intake of anti-inflammatory and/or immunosuppressive drugs, leading to significant adverse events. To evaluate the role solid lipid nanoparticles (SLN) play as drug delivery system in enhancing anti-inflammatory activity for drugs such as dexamethasone and butyrate in a human inflammatory bowel diseases whole-blood model. ELISA assay and the peripheral blood mononuclear cell (PBMC) cytokine mRNA expression levels were evaluated by quantitative SYBR Green real-time RT-PCR to determine the IL-1beta, TNF-alpha, IFN-gamma and IL-10 secretion in inflammatory bowel diseases patients' PBMC culture supernatants. There was a significant decrease in IL-1beta (p<0.01) and TNF-alpha (p<0.001) secretion, whilst IL-10 (p<0.05) secretion significantly increased after cholesteryl butyrate administration, compared to that of butyrate alone at the highest concentration tested (100 microM), at 24h exposure. There was a significant decrease in IL-1beta (p<0.01), TNF-alpha (p<0.001) and IL-10 (p<0.001) secretion after dexamethasone loaded SLN administration, compared to dexamethasone alone at the highest concentration tested (250 nM) at 24h exposure. No IFN-gamma was detected under any conditions and no cytotoxic effects observed even at the highest concentration tested. The incorporation of butyrate and dexamethasone into SLN has a significant positive anti-inflammatory effect in the human inflammatory bowel disease whole-blood model. Copyright 2010 Elsevier B.V. All rights reserved.
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Ioset, Jean-Robert; Chang, Shing
2011-09-01
The Drugs for Neglected Diseases initiative (DNDi) is a patients' needs-driven organization committed to the development of new treatments for neglected diseases. Created in 2003, DNDi has delivered four improved treatments for malaria, sleeping sickness and visceral leishmaniasis. A main DNDi challenge is to build a solid R&D portfolio for neglected diseases and to deliver preclinical candidates in a timely manner using an original model based on partnership. To address this challenge DNDi has remodeled its discovery activities from a project-based academic-bound network to a fully integrated process-oriented platform in close collaboration with pharmaceutical companies. This discovery platform relies on dedicated screening capacity and lead-optimization consortia supported by a pragmatic, structured and pharmaceutical-focused compound sourcing strategy.
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DenguePredict: An Integrated Drug Repositioning Approach towards Drug Discovery for Dengue.
Wang, QuanQiu; Xu, Rong
2015-01-01
Dengue is a viral disease of expanding global incidence without cures. Here we present a drug repositioning system (DenguePredict) leveraging upon a unique drug treatment database and vast amounts of disease- and drug-related data. We first constructed a large-scale genetic disease network with enriched dengue genetics data curated from biomedical literature. We applied a network-based ranking algorithm to find dengue-related diseases from the disease network. We then developed a novel algorithm to prioritize FDA-approved drugs from dengue-related diseases to treat dengue. When tested in a de-novo validation setting, DenguePredict found the only two drugs tested in clinical trials for treating dengue and ranked them highly: chloroquine ranked at top 0.96% and ivermectin at top 22.75%. We showed that drugs targeting immune systems and arachidonic acid metabolism-related apoptotic pathways might represent innovative drugs to treat dengue. In summary, DenguePredict, by combining comprehensive disease- and drug-related data and novel algorithms, may greatly facilitate drug discovery for dengue.
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Constructing an integrated gene similarity network for the identification of disease genes.
Tian, Zhen; Guo, Maozu; Wang, Chunyu; Xing, LinLin; Wang, Lei; Zhang, Yin
2017-09-20
Discovering novel genes that are involved human diseases is a challenging task in biomedical research. In recent years, several computational approaches have been proposed to prioritize candidate disease genes. Most of these methods are mainly based on protein-protein interaction (PPI) networks. However, since these PPI networks contain false positives and only cover less half of known human genes, their reliability and coverage are very low. Therefore, it is highly necessary to fuse multiple genomic data to construct a credible gene similarity network and then infer disease genes on the whole genomic scale. We proposed a novel method, named RWRB, to infer causal genes of interested diseases. First, we construct five individual gene (protein) similarity networks based on multiple genomic data of human genes. Then, an integrated gene similarity network (IGSN) is reconstructed based on similarity network fusion (SNF) method. Finally, we employee the random walk with restart algorithm on the phenotype-gene bilayer network, which combines phenotype similarity network, IGSN as well as phenotype-gene association network, to prioritize candidate disease genes. We investigate the effectiveness of RWRB through leave-one-out cross-validation methods in inferring phenotype-gene relationships. Results show that RWRB is more accurate than state-of-the-art methods on most evaluation metrics. Further analysis shows that the success of RWRB is benefited from IGSN which has a wider coverage and higher reliability comparing with current PPI networks. Moreover, we conduct a comprehensive case study for Alzheimer's disease and predict some novel disease genes that supported by literature. RWRB is an effective and reliable algorithm in prioritizing candidate disease genes on the genomic scale. Software and supplementary information are available at http://nclab.hit.edu.cn/~tianzhen/RWRB/ .
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Drug repositioning for enzyme modulator based on human metabolite-likeness.
Lee, Yoon Hyeok; Choi, Hojae; Park, Seongyong; Lee, Boah; Yi, Gwan-Su
2017-05-31
Recently, the metabolite-likeness of the drug space has emerged and has opened a new possibility for exploring human metabolite-like candidates in drug discovery. However, the applicability of metabolite-likeness in drug discovery has been largely unexplored. Moreover, there are no reports on its applications for the repositioning of drugs to possible enzyme modulators, although enzyme-drug relations could be directly inferred from the similarity relationships between enzyme's metabolites and drugs. We constructed a drug-metabolite structural similarity matrix, which contains 1,861 FDA-approved drugs and 1,110 human intermediary metabolites scored with the Tanimoto similarity. To verify the metabolite-likeness measure for drug repositioning, we analyzed 17 known antimetabolite drugs that resemble the innate metabolites of their eleven target enzymes as the gold standard positives. Highly scored drugs were selected as possible modulators of enzymes for their corresponding metabolites. Then, we assessed the performance of metabolite-likeness with a receiver operating characteristic analysis and compared it with other drug-target prediction methods. We set the similarity threshold for drug repositioning candidates of new enzyme modulators based on maximization of the Youden's index. We also carried out literature surveys for supporting the drug repositioning results based on the metabolite-likeness. In this paper, we applied metabolite-likeness to repurpose FDA-approved drugs to disease-associated enzyme modulators that resemble human innate metabolites. All antimetabolite drugs were mapped with their known 11 target enzymes with statistically significant similarity values to the corresponding metabolites. The comparison with other drug-target prediction methods showed the higher performance of metabolite-likeness for predicting enzyme modulators. After that, the drugs scored higher than similarity score of 0.654 were selected as possible modulators of enzymes for
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Design of a tripartite network for the prediction of drug targets
Kunimoto, Ryo; Bajorath, Jürgen
2018-02-01
Drug-target networks have aided in many target prediction studies aiming at drug repurposing or the analysis of side effects. Conventional drug-target networks are bipartite. They contain two different types of nodes representing drugs and targets, respectively, and edges indicating pairwise drug-target interactions. In this work, we introduce a tripartite network consisting of drugs, other bioactive compounds, and targets from different sources. On the basis of analog relationships captured in the network and so-called neighbor targets of drugs, new drug targets can be inferred. The tripartite network was found to have a stable structure and simulated network growth was accompanied by a steady increase in assortativity, reflecting increasing correlation between degrees of connected nodes leading to even network connectivity. Local drug environments in the tripartite network typically contained neighbor targets and revealed interesting drug-compound-target relationships for further analysis. Candidate targets were prioritized. The tripartite network design extends standard drug-target networks and provides additional opportunities for drug target prediction.
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Coupled disease-behavior dynamics on complex networks: A review
Wang, Zhen; Andrews, Michael A.; Wu, Zhi-Xi; Wang, Lin; Bauch, Chris T.
2015-12-01
It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease-behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease-behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years.
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Molecular networks and the evolution of human cognitive specializations.
Fontenot, Miles; Konopka, Genevieve
2014-12-01
Inroads into elucidating the origins of human cognitive specializations have taken many forms, including genetic, genomic, anatomical, and behavioral assays that typically compare humans to non-human primates. While the integration of all of these approaches is essential for ultimately understanding human cognition, here, we review the usefulness of coexpression network analysis for specifically addressing this question. An increasing number of studies have incorporated coexpression networks into brain expression studies comparing species, disease versus control tissue, brain regions, or developmental time periods. A clearer picture has emerged of the key genes driving brain evolution, as well as the developmental and regional contributions of gene expression patterns important for normal brain development and those misregulated in cognitive diseases. Copyright © 2014 Elsevier Ltd. All rights reserved.
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iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking.
Directory of Open Access Journals (Sweden)
Xuan Xiao
Full Text Available Involved in many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, G-protein-coupled receptors (GPCRs are among the most frequent targets of therapeutic drugs. It is time-consuming and expensive to determine whether a drug and a GPCR are to interact with each other in a cellular network purely by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (dimensional structure of protein, unfortunately their usage is quite limited because the 3D structures for most GPCRs are still unknown. To overcome the situation, a sequence-based classifier, called "iGPCR-drug", was developed to predict the interactions between GPCRs and drugs in cellular networking. In the predictor, the drug compound is formulated by a 2D (dimensional fingerprint via a 256D vector, GPCR by the PseAAC (pseudo amino acid composition generated with the grey model theory, and the prediction engine is operated by the fuzzy K-nearest neighbour algorithm. Moreover, a user-friendly web-server for iGPCR-drug was established at http://www.jci-bioinfo.cn/iGPCR-Drug/. For the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in this paper just for its integrity. The overall success rate achieved by iGPCR-drug via the jackknife test was 85.5%, which is remarkably higher than the rate by the existing peer method developed in 2010 although no web server was ever established for it. It is anticipated that iGPCR-Drug may become a useful high throughput tool for both basic research and drug development, and that the approach presented here can also be extended to study other drug - target interaction networks.
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DEGAS: de novo discovery of dysregulated pathways in human diseases.
Directory of Open Access Journals (Sweden)
Igor Ulitsky
Full Text Available BACKGROUND: Molecular studies of the human disease transcriptome typically involve a search for genes whose expression is significantly dysregulated in sick individuals compared to healthy controls. Recent studies have found that only a small number of the genes in human disease-related pathways show consistent dysregulation in sick individuals. However, those studies found that some pathway genes are affected in most sick individuals, but genes can differ among individuals. While a pathway is usually defined as a set of genes known to share a specific function, pathway boundaries are frequently difficult to assign, and methods that rely on such definition cannot discover novel pathways. Protein interaction networks can potentially be used to overcome these problems. METHODOLOGY/PRINCIPAL FINDINGS: We present DEGAS (DysrEgulated Gene set Analysis via Subnetworks, a method for identifying connected gene subnetworks significantly enriched for genes that are dysregulated in specimens of a disease. We applied DEGAS to seven human diseases and obtained statistically significant results that appear to home in on compact pathways enriched with hallmarks of the diseases. In Parkinson's disease, we provide novel evidence for involvement of mRNA splicing, cell proliferation, and the 14-3-3 complex in the disease progression. DEGAS is available as part of the MATISSE software package (http://acgt.cs.tau.ac.il/matisse. CONCLUSIONS/SIGNIFICANCE: The subnetworks identified by DEGAS can provide a signature of the disease potentially useful for diagnosis, pinpoint possible pathways affected by the disease, and suggest targets for drug intervention.
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Directory of Open Access Journals (Sweden)
Jingchun Sun
2013-01-01
Full Text Available Antipsychotic drugs are medications commonly for schizophrenia (SCZ treatment, which include two groups: typical and atypical. SCZ patients have multiple comorbidities, and the coadministration of drugs is quite common. This may result in adverse drug-drug interactions, which are events that occur when the effect of a drug is altered by the coadministration of another drug. Therefore, it is important to provide a comprehensive view of these interactions for further coadministration improvement. Here, we extracted SCZ drugs and their adverse drug interactions from the DrugBank and compiled a SCZ-specific adverse drug interaction network. This network included 28 SCZ drugs, 241 non-SCZs, and 991 interactions. By integrating the Anatomical Therapeutic Chemical (ATC classification with the network analysis, we characterized those interactions. Our results indicated that SCZ drugs tended to have more adverse drug interactions than other drugs. Furthermore, SCZ typical drugs had significant interactions with drugs of the “alimentary tract and metabolism” category while SCZ atypical drugs had significant interactions with drugs of the categories “nervous system” and “antiinfectives for systemic uses.” This study is the first to characterize the adverse drug interactions in the course of SCZ treatment and might provide useful information for the future SCZ treatment.
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Chen, Bi-Wen; Li, Wen-Xing; Wang, Guang-Hui; Li, Gong-Hua; Liu, Jia-Qian; Zheng, Jun-Juan; Wang, Qian; Li, Hui-Juan; Dai, Shao-Xing; Huang, Jing-Fei
2018-01-01
Alzheimer' disease (AD) is an ultimately fatal degenerative brain disorder that has an increasingly large burden on health and social care systems. There are only five drugs for AD on the market, and no new effective medicines have been discovered for many years. Chinese medicinal plants have been used to treat diseases for thousands of years, and screening herbal remedies is a way to develop new drugs. We used molecular docking to screen 30,438 compounds from Traditional Chinese Medicine (TCM) against a comprehensive list of AD target proteins. TCM compounds in the top 0.5% of binding affinity scores for each target protein were selected as our research objects. Structural similarities between existing drugs from DrugBank database and selected TCM compounds as well as the druggability of our candidate compounds were studied. Finally, we searched the CNKI database to obtain studies on anti-AD Chinese plants from 2007 to 2017, and only clinical studies were included. A total of 1,476 compounds (top 0.5%) were selected as drug candidates. Most of these compounds are abundantly found in plants used for treating AD in China, especially the plants from two genera Panax and Morus. We classified the compounds by single target and multiple targets and analyzed the interactions between target proteins and compounds. Analysis of structural similarity revealed that 17 candidate anti-AD compounds were structurally identical to 14 existing approved drugs. Most of them have been reported to have a positive effect in AD. After filtering for compound druggability, we identified 11 anti-AD compounds with favorable properties, seven of which are found in anti-AD Chinese plants. Of 11 anti-AD compounds, four compounds 5,862, 5,863, 5,868, 5,869 have anti-inflammatory activity. The compound 28,814 mainly has immunoregulatory activity. The other six compounds have not yet been reported for any biology activity at present. Natural compounds from TCM provide a broad prospect for the
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Science of the science, drug discovery and artificial neural networks.
Patel, Jigneshkumar
2013-03-01
Drug discovery process many times encounters complex problems, which may be difficult to solve by human intelligence. Artificial Neural Networks (ANNs) are one of the Artificial Intelligence (AI) technologies used for solving such complex problems. ANNs are widely used for primary virtual screening of compounds, quantitative structure activity relationship studies, receptor modeling, formulation development, pharmacokinetics and in all other processes involving complex mathematical modeling. Despite having such advanced technologies and enough understanding of biological systems, drug discovery is still a lengthy, expensive, difficult and inefficient process with low rate of new successful therapeutic discovery. In this paper, author has discussed the drug discovery science and ANN from very basic angle, which may be helpful to understand the application of ANN for drug discovery to improve efficiency.
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Drug dosing in chronic kidney disease.
Gabardi, Steven; Abramson, Stuart
2005-05-01
Patients with chronic kidney disease (CKD) are at high risk for adverse drug reactions and drug-drug interactions. Drug dosing in these patients often proves to be a difficult task. Renal dysfunction-induced changes in human pathophysiology regularly results may alter medication pharmacodynamics and handling. Several pharmacokinetic parameters are adversely affected by CKD, secondary to a reduced oral absorption and glomerular filtration; altered tubular secretion; and reabsorption and changes in intestinal, hepatic, and renal metabolism. In general, drug dosing can be accomplished by multiple methods; however, the most common recommendations are often to reduce the dose or expand the dosing interval, or use both methods simultaneously. Some medications need to be avoided all together in CKD either because of lack of efficacy or increased risk of toxicity. Nevertheless, specific recommendations are available for dosing of certain medications and are an important resource, because most are based on clinical or pharmacokinetic trials.
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Contextualization of drug-mediator relations using evidence networks.
Tran, Hai Joey; Speyer, Gil; Kiefer, Jeff; Kim, Seungchan
2017-05-31
Genomic analysis of drug response can provide unique insights into therapies that can be used to match the "right drug to the right patient." However, the process of discovering such therapeutic insights using genomic data is not straightforward and represents an area of active investigation. EDDY (Evaluation of Differential DependencY), a statistical test to detect differential statistical dependencies, is one method that leverages genomic data to identify differential genetic dependencies. EDDY has been used in conjunction with the Cancer Therapeutics Response Portal (CTRP), a dataset with drug-response measurements for more than 400 small molecules, and RNAseq data of cell lines in the Cancer Cell Line Encyclopedia (CCLE) to find potential drug-mediator pairs. Mediators were identified as genes that showed significant change in genetic statistical dependencies within annotated pathways between drug sensitive and drug non-sensitive cell lines, and the results are presented as a public web-portal (EDDY-CTRP). However, the interpretability of drug-mediator pairs currently hinders further exploration of these potentially valuable results. In this study, we address this challenge by constructing evidence networks built with protein and drug interactions from the STITCH and STRING interaction databases. STITCH and STRING are sister databases that catalog known and predicted drug-protein interactions and protein-protein interactions, respectively. Using these two databases, we have developed a method to construct evidence networks to "explain" the relation between a drug and a mediator. RESULTS: We applied this approach to drug-mediator relations discovered in EDDY-CTRP analysis and identified evidence networks for ~70% of drug-mediator pairs where most mediators were not known direct targets for the drug. Constructed evidence networks enable researchers to contextualize the drug-mediator pair with current research and knowledge. Using evidence networks, we were
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Keenan, Alexandra B; Jenkins, Sherry L; Jagodnik, Kathleen M; Koplev, Simon; He, Edward; Torre, Denis; Wang, Zichen; Dohlman, Anders B; Silverstein, Moshe C; Lachmann, Alexander; Kuleshov, Maxim V; Ma'ayan, Avi; Stathias, Vasileios; Terryn, Raymond; Cooper, Daniel; Forlin, Michele; Koleti, Amar; Vidovic, Dusica; Chung, Caty; Schürer, Stephan C; Vasiliauskas, Jouzas; Pilarczyk, Marcin; Shamsaei, Behrouz; Fazel, Mehdi; Ren, Yan; Niu, Wen; Clark, Nicholas A; White, Shana; Mahi, Naim; Zhang, Lixia; Kouril, Michal; Reichard, John F; Sivaganesan, Siva; Medvedovic, Mario; Meller, Jaroslaw; Koch, Rick J; Birtwistle, Marc R; Iyengar, Ravi; Sobie, Eric A; Azeloglu, Evren U; Kaye, Julia; Osterloh, Jeannette; Haston, Kelly; Kalra, Jaslin; Finkbiener, Steve; Li, Jonathan; Milani, Pamela; Adam, Miriam; Escalante-Chong, Renan; Sachs, Karen; Lenail, Alex; Ramamoorthy, Divya; Fraenkel, Ernest; Daigle, Gavin; Hussain, Uzma; Coye, Alyssa; Rothstein, Jeffrey; Sareen, Dhruv; Ornelas, Loren; Banuelos, Maria; Mandefro, Berhan; Ho, Ritchie; Svendsen, Clive N; Lim, Ryan G; Stocksdale, Jennifer; Casale, Malcolm S; Thompson, Terri G; Wu, Jie; Thompson, Leslie M; Dardov, Victoria; Venkatraman, Vidya; Matlock, Andrea; Van Eyk, Jennifer E; Jaffe, Jacob D; Papanastasiou, Malvina; Subramanian, Aravind; Golub, Todd R; Erickson, Sean D; Fallahi-Sichani, Mohammad; Hafner, Marc; Gray, Nathanael S; Lin, Jia-Ren; Mills, Caitlin E; Muhlich, Jeremy L; Niepel, Mario; Shamu, Caroline E; Williams, Elizabeth H; Wrobel, David; Sorger, Peter K; Heiser, Laura M; Gray, Joe W; Korkola, James E; Mills, Gordon B; LaBarge, Mark; Feiler, Heidi S; Dane, Mark A; Bucher, Elmar; Nederlof, Michel; Sudar, Damir; Gross, Sean; Kilburn, David F; Smith, Rebecca; Devlin, Kaylyn; Margolis, Ron; Derr, Leslie; Lee, Albert; Pillai, Ajay
2018-01-24
The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic, and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies. Perturbations under study include drugs, genetic perturbations, tissue micro-environments, antibodies, and disease-causing mutations. Responses to perturbations are measured by transcript profiling, mass spectrometry, cell imaging, and biochemical methods, among other assays. The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders. This Perspective describes LINCS technologies, datasets, tools, and approaches to data accessibility and reusability. Copyright © 2017 Elsevier Inc. All rights reserved.
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Reppe, Sjur; Datta, Harish K; Gautvik, Kaare M
2017-08-01
The skeleton is a metabolically active organ throughout life where specific bone cell activity and paracrine/endocrine factors regulate its morphogenesis and remodeling. In recent years, an increasing number of reports have used multi-omics technologies to characterize subsets of bone biological molecular networks. The skeleton is affected by primary and secondary disease, lifestyle and many drugs. Therefore, to obtain relevant and reliable data from well characterized patient and control cohorts are vital. Here we provide a brief overview of omics studies performed on human bone, of which our own studies performed on trans-iliacal bone biopsies from postmenopausal women with osteoporosis (OP) and healthy controls are among the first and largest. Most other studies have been performed on smaller groups of patients, undergoing hip replacement for osteoarthritis (OA) or fracture, and without healthy controls. The major findings emerging from the combined studies are: 1. Unstressed and stressed bone show profoundly different gene expression reflecting differences in bone turnover and remodeling and 2. Omics analyses comparing healthy/OP and control/OA cohorts reveal characteristic changes in transcriptomics, epigenomics (DNA methylation), proteomics and metabolomics. These studies, together with genome-wide association studies, in vitro observations and transgenic animal models have identified a number of genes and gene products that act via Wnt and other signaling systems and are highly associated to bone density and fracture. Future challenge is to understand the functional interactions between bone-related molecular networks and their significance in OP and OA pathogenesis, and also how the genomic architecture is affected in health and disease. Copyright © 2017 Elsevier Inc. All rights reserved.
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Daniell, Henry; Chan, Hui-Ting; Pasoreck, Elise K
2016-11-23
Plastid-made biopharmaceuticals treat major metabolic or genetic disorders, including Alzheimer's, diabetes, hypertension, hemophilia, and retinopathy. Booster vaccines made in chloroplasts prevent global infectious diseases, such as tuberculosis, malaria, cholera, and polio, and biological threats, such as anthrax and plague. Recent advances in this field include commercial-scale production of human therapeutic proteins in FDA-approved cGMP facilities, development of tags to deliver protein drugs to targeted human cells or tissues, methods to deliver precise doses, and long-term stability of protein drugs at ambient temperature, maintaining their efficacy. Codon optimization utilizing valuable information from sequenced chloroplast genomes enhanced expression of eukaryotic human or viral genes in chloroplasts and offered unique insights into translation in chloroplasts. Support from major biopharmaceutical companies, development of hydroponic production systems, and evaluation by regulatory agencies, including the CDC, FDA, and USDA, augur well for advancing this novel concept to the clinic and revolutionizing affordable healthcare.
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TCMSP: a database of systems pharmacology for drug discovery from herbal medicines.
Ru, Jinlong; Li, Peng; Wang, Jinan; Zhou, Wei; Li, Bohui; Huang, Chao; Li, Pidong; Guo, Zihu; Tao, Weiyang; Yang, Yinfeng; Xu, Xue; Li, Yan; Wang, Yonghua; Yang, Ling
2014-01-01
Modern medicine often clashes with traditional medicine such as Chinese herbal medicine because of the little understanding of the underlying mechanisms of action of the herbs. In an effort to promote integration of both sides and to accelerate the drug discovery from herbal medicines, an efficient systems pharmacology platform that represents ideal information convergence of pharmacochemistry, ADME properties, drug-likeness, drug targets, associated diseases and interaction networks, are urgently needed. The traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) was built based on the framework of systems pharmacology for herbal medicines. It consists of all the 499 Chinese herbs registered in the Chinese pharmacopoeia with 29,384 ingredients, 3,311 targets and 837 associated diseases. Twelve important ADME-related properties like human oral bioavailability, half-life, drug-likeness, Caco-2 permeability, blood-brain barrier and Lipinski's rule of five are provided for drug screening and evaluation. TCMSP also provides drug targets and diseases of each active compound, which can automatically establish the compound-target and target-disease networks that let users view and analyze the drug action mechanisms. It is designed to fuel the development of herbal medicines and to promote integration of modern medicine and traditional medicine for drug discovery and development. The particular strengths of TCMSP are the composition of the large number of herbal entries, and the ability to identify drug-target networks and drug-disease networks, which will help revealing the mechanisms of action of Chinese herbs, uncovering the nature of TCM theory and developing new herb-oriented drugs. TCMSP is freely available at http://sm.nwsuaf.edu.cn/lsp/tcmsp.php.
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Genetic engineering in nonhuman primates for human disease modeling.
Sato, Kenya; Sasaki, Erika
2018-02-01
Nonhuman primate (NHP) experimental models have contributed greatly to human health research by assessing the safety and efficacy of newly developed drugs, due to their physiological and anatomical similarities to humans. To generate NHP disease models, drug-inducible methods, and surgical treatment methods have been employed. Recent developments in genetic and developmental engineering in NHPs offer new options for producing genetically modified disease models. Moreover, in recent years, genome-editing technology has emerged to further promote this trend and the generation of disease model NHPs has entered a new era. In this review, we summarize the generation of conventional disease model NHPs and discuss new solutions to the problem of mosaicism in genome-editing technology.
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Illegal Drugs and Heart Disease
... Venous Thromboembolism Aortic Aneurysm More Illegal Drugs and Heart Disease Updated:May 3,2018 Most illegal drugs can ... www.dea.gov/druginfo/factsheets.shtml Alcohol and Heart Disease Caffeine and Heart Disease Tobacco and Heart Disease ...
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Disease-modifying drugs in Alzheimer's disease
Directory of Open Access Journals (Sweden)
Ghezzi L
2013-12-01
Full Text Available Laura Ghezzi, Elio Scarpini, Daniela Galimberti Neurology Unit, Department of Pathophysiology and Transplantation, University of Milan, Fondazione Cà Granda, IRCCS Ospedale Maggiore Policlinico, Milan, Italy Abstract: Alzheimer's disease (AD is an age-dependent neurodegenerative disorder and the most common cause of dementia. The early stages of AD are characterized by short-term memory loss. Once the disease progresses, patients experience difficulties in sense of direction, oral communication, calculation, ability to learn, and cognitive thinking. The median duration of the disease is 10 years. The pathology is characterized by deposition of amyloid beta peptide (so-called senile plaques and tau protein in the form of neurofibrillary tangles. Currently, two classes of drugs are licensed by the European Medicines Agency for the treatment of AD, ie, acetylcholinesterase inhibitors for mild to moderate AD, and memantine, an N-methyl-D-aspartate receptor antagonist, for moderate and severe AD. Treatment with acetylcholinesterase inhibitors or memantine aims at slowing progression and controlling symptoms, whereas drugs under development are intended to modify the pathologic steps leading to AD. Herein, we review the clinical features, pharmacologic properties, and cost-effectiveness of the available acetylcholinesterase inhibitors and memantine, and focus on disease-modifying drugs aiming to interfere with the amyloid beta peptide, including vaccination, passive immunization, and tau deposition. Keywords: Alzheimer's disease, acetylcholinesterase inhibitors, memantine, disease-modifying drugs, diagnosis, treatment
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Spreading of diseases through comorbidity networks across life and gender
International Nuclear Information System (INIS)
Chmiel, Anna; Klimek, Peter; Thurner, Stefan
2014-01-01
The state of health of patients is typically not characterized by a single disease alone but by multiple (comorbid) medical conditions. These comorbidities may depend strongly on age and gender. We propose a specific phenomenological comorbidity network of human diseases that is based on medical claims data of the entire population of Austria. The network is constructed from a two-layer multiplex network, where in one layer the links represent the conditional probability for a comorbidity, and in the other the links contain the respective statistical significance. We show that the network undergoes dramatic structural changes across the lifetime of patients. Disease networks for children consist of a single, strongly interconnected cluster. During adolescence and adulthood further disease clusters emerge that are related to specific classes of diseases, such as circulatory, mental, or genitourinary disorders. For people over 65 these clusters start to merge, and highly connected hubs dominate the network. These hubs are related to hypertension, chronic ischemic heart diseases, and chronic obstructive pulmonary diseases. We introduce a simple diffusion model to understand the spreading of diseases on the disease network at the population level. For the first time we are able to show that patients predominantly develop diseases that are in close network proximity to disorders that they already suffer. The model explains more than 85% of the variance of all disease incidents in the population. The presented methodology could be of importance for anticipating age-dependent disease profiles for entire populations, and for design and validation of prevention strategies. (paper)
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Directory of Open Access Journals (Sweden)
Huthmacher Carola
2010-08-01
Full Text Available Abstract Background Despite enormous efforts to combat malaria the disease still afflicts up to half a billion people each year of which more than one million die. Currently no approved vaccine is available and resistances to antimalarials are widely spread. Hence, new antimalarial drugs are urgently needed. Results Here, we present a computational analysis of the metabolism of Plasmodium falciparum, the deadliest malaria pathogen. We assembled a compartmentalized metabolic model and predicted life cycle stage specific metabolism with the help of a flux balance approach that integrates gene expression data. Predicted metabolite exchanges between parasite and host were found to be in good accordance with experimental findings when the parasite's metabolic network was embedded into that of its host (erythrocyte. Knock-out simulations identified 307 indispensable metabolic reactions within the parasite. 35 out of 57 experimentally demonstrated essential enzymes were recovered and another 16 enzymes, if additionally the assumption was made that nutrient uptake from the host cell is limited and all reactions catalyzed by the inhibited enzyme are blocked. This predicted set of putative drug targets, shown to be enriched with true targets by a factor of at least 2.75, was further analyzed with respect to homology to human enzymes, functional similarity to therapeutic targets in other organisms and their predicted potency for prophylaxis and disease treatment. Conclusions The results suggest that the set of essential enzymes predicted by our flux balance approach represents a promising starting point for further drug development.
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Directory of Open Access Journals (Sweden)
Qian Li
Full Text Available BACKGROUND: Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. METHODOLOGY: We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671 between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. CONCLUSIONS: This article proposes a network-based multi-target computational estimation
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Li, Qian; Li, Xudong; Li, Canghai; Chen, Lirong; Song, Jun; Tang, Yalin; Xu, Xiaojie
2011-03-22
Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by
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Hanlon, J T; Perera, S; Newman, A B; Thorpe, J M; Donohue, J M; Simonsick, E M; Shorr, R I; Bauer, D C; Marcum, Z A
2017-04-01
There are few studies examining both drug-drug and drug-disease interactions in older adults. Therefore, the objective of this study was to describe the prevalence of potential drug-drug and drug-disease interactions and associated factors in community-dwelling older adults. This cross-sectional study included 3055 adults aged 70-79 without mobility limitations at their baseline visit in the Health Aging and Body Composition Study conducted in the communities of Pittsburgh PA and Memphis TN, USA. The outcome factors were potential drug-drug and drug-disease interactions as per the application of explicit criteria drawn from a number of sources to self-reported prescription and non-prescription medication use. Over one-third of participants had at least one type of interaction. Approximately one quarter (25·1%) had evidence of had one or more drug-drug interactions. Nearly 10·7% of the participants had a drug-drug interaction that involved a non-prescription medication. % The most common drug-drug interaction was non-steroidal anti-inflammatory drugs (NSAIDs) affecting antihypertensives. Additionally, 16·0% had a potential drug-disease interaction with 3·7% participants having one involving non-prescription medications. The most common drug-disease interaction was aspirin/NSAID use in those with history of peptic ulcer disease without gastroprotection. Over one-third (34·0%) had at least one type of drug interaction. Each prescription medication increased the odds of having at least one type of drug interaction by 35-40% [drug-drug interaction adjusted odds ratio (AOR) = 1·35, 95% confidence interval (CI) = 1·27-1·42; drug-disease interaction AOR = 1·30; CI = 1·21-1·40; and both AOR = 1·45; CI = 1·34-1·57]. A prior hospitalization increased the odds of having at least one type of drug interaction by 49-84% compared with those not hospitalized (drug-drug interaction AOR = 1·49, 95% CI = 1·11-2·01; drug-disease interaction AOR = 1·69, CI = 1·15-2
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Directory of Open Access Journals (Sweden)
Naoki Yahata
Full Text Available BACKGROUND: Alzheimer's disease (AD is a neurodegenerative disorder that causes progressive memory and cognitive decline during middle to late adult life. The AD brain is characterized by deposition of amyloid β peptide (Aβ, which is produced from amyloid precursor protein by β- and γ-secretase (presenilin complex-mediated sequential cleavage. Induced pluripotent stem (iPS cells potentially provide an opportunity to generate a human cell-based model of AD that would be crucial for drug discovery as well as for investigating mechanisms of the disease. METHODOLOGY/PRINCIPAL FINDINGS: We differentiated human iPS (hiPS cells into neuronal cells expressing the forebrain marker, Foxg1, and the neocortical markers, Cux1, Satb2, Ctip2, and Tbr1. The iPS cell-derived neuronal cells also expressed amyloid precursor protein, β-secretase, and γ-secretase components, and were capable of secreting Aβ into the conditioned media. Aβ production was inhibited by β-secretase inhibitor, γ-secretase inhibitor (GSI, and an NSAID; however, there were different susceptibilities to all three drugs between early and late differentiation stages. At the early differentiation stage, GSI treatment caused a fast increase at lower dose (Aβ surge and drastic decline of Aβ production. CONCLUSIONS/SIGNIFICANCE: These results indicate that the hiPS cell-derived neuronal cells express functional β- and γ-secretases involved in Aβ production; however, anti-Aβ drug screening using these hiPS cell-derived neuronal cells requires sufficient neuronal differentiation.
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Disease emergence and resurgence—the wildlife-human connection
Friend, Milton; Hurley, James W.; Nol, Pauline; Wesenberg, Katherine
2006-01-01
In 2000, the Global Outbreak Alert and Response Network (GOARN) was organized as a global disease watchdog group to coordinate disease outbreak information and health crisis response. The World Health Organization (WHO) is the headquarters for this network. Understandably, the primary focus for WHO is human health. However, diseases such as the H5N1 avian influenza epizootic in Asian bird populations demonstrate the need for integrating knowledge about disease emergence in animals and in humans.Aside from human disease concerns, H5N1 avian influenza has major economic consequences for the poultry industry worldwide. Many other emerging diseases, such as severe acute respiratory syndrome (SARS), monkeypox, Ebola fever, and West Nile fever, also have an important wildlife component. Despite these wildlife associations, the true integration of the wildlife component in approaches towards disease emergence remains elusive. This separation between wildlife and other species’ interests is counterproductive because the emergence of zoonotic viruses and other pathogens maintained by wildlife reservoir hosts is poorly understood.This book is about the wildlife component of emerging diseases. It is intended to enhance the reader’s awareness of the role of wildlife in disease emergence. By doing so, perhaps a more holistic approach to disease prevention and control will emerge for the benefit of human, domestic animal, and free-ranging wildlife populations alike. The perspectives offered are influenced by more than four decades of my experiences as a wildlife disease practitioner. Although wildlife are victims to many of the same disease agents affecting humans and domestic animals, many aspects of disease in free-ranging wildlife require different approaches than those commonly applied to address disease in humans or domestic animals. Nevertheless, the broader community of disease investigators and health care professionals has largely pursued a separatist approach for
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Mezlini, Aziz M; Goldenberg, Anna
2017-10-01
Discovering genetic mechanisms driving complex diseases is a hard problem. Existing methods often lack power to identify the set of responsible genes. Protein-protein interaction networks have been shown to boost power when detecting gene-disease associations. We introduce a Bayesian framework, Conflux, to find disease associated genes from exome sequencing data using networks as a prior. There are two main advantages to using networks within a probabilistic graphical model. First, networks are noisy and incomplete, a substantial impediment to gene discovery. Incorporating networks into the structure of a probabilistic models for gene inference has less impact on the solution than relying on the noisy network structure directly. Second, using a Bayesian framework we can keep track of the uncertainty of each gene being associated with the phenotype rather than returning a fixed list of genes. We first show that using networks clearly improves gene detection compared to individual gene testing. We then show consistently improved performance of Conflux compared to the state-of-the-art diffusion network-based method Hotnet2 and a variety of other network and variant aggregation methods, using randomly generated and literature-reported gene sets. We test Hotnet2 and Conflux on several network configurations to reveal biases and patterns of false positives and false negatives in each case. Our experiments show that our novel Bayesian framework Conflux incorporates many of the advantages of the current state-of-the-art methods, while offering more flexibility and improved power in many gene-disease association scenarios.
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Directory of Open Access Journals (Sweden)
Jüri Reimand
2015-01-01
Full Text Available Interpreting the impact of human genome variation on phenotype is challenging. The functional effect of protein-coding variants is often predicted using sequence conservation and population frequency data, however other factors are likely relevant. We hypothesized that variants in protein post-translational modification (PTM sites contribute to phenotype variation and disease. We analyzed fraction of rare variants and non-synonymous to synonymous variant ratio (Ka/Ks in 7,500 human genomes and found a significant negative selection signal in PTM regions independent of six factors, including conservation, codon usage, and GC-content, that is widely distributed across tissue-specific genes and function classes. PTM regions are also enriched in known disease mutations, suggesting that PTM variation is more likely deleterious. PTM constraint also affects flanking sequence around modified residues and increases around clustered sites, indicating presence of functionally important short linear motifs. Using target site motifs of 124 kinases, we predict that at least ∼180,000 motif-breaker amino acid residues that disrupt PTM sites when substituted, and highlight kinase motifs that show specific negative selection and enrichment of disease mutations. We provide this dataset with corresponding hypothesized mechanisms as a community resource. As an example of our integrative approach, we propose that PTPN11 variants in Noonan syndrome aberrantly activate the protein by disrupting an uncharacterized cluster of phosphorylation sites. Further, as PTMs are molecular switches that are modulated by drugs, we study mutated binding sites of PTM enzymes in disease genes and define a drug-disease network containing 413 novel predicted disease-gene links.
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Sharma, Arun; Wu, Joseph C; Wu, Sean M
2013-12-24
Human induced pluripotent stem cells (hiPSCs) have emerged as a novel tool for drug discovery and therapy in cardiovascular medicine. hiPSCs are functionally similar to human embryonic stem cells (hESCs) and can be derived autologously without the ethical challenges associated with hESCs. Given the limited regenerative capacity of the human heart following myocardial injury, cardiomyocytes derived from hiPSCs (hiPSC-CMs) have garnered significant attention from basic and translational scientists as a promising cell source for replacement therapy. However, ongoing issues such as cell immaturity, scale of production, inter-line variability, and cell purity will need to be resolved before human clinical trials can begin. Meanwhile, the use of hiPSCs to explore cellular mechanisms of cardiovascular diseases in vitro has proven to be extremely valuable. For example, hiPSC-CMs have been shown to recapitulate disease phenotypes from patients with monogenic cardiovascular disorders. Furthermore, patient-derived hiPSC-CMs are now providing new insights regarding drug efficacy and toxicity. This review will highlight recent advances in utilizing hiPSC-CMs for cardiac disease modeling in vitro and as a platform for drug validation. The advantages and disadvantages of using hiPSC-CMs for drug screening purposes will be explored as well.
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Wu, Di; Wang, Zhenhong; Jiang, Zhenxia; Fu, Xiaojing; Li, Hui; Zhang, Dapeng; Liu, Hui; Hu, Yifei
2014-11-01
To understand the characteristics of recreational drug users' behaviors and social network, as well as their potential impact to the transmission of sexual transmitted infections (STI). Qualitative interview was used to collect information on rough estimation of population size and behavior change before and after recreational drug use. A total of 120 participants were recruited by convenient sampling from April to October, 2013 in a community of Qingdao city. Blood specimens were taken for HIV/syphilis serological testing and social network analysis was performed to understand the characteristics of their behavior and social network. All participants used methamphetamine and 103 of them showed social connection. The prevalence of syphilis and HIV were 24.2% (29/120) and 2.5% (3/120) respectively. The estimated size of recreational drug users was big with a wide diversity of occupations and age range, and males were more frequent than females. Drug use may affect condom use and frequent drug users showed symptom of psychosis and neuro-toxicities. The size of social network was 2.45 ± 1.63 in the past 6 months, which indicated an increasing trend of the sexual partner number and risky behaviors. Recreational drug use could increase the size of social network among sex partners, the frequency of risky sexual behaviors and syphilis prevalence, which indicate a high risk of HIV/STI among this population as well as a huge burden of disease prevention and control in the future.
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The thalamus in drug addiction: from rodents to humans.
Huang, Anna S; Mitchell, Jameson A; Haber, Suzanne N; Alia-Klein, Nelly; Goldstein, Rita Z
2018-03-19
Impairments in response inhibition and salience attribution (iRISA) have been proposed to underlie the clinical symptoms of drug addiction as mediated by cortico-striatal-thalamo-cortical networks. The bulk of evidence supporting the iRISA model comes from neuroimaging research that has focused on cortical and striatal influences with less emphasis on the role of the thalamus. Here, we highlight the importance of the thalamus in drug addiction, focusing on animal literature findings on thalamic nuclei in the context of drug-seeking, structural and functional changes of the thalamus as measured by imaging studies in human drug addiction, particularly during drug cue and non-drug reward processing, and response inhibition tasks. Findings from the animal literature suggest that the paraventricular nucleus of the thalamus, the lateral habenula and the mediodorsal nucleus may be involved in the reinstatement, extinction and expression of drug-seeking behaviours. In support of the iRISA model, the human addiction imaging literature demonstrates enhanced thalamus activation when reacting to drug cues and reduced thalamus activation during response inhibition. This pattern of response was further associated with the severity of, and relapse in, drug addiction. Future animal studies could widen their field of focus by investigating the specific role(s) of different thalamic nuclei in different phases of the addiction cycle. Similarly, future human imaging studies should aim to specifically delineate the structure and function of different thalamic nuclei, for example, through the application of advanced imaging protocols at higher magnetic fields (7 Tesla).This article is part of a discussion meeting issue 'Of mice and mental health: facilitating dialogue between basic and clinical neuroscientists'. © 2018 The Author(s).
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De, Prithwish; Cox, Joseph; Boivin, Jean-François; Platt, Robert W; Jolly, Ann M
2007-11-01
To examine the scientific evidence regarding the association between characteristics of social networks of injection drug users (IDUs) and the sharing of drug injection equipment. A search was performed on MEDLINE, EMBASE, BIOSIS, Current Contents, PsycINFO databases and other sources to identify published studies on social networks of IDUs. Papers were selected based on their examination of social network factors in relation to the sharing of syringes and drug preparation equipment (e.g. containers, filters, water). Additional relevant papers were found from the reference list of identified articles. Network correlates of drug equipment sharing are multi-factorial and include structural factors (network size, density, position, turnover), compositional factors (network member characteristics, role and quality of relationships with members) and behavioural factors (injecting norms, patterns of drug use, severity of drug addiction). Factors appear to be related differentially to equipment sharing. Social network characteristics are associated with drug injection risk behaviours and should be considered alongside personal risk behaviours in prevention programmes. Recommendations for future research into the social networks of IDUs are proposed.
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Li, Bao-Dong; Cui, Jing-Jun; Song, Jia; Qi, Ce; Ma, Pei-Feng; Wang, Ya-Rong; Bai, Jing
2018-01-01
A network meta-analysis is used to compare the efficacy of ropinirole, rasagiline, rotigotine, entacapone, apomorphine, pramipexole, sumanirole, bromocriptine, piribedil and levodopa, with placebo as a control, for non-motor symptoms in Parkinson's disease (PD). PubMed, Embase and the Cochrane Library were searched from their establishment dates up to January 2017 for randomized controlled trials (RCTs) investigating the efficacy of the above ten drugs on the non-motor symptoms of PD. A network meta-analysis combined the evidence from direct comparisons and indirect comparisons and evaluated the pooled weighted mean difference (WMD) values and surfaces under the cumulative ranking curves (SUCRA). The network meta-analysis included 21 RCTs. The analysis results indicated that, using the United Parkinson's Disease Rating Scale (UPDRS) III, the efficacies of placebo, ropinirole, rasagiline, rotigotine, entacapone, pramipexole, sumanirole and levodopa in treating PD were lower than that of apomorphine (WMD = -10.90, 95% CI = -16.12∼-5.48; WMD = -11.85, 95% CI = -17.31∼-6.16; WMD = -11.15, 95% CI = -16.64∼-5.04; WMD = -11.70, 95% CI = -16.98∼-5.60; WMD = -11.04, 95% CI = -16.97∼-5.34; WMD = -13.27, 95% CI = -19.22∼-7.40; WMD = -10.25, 95% CI = -15.66∼-4.32; and WMD = -11.60, 95% CI = -17.89∼-5.57, respectively). Treatment with ropinirole, rasagiline, rotigotine, entacapone, pramipexole, sumanirole, bromocriptine, piribedil or levodopa, with placebo as a control, on PD exhibited no significant differences on PD symptoms when the UPDRS II was used for evaluation. Moreover, using the UPDRS III, the SUCRA values indicated that a pomorphine had the best efficacy on the non-motor symptoms of PD (99.0%). Using the UPDRS II, the SUCRA values for ropinirole, rasagiline, rotigotine, entacapone, pramipexole, sumanirole, bromocriptine, piribedil and levodopa treatments, with placebo as a control, indicated that bromocriptine showed the best efficacy on the non
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International Nuclear Information System (INIS)
Schaefer-Prokop, Cornelia; Eisenhuber, Edith
2010-01-01
There is an ever increasing number of drugs that can cause lung disease. Imaging plays an important role in the diagnosis, since the clinical symptoms are mostly nonspecific. Various HRCT patterns can be correlated - though with overlaps - to lung changes caused by certain groups of drugs. Alternative diagnosis such as infection, edema or underlying lung disease has to be excluded by clinical-radiological means. Herefore is profound knowledge of the correlations of drug effects and imaging findings essential. History of drug exposure, suitable radiological findings and response to treatment (corticosteroids and stop of medication) mostly provide the base for the diagnosis. (orig.)
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Next generation human skin constructs as advanced tools for drug development.
Abaci, H E; Guo, Zongyou; Doucet, Yanne; Jacków, Joanna; Christiano, Angela
2017-11-01
Many diseases, as well as side effects of drugs, manifest themselves through skin symptoms. Skin is a complex tissue that hosts various specialized cell types and performs many roles including physical barrier, immune and sensory functions. Therefore, modeling skin in vitro presents technical challenges for tissue engineering. Since the first attempts at engineering human epidermis in 1970s, there has been a growing interest in generating full-thickness skin constructs mimicking physiological functions by incorporating various skin components, such as vasculature and melanocytes for pigmentation. Development of biomimetic in vitro human skin models with these physiological functions provides a new tool for drug discovery, disease modeling, regenerative medicine and basic research for skin biology. This goal, however, has long been delayed by the limited availability of different cell types, the challenges in establishing co-culture conditions, and the ability to recapitulate the 3D anatomy of the skin. Recent breakthroughs in induced pluripotent stem cell (iPSC) technology and microfabrication techniques such as 3D-printing have allowed for building more reliable and complex in vitro skin models for pharmaceutical screening. In this review, we focus on the current developments and prevailing challenges in generating skin constructs with vasculature, skin appendages such as hair follicles, pigmentation, immune response, innervation, and hypodermis. Furthermore, we discuss the promising advances that iPSC technology offers in order to generate in vitro models of genetic skin diseases, such as epidermolysis bullosa and psoriasis. We also discuss how future integration of the next generation human skin constructs onto microfluidic platforms along with other tissues could revolutionize the early stages of drug development by creating reliable evaluation of patient-specific effects of pharmaceutical agents. Impact statement Skin is a complex tissue that hosts various
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MyGeneFriends: A Social Network Linking Genes, Genetic Diseases, and Researchers.
Allot, Alexis; Chennen, Kirsley; Nevers, Yannis; Poidevin, Laetitia; Kress, Arnaud; Ripp, Raymond; Thompson, Julie Dawn; Poch, Olivier; Lecompte, Odile
2017-06-16
The constant and massive increase of biological data offers unprecedented opportunities to decipher the function and evolution of genes and their roles in human diseases. However, the multiplicity of sources and flow of data mean that efficient access to useful information and knowledge production has become a major challenge. This challenge can be addressed by taking inspiration from Web 2.0 and particularly social networks, which are at the forefront of big data exploration and human-data interaction. MyGeneFriends is a Web platform inspired by social networks, devoted to genetic disease analysis, and organized around three types of proactive agents: genes, humans, and genetic diseases. The aim of this study was to improve exploration and exploitation of biological, postgenomic era big data. MyGeneFriends leverages conventions popularized by top social networks (Facebook, LinkedIn, etc), such as networks of friends, profile pages, friendship recommendations, affinity scores, news feeds, content recommendation, and data visualization. MyGeneFriends provides simple and intuitive interactions with data through evaluation and visualization of connections (friendships) between genes, humans, and diseases. The platform suggests new friends and publications and allows agents to follow the activity of their friends. It dynamically personalizes information depending on the user's specific interests and provides an efficient way to share information with collaborators. Furthermore, the user's behavior itself generates new information that constitutes an added value integrated in the network, which can be used to discover new connections between biological agents. We have developed MyGeneFriends, a Web platform leveraging conventions from popular social networks to redefine the relationship between humans and biological big data and improve human processing of biomedical data. MyGeneFriends is available at lbgi.fr/mygenefriends. ©Alexis Allot, Kirsley Chennen, Yannis
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Molecular Networking As a Drug Discovery, Drug Metabolism, and Precision Medicine Strategy.
Quinn, Robert A; Nothias, Louis-Felix; Vining, Oliver; Meehan, Michael; Esquenazi, Eduardo; Dorrestein, Pieter C
2017-02-01
Molecular networking is a tandem mass spectrometry (MS/MS) data organizational approach that has been recently introduced in the drug discovery, metabolomics, and medical fields. The chemistry of molecules dictates how they will be fragmented by MS/MS in the gas phase and, therefore, two related molecules are likely to display similar fragment ion spectra. Molecular networking organizes the MS/MS data as a relational spectral network thereby mapping the chemistry that was detected in an MS/MS-based metabolomics experiment. Although the wider utility of molecular networking is just beginning to be recognized, in this review we highlight the principles behind molecular networking and its use for the discovery of therapeutic leads, monitoring drug metabolism, clinical diagnostics, and emerging applications in precision medicine. Copyright © 2016. Published by Elsevier Ltd.
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Computational study of ‘HUB’ microRNA in human cardiac diseases
Krishnan, Remya; Nair, Achuthsankar S.; Dhar, Pawan K.
2017-01-01
MicroRNAs (miRNAs) are small non-coding RNAs ~22 nucleotides long that do not encode for proteins but have been reported to influence gene expression in normal and abnormal health conditions. Though a large body of scientific literature on miRNAs exists, their network level profile linking molecules with their corresponding phenotypes, is less explored. Here, we studied a network of 191 human miRNAs reported to play a role in 30 human cardiac diseases. Our aim was to study miRNA network properties like hubness and preferred associations, using data mining, network graph theory and statistical analysis. A total of 16 miRNAs were found to have a disease node connectivity of >5 edges (i.e., they were linked to more than 5 diseases) and were considered hubs in the miRNAcardiac disease network. Alternatively, when diseases were considered as hubs, >10 of miRNAs showed up on each ‘disease hub node’. Of all the miRNAs associated with diseases, 19 miRNAs (19/24= 79.1% of upregulated events) were found to be upregulated in atherosclerosis. The data suggest micro RNAs as early stage biological markers in cardiac conditions with potential towards microRNA based therapeutics. PMID:28479745
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Chelminiak, P.; Dixon, J. M.; Tuszyński, J. A.; Marsh, R. E.
2006-05-01
This paper discusses an application of a random network with a variable number of links and traps to the elimination of drug molecules from the body by the liver. The nodes and links represent the transport vessels, and the traps represent liver cells with metabolic enzymes that eliminate drug molecules. By varying the number and configuration of links and nodes, different disease states of the liver related to vascular damage have been simulated, and the effects on the rate of elimination of a drug have been investigated. Results of numerical simulations show the prevalence of exponential decay curves with rates that depend on the concentration of links. In the case of fractal lattices at the percolation threshold, we find that the decay of the concentration is described by exponential functions for high trap concentrations but transitions to stretched exponential behavior at low trap concentrations.
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Rosati, Dora P.; Molina, Chai; Earn, David J. D.
2015-12-01
Human behaviour and disease dynamics can greatly influence each other. In particular, people often engage in self-protective behaviours that affect epidemic patterns (e.g., vaccination, use of barrier precautions, isolation, etc.). Self-protective measures usually have a mitigating effect on an epidemic [16], but can in principle have negative impacts at the population level [12,15,18]. The structure of underlying social and biological contact networks can significantly influence the specific ways in which population-level effects are manifested. Using a different contact network in a disease dynamics model-keeping all else equal-can yield very different epidemic patterns. For example, it has been shown that when individuals imitate their neighbours' vaccination decisions with some probability, this can lead to herd immunity in some networks [9], yet for other networks it can preserve clusters of susceptible individuals that can drive further outbreaks of infectious disease [12].
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Chun, Yong Soon; Chaudhari, Pooja; Jang, Yoon-Young
2010-12-14
The recent advances in the induced pluripotent stem cell (iPSC) research have significantly changed our perspectives on regenerative medicine by providing researchers with a unique tool to derive disease-specific stem cells for study. In this review, we describe the human iPSC generation from developmentally diverse origins (i.e. endoderm-, mesoderm-, and ectoderm- tissue derived human iPSCs) and multistage hepatic differentiation protocols, and discuss both basic and clinical applications of these cells including disease modeling, drug toxicity screening/drug discovery, gene therapy and cell replacement therapy.
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Network-based association of hypoxia-responsive genes with cardiovascular diseases
International Nuclear Information System (INIS)
Wang, Rui-Sheng; Oldham, William M; Loscalzo, Joseph
2014-01-01
Molecular oxygen is indispensable for cellular viability and function. Hypoxia is a stress condition in which oxygen demand exceeds supply. Low cellular oxygen content induces a number of molecular changes to activate regulatory pathways responsible for increasing the oxygen supply and optimizing cellular metabolism under limited oxygen conditions. Hypoxia plays critical roles in the pathobiology of many diseases, such as cancer, heart failure, myocardial ischemia, stroke, and chronic lung diseases. Although the complicated associations between hypoxia and cardiovascular (and cerebrovascular) diseases (CVD) have been recognized for some time, there are few studies that investigate their biological link from a systems biology perspective. In this study, we integrate hypoxia genes, CVD genes, and the human protein interactome in order to explore the relationship between hypoxia and cardiovascular diseases at a systems level. We show that hypoxia genes are much closer to CVD genes in the human protein interactome than that expected by chance. We also find that hypoxia genes play significant bridging roles in connecting different cardiovascular diseases. We construct a hypoxia-CVD bipartite network and find several interesting hypoxia-CVD modules with significant gene ontology similarity. Finally, we show that hypoxia genes tend to have more CVD interactors in the human interactome than in random networks of matching topology. Based on these observations, we can predict novel genes that may be associated with CVD. This network-based association study gives us a broad view of the relationships between hypoxia and cardiovascular diseases and provides new insights into the role of hypoxia in cardiovascular biology. (paper)
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Human Drug Discrimination: Elucidating the Neuropharmacology of Commonly Abused Illicit Drugs.
Bolin, B Levi; Alcorn, Joseph L; Reynolds, Anna R; Lile, Joshua A; Stoops, William W; Rush, Craig R
2016-06-07
Drug-discrimination procedures empirically evaluate the control that internal drug states have over behavior. They provide a highly selective method to investigate the neuropharmacological underpinnings of the interoceptive effects of drugs in vivo. As a result, drug discrimination has been one of the most widely used assays in the field of behavioral pharmacology. Drug-discrimination procedures have been adapted for use with humans and are conceptually similar to preclinical drug-discrimination techniques in that a behavior is differentially reinforced contingent on the presence or absence of a specific interoceptive drug stimulus. This chapter provides a basic overview of human drug-discrimination procedures and reviews the extant literature concerning the use of these procedures to elucidate the underlying neuropharmacological mechanisms of commonly abused illicit drugs (i.e., stimulants, opioids, and cannabis) in humans. This chapter is not intended to review every available study that used drug-discrimination procedures in humans. Instead, when possible, exemplary studies that used a stimulant, opioid, or Δ 9 -tetrahydrocannabinol (the primary psychoactive constituent of cannabis) to assess the discriminative-stimulus effects of drugs in humans are reviewed for illustrative purposes. We conclude by commenting on the current state and future of human drug-discrimination research.
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Smith, Alec S T; Macadangdang, Jesse; Leung, Winnie; Laflamme, Michael A; Kim, Deok-Ho
Improved methodologies for modeling cardiac disease phenotypes and accurately screening the efficacy and toxicity of potential therapeutic compounds are actively being sought to advance drug development and improve disease modeling capabilities. To that end, much recent effort has been devoted to the development of novel engineered biomimetic cardiac tissue platforms that accurately recapitulate the structure and function of the human myocardium. Within the field of cardiac engineering, induced pluripotent stem cells (iPSCs) are an exciting tool that offer the potential to advance the current state of the art, as they are derived from somatic cells, enabling the development of personalized medical strategies and patient specific disease models. Here we review different aspects of iPSC-based cardiac engineering technologies. We highlight methods for producing iPSC-derived cardiomyocytes (iPSC-CMs) and discuss their application to compound efficacy/toxicity screening and in vitro modeling of prevalent cardiac diseases. Special attention is paid to the application of micro- and nano-engineering techniques for the development of novel iPSC-CM based platforms and their potential to advance current preclinical screening modalities. Published by Elsevier Inc.
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Directory of Open Access Journals (Sweden)
Heikki Kiiski
2013-05-01
The possibilities of human pluripotent stem cell-derived neural cells from the basic research tool to a treatment option in regenerative medicine have been well recognized. These cells also offer an interesting tool for in vitro models of neuronal networks to be used for drug screening and neurotoxicological studies and for patient/disease specific in vitro models. Here, as aiming to develop a reductionistic in vitro human neuronal network model, we tested whether human embryonic stem cell (hESC-derived neural cells could be cultured in human cerebrospinal fluid (CSF in order to better mimic the in vivo conditions. Our results showed that CSF altered the differentiation of hESC-derived neural cells towards glial cells at the expense of neuronal differentiation. The proliferation rate was reduced in CSF cultures. However, even though the use of CSF as the culture medium altered the glial vs. neuronal differentiation rate, the pre-existing spontaneous activity of the neuronal networks persisted throughout the study. These results suggest that it is possible to develop fully human cell and culture-based environments that can further be modified for various in vitro modeling purposes.
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Some Remarks on Prediction of Drug-Target Interaction with Network Models.
Zhang, Shao-Wu; Yan, Xiao-Ying
2017-01-01
System-level understanding of the relationships between drugs and targets is very important for enhancing drug research, especially for drug function repositioning. The experimental methods used to determine drug-target interactions are usually time-consuming, tedious and expensive, and sometimes lack reproducibility. Thus, it is highly desired to develop computational methods for efficiently and effectively analyzing and detecting new drug-target interaction pairs. With the explosive growth of different types of omics data, such as genome, pharmacology, phenotypic, and other kinds of molecular networks, numerous computational approaches have been developed to predict Drug-Target Interactions (DTI). In this review, we make a survey on the recent advances in predicting drug-target interaction with network-based models from the following aspects: i) Available public data sources and benchmark datasets; ii) Drug/target similarity metrics; iii) Network construction; iv) Common network algorithms; v) Performance comparison of existing network-based DTI predictors. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
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Tinnitus: network pathophysiology-network pharmacology.
Elgoyhen, Ana B; Langguth, Berthold; Vanneste, Sven; De Ridder, Dirk
2012-01-01
Tinnitus, the phantom perception of sound, is a prevalent disorder. One in 10 adults has clinically significant subjective tinnitus, and for one in 100, tinnitus severely affects their quality of life. Despite the significant unmet clinical need for a safe and effective drug targeting tinnitus relief, there is currently not a single Food and Drug Administration (FDA)-approved drug on the market. The search for drugs that target tinnitus is hampered by the lack of a deep knowledge of the underlying neural substrates of this pathology. Recent studies are increasingly demonstrating that, as described for other central nervous system (CNS) disorders, tinnitus is a pathology of brain networks. The application of graph theoretical analysis to brain networks has recently provided new information concerning their topology, their robustness and their vulnerability to attacks. Moreover, the philosophy behind drug design and pharmacotherapy in CNS pathologies is changing from that of "magic bullets" that target individual chemoreceptors or "disease-causing genes" into that of "magic shotguns," "promiscuous" or "dirty drugs" that target "disease-causing networks," also known as network pharmacology. In the present work we provide some insight into how this knowledge could be applied to tinnitus pathophysiology and pharmacotherapy.
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Allarakhia, Minna
2013-01-01
Repurposing has the objective of targeting existing drugs and failed, abandoned, or yet-to-be-pursued clinical candidates to new disease areas. The open-source model permits for the sharing of data, resources, compounds, clinical molecules, small libraries, and screening platforms to cost-effectively advance old drugs and/or candidates into clinical re-development. Clearly, at the core of drug-repurposing activities is collaboration, in many cases progressing beyond the open sharing of resources, technology, and intellectual property, to the sharing of facilities and joint program development to foster drug-repurposing human-capacity development. A variety of initiatives under way for drug repurposing, including those targeting rare and neglected diseases, are discussed in this review and provide insight into the stakeholders engaged in drug-repurposing discovery, the models of collaboration used, the intellectual property-management policies crafted, and human capacity developed. In the case of neglected tropical diseases, it is suggested that the development of human capital be a central aspect of drug-repurposing programs. Open-source models can support human-capital development through collaborative data generation, open compound access, open and collaborative screening, preclinical and possibly clinical studies. Given the urgency of drug development for neglected tropical diseases, the review suggests elements from current repurposing programs be extended to the neglected tropical diseases arena.
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Associating Human-Centered Concepts with Social Networks Using Fuzzy Sets
Yager, Ronald R.
The rapidly growing global interconnectivity, brought about to a large extent by the Internet, has dramatically increased the importance and diversity of social networks. Modern social networks cut across a spectrum from benign recreational focused websites such as Facebook to occupationally oriented websites such as LinkedIn to criminally focused groups such as drug cartels to devastation and terror focused groups such as Al-Qaeda. Many organizations are interested in analyzing and extracting information related to these social networks. Among these are governmental police and security agencies as well marketing and sales organizations. To aid these organizations there is a need for technologies to model social networks and intelligently extract information from these models. While established technologies exist for the modeling of relational networks [1-7] few technologies exist to extract information from these, compatible with human perception and understanding. Data bases is an example of a technology in which we have tools for representing our information as well as tools for querying and extracting the information contained. Our goal is in some sense analogous. We want to use the relational network model to represent information, in this case about relationships and interconnections, and then be able to query the social network using intelligent human-centered concepts. To extend our capabilities to interact with social relational networks we need to associate with these network human concepts and ideas. Since human beings predominantly use linguistic terms in which to reason and understand we need to build bridges between human conceptualization and the formal mathematical representation of the social network. Consider for example a concept such as "leader". An analyst may be able to express, in linguistic terms, using a network relevant vocabulary, properties of a leader. Our task is to translate this linguistic description into a mathematical formalism
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Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease.
Johnson, Michael R; Shkura, Kirill; Langley, Sarah R; Delahaye-Duriez, Andree; Srivastava, Prashant; Hill, W David; Rackham, Owen J L; Davies, Gail; Harris, Sarah E; Moreno-Moral, Aida; Rotival, Maxime; Speed, Doug; Petrovski, Slavé; Katz, Anaïs; Hayward, Caroline; Porteous, David J; Smith, Blair H; Padmanabhan, Sandosh; Hocking, Lynne J; Starr, John M; Liewald, David C; Visconti, Alessia; Falchi, Mario; Bottolo, Leonardo; Rossetti, Tiziana; Danis, Bénédicte; Mazzuferi, Manuela; Foerch, Patrik; Grote, Alexander; Helmstaedter, Christoph; Becker, Albert J; Kaminski, Rafal M; Deary, Ian J; Petretto, Enrico
2016-02-01
Genetic determinants of cognition are poorly characterized, and their relationship to genes that confer risk for neurodevelopmental disease is unclear. Here we performed a systems-level analysis of genome-wide gene expression data to infer gene-regulatory networks conserved across species and brain regions. Two of these networks, M1 and M3, showed replicable enrichment for common genetic variants underlying healthy human cognitive abilities, including memory. Using exome sequence data from 6,871 trios, we found that M3 genes were also enriched for mutations ascertained from patients with neurodevelopmental disease generally, and intellectual disability and epileptic encephalopathy in particular. M3 consists of 150 genes whose expression is tightly developmentally regulated, but which are collectively poorly annotated for known functional pathways. These results illustrate how systems-level analyses can reveal previously unappreciated relationships between neurodevelopmental disease-associated genes in the developed human brain, and provide empirical support for a convergent gene-regulatory network influencing cognition and neurodevelopmental disease.
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Chronotherapeutic drug delivery systems: an approach to circadian rhythms diseases.
Sunil, S A; Srikanth, M V; Rao, N Sreenivasa; Uhumwangho, M U; Latha, K; Murthy, K V Ramana
2011-11-01
The purpose of writing this review on chronotherapeutic drug delivery systems (ChrDDs) is to review the literatures with special focus on ChrDDs and the various dosage forms, techniques that are used to target the circadian rhythms (CR) of various diseases. Many functions of the human body vary considerably in a day. ChrDDs refers to a treatment method in which in vivo drug availability is timed to match circadian rhythms of disease in order to optimize therapeutic outcomes and minimize side effects. Several techniques have been developed but not many dosage forms for all the diseases are available in the market. ChrDDs are gaining importance in the field of pharmaceutical technology as these systems reduce dosing frequency, toxicity and deliver the drug that matches the CR of that particular disease when the symptoms are maximum to worse. Finally, the ultimate benefit goes to the patient due the compliance and convenience of the dosage form. Some diseases that follow circadian rhythms include cardiovascular diseases, asthma, arthritis, ulcers, diabetes etc. ChrDDs in the market were also discussed and the current technologies used to formulate were also stated. These technologies include Contin® , Chronotopic®, Pulsincaps®, Ceform®, Timerx®, Oros®, Codas®, Diffucaps®, Egalet®, Tablet in capsule device, Core-in-cup tablet technology. A coated drug-core tablet matrix, A bi-layered tablet, Multiparticulate-based chronotherapeutic drug delivery systems, Chronoset and Controlled release microchips.
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Disease modeling using human induced pluripotent stem cells: lessons from the liver.
Gieseck, Richard L; Colquhoun, Jennifer; Hannan, Nicholas R F
2015-01-01
Human pluripotent stem cells (hPSCs) have the capacity to differentiate into any of the hundreds of distinct cell types that comprise the human body. This unique characteristic has resulted in considerable interest in the field of regenerative medicine, given the potential for these cells to be used to protect, repair, or replace diseased, injured, and aged cells within the human body. In addition to their potential in therapeutics, hPSCs can be used to study the earliest stages of human development and to provide a platform for both drug screening and disease modeling using human cells. Recently, the description of human induced pluripotent stem cells (hIPSCs) has allowed the field of disease modeling to become far more accessible and physiologically relevant, as pluripotent cells can be generated from patients of any genetic background. Disease models derived from hIPSCs that manifest cellular disease phenotypes have been established to study several monogenic diseases; furthermore, hIPSCs can be used for phenotype-based drug screens to investigate complex diseases for which the underlying genetic mechanism is unknown. As a result, the use of stem cells as research tools has seen an unprecedented growth within the last decade as researchers look for in vitro disease models which closely mimic in vivo responses in humans. Here, we discuss the beginnings of hPSCs, starting with isolation of human embryonic stem cells, moving into the development and optimization of hIPSC technology, and ending with the application of hIPSCs towards disease modeling and drug screening applications, with specific examples highlighting the modeling of inherited metabolic disorders of the liver. This article is part of a Special Issue entitled Linking transcription to physiology in lipodomics. Crown Copyright © 2014. Published by Elsevier B.V. All rights reserved.
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Directory of Open Access Journals (Sweden)
S. Vijayakumar
Full Text Available Parkinson's disease (PD is caused by human physiological function and is ranked as the second most common neurodegenerative disorder. One of the prominent therapies currently available for PD is the use of dopamine agonists which mimic the natural action of dopamine in the brain and stimulate dopamine receptors directly. Currently, available pharmaceutical drugs provide only temporary relief of the disease. Phytocompounds have been identified as promising target of research in the quest for new pharmaceutical compounds as they can produce secondary metabolites with novel chemical structure. In this review the drug development of Parkinson disease has been analyzed using computational tools. Keywords: Parkinson disease, Phytocompounds, Computational methods, Drug development and design
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Takeda, Takako; Hao, Ming; Cheng, Tiejun; Bryant, Stephen H.; Wang, Yanli
2017-01-01
Drug?drug interactions (DDIs) may lead to adverse effects and potentially result in drug withdrawal from the market. Predicting DDIs during drug development would help reduce development costs and time by rigorous evaluation of drug candidates. The primary mechanisms of DDIs are based on pharmacokinetics (PK) and pharmacodynamics (PD). This study examines the effects of 2D structural similarities of drugs on DDI prediction through interaction networks including both PD and PK knowledge. Our a...
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Controlling infectious disease through the targeted manipulation of contact network structure
Directory of Open Access Journals (Sweden)
M. Carolyn Gates
2015-09-01
Full Text Available Individuals in human and animal populations are linked through dynamic contact networks with characteristic structural features that drive the epidemiology of directly transmissible infectious diseases. Using animal movement data from the British cattle industry as an example, this analysis explores whether disease dynamics can be altered by placing targeted restrictions on contact formation to reconfigure network topology. This was accomplished using a simple network generation algorithm that combined configuration wiring with stochastic block modelling techniques to preserve the weighted in- and out-degree of individual nodes (farms as well as key demographic characteristics of the individual network connections (movement date, livestock market, and animal production type. We then tested a control strategy based on introducing additional constraints into the network generation algorithm to prevent farms with a high in-degree from selling cattle to farms with a high out-degree as these particular network connections are predicted to have a disproportionately strong role in spreading disease. Results from simple dynamic disease simulation models predicted significantly lower endemic disease prevalences on the trade restricted networks compared to the baseline generated networks. As expected, the relative magnitude of the predicted changes in endemic prevalence was greater for diseases with short infectious periods and low transmission probabilities. Overall, our study findings demonstrate that there is significant potential for controlling multiple infectious diseases simultaneously by manipulating networks to have more epidemiologically favourable topological configurations. Further research is needed to determine whether the economic and social benefits of controlling disease can justify the costs of restricting contact formation.
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Controlling infectious disease through the targeted manipulation of contact network structure
Gates, M. Carolyn; Woolhouse, Mark E.J.
2015-01-01
Individuals in human and animal populations are linked through dynamic contact networks with characteristic structural features that drive the epidemiology of directly transmissible infectious diseases. Using animal movement data from the British cattle industry as an example, this analysis explores whether disease dynamics can be altered by placing targeted restrictions on contact formation to reconfigure network topology. This was accomplished using a simple network generation algorithm that combined configuration wiring with stochastic block modelling techniques to preserve the weighted in- and out-degree of individual nodes (farms) as well as key demographic characteristics of the individual network connections (movement date, livestock market, and animal production type). We then tested a control strategy based on introducing additional constraints into the network generation algorithm to prevent farms with a high in-degree from selling cattle to farms with a high out-degree as these particular network connections are predicted to have a disproportionately strong role in spreading disease. Results from simple dynamic disease simulation models predicted significantly lower endemic disease prevalences on the trade restricted networks compared to the baseline generated networks. As expected, the relative magnitude of the predicted changes in endemic prevalence was greater for diseases with short infectious periods and low transmission probabilities. Overall, our study findings demonstrate that there is significant potential for controlling multiple infectious diseases simultaneously by manipulating networks to have more epidemiologically favourable topological configurations. Further research is needed to determine whether the economic and social benefits of controlling disease can justify the costs of restricting contact formation. PMID:26342238
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Le, Duc-Hau; Dao, Lan T M
2018-05-23
Recently, many long non-coding RNAs (lncRNAs) have been identified and their biological function has been characterized; however, our understanding of their underlying molecular mechanisms related to disease is still limited. To overcome the limitation in experimentally identifying disease-lncRNA associations, computational methods have been proposed as a powerful tool to predict such associations. These methods are usually based on the similarities between diseases or lncRNAs since it was reported that similar diseases are associated with functionally similar lncRNAs. Therefore, prediction performance is highly dependent on how well the similarities can be captured. Previous studies have calculated the similarity between two diseases by mapping exactly each disease to a single Disease Ontology (DO) term, and then use a semantic similarity measure to calculate the similarity between them. However, the problem of this approach is that a disease can be described by more than one DO terms. Until now, there is no annotation database of DO terms for diseases except for genes. In contrast, Human Phenotype Ontology (HPO) is designed to fully annotate human disease phenotypes. Therefore, in this study, we constructed disease similarity networks/matrices using HPO instead of DO. Then, we used these networks/matrices as inputs of two representative machine learning-based and network-based ranking algorithms, that is, regularized least square and heterogeneous graph-based inference, respectively. The results showed that the prediction performance of the two algorithms on HPO-based is better than that on DO-based networks/matrices. In addition, our method can predict 11 novel cancer-associated lncRNAs, which are supported by literature evidence. Copyright © 2018 Elsevier Ltd. All rights reserved.
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Human cancer protein-protein interaction network: a structural perspective.
Directory of Open Access Journals (Sweden)
Gozde Kar
2009-12-01
Full Text Available Protein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network. The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%. We illustrate the interface related affinity properties of two cancer-related hub
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Automatic extraction of drug indications from FDA drug labels.
Khare, Ritu; Wei, Chih-Hsuan; Lu, Zhiyong
2014-01-01
Extracting computable indications, i.e. drug-disease treatment relationships, from narrative drug resources is the key for building a gold standard drug indication repository. The two steps to the extraction problem are disease named-entity recognition (NER) to identify disease mentions from a free-text description and disease classification to distinguish indications from other disease mentions in the description. While there exist many tools for disease NER, disease classification is mostly achieved through human annotations. For example, we recently resorted to human annotations to prepare a corpus, LabeledIn, capturing structured indications from the drug labels submitted to FDA by pharmaceutical companies. In this study, we present an automatic end-to-end framework to extract structured and normalized indications from FDA drug labels. In addition to automatic disease NER, a key component of our framework is a machine learning method that is trained on the LabeledIn corpus to classify the NER-computed disease mentions as "indication vs. non-indication." Through experiments with 500 drug labels, our end-to-end system delivered 86.3% F1-measure in drug indication extraction, with 17% improvement over baseline. Further analysis shows that the indication classifier delivers a performance comparable to human experts and that the remaining errors are mostly due to disease NER (more than 50%). Given its performance, we conclude that our end-to-end approach has the potential to significantly reduce human annotation costs.
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Directory of Open Access Journals (Sweden)
Bao-Dong Li
2018-01-01
Full Text Available Background/Aims: A network meta-analysis is used to compare the efficacy of ropinirole, rasagiline, rotigotine, entacapone, apomorphine, pramipexole, sumanirole, bromocriptine, piribedil and levodopa, with placebo as a control, for non-motor symptoms in Parkinson’s disease (PD. Methods: PubMed, Embase and the Cochrane Library were searched from their establishment dates up to January 2017 for randomized controlled trials (RCTs investigating the efficacy of the above ten drugs on the non-motor symptoms of PD. A network meta-analysis combined the evidence from direct comparisons and indirect comparisons and evaluated the pooled weighted mean difference (WMD values and surfaces under the cumulative ranking curves (SUCRA. The network meta-analysis included 21 RCTs. Results: The analysis results indicated that, using the United Parkinson’s Disease Rating Scale (UPDRS III, the efficacies of placebo, ropinirole, rasagiline, rotigotine, entacapone, pramipexole, sumanirole and levodopa in treating PD were lower than that of apomorphine (WMD = -10.90, 95% CI = -16.12∼-5.48; WMD = -11.85, 95% CI = -17.31∼-6.16; WMD = -11.15, 95% CI = -16.64∼-5.04; WMD = -11.70, 95% CI = -16.98∼-5.60; WMD = -11.04, 95% CI = -16.97∼-5.34; WMD = -13.27, 95% CI = -19.22∼-7.40; WMD = -10.25, 95% CI = -15.66∼-4.32; and WMD = -11.60, 95% CI = -17.89∼-5.57, respectively. Treatment with ropinirole, rasagiline, rotigotine, entacapone, pramipexole, sumanirole, bromocriptine, piribedil or levodopa, with placebo as a control, on PD exhibited no significant differences on PD symptoms when the UPDRS II was used for evaluation. Moreover, using the UPDRS III, the SUCRA values indicated that a pomorphine had the best efficacy on the non-motor symptoms of PD (99.0%. Using the UPDRS II, the SUCRA values for ropinirole, rasagiline, rotigotine, entacapone, pramipexole, sumanirole, bromocriptine, piribedil and levodopa treatments, with placebo as a control, indicated that
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Drawnel, Faye M; Boccardo, Stefano; Prummer, Michael; Delobel, Frédéric; Graff, Alexandra; Weber, Michael; Gérard, Régine; Badi, Laura; Kam-Thong, Tony; Bu, Lei; Jiang, Xin; Hoflack, Jean-Christophe; Kiialainen, Anna; Jeworutzki, Elena; Aoyama, Natsuyo; Carlson, Coby; Burcin, Mark; Gromo, Gianni; Boehringer, Markus; Stahlberg, Henning; Hall, Benjamin J; Magnone, Maria Chiara; Kolaja, Kyle; Chien, Kenneth R; Bailly, Jacques; Iacone, Roberto
2014-11-06
Diabetic cardiomyopathy is a complication of type 2 diabetes, with known contributions of lifestyle and genetics. We develop environmentally and genetically driven in vitro models of the condition using human-induced-pluripotent-stem-cell-derived cardiomyocytes. First, we mimic diabetic clinical chemistry to induce a phenotypic surrogate of diabetic cardiomyopathy, observing structural and functional disarray. Next, we consider genetic effects by deriving cardiomyocytes from two diabetic patients with variable disease progression. The cardiomyopathic phenotype is recapitulated in the patient-specific cells basally, with a severity dependent on their original clinical status. These models are incorporated into successive levels of a screening platform, identifying drugs that preserve cardiomyocyte phenotype in vitro during diabetic stress. In this work, we present a patient-specific induced pluripotent stem cell (iPSC) model of a complex metabolic condition, showing the power of this technique for discovery and testing of therapeutic strategies for a disease with ever-increasing clinical significance. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.
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Directory of Open Access Journals (Sweden)
Faye M. Drawnel
2014-11-01
Full Text Available Diabetic cardiomyopathy is a complication of type 2 diabetes, with known contributions of lifestyle and genetics. We develop environmentally and genetically driven in vitro models of the condition using human-induced-pluripotent-stem-cell-derived cardiomyocytes. First, we mimic diabetic clinical chemistry to induce a phenotypic surrogate of diabetic cardiomyopathy, observing structural and functional disarray. Next, we consider genetic effects by deriving cardiomyocytes from two diabetic patients with variable disease progression. The cardiomyopathic phenotype is recapitulated in the patient-specific cells basally, with a severity dependent on their original clinical status. These models are incorporated into successive levels of a screening platform, identifying drugs that preserve cardiomyocyte phenotype in vitro during diabetic stress. In this work, we present a patient-specific induced pluripotent stem cell (iPSC model of a complex metabolic condition, showing the power of this technique for discovery and testing of therapeutic strategies for a disease with ever-increasing clinical significance.
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Insect Peptides - Perspectives in Human Diseases Treatment.
Chowanski, Szymon; Adamski, Zbigniew; Lubawy, Jan; Marciniak, Pawel; Pacholska-Bogalska, Joanna; Slocinska, Malgorzata; Spochacz, Marta; Szymczak, Monika; Urbanski, Arkadiusz; Walkowiak-Nowicka, Karolina; Rosinski, Grzegorz
2017-01-01
Insects are the largest and the most widely distributed group of animals in the world. Their diversity is a source of incredible variety of different mechanisms of life processes regulation. There are many agents that regulate immunology, reproduction, growth and development or metabolism. Hence, it seems that insects may be a source of numerous substances useful in human diseases treatment. Especially important in the regulation of insect physiology are peptides, like neuropeptides, peptide hormones or antimicrobial peptides. There are two main aspects where they can be helpful, 1) Peptides isolated from insects may become potential drugs in therapy of different diseases, 2) A lot of insect peptide hormones show structural or functional homology to mammalian peptide hormones and the comparative studies may give a new look on human disorders. In our review we focused on three group of insect derived peptides: 1) immune-active peptides, 2) peptide hormones and 3) peptides present in venoms. In our review we try to show the considerable potential of insect peptides in searching for new solutions for mammalian diseases treatment. We summarise the knowledge about properties of insect peptides against different virulent agents, anti-inflammatory or anti-nociceptive properties as well as compare insect and mammalian/vertebrate peptide endocrine system to indicate usefulness of knowledge about insect peptide hormones in drug design. The field of possible using of insect delivered peptide to therapy of various human diseases is still not sufficiently explored. Undoubtedly, more attention should be paid to insects due to searching new drugs. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
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Tinnitus: Network pathophysiology-network pharmacology
Directory of Open Access Journals (Sweden)
Ana Belen eElgoyhen
2012-01-01
Full Text Available Tinnitus, the phantom perception of sound, is a prevalent disorder. One in 10 adults has clinically significant subjective tinnitus, and for 1 in 100, tinnitus severely affects their quality of life. Despite the significant unmet clinical need for a safe and effective drug targeting tinnitus relief, there is currently not a single FDA-approved drug on the market. The search for drugs that target tinnitus is hampered by the lack of a deep knowledge of the underlying neural substrates of this pathology. Recent studies are increasingly demonstrating that, as described for other central nervous system disorders, tinnitus is a pathology of brain networks. The application of graph theoretical analysis to brain networks has recently provided new information concerning their topology, their robustness and their vulnerability to attacks. Moreover, the philosophy behind drug design and pharmacotherapy in central nervous system pathologies is changing from that of magic bullets that target individual chemoreceptors or disease-causing genes into that of magic shotguns, promiscuous or dirty drugs that target disease-causing networks, also known as network pharmacology. In the present work we provide some insight into how this knowledge could be applied to tinnitus pathophysiology and pharmacotherapy.
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NATO Human View Architecture and Human Networks
Handley, Holly A. H.; Houston, Nancy P.
2010-01-01
The NATO Human View is a system architectural viewpoint that focuses on the human as part of a system. Its purpose is to capture the human requirements and to inform on how the human impacts the system design. The viewpoint contains seven static models that include different aspects of the human element, such as roles, tasks, constraints, training and metrics. It also includes a Human Dynamics component to perform simulations of the human system under design. One of the static models, termed Human Networks, focuses on the human-to-human communication patterns that occur as a result of ad hoc or deliberate team formation, especially teams distributed across space and time. Parameters of human teams that effect system performance can be captured in this model. Human centered aspects of networks, such as differences in operational tempo (sense of urgency), priorities (common goal), and team history (knowledge of the other team members), can be incorporated. The information captured in the Human Network static model can then be included in the Human Dynamics component so that the impact of distributed teams is represented in the simulation. As the NATO militaries transform to a more networked force, the Human View architecture is an important tool that can be used to make recommendations on the proper mix of technological innovations and human interactions.
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Dominating biological networks.
Directory of Open Access Journals (Sweden)
Tijana Milenković
Full Text Available Proteins are essential macromolecules of life that carry out most cellular processes. Since proteins aggregate to perform function, and since protein-protein interaction (PPI networks model these aggregations, one would expect to uncover new biology from PPI network topology. Hence, using PPI networks to predict protein function and role of protein pathways in disease has received attention. A debate remains open about whether network properties of "biologically central (BC" genes (i.e., their protein products, such as those involved in aging, cancer, infectious diseases, or signaling and drug-targeted pathways, exhibit some topological centrality compared to the rest of the proteins in the human PPI network.To help resolve this debate, we design new network-based approaches and apply them to get new insight into biological function and disease. We hypothesize that BC genes have a topologically central (TC role in the human PPI network. We propose two different concepts of topological centrality. We design a new centrality measure to capture complex wirings of proteins in the network that identifies as TC those proteins that reside in dense extended network neighborhoods. Also, we use the notion of domination and find dominating sets (DSs in the PPI network, i.e., sets of proteins such that every protein is either in the DS or is a neighbor of the DS. Clearly, a DS has a TC role, as it enables efficient communication between different network parts. We find statistically significant enrichment in BC genes of TC nodes and outperform the existing methods indicating that genes involved in key biological processes occupy topologically complex and dense regions of the network and correspond to its "spine" that connects all other network parts and can thus pass cellular signals efficiently throughout the network. To our knowledge, this is the first study that explores domination in the context of PPI networks.
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Surveillance of gastrointestinal disease in France using drug sales data.
Pivette, Mathilde; Mueller, Judith E; Crépey, Pascal; Bar-Hen, Avner
2014-09-01
Drug sales data have increasingly been used for disease surveillance during recent years. Our objective was to assess the value of drug sales data as an operational early detection tool for gastroenteritis epidemics at national and regional level in France. For the period 2008-2013, we compared temporal trends of drug sales for the treatment of gastroenteritis with trends of cases reported by a Sentinel Network of general practitioners. We benchmarked detection models to select the one with the best sensitivity, false alert proportion and timeliness, and developed a prospective framework to assess the operational performance of the system. Drug sales data allowed the detection of seasonal gastrointestinal epidemics occurring in winter with a distinction between prescribed and non-prescribed drugs. Sales of non-prescribed drugs allowed epidemic detection on average 2.25 weeks earlier than Sentinel data. These results confirm the value of drug sales data for real-time monitoring of gastroenteritis epidemic activity. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.
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Controlling infectious disease through the targeted manipulation of contact network structure.
Gates, M Carolyn; Woolhouse, Mark E J
2015-09-01
Individuals in human and animal populations are linked through dynamic contact networks with characteristic structural features that drive the epidemiology of directly transmissible infectious diseases. Using animal movement data from the British cattle industry as an example, this analysis explores whether disease dynamics can be altered by placing targeted restrictions on contact formation to reconfigure network topology. This was accomplished using a simple network generation algorithm that combined configuration wiring with stochastic block modelling techniques to preserve the weighted in- and out-degree of individual nodes (farms) as well as key demographic characteristics of the individual network connections (movement date, livestock market, and animal production type). We then tested a control strategy based on introducing additional constraints into the network generation algorithm to prevent farms with a high in-degree from selling cattle to farms with a high out-degree as these particular network connections are predicted to have a disproportionately strong role in spreading disease. Results from simple dynamic disease simulation models predicted significantly lower endemic disease prevalences on the trade restricted networks compared to the baseline generated networks. As expected, the relative magnitude of the predicted changes in endemic prevalence was greater for diseases with short infectious periods and low transmission probabilities. Overall, our study findings demonstrate that there is significant potential for controlling multiple infectious diseases simultaneously by manipulating networks to have more epidemiologically favourable topological configurations. Further research is needed to determine whether the economic and social benefits of controlling disease can justify the costs of restricting contact formation. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.
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Tissue-specific functional networks for prioritizing phenotype and disease genes.
Directory of Open Access Journals (Sweden)
Yuanfang Guan
Full Text Available Integrated analyses of functional genomics data have enormous potential for identifying phenotype-associated genes. Tissue-specificity is an important aspect of many genetic diseases, reflecting the potentially different roles of proteins and pathways in diverse cell lineages. Accounting for tissue specificity in global integration of functional genomics data is challenging, as "functionality" and "functional relationships" are often not resolved for specific tissue types. We address this challenge by generating tissue-specific functional networks, which can effectively represent the diversity of protein function for more accurate identification of phenotype-associated genes in the laboratory mouse. Specifically, we created 107 tissue-specific functional relationship networks through integration of genomic data utilizing knowledge of tissue-specific gene expression patterns. Cross-network comparison revealed significantly changed genes enriched for functions related to specific tissue development. We then utilized these tissue-specific networks to predict genes associated with different phenotypes. Our results demonstrate that prediction performance is significantly improved through using the tissue-specific networks as compared to the global functional network. We used a testis-specific functional relationship network to predict genes associated with male fertility and spermatogenesis phenotypes, and experimentally confirmed one top prediction, Mbyl1. We then focused on a less-common genetic disease, ataxia, and identified candidates uniquely predicted by the cerebellum network, which are supported by both literature and experimental evidence. Our systems-level, tissue-specific scheme advances over traditional global integration and analyses and establishes a prototype to address the tissue-specific effects of genetic perturbations, diseases and drugs.
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Fujiwara, Ryoichi; Yoda, Emiko; Tukey, Robert H
2018-02-01
More than 20% of clinically used drugs are glucuronidated by a microsomal enzyme UDP-glucuronosyltransferase (UGT). Inhibition or induction of UGT can result in an increase or decrease in blood drug concentration. To avoid drug-drug interactions and adverse drug reactions in individuals, therefore, it is important to understand whether UGTs are involved in metabolism of drugs and drug candidates. While most of glucuronides are inactive metabolites, acyl-glucuronides that are formed from compounds with a carboxylic acid group can be highly toxic. Animals such as mice and rats are widely used to predict drug metabolism and drug-induced toxicity in humans. However, there are marked species differences in the expression and function of drug-metabolizing enzymes including UGTs. To overcome the species differences, mice in which certain drug-metabolizing enzymes are humanized have been recently developed. Humanized UGT1 (hUGT1) mice were created in 2010 by crossing Ugt1-null mice with human UGT1 transgenic mice in a C57BL/6 background. hUGT1 mice can be promising tools to predict human drug glucuronidation and acyl-glucuronide-associated toxicity. In this review article, studies of drug metabolism and toxicity in the hUGT1 mice are summarized. We further discuss research and strategic directions to advance the understanding of drug glucuronidation in humans. Copyright © 2017 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.
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Liu, Yifeng; Liang, Yongjie; Wishart, David
2015-07-01
PolySearch2 (http://polysearch.ca) is an online text-mining system for identifying relationships between biomedical entities such as human diseases, genes, SNPs, proteins, drugs, metabolites, toxins, metabolic pathways, organs, tissues, subcellular organelles, positive health effects, negative health effects, drug actions, Gene Ontology terms, MeSH terms, ICD-10 medical codes, biological taxonomies and chemical taxonomies. PolySearch2 supports a generalized 'Given X, find all associated Ys' query, where X and Y can be selected from the aforementioned biomedical entities. An example query might be: 'Find all diseases associated with Bisphenol A'. To find its answers, PolySearch2 searches for associations against comprehensive collections of free-text collections, including local versions of MEDLINE abstracts, PubMed Central full-text articles, Wikipedia full-text articles and US Patent application abstracts. PolySearch2 also searches 14 widely used, text-rich biological databases such as UniProt, DrugBank and Human Metabolome Database to improve its accuracy and coverage. PolySearch2 maintains an extensive thesaurus of biological terms and exploits the latest search engine technology to rapidly retrieve relevant articles and databases records. PolySearch2 also generates, ranks and annotates associative candidates and present results with relevancy statistics and highlighted key sentences to facilitate user interpretation. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.
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Liu, Kunmeng; Lin, Hui-Heng; Pi, Rongbiao; Mak, Shinghung; Han, Yifan; Hu, Yuanjia
2018-04-01
Today, over 20 million people suffer from Alzheimer's disease (AD) worldwide. AD has become a critical issue to human health, especially in aging societies, and therefore it is a research hotspot in the global scientific community. The technology flow method differs from traditional reviews generating an informative overview of the research and development (R&D) landscape in a specific technological area. We need such an updated method to get a general overview of the R&D of anti-AD drugs in light of the dramatic developments in this area in recent years. Areas covered: This study collects patent data from the Integrity database. A total of 399 patents with 821 internal citation pairs in the US from 1978 to 2017 were analyzed. Patent citation network analysis was used to visualize the technology relationship. Expert opinion: For better production of anti-AD drugs, governments should emphasize the multi-target drug design, provide policy support for private companies, and encourage multilateral cooperation. The β-amyloid peptide (Aβ) theory leaves much to be desired; neurotransmitter and tau protein hypotheses are worth further examination. The use of old drugs for new indications is promising, as are traditional herbal medicines.
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Stable isotope-resolved metabolomics and applications for drug development
Fan, Teresa W-M.; Lorkiewicz, Pawel; Sellers, Katherine; Moseley, Hunter N.B.; Higashi, Richard M.; Lane, Andrew N.
2012-01-01
Advances in analytical methodologies, principally nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS), during the last decade have made large-scale analysis of the human metabolome a reality. This is leading to the reawakening of the importance of metabolism in human diseases, particularly cancer. The metabolome is the functional readout of the genome, functional genome, and proteome; it is also an integral partner in molecular regulations for homeostasis. The interrogation of the metabolome, or metabolomics, is now being applied to numerous diseases, largely by metabolite profiling for biomarker discovery, but also in pharmacology and therapeutics. Recent advances in stable isotope tracer-based metabolomic approaches enable unambiguous tracking of individual atoms through compartmentalized metabolic networks directly in human subjects, which promises to decipher the complexity of the human metabolome at an unprecedented pace. This knowledge will revolutionize our understanding of complex human diseases, clinical diagnostics, as well as individualized therapeutics and drug response. In this review, we focus on the use of stable isotope tracers with metabolomics technologies for understanding metabolic network dynamics in both model systems and in clinical applications. Atom-resolved isotope tracing via the two major analytical platforms, NMR and MS, has the power to determine novel metabolic reprogramming in diseases, discover new drug targets, and facilitates ADME studies. We also illustrate new metabolic tracer-based imaging technologies, which enable direct visualization of metabolic processes in vivo. We further outline current practices and future requirements for biochemoinformatics development, which is an integral part of translating stable isotope-resolved metabolomics into clinical reality. PMID:22212615
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Outlier populations: individual and social network correlates of solvent-using injection drug users.
Directory of Open Access Journals (Sweden)
Souradet Y Shaw
Full Text Available We previously identified a high prevalence of Hepatitis C (HCV amongst solvent-using injection drug users (S-IDU relative to other injection drug users within the same locality. Here we incorporated social network variables to better characterize some of the behavioural characteristics that may be putting this specific subgroup of IDU at elevated disease risk.A cross-sectional survey of at-risk populations was carried out in Winnipeg, Canada in 2009. Individuals reporting any history of injection drug and/or solvent use were included in the study. Associations between subgroup membership, infection with HCV and HIV and individual and social network variables were examined.In relation to other IDU, S-IDU were more likely to be infected with HCV, to report ever having shared a syringe, and to associate with other IDU. They were further differentiated in terms of their self-reported sexual orientation, ethnicity and in the injection drugs typically used.Solvent use stands as a proxy measure of numerous other characteristics that put this group of IDU at higher risk of infection. Provision of adequate services to ostracized subpopulations may result in wider population-level benefits.
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Human placental perfusion method in the assessment of transplacental passage of antiepileptic drugs
International Nuclear Information System (INIS)
Myllynen, Paeivi; Pienimaeki, Paeivi; Vaehaekangas, Kirsi
2005-01-01
Epilepsy is one of the most common neurological diseases, affecting about 0.5 to 1% of pregnant women. It is commonly accepted that older antiepileptic drugs bear teratogenic potential. So far, no agreement has been reached about the safest antiepileptic drug during pregnancy. It is known that nearly all drugs cross the placenta at least to some extent. Nowadays, there is very little information available of the pharmacokinetics of drugs in the feto-placental unit. Detailed information about drug transport across the placenta would be valuable for the development of safe and effective treatments. For reasons of safety, human studies on placental transfer are restricted to a limited number of drugs. Interspecies differences limit the extrapolation of animal data to humans. Several in vitro methods for the study of placental transfer have been developed over the past decades. The placental perfusion method is the only experimental method that has been used to study human placental transfer of substances in organized placental tissue. The aim of this article is to review human placental perfusion data on antiepileptic drugs. According to perfusion data, it seems that most of the antiepileptic drugs are transferred across the placenta meaning significant fetal exposure
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Chong, Joanna Su Xian; Liu, Siwei; Loke, Yng Miin; Hilal, Saima; Ikram, Mohammad Kamran; Xu, Xin; Tan, Boon Yeow; Venketasubramanian, Narayanaswamy; Chen, Christopher Li-Hsian; Zhou, Juan
2017-11-01
Network-sensitive neuroimaging methods have been used to characterize large-scale brain network degeneration in Alzheimer's disease and its prodrome. However, few studies have investigated the combined effect of Alzheimer's disease and cerebrovascular disease on brain network degeneration. Our study sought to examine the intrinsic functional connectivity and structural covariance network changes in 235 prodromal and clinical Alzheimer's disease patients with and without cerebrovascular disease. We focused particularly on two higher-order cognitive networks-the default mode network and the executive control network. We found divergent functional connectivity and structural covariance patterns in Alzheimer's disease patients with and without cerebrovascular disease. Alzheimer's disease patients without cerebrovascular disease, but not Alzheimer's disease patients with cerebrovascular disease, showed reductions in posterior default mode network functional connectivity. By comparison, while both groups exhibited parietal reductions in executive control network functional connectivity, only Alzheimer's disease patients with cerebrovascular disease showed increases in frontal executive control network connectivity. Importantly, these distinct executive control network changes were recapitulated in prodromal Alzheimer's disease patients with and without cerebrovascular disease. Across Alzheimer's disease patients with and without cerebrovascular disease, higher default mode network functional connectivity z-scores correlated with greater hippocampal volumes while higher executive control network functional connectivity z-scores correlated with greater white matter changes. In parallel, only Alzheimer's disease patients without cerebrovascular disease showed increased default mode network structural covariance, while only Alzheimer's disease patients with cerebrovascular disease showed increased executive control network structural covariance compared to controls. Our
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Canet, Mark J.; Hardwick, Rhiannon N.; Lake, April D.; Dzierlenga, Anika L.; Clarke, John D.; Cherrington, Nathan J.
2014-01-01
Nonalcoholic fatty liver disease is a prevalent form of chronic liver disease that can progress to the more advanced stage of nonalcoholic steatohepatitis (NASH). NASH has been shown to alter drug transporter regulation and may have implications in the development of adverse drug reactions. Several experimental rodent models have been proposed for the study of NASH, but no single model fully recapitulates all aspects of the human disease. The purpose of the current study was to determine whic...
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Stein, ML
2016-01-01
A broad range of infectious diseases such as influenza, measles and Ebola are transmitted through direct or close human contact. These pathogens do not spread randomly through a population, but follow the structure of human contact networks. Making use of these networks may help to understand and
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Novel transcriptional networks regulated by CLOCK in human neurons.
Fontenot, Miles R; Berto, Stefano; Liu, Yuxiang; Werthmann, Gordon; Douglas, Connor; Usui, Noriyoshi; Gleason, Kelly; Tamminga, Carol A; Takahashi, Joseph S; Konopka, Genevieve
2017-11-01
The molecular mechanisms underlying human brain evolution are not fully understood; however, previous work suggested that expression of the transcription factor CLOCK in the human cortex might be relevant to human cognition and disease. In this study, we investigated this novel transcriptional role for CLOCK in human neurons by performing chromatin immunoprecipitation sequencing for endogenous CLOCK in adult neocortices and RNA sequencing following CLOCK knockdown in differentiated human neurons in vitro. These data suggested that CLOCK regulates the expression of genes involved in neuronal migration, and a functional assay showed that CLOCK knockdown increased neuronal migratory distance. Furthermore, dysregulation of CLOCK disrupts coexpressed networks of genes implicated in neuropsychiatric disorders, and the expression of these networks is driven by hub genes with human-specific patterns of expression. These data support a role for CLOCK-regulated transcriptional cascades involved in human brain evolution and function. © 2017 Fontenot et al.; Published by Cold Spring Harbor Laboratory Press.
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Therapeutic Drug Monitoring in Rheumatic Diseases
Directory of Open Access Journals (Sweden)
NG Hoi-Yan Alexandra
2016-12-01
Full Text Available The ultimate goal of treating rheumatic disease is to achieve rapid suppression of inflammation, while at the same time minimizing the toxicities from rheumatic drugs. Different patients have different individual pharmacokinetics that can affect the drug level. Moreover, different factors, such as renal function, age or even different underlying diseases, can affect the drug level. Therefore, giving the same dosage of drugs to different patients may result in different drug levels. This article will review the usefulness of therapeutic drug monitoring in maximizing drug efficacy, while reducing the risk of toxicities in Hydroxychloroquine, Mycophenolate Mofetil, Tacrolimus and Tumor Necrosis Factor inhibitors (TNF Inhibitors.
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Altered network communication following a neuroprotective drug treatment.
Directory of Open Access Journals (Sweden)
Kathleen Vincent
Full Text Available Preconditioning is defined as a range of stimuli that allow cells to withstand subsequent anaerobic and other deleterious conditions. While cell protection under preconditioning is well established, this paper investigates the influence of neuroprotective preconditioning drugs, 4-aminopyridine and bicuculline (4-AP/bic, on synaptic communication across a broad network of in vitro rat cortical neurons. Using a permutation test, we evaluated cross-correlations of extracellular spiking activity across all pairs of recording electrodes on a 64-channel multielectrode array. The resulting functional connectivity maps were analyzed in terms of their graph-theoretic properties. A small-world effect was found, characterized by a functional network with high clustering coefficient and short average path length. Twenty-four hours after exposure to 4-AP/bic, small-world properties were comparable to control cultures that were not treated with the drug. Four hours following drug washout, however, the density of functional connections increased, while path length decreased and clustering coefficient increased. These alterations in functional connectivity were maintained at four days post-washout, suggesting that 4-AP/bic preconditioning leads to long-term effects on functional networks of cortical neurons. Because of their influence on communication efficiency in neuronal networks, alterations in small-world properties hold implications for information processing in brain systems. The observed relationship between density, path length, and clustering coefficient is captured by a phenomenological model where connections are added randomly within a spatially-embedded network. Taken together, results provide information regarding functional consequences of drug therapies that are overlooked in traditional viability studies and present the first investigation of functional networks under neuroprotective preconditioning.
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Human factors in network security
Jones, Francis B.
1991-01-01
Human factors, such as ethics and education, are important factors in network information security. This thesis determines which human factors have significant influence on network security. Those factors are examined in relation to current security devices and procedures. Methods are introduced to evaluate security effectiveness by incorporating the appropriate human factors into network security controls
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Dynamics of epidemic spreading model with drug-resistant variation on scale-free networks
Wan, Chen; Li, Tao; Zhang, Wu; Dong, Jing
2018-03-01
Considering the influence of the virus' drug-resistant variation, a novel SIVRS (susceptible-infected-variant-recovered-susceptible) epidemic spreading model with variation characteristic on scale-free networks is proposed in this paper. By using the mean-field theory, the spreading dynamics of the model is analyzed in detail. Then, the basic reproductive number R0 and equilibriums are derived. Studies show that the existence of disease-free equilibrium is determined by the basic reproductive number R0. The relationships between the basic reproductive number R0, the variation characteristic and the topology of the underlying networks are studied in detail. Furthermore, our studies prove the global stability of the disease-free equilibrium, the permanence of epidemic and the global attractivity of endemic equilibrium. Numerical simulations are performed to confirm the analytical results.
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Adaptive contact networks change effective disease infectiousness and dynamics.
Van Segbroeck, Sven; Santos, Francisco C; Pacheco, Jorge M
2010-08-19
Human societies are organized in complex webs that are constantly reshaped by a social dynamic which is influenced by the information individuals have about others. Similarly, epidemic spreading may be affected by local information that makes individuals aware of the health status of their social contacts, allowing them to avoid contact with those infected and to remain in touch with the healthy. Here we study disease dynamics in finite populations in which infection occurs along the links of a dynamical contact network whose reshaping may be biased based on each individual's health status. We adopt some of the most widely used epidemiological models, investigating the impact of the reshaping of the contact network on the disease dynamics. We derive analytical results in the limit where network reshaping occurs much faster than disease spreading and demonstrate numerically that this limit extends to a much wider range of time scales than one might anticipate. Specifically, we show that from a population-level description, disease propagation in a quickly adapting network can be formulated equivalently as disease spreading on a well-mixed population but with a rescaled infectiousness. We find that for all models studied here--SI, SIS and SIR--the effective infectiousness of a disease depends on the population size, the number of infected in the population, and the capacity of healthy individuals to sever contacts with the infected. Importantly, we indicate how the use of available information hinders disease progression, either by reducing the average time required to eradicate a disease (in case recovery is possible), or by increasing the average time needed for a disease to spread to the entire population (in case recovery or immunity is impossible).
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Research priorities for Chagas disease, human African trypanosomiasis and leishmaniasis.
2012-01-01
This report provides a review and analysis of the research landscape for three diseases - Chagas disease, human African trypanosomiasis and leishmaniasis - that disproportionately afflict poor and remote populations with limited access to health services. It represents the work of the disease reference group on Chagas Disease, Human African Trypanosomiasis and Leishmaniasis (DRG3) which was established to identify key research priorities through review of research evidence and input from stakeholders' consultations. The diseases, which are caused by related protozoan parasites, are described in terms of their epidemiology and diseases burden, clinical forms and pathogenesis, HIV coinfection, diagnosis, drugs and drug resistance, vaccines, vector control, and health-care interventions. Priority areas for research are identified based on criteria such as public health relevance, benefit and impact on poor populations and equity, and feasibility. The priorities are found in the areas of diagnostics, drugs, vector control, asymptomatic infection, economic analysis of treatment and vector control methods, and in some specific issues such as surveillance methods or transmission-blocking vaccines for particular diseases. This report will be useful to researchers, policy and decision-makers, funding bodies, implementation organizations, and civil society. This is one of ten disease and thematic reference group reports that have come out of the TDR Think Tank, all of which have contributed to the development of the Global Report for Research on Infectious Diseases of Poverty, available at: www.who.int/tdr/stewardship/global_report/en/index.html.
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Lebedeva, Galina; Sorokin, Anatoly; Faratian, Dana; Mullen, Peter; Goltsov, Alexey; Langdon, Simon P.; Harrison, David J.; Goryanin, Igor
2012-01-01
High levels of variability in cancer-related cellular signalling networks and a lack of parameter identifiability in large-scale network models hamper translation of the results of modelling studies into the process of anti-cancer drug development. Recently global sensitivity analysis (GSA) has been recognised as a useful technique, capable of addressing the uncertainty of the model parameters and generating valid predictions on parametric sensitivities. Here we propose a novel implementation of model-based GSA specially designed to explore how multi-parametric network perturbations affect signal propagation through cancer-related networks. We use area-under-the-curve for time course of changes in phosphorylation of proteins as a characteristic for sensitivity analysis and rank network parameters with regard to their impact on the level of key cancer-related outputs, separating strong inhibitory from stimulatory effects. This allows interpretation of the results in terms which can incorporate the effects of potential anti-cancer drugs on targets and the associated biological markers of cancer. To illustrate the method we applied it to an ErbB signalling network model and explored the sensitivity profile of its key model readout, phosphorylated Akt, in the absence and presence of the ErbB2 inhibitor pertuzumab. The method successfully identified the parameters associated with elevation or suppression of Akt phosphorylation in the ErbB2/3 network. From analysis and comparison of the sensitivity profiles of pAkt in the absence and presence of targeted drugs we derived predictions of drug targets, cancer-related biomarkers and generated hypotheses for combinatorial therapy. Several key predictions have been confirmed in experiments using human ovarian carcinoma cell lines. We also compared GSA-derived predictions with the results of local sensitivity analysis and discuss the applicability of both methods. We propose that the developed GSA procedure can serve as a
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Micro RNA, A Review: Pharmacogenomic drug targets for complex diseases
Directory of Open Access Journals (Sweden)
Sandhya Bawa
2010-01-01
Full Text Available
Micro RNAs (miRNAs are non-coding RNAs that can regulate gene expression to target several mRNAs in a gene regulatory network. MiRNA related Single Nucleotide Polymorphisms (S.N.P.s represent a newly identified type of genetic variability that can be of influence to the risk of certain human diseases and also affect how drugs can be activated and metabolized by patients. This will help in personalized medicines which are used for administrating the correct dosage of drug and drug efficacy. miRNA deregulated expression has been extensively described in a variety of diseases such as Cancer, Obesity , Diabetes, Schizophrenia and control and self renewal of stem cells. MiRNA can function as oncogenes and/or tumor suppressors. MiRNAs may act as key regulators of processes as diverse as early development, cell proliferation and cell death, apoptosis and fat metabolism and cell differentiation .miRNA expression have shown their role in brain development chronic lymphocytic leukemia, colonic adeno carcinoma, Burkiff’s lymphoma and viral infection. These show their links with viral disease, neurodevelopment and cancer. It has been shown that they play a key role in melanoma metastasis. These may be
Drugs Approved for Gestational Trophoblastic Disease
This page lists cancer drugs approved by the Food and Drug Administration (FDA) for gestational trophoblastic disease. The list includes generic names and brand names. The drug names link to NCI's Cancer Drug Information summaries.
Directory of Open Access Journals (Sweden)
Ying eTan
2015-08-01
Full Text Available Background: The symptoms and pathogenesis of Parkinson’s disease (PD are complicated and accurate diagnosis is difficult, particularly in early-stage. Functional magnetic resonance imaging is noninvasive and characterized by the integration of different brain areas at functional connectivity (FC. Considering pain process in PD, we hypothesized that pain is one of the earliest symptoms and investigated whether FC of the pain network was disrupted in PD without pain.Methods: Fourteen early drug-naïve PD without pain and 17 age- and sex-matched healthy controls (HC participated in our test. We investigate abnormalities in FC and in functional network connectivity in PD compared with HC during the task (51 °C heat pain stimuli and at rest.Results: Compared with HC, PD showed decreased FC in basal ganglia network (BGN, salience network (SN and sensorimotor network in two states respectively. FNC between the BGN and the SN are reduced during both states in PD compared with HC. In addition, the FNC associated with right frontoparietal network (RFPN was also significantly disturbed during the task.Conclusion: These findings suggest that BGN plays a role in the pathological mechanisms of pain underlying PD, and RFPN likely contributes greatly to harmonization between intrinsic brain activity and external stimuli.
Shimizu, Reiko; Ogata, Katsuhisa; Tamaura, Akemi; Kimura, En; Ohata, Maki; Takeshita, Eri; Nakamura, Harumasa; Takeda, Shin'ichi; Komaki, Hirofumi
2016-07-11
Duchenne muscular dystrophy (DMD) is the most commonly inherited neuromuscular disease. Therapeutic agents for the treatment of rare disease, namely "orphan drugs", have recently drawn the attention of researchers and pharmaceutical companies. To ensure the successful conduction of clinical trials to evaluate novel treatments for patients with rare diseases, an appropriate infrastructure is needed. One of the effective solutions for the lack of infrastructure is to establish a network of rare diseases. To accomplish the conduction of clinical trials in Japan, the Muscular dystrophy clinical trial network (MDCTN) was established by the clinical research group for muscular dystrophy, including the National Center of Neurology and Psychiatry, as well as national and university hospitals, all which have a long-standing history of research cooperation. Thirty-one medical institutions (17 national hospital organizations, 10 university hospitals, 1 national center, 2 public hospitals, and 1 private hospital) belong to this network and collaborate to facilitate clinical trials. The Care and Treatment Site Registry (CTSR) calculates and reports the proportion of patients with neuromuscular diseases in the cooperating sites. In total, there are 5,589 patients with neuromuscular diseases in Japan and the proportion of patients with each disease is as follows: DMD, 29 %; myotonic dystrophy type 1, 23 %; limb girdle muscular dystrophy, 11 %; Becker muscular dystrophy, 10 %. We work jointly to share updated health care information and standardized evaluations of clinical outcomes as well. The collaboration with the patient registry (CTSR), allows the MDCTN to recruit DMD participants with specific mutations and conditions, in a remarkably short period of time. Counting with a network that operates at a national level is important to address the corresponding national issues. Thus, our network will be able to contribute with international research activity, which can lead to
21 CFR 25.31 - Human drugs and biologics.
2010-04-01
... 21 Food and Drugs 1 2010-04-01 2010-04-01 false Human drugs and biologics. 25.31 Section 25.31 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES GENERAL ENVIRONMENTAL IMPACT CONSIDERATIONS Categorical Exclusions § 25.31 Human drugs and biologics. The classes of...
Graph theoretical analysis and application of fMRI-based brain network in Alzheimer's disease
Directory of Open Access Journals (Sweden)
LIU Xue-na
2012-08-01
Full Text Available Alzheimer's disease (AD, a progressive neurodegenerative disease, is clinically characterized by impaired memory and many other cognitive functions. However, the pathophysiological mechanisms underlying the disease are not thoroughly understood. In recent years, using functional magnetic resonance imaging (fMRI as well as advanced graph theory based network analysis approach, several studies of patients with AD suggested abnormal topological organization in both global and regional properties of functional brain networks, specifically, as demonstrated by a loss of small-world network characteristics. These studies provide novel insights into the pathophysiological mechanisms of AD and could be helpful in developing imaging biomarkers for disease diagnosis. In this paper we introduce the essential concepts of complex brain networks theory, and review recent advances of the study on human functional brain networks in AD, especially focusing on the graph theoretical analysis of small-world network based on fMRI. We also propound the existent problems and research orientation.
Cordes, Henrik; Thiel, Christoph; Baier, Vanessa; Blank, Lars M; Kuepfer, Lars
2018-01-01
Drug-induced perturbations of the endogenous metabolic network are a potential root cause of cellular toxicity. A mechanistic understanding of such unwanted side effects during drug therapy is therefore vital for patient safety. The comprehensive assessment of such drug-induced injuries requires the simultaneous consideration of both drug exposure at the whole-body and resulting biochemical responses at the cellular level. We here present a computational multi-scale workflow that combines whole-body physiologically based pharmacokinetic (PBPK) models and organ-specific genome-scale metabolic network (GSMN) models through shared reactions of the xenobiotic metabolism. The applicability of the proposed workflow is illustrated for isoniazid, a first-line antibacterial agent against Mycobacterium tuberculosis , which is known to cause idiosyncratic drug-induced liver injuries (DILI). We combined GSMN models of a human liver with N-acetyl transferase 2 (NAT2)-phenotype-specific PBPK models of isoniazid. The combined PBPK-GSMN models quantitatively describe isoniazid pharmacokinetics, as well as intracellular responses, and changes in the exometabolome in a human liver following isoniazid administration. Notably, intracellular and extracellular responses identified with the PBPK-GSMN models are in line with experimental and clinical findings. Moreover, the drug-induced metabolic perturbations are distributed and attenuated in the metabolic network in a phenotype-dependent manner. Our simulation results show that a simultaneous consideration of both drug pharmacokinetics at the whole-body and metabolism at the cellular level is mandatory to explain drug-induced injuries at the patient level. The proposed workflow extends our mechanistic understanding of the biochemistry underlying adverse events and may be used to prevent drug-induced injuries in the future.
Social networks in cardiovascular disease management.
Shaya, Fadia T; Yan, Xia; Farshid, Maryam; Barakat, Samer; Jung, Miah; Low, Sara; Fedder, Donald
2010-12-01
Cardiovascular disease remains the leading cause of death in the USA. Social networks have a positive association with obesity, smoking cessation and weight loss. This article summarizes studies evaluating the impact of social networks on the management of cardiovascular disease. The 35 studies included in the article describe the impact of social networks on a decreased incidence of cardiovascular disease, depression and mortality. In addition, having a large-sized social network is also associated with better outcomes and improved health. The role of pharmacists is beginning to play an important role in the patient-centered medical home, which needs to be incorporated into social networks. The patient-centered medical home can serve as an adaptive source for social network evolvement.
DEFF Research Database (Denmark)
Imamovic, Lejla; Sommer, Morten
2013-01-01
collateral sensitivity and resistance profiles, revealing a complex collateral sensitivity network. On the basis of these data, we propose a new treatment framework-collateral sensitivity cycling-in which drugs with compatible collateral sensitivity profiles are used sequentially to treat infection...... pathogens. These results provide proof of principle for collateral sensitivity cycling as a sustainable treatment paradigm that may be generally applicable to infectious diseases and cancer....
Directory of Open Access Journals (Sweden)
Hongwei Chu
Full Text Available Epilepsy is a complex neurological disorder and a significant health problem. The pathogenesis of epilepsy remains obscure in a significant number of patients and the current treatment options are not adequate in about a third of individuals which were known as refractory epilepsies (RE. Network medicine provides an effective approach for studying the molecular mechanisms underlying complex diseases. Here we integrated 1876 disease-gene associations of RE and located those genes to human protein-protein interaction (PPI network to obtain 42 significant RE-associated disease modules. The functional analysis of these disease modules showed novel molecular pathological mechanisms of RE, such as the novel enriched pathways (e.g., "presynaptic nicotinic acetylcholine receptors", "signaling by insulin receptor". Further analysis on the relationships between current drug targets and the RE-related disease genes showed the rational mechanisms of most antiepileptic drugs. In addition, we detected ten potential novel drug targets (e.g., KCNA1, KCNA4-6, KCNC3, KCND2, KCNMA1, CAMK2G, CACNB4 and GRM1 located in three RE related disease modules, which might provide novel insights into the new drug discovery for RE therapy.
From Single Target to Multitarget/Network Therapeutics in Alzheimer’s Therapy
Directory of Open Access Journals (Sweden)
Hailin Zheng
2014-01-01
Full Text Available Brain network dysfunction in Alzheimer’s disease (AD involves many proteins (enzymes, processes and pathways, which overlap and influence one another in AD pathogenesis. This complexity challenges the dominant paradigm in drug discovery or a single-target drug for a single mechanism. Although this paradigm has achieved considerable success in some particular diseases, it has failed to provide effective approaches to AD therapy. Network medicines may offer alternative hope for effective treatment of AD and other complex diseases. In contrast to the single-target drug approach, network medicines employ a holistic approach to restore network dysfunction by simultaneously targeting key components in disease networks. In this paper, we explore several drugs either in the clinic or under development for AD therapy in term of their design strategies, diverse mechanisms of action and disease-modifying potential. These drugs act as multi-target ligands and may serve as leads for further development as network medicines.
Multilayer modeling and analysis of human brain networks
2017-01-01
Abstract Understanding how the human brain is structured, and how its architecture is related to function, is of paramount importance for a variety of applications, including but not limited to new ways to prevent, deal with, and cure brain diseases, such as Alzheimer’s or Parkinson’s, and psychiatric disorders, such as schizophrenia. The recent advances in structural and functional neuroimaging, together with the increasing attitude toward interdisciplinary approaches involving computer science, mathematics, and physics, are fostering interesting results from computational neuroscience that are quite often based on the analysis of complex network representation of the human brain. In recent years, this representation experienced a theoretical and computational revolution that is breaching neuroscience, allowing us to cope with the increasing complexity of the human brain across multiple scales and in multiple dimensions and to model structural and functional connectivity from new perspectives, often combined with each other. In this work, we will review the main achievements obtained from interdisciplinary research based on magnetic resonance imaging and establish de facto, the birth of multilayer network analysis and modeling of the human brain. PMID:28327916
Drozdzik, M; Oswald, S
2016-01-01
Orally administered drugs must pass through the intestinal wall and then through the liver before reaching systemic circulation. During this process drugs are subjected to different processes that may determine the therapeutic value. The intestinal barrier with active drug metabolizing enzymes and drug transporters in enterocytes plays an important role in the determination of drug bioavailability. Accumulating information demonstrates variable distribution of drug metabolizing enzymes and transporters along the human gastrointestinal tract (GI), that creates specific barrier characteristics in different segments of the GI. In this review, expression of drug metabolizing enzymes and transporters in the healthy and diseased human GI as well as their regulatory aspects: genetic, miRNA, DNA methylation are outlined. The knowledge of unique interplay between drug metabolizing enzymes and transporters in specific segments of the GI tract allows more precise definition of drug release sites within the GI in order to assure more complete bioavailability and prediction of drug interactions.
Network-based ranking methods for prediction of novel disease associated microRNAs.
Le, Duc-Hau
2015-10-01
Many studies have shown roles of microRNAs on human disease and a number of computational methods have been proposed to predict such associations by ranking candidate microRNAs according to their relevance to a disease. Among them, machine learning-based methods usually have a limitation in specifying non-disease microRNAs as negative training samples. Meanwhile, network-based methods are becoming dominant since they well exploit a "disease module" principle in microRNA functional similarity networks. Of which, random walk with restart (RWR) algorithm-based method is currently state-of-the-art. The use of this algorithm was inspired from its success in predicting disease gene because the "disease module" principle also exists in protein interaction networks. Besides, many algorithms designed for webpage ranking have been successfully applied in ranking disease candidate genes because web networks share topological properties with protein interaction networks. However, these algorithms have not yet been utilized for disease microRNA prediction. We constructed microRNA functional similarity networks based on shared targets of microRNAs, and then we integrated them with a microRNA functional synergistic network, which was recently identified. After analyzing topological properties of these networks, in addition to RWR, we assessed the performance of (i) PRINCE (PRIoritizatioN and Complex Elucidation), which was proposed for disease gene prediction; (ii) PageRank with Priors (PRP) and K-Step Markov (KSM), which were used for studying web networks; and (iii) a neighborhood-based algorithm. Analyses on topological properties showed that all microRNA functional similarity networks are small-worldness and scale-free. The performance of each algorithm was assessed based on average AUC values on 35 disease phenotypes and average rankings of newly discovered disease microRNAs. As a result, the performance on the integrated network was better than that on individual ones. In
Orphan diseases: state of the drug discovery art.
Volmar, Claude-Henry; Wahlestedt, Claes; Brothers, Shaun P
2017-06-01
Since 1983 more than 300 drugs have been developed and approved for orphan diseases. However, considering the development of novel diagnosis tools, the number of rare diseases vastly outpaces therapeutic discovery. Academic centers and nonprofit institutes are now at the forefront of rare disease R&D, partnering with pharmaceutical companies when academic researchers discover novel drugs or targets for specific diseases, thus reducing the failure risk and cost for pharmaceutical companies. Considerable progress has occurred in the art of orphan drug discovery, and a symbiotic relationship now exists between pharmaceutical industry, academia, and philanthropists that provides a useful framework for orphan disease therapeutic discovery. Here, the current state-of-the-art of drug discovery for orphan diseases is reviewed. Current technological approaches and challenges for drug discovery are considered, some of which can present somewhat unique challenges and opportunities in orphan diseases, including the potential for personalized medicine, gene therapy, and phenotypic screening.
Naritomi, Yoichi; Sanoh, Seigo; Ohta, Shigeru
2018-02-01
Predicting human drug metabolism and pharmacokinetics (PK) is key to drug discovery. In particular, it is important to predict human PK, metabolite profiles and drug-drug interactions (DDIs). Various methods have been used for such predictions, including in vitro metabolic studies using human biological samples, such as hepatic microsomes and hepatocytes, and in vivo studies using experimental animals. However, prediction studies using these methods are often inconclusive due to discrepancies between in vitro and in vivo results, and interspecies differences in drug metabolism. Further, the prediction methods have changed from qualitative to quantitative to solve these issues. Chimeric mice with humanized liver have been developed, in which mouse liver cells are mostly replaced with human hepatocytes. Since human drug metabolizing enzymes are expressed in the liver of these mice, they are regarded as suitable models for mimicking the drug metabolism and PK observed in humans; therefore, these mice are useful for predicting human drug metabolism and PK. In this review, we discuss the current state, issues, and future directions of predicting human drug metabolism and PK using chimeric mice with humanized liver in drug discovery. Copyright © 2017 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.
Guo, Wei-Feng; Zhang, Shao-Wu; Shi, Qian-Qian; Zhang, Cheng-Ming; Zeng, Tao; Chen, Luonan
2018-01-19
The advances in target control of complex networks not only can offer new insights into the general control dynamics of complex systems, but also be useful for the practical application in systems biology, such as discovering new therapeutic targets for disease intervention. In many cases, e.g. drug target identification in biological networks, we usually require a target control on a subset of nodes (i.e., disease-associated genes) with minimum cost, and we further expect that more driver nodes consistent with a certain well-selected network nodes (i.e., prior-known drug-target genes). Therefore, motivated by this fact, we pose and address a new and practical problem called as target control problem with objectives-guided optimization (TCO): how could we control the interested variables (or targets) of a system with the optional driver nodes by minimizing the total quantity of drivers and meantime maximizing the quantity of constrained nodes among those drivers. Here, we design an efficient algorithm (TCOA) to find the optional driver nodes for controlling targets in complex networks. We apply our TCOA to several real-world networks, and the results support that our TCOA can identify more precise driver nodes than the existing control-fucus approaches. Furthermore, we have applied TCOA to two bimolecular expert-curate networks. Source code for our TCOA is freely available from http://sysbio.sibcb.ac.cn/cb/chenlab/software.htm or https://github.com/WilfongGuo/guoweifeng . In the previous theoretical research for the full control, there exists an observation and conclusion that the driver nodes tend to be low-degree nodes. However, for target control the biological networks, we find interestingly that the driver nodes tend to be high-degree nodes, which is more consistent with the biological experimental observations. Furthermore, our results supply the novel insights into how we can efficiently target control a complex system, and especially many evidences on the
Hoehndorf, Robert
2015-06-08
Phenotypes are the observable characteristics of an organism arising from its response to the environment. Phenotypes associated with engineered and natural genetic variation are widely recorded using phenotype ontologies in model organisms, as are signs and symptoms of human Mendelian diseases in databases such as OMIM and Orphanet. Exploiting these resources, several computational methods have been developed for integration and analysis of phenotype data to identify the genetic etiology of diseases or suggest plausible interventions. A similar resource would be highly useful not only for rare and Mendelian diseases, but also for common, complex and infectious diseases. We apply a semantic text-mining approach to identify the phenotypes (signs and symptoms) associated with over 6,000 diseases. We evaluate our text-mined phenotypes by demonstrating that they can correctly identify known disease-associated genes in mice and humans with high accuracy. Using a phenotypic similarity measure, we generate a human disease network in which diseases that have similar signs and symptoms cluster together, and we use this network to identify closely related diseases based on common etiological, anatomical as well as physiological underpinnings.
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Hoehndorf, Robert; Schofield, Paul N.; Gkoutos, Georgios V.
2015-06-01
Phenotypes are the observable characteristics of an organism arising from its response to the environment. Phenotypes associated with engineered and natural genetic variation are widely recorded using phenotype ontologies in model organisms, as are signs and symptoms of human Mendelian diseases in databases such as OMIM and Orphanet. Exploiting these resources, several computational methods have been developed for integration and analysis of phenotype data to identify the genetic etiology of diseases or suggest plausible interventions. A similar resource would be highly useful not only for rare and Mendelian diseases, but also for common, complex and infectious diseases. We apply a semantic text-mining approach to identify the phenotypes (signs and symptoms) associated with over 6,000 diseases. We evaluate our text-mined phenotypes by demonstrating that they can correctly identify known disease-associated genes in mice and humans with high accuracy. Using a phenotypic similarity measure, we generate a human disease network in which diseases that have similar signs and symptoms cluster together, and we use this network to identify closely related diseases based on common etiological, anatomical as well as physiological underpinnings.
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Approaching human language with complex networks
Cong, Jin; Liu, Haitao
2014-12-01
The interest in modeling and analyzing human language with complex networks is on the rise in recent years and a considerable body of research in this area has already been accumulated. We survey three major lines of linguistic research from the complex network approach: 1) characterization of human language as a multi-level system with complex network analysis; 2) linguistic typological research with the application of linguistic networks and their quantitative measures; and 3) relationships between the system-level complexity of human language (determined by the topology of linguistic networks) and microscopic linguistic (e.g., syntactic) features (as the traditional concern of linguistics). We show that the models and quantitative tools of complex networks, when exploited properly, can constitute an operational methodology for linguistic inquiry, which contributes to the understanding of human language and the development of linguistics. We conclude our review with suggestions for future linguistic research from the complex network approach: 1) relationships between the system-level complexity of human language and microscopic linguistic features; 2) expansion of research scope from the global properties to other levels of granularity of linguistic networks; and 3) combination of linguistic network analysis with other quantitative studies of language (such as quantitative linguistics).
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Directory of Open Access Journals (Sweden)
Joseph S. D’Arrigo
2017-11-01
Full Text Available Owing to the complexity of neurodegenerative diseases, multiple cellular types need to be targeted simultaneously in order for a given therapy to demonstrate any major effectiveness. Ultrasound-sensitive coated microbubbles (in a targeted nanoemulsion are available. Versatile small-molecule drug(s targeting multiple pathways of Alzheimer’s disease pathogenesis are known. By incorporating such drug(s into the targeted lipid-coated microbubble/nanoparticle-derived (LCM/ND lipid nanoemulsion type, one obtains a multitasking combination therapeutic for translational medicine. This multitasking therapeutic targets cell-surface scavenger receptors (mainly scavenger receptor class B type I (SR-BI, making it possible for various Alzheimer’s-related cell types to be simultaneously sought for localized drug treatment in vivo. Besides targeting cell-surface SR-BI, the proposed LCM/ND-nanoemulsion combination therapeutic(s include a characteristic lipid-coated microbubble (LCM subpopulation (i.e., a stable LCM suspension; such LCM substantially reduce the acoustic power levels needed for accomplishing temporary noninvasive (transcranial ultrasound treatment, or sonoporation, if additionally desired for the Alzheimer’s patient.
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A probabilistic approach to identify putative drug targets in biochemical networks.
Murabito, E.; Smalbone, K.; Swinton, J.; Westerhoff, H.V.; Steuer, R.
2011-01-01
Network-based drug design holds great promise in clinical research as a way to overcome the limitations of traditional approaches in the development of drugs with high efficacy and low toxicity. This novel strategy aims to study how a biochemical network as a whole, rather than its individual
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Modelling fast spreading patterns of airborne infectious diseases using complex networks
Brenner, Frank; Marwan, Norbert; Hoffmann, Peter
2017-04-01
The pandemics of SARS (2002/2003) and H1N1 (2009) have impressively shown the potential of epidemic outbreaks of infectious diseases in a world that is strongly connected. Global air travelling established an easy and fast opportunity for pathogens to migrate globally in only a few days. This made epidemiological prediction harder. By understanding this complex development and its link to climate change we can suggest actions to control a part of global human health affairs. In this study we combine the following data components to simulate the outbreak of an airborne infectious disease that is directly transmitted from human to human: em{Global Air Traffic Network (from openflights.org) with information on airports, airport location, direct flight connection, airplane type} em{Global population dataset (from SEDAC, NASA)} em{Susceptible-Infected-Recovered (SIR) compartmental model to simulate disease spreading in the vicinity of airports. A modified Susceptible-Exposed-Infected-Recovered (SEIR) model to analyze the impact of the incubation period.} em{WATCH-Forcing-Data-ERA-Interim (WFDEI) climate data: temperature, specific humidity, surface air pressure, and water vapor pressure} These elements are implemented into a complex network. Nodes inside the network represent airports. Each single node is equipped with its own SIR/SEIR compartmental model with node specific attributes. Edges between those nodes represent direct flight connections that allow infected individuals to move between linked nodes. Therefore the interaction of the set of unique SIR models creates the model dynamics we will analyze. To better figure out the influence on climate change on disease spreading patterns, we focus on Influenza-like-Illnesses (ILI). The transmission rate of ILI has a dependency on climate parameters like humidity and temperature. Even small changes of environmental variables can trigger significant differences in the global outbreak behavior. Apart from the direct
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Directory of Open Access Journals (Sweden)
Nadia K. Litterman
2014-10-01
Full Text Available Rare disease research has reached a tipping point, with the confluence of scientific and technologic developments that if appropriately harnessed, could lead to key breakthroughs and treatments for this set of devastating disorders. Industry-wide trends have revealed that the traditional drug discovery research and development (R&D model is no longer viable, and drug companies are evolving their approach. Rather than only pursue blockbuster therapeutics for heterogeneous, common diseases, drug companies have increasingly begun to shift their focus to rare diseases. In academia, advances in genetics analyses and disease mechanisms have allowed scientific understanding to mature, but the lack of funding and translational capability severely limits the rare disease research that leads to clinical trials. Simultaneously, there is a movement towards increased research collaboration, more data sharing, and heightened engagement and active involvement by patients, advocates, and foundations. The growth in networks and social networking tools presents an opportunity to help reach other patients but also find researchers and build collaborations. The growth of collaborative software that can enable researchers to share their data could also enable rare disease patients and foundations to manage their portfolio of funded projects for developing new therapeutics and suggest drug repurposing opportunities. Still there are many thousands of diseases without treatments and with only fragmented research efforts. We will describe some recent progress in several rare diseases used as examples and propose how collaborations could be facilitated. We propose that the development of a center of excellence that integrates and shares informatics resources for rare diseases sponsored by all of the stakeholders would help foster these initiatives.
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Addictive drugs and their relationship with infectious diseases.
Friedman, Herman; Pross, Susan; Klein, Thomas W
2006-08-01
The use of drugs of abuse, both recreationally and medicinally, may be related to serious public health concerns. There is a relationship between addictive drugs of abuse such as alcohol and nicotine in cigarette smoke, as well as illegal drugs such as opiates, cocaine and marijuana, and increased susceptibility to infections. The nature and mechanisms of immunomodulation induced by such drugs of abuse are described in this review. The effects of opiates and marijuana, using animal models as well as in vitro studies with immune cells from experimental animals and humans, have shown that immunomodulation induced by these drugs is mainly receptor-mediated, either directly by interaction with specific receptors on immune cells or indirectly by reaction with similar receptors on cells of the nervous system. Similar studies also show that cocaine and nicotine have marked immunomodulatory effects, which are mainly receptor-mediated. Both cocaine, an illegal drug, and nicotine, a widely used legal addictive component of cigarettes, are markedly immunomodulatory and increase susceptibility to infection. The nature and mechanism of immunomodulation induced by alcohol, the most widely used addictive substance of abuse, are similar but immunomodulatory effects, although not receptor-mediated. The many research studies on the effects of these drugs on immunity and increased susceptibility to infectious diseases, including AIDS, are providing a better understanding of the complex interactions between immunity, infections and substance abuse.
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Li, Wan; Wei, Wenqing; Li, Yiran; Xie, Ruiqiang; Guo, Shanshan; Wang, Yahui; Jiang, Jing; Chen, Binbin; Lv, Junjie; Zhang, Nana; Chen, Lina; He, Weiming
2016-01-01
Polycystic ovary syndrome (PCOS) is one of the most common endocrinological disorders in reproductive aged women. PCOS and Type 2 Diabetes (T2D) are closely linked in multiple levels and possess high pathobiological similarity. Here, we put forward a new computational approach based on the pathobiological similarity to identify PCOS potential drug target modules (PPDT-Modules) and PCOS potential drug targets in the protein-protein interaction network (PPIN). From the systems level and biological background, 1 PPDT-Module and 22 PCOS potential drug targets were identified, 21 of which were verified by literatures to be associated with the pathogenesis of PCOS. 42 drugs targeting to 13 PCOS potential drug targets were investigated experimentally or clinically for PCOS. Evaluated by independent datasets, the whole PPDT-Module and 22 PCOS potential drug targets could not only reveal the drug response, but also distinguish the statuses between normal and disease. Our identified PPDT-Module and PCOS potential drug targets would shed light on the treatment of PCOS. And our approach would provide valuable insights to research on the pathogenesis and drug response of other diseases. PMID:27191267
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Subramani, Suresh; Kalpana, Raja; Monickaraj, Pankaj Moses; Natarajan, Jeyakumar
2015-04-01
The knowledge on protein-protein interactions (PPI) and their related pathways are equally important to understand the biological functions of the living cell. Such information on human proteins is highly desirable to understand the mechanism of several diseases such as cancer, diabetes, and Alzheimer's disease. Because much of that information is buried in biomedical literature, an automated text mining system for visualizing human PPI and pathways is highly desirable. In this paper, we present HPIminer, a text mining system for visualizing human protein interactions and pathways from biomedical literature. HPIminer extracts human PPI information and PPI pairs from biomedical literature, and visualize their associated interactions, networks and pathways using two curated databases HPRD and KEGG. To our knowledge, HPIminer is the first system to build interaction networks from literature as well as curated databases. Further, the new interactions mined only from literature and not reported earlier in databases are highlighted as new. A comparative study with other similar tools shows that the resultant network is more informative and provides additional information on interacting proteins and their associated networks. Copyright © 2015 Elsevier Inc. All rights reserved.
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Human gut microbiota plays a role in the metabolism of drugs.
Jourova, Lenka; Anzenbacher, Pavel; Anzenbacherova, Eva
2016-09-01
The gut microbiome, an aggregate genome of trillions of microorganisms residing in the human gastrointestinal tract, is now known to play a critical role in human health and predisposition to disease. It is also involved in the biotransformation of xenobiotics and several recent studies have shown that the gut microbiota can affect the pharmacokinetics of orally taken drugs with implications for their oral bioavailability. Review of Pubmed, Web of Science and Science Direct databases for the years 1957-2016. Recent studies make it clear that the human gut microbiota can play a major role in the metabolism of xenobiotics and, the stability and oral bioavailability of drugs. Over the past 50 years, more than 30 drugs have been identified as a substrate for intestinal bacteria. Questions concerning the impact of the gut microbiota on drug metabolism, remain unanswered or only partially answered, namely (i) what are the molecular mechanisms and which bacterial species are involved? (ii) What is the impact of host genotype and environmental factors on the composition and function of the gut microbiota, (iii) To what extent is the composition of the intestinal microbiome stable, transmissible, and resilient to perturbation? (iv) Has past exposure to a given drug any impact on future microbial response, and, if so, for how long? Answering such questions should be an integral part of pharmaceutical research and personalised health care.
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Li, Xiu-Qing; Xing, Tim; Du, Donglei
2016-01-01
Somatic mutation of signal transduction genes or key nodes of the cellular protein network can cause severe diseases in humans but can sometimes genetically improve plants, likely because growth is determinate in animals but indeterminate in plants. This article reviews protein networks; human protein ranking; the mitogen-activated protein kinase (MAPK) and insulin (phospho- inositide 3kinase [PI3K]/phosphatase and tensin homolog [PTEN]/protein kinase B [AKT]) signaling pathways; human diseases caused by somatic mutations to the PI3K/PTEN/ AKT pathway; use of the MAPK pathway in plant molecular breeding; and protein domain evolution. Casitas B-lineage lymphoma (CBL), PTEN, MAPK1 and PIK3CA are among PIK3CA the top-ranked proteins in directional rankings. Eight proteins (ACVR1, CDC42, RAC1, RAF1, RHOA, TGFBR1, TRAF2, and TRAF6) are ranked in the top 50 key players in both signal emission and signal reception and in interaction with many other proteins. Top-ranked proteins likely have major impacts on the network function. Such proteins are targets for drug discovery, because their mutations are implicated in various cancers and overgrowth syndromes. Appropriately managing food intake may help reduce the growth of tumors or malformation of tissues. The role of the protein kinase C/ fatty acid synthase pathway in fat deposition in PTEN/PI3K patients should be investigated. Both the MAPK and insulin signaling pathways exist in plants, and MAPK pathway engineering can improve plant tolerance to biotic and abiotic stresses such as salinity.
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Human tracking over camera networks: a review
Hou, Li; Wan, Wanggen; Hwang, Jenq-Neng; Muhammad, Rizwan; Yang, Mingyang; Han, Kang
2017-12-01
In recent years, automated human tracking over camera networks is getting essential for video surveillance. The tasks of tracking human over camera networks are not only inherently challenging due to changing human appearance, but also have enormous potentials for a wide range of practical applications, ranging from security surveillance to retail and health care. This review paper surveys the most widely used techniques and recent advances for human tracking over camera networks. Two important functional modules for the human tracking over camera networks are addressed, including human tracking within a camera and human tracking across non-overlapping cameras. The core techniques of human tracking within a camera are discussed based on two aspects, i.e., generative trackers and discriminative trackers. The core techniques of human tracking across non-overlapping cameras are then discussed based on the aspects of human re-identification, camera-link model-based tracking and graph model-based tracking. Our survey aims to address existing problems, challenges, and future research directions based on the analyses of the current progress made toward human tracking techniques over camera networks.
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Drug-Drug Interaction Extraction via Convolutional Neural Networks
Directory of Open Access Journals (Sweden)
Shengyu Liu
2016-01-01
Full Text Available Drug-drug interaction (DDI extraction as a typical relation extraction task in natural language processing (NLP has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM with a large number of manually defined features. Recently, convolutional neural networks (CNN, a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extraction. Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction. The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%.
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Financing drug discovery for orphan diseases.
Fagnan, David E; Gromatzky, Austin A; Stein, Roger M; Fernandez, Jose-Maria; Lo, Andrew W
2014-05-01
Recently proposed 'megafund' financing methods for funding translational medicine and drug development require billions of dollars in capital per megafund to de-risk the drug discovery process enough to issue long-term bonds. Here, we demonstrate that the same financing methods can be applied to orphan drug development but, because of the unique nature of orphan diseases and therapeutics (lower development costs, faster FDA approval times, lower failure rates and lower correlation of failures among disease targets) the amount of capital needed to de-risk such portfolios is much lower in this field. Numerical simulations suggest that an orphan disease megafund of only US$575 million can yield double-digit expected rates of return with only 10-20 projects in the portfolio. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.
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Gene co-expression networks shed light into diseases of brain iron accumulation.
Bettencourt, Conceição; Forabosco, Paola; Wiethoff, Sarah; Heidari, Moones; Johnstone, Daniel M; Botía, Juan A; Collingwood, Joanna F; Hardy, John; Milward, Elizabeth A; Ryten, Mina; Houlden, Henry
2016-03-01
Aberrant brain iron deposition is observed in both common and rare neurodegenerative disorders, including those categorized as Neurodegeneration with Brain Iron Accumulation (NBIA), which are characterized by focal iron accumulation in the basal ganglia. Two NBIA genes are directly involved in iron metabolism, but whether other NBIA-related genes also regulate iron homeostasis in the human brain, and whether aberrant iron deposition contributes to neurodegenerative processes remains largely unknown. This study aims to expand our understanding of these iron overload diseases and identify relationships between known NBIA genes and their main interacting partners by using a systems biology approach. We used whole-transcriptome gene expression data from human brain samples originating from 101 neuropathologically normal individuals (10 brain regions) to generate weighted gene co-expression networks and cluster the 10 known NBIA genes in an unsupervised manner. We investigated NBIA-enriched networks for relevant cell types and pathways, and whether they are disrupted by iron loading in NBIA diseased tissue and in an in vivo mouse model. We identified two basal ganglia gene co-expression modules significantly enriched for NBIA genes, which resemble neuronal and oligodendrocytic signatures. These NBIA gene networks are enriched for iron-related genes, and implicate synapse and lipid metabolism related pathways. Our data also indicates that these networks are disrupted by excessive brain iron loading. We identified multiple cell types in the origin of NBIA disorders. We also found unforeseen links between NBIA networks and iron-related processes, and demonstrate convergent pathways connecting NBIAs and phenotypically overlapping diseases. Our results are of further relevance for these diseases by providing candidates for new causative genes and possible points for therapeutic intervention. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
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2013-05-21
... DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration [Docket No. FDA-2013-N-0473] Human Immunodeficiency Virus Patient-Focused Drug Development and Human Immunodeficiency Virus Cure... an opportunity for public comment on human immunodeficiency virus (HIV) Patient-Focused Drug...
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The spread of sleep loss influences drug use in adolescent social networks.
Directory of Open Access Journals (Sweden)
Sara C Mednick
2010-03-01
Full Text Available Troubled sleep is a commonly cited consequence of adolescent drug use, but it has rarely been studied as a cause. Nor have there been any studies of the extent to which sleep behavior can spread in social networks from person to person to person. Here we map the social networks of 8,349 adolescents in order to study how sleep behavior spreads, how drug use behavior spreads, and how a friend's sleep behavior influences one's own drug use. We find clusters of poor sleep behavior and drug use that extend up to four degrees of separation (to one's friends' friends' friends' friends in the social network. Prospective regression models show that being central in the network negatively influences future sleep outcomes, but not vice versa. Moreover, if a friend sleeps drugs increases by 19% when a friend sleeps < or =7 hours, and a mediation analysis shows that 20% of this effect results from the spread of sleep behavior from one person to another. This is the first study to suggest that the spread of one behavior in social networks influences the spread of another. The results indicate that interventions should focus on healthy sleep to prevent drug use and targeting specific individuals may improve outcomes across the entire social network.
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The integration of weighted human gene association networks based on link prediction.
Yang, Jian; Yang, Tinghong; Wu, Duzhi; Lin, Limei; Yang, Fan; Zhao, Jing
2017-01-31
Physical and functional interplays between genes or proteins have important biological meaning for cellular functions. Some efforts have been made to construct weighted gene association meta-networks by integrating multiple biological resources, where the weight indicates the confidence of the interaction. However, it is found that these existing human gene association networks share only quite limited overlapped interactions, suggesting their incompleteness and noise. Here we proposed a workflow to construct a weighted human gene association network using information of six existing networks, including two weighted specific PPI networks and four gene association meta-networks. We applied link prediction algorithm to predict possible missing links of the networks, cross-validation approach to refine each network and finally integrated the refined networks to get the final integrated network. The common information among the refined networks increases notably, suggesting their higher reliability. Our final integrated network owns much more links than most of the original networks, meanwhile its links still keep high functional relevance. Being used as background network in a case study of disease gene prediction, the final integrated network presents good performance, implying its reliability and application significance. Our workflow could be insightful for integrating and refining existing gene association data.
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2013-08-02
... DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration [Docket No. FDA-2013-N-0473] Human Immunodeficiency Virus Patient-Focused Drug Development and Human Immunodeficiency Virus Cure... for the notice of public meeting entitled ``Human Immunodeficiency Virus (HIV) Patient-Focused Drug...
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Directory of Open Access Journals (Sweden)
Susan T Mashiyama
Full Text Available We performed a genome-level computational study of sequence and structure similarity, the latter using crystal structures and models, of the proteases of Homo sapiens and the human parasite Trypanosoma brucei. Using sequence and structure similarity networks to summarize the results, we constructed global views that show visually the relative abundance and variety of proteases in the degradome landscapes of these two species, and provide insights into evolutionary relationships between proteases. The results also indicate how broadly these sequence sets are covered by three-dimensional structures. These views facilitate cross-species comparisons and offer clues for drug design from knowledge about the sequences and structures of potential drug targets and their homologs. Two protease groups ("M32" and "C51" that are very different in sequence from human proteases are examined in structural detail, illustrating the application of this global approach in mining new pathogen genomes for potential drug targets. Based on our analyses, a human ACE2 inhibitor was selected for experimental testing on one of these parasite proteases, TbM32, and was shown to inhibit it. These sequence and structure data, along with interactive versions of the protein similarity networks generated in this study, are available at http://babbittlab.ucsf.edu/resources.html.
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Mashiyama, Susan T; Koupparis, Kyriacos; Caffrey, Conor R; McKerrow, James H; Babbitt, Patricia C
2012-01-01
We performed a genome-level computational study of sequence and structure similarity, the latter using crystal structures and models, of the proteases of Homo sapiens and the human parasite Trypanosoma brucei. Using sequence and structure similarity networks to summarize the results, we constructed global views that show visually the relative abundance and variety of proteases in the degradome landscapes of these two species, and provide insights into evolutionary relationships between proteases. The results also indicate how broadly these sequence sets are covered by three-dimensional structures. These views facilitate cross-species comparisons and offer clues for drug design from knowledge about the sequences and structures of potential drug targets and their homologs. Two protease groups ("M32" and "C51") that are very different in sequence from human proteases are examined in structural detail, illustrating the application of this global approach in mining new pathogen genomes for potential drug targets. Based on our analyses, a human ACE2 inhibitor was selected for experimental testing on one of these parasite proteases, TbM32, and was shown to inhibit it. These sequence and structure data, along with interactive versions of the protein similarity networks generated in this study, are available at http://babbittlab.ucsf.edu/resources.html.
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Neural/Bayes network predictor for inheritable cardiac disease pathogenicity and phenotype.
Burghardt, Thomas P; Ajtai, Katalin
2018-04-11
The cardiac muscle sarcomere contains multiple proteins contributing to contraction energy transduction and its regulation during a heartbeat. Inheritable heart disease mutants affect most of them but none more frequently than the ventricular myosin motor and cardiac myosin binding protein c (mybpc3). These co-localizing proteins have mybpc3 playing a regulatory role to the energy transducing motor. Residue substitution and functional domain assignment of each mutation in the protein sequence decides, under the direction of a sensible disease model, phenotype and pathogenicity. The unknown model mechanism is decided here using a method combing neural and Bayes networks. Missense single nucleotide polymorphisms (SNPs) are clues for the disease mechanism summarized in an extensive database collecting mutant sequence location and residue substitution as independent variables that imply the dependent disease phenotype and pathogenicity characteristics in 4 dimensional data points (4ddps). The SNP database contains entries with the majority having one or both dependent data entries unfulfilled. A neural network relating causes (mutant residue location and substitution) and effects (phenotype and pathogenicity) is trained, validated, and optimized using fulfilled 4ddps. It then predicts unfulfilled 4ddps providing the implicit disease model. A discrete Bayes network interprets fulfilled and predicted 4ddps with conditional probabilities for phenotype and pathogenicity given mutation location and residue substitution thus relating the neural network implicit model to explicit features of the motor and mybpc3 sequence and structural domains. Neural/Bayes network forecasting automates disease mechanism modeling by leveraging the world wide human missense SNP database that is in place and expanding. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.
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Non-alcoholic fatty liver disease (NAFLD) models in drug discovery.
Cole, Banumathi K; Feaver, Ryan E; Wamhoff, Brian R; Dash, Ajit
2018-02-01
The progressive disease spectrum of non-alcoholic fatty liver disease (NAFLD), which includes non-alcoholic steatohepatitis (NASH), is a rapidly emerging public health crisis with no approved therapy. The diversity of various therapies under development highlights the lack of consensus around the most effective target, underscoring the need for better translatable preclinical models to study the complex progressive disease and effective therapies. Areas covered: This article reviews published literature of various mouse models of NASH used in preclinical studies, as well as complex organotypic in vitro and ex vivo liver models being developed. It discusses translational challenges associated with both kinds of models, and describes some of the studies that validate their application in NAFLD. Expert opinion: Animal models offer advantages of understanding drug distribution and effects in a whole body context, but are limited by important species differences. Human organotypic in vitro and ex vivo models with physiological relevance and translatability need to be used in a tiered manner with simpler screens. Leveraging newer technologies, like metabolomics, proteomics, and transcriptomics, and the future development of validated disease biomarkers will allow us to fully utilize the value of these models to understand disease and evaluate novel drugs in isolation or combination.
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The integrated disease network.
Sun, Kai; Buchan, Natalie; Larminie, Chris; Pržulj, Nataša
2014-11-01
The growing body of transcriptomic, proteomic, metabolomic and genomic data generated from disease states provides a great opportunity to improve our current understanding of the molecular mechanisms driving diseases and shared between diseases. The use of both clinical and molecular phenotypes will lead to better disease understanding and classification. In this study, we set out to gain novel insights into diseases and their relationships by utilising knowledge gained from system-level molecular data. We integrated different types of biological data including genome-wide association studies data, disease-chemical associations, biological pathways and Gene Ontology annotations into an Integrated Disease Network (IDN), a heterogeneous network where nodes are bio-entities and edges between nodes represent their associations. We also introduced a novel disease similarity measure to infer disease-disease associations from the IDN. Our predicted associations were systemically evaluated against the Medical Subject Heading classification and a statistical measure of disease co-occurrence in PubMed. The strong correlation between our predictions and co-occurrence associations indicated the ability of our approach to recover known disease associations. Furthermore, we presented a case study of Crohn's disease. We demonstrated that our approach not only identified well-established connections between Crohn's disease and other diseases, but also revealed new, interesting connections consistent with emerging literature. Our approach also enabled ready access to the knowledge supporting these new connections, making this a powerful approach for exploring connections between diseases.
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World health dilemmas: Orphan and rare diseases, orphan drugs and orphan patients.
Kontoghiorghe, Christina N; Andreou, Nicholas; Constantinou, Katerina; Kontoghiorghes, George J
2014-09-26
According to global annual estimates hunger/malnutrition is the major cause of death (36 of 62 million). Cardiovascular diseases and cancer (5.44 of 13.43 million) are the major causes of death in developed countries, while lower respiratory tract infections, human immunodeficiency virus infection/acquired immunodeficiency syndrome, diarrhoeal disease, malaria and tuberculosis (10.88 of 27.12 million) are the major causes of death in developing countries with more than 70% of deaths occurring in children. The majority of approximately 800 million people with other rare diseases, including 100000 children born with thalassaemia annually receive no treatment. There are major ethical dilemmas in dealing with global health issues such as poverty and the treatment of orphan and rare diseases. Of approximately 50000 drugs about 10% are orphan drugs, with annual sales of the latter approaching 100 billion USD. In comparison, the annual revenue in 2009 from the top 12 pharmaceutical companies in Western countries was 445 billion USD and the top drug, atorvastatin, reached 100 billion USD. In the same year, the total government expenditure for health in the developing countries was 410 billion USD with only 6%-7% having been received as aid from developed countries. Drugs cost the National Health Service in the United Kingdom more than 20 billion USD or 10% of the annual health budget. Uncontrollable drug prices and marketing policies affect global health budgets, clinical practice, patient safety and survival. Fines of 5.3 billion USD were imposed on two pharmaceutical companies in the United States, the regulatory authority in France was replaced and clinicians were charged with bribery in order to overcome recent illegal practises affecting patient care. High expenditure for drug development is mainly related to marketing costs. However, only 2 million USD was spent developing the drug deferiprone (L1) for thalassaemia up to the stage of multicentre clinical trials. The
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World health dilemmas: Orphan and rare diseases, orphan drugs and orphan patients
Kontoghiorghe, Christina N; Andreou, Nicholas; Constantinou, Katerina; Kontoghiorghes, George J
2014-01-01
According to global annual estimates hunger/malnutrition is the major cause of death (36 of 62 million). Cardiovascular diseases and cancer (5.44 of 13.43 million) are the major causes of death in developed countries, while lower respiratory tract infections, human immunodeficiency virus infection/acquired immunodeficiency syndrome, diarrhoeal disease, malaria and tuberculosis (10.88 of 27.12 million) are the major causes of death in developing countries with more than 70% of deaths occurring in children. The majority of approximately 800 million people with other rare diseases, including 100000 children born with thalassaemia annually receive no treatment. There are major ethical dilemmas in dealing with global health issues such as poverty and the treatment of orphan and rare diseases. Of approximately 50000 drugs about 10% are orphan drugs, with annual sales of the latter approaching 100 billion USD. In comparison, the annual revenue in 2009 from the top 12 pharmaceutical companies in Western countries was 445 billion USD and the top drug, atorvastatin, reached 100 billion USD. In the same year, the total government expenditure for health in the developing countries was 410 billion USD with only 6%-7% having been received as aid from developed countries. Drugs cost the National Health Service in the United Kingdom more than 20 billion USD or 10% of the annual health budget. Uncontrollable drug prices and marketing policies affect global health budgets, clinical practice, patient safety and survival. Fines of 5.3 billion USD were imposed on two pharmaceutical companies in the United States, the regulatory authority in France was replaced and clinicians were charged with bribery in order to overcome recent illegal practises affecting patient care. High expenditure for drug development is mainly related to marketing costs. However, only 2 million USD was spent developing the drug deferiprone (L1) for thalassaemia up to the stage of multicentre clinical trials. The
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Huang, Sing-Han; Lo, Yu-Shu; Luo, Yong-Chun; Tseng, Yu-Yao; Yang, Jinn-Moon
2018-03-19
One of the crucial steps toward understanding the associations among molecular interactions, pathways, and diseases in a cell is to investigate detailed atomic protein-protein interactions (PPIs) in the structural interactome. Despite the availability of large-scale methods for analyzing PPI networks, these methods often focused on PPI networks using genome-scale data and/or known experimental PPIs. However, these methods are unable to provide structurally resolved interaction residues and their conservations in PPI networks. Here, we reconstructed a human three-dimensional (3D) structural PPI network (hDiSNet) with the detailed atomic binding models and disease-associated mutations by enhancing our PPI families and 3D-domain interologs from 60,618 structural complexes and complete genome database with 6,352,363 protein sequences across 2274 species. hDiSNet is a scale-free network (γ = 2.05), which consists of 5177 proteins and 19,239 PPIs with 5843 mutations. These 19,239 structurally resolved PPIs not only expanded the number of PPIs compared to present structural PPI network, but also achieved higher agreement with gene ontology similarities and higher co-expression correlation than the ones of 181,868 experimental PPIs recorded in public databases. Among 5843 mutations, 1653 and 790 mutations involved in interacting domains and contacting residues, respectively, are highly related to diseases. Our hDiSNet can provide detailed atomic interactions of human disease and their associated proteins with mutations. Our results show that the disease-related mutations are often located at the contacting residues forming the hydrogen bonds or conserved in the PPI family. In addition, hDiSNet provides the insights of the FGFR (EGFR)-MAPK pathway for interpreting the mechanisms of breast cancer and ErbB signaling pathway in brain cancer. Our results demonstrate that hDiSNet can explore structural-based interactions insights for understanding the mechanisms of disease
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Reduced integration and improved segregation of functional brain networks in Alzheimer's disease.
Kabbara, A; Eid, H; El Falou, W; Khalil, M; Wendling, F; Hassan, M
2018-04-01
Emerging evidence shows that cognitive deficits in Alzheimer's disease (AD) are associated with disruptions in brain functional connectivity. Thus, the identification of alterations in AD functional networks has become a topic of increasing interest. However, to what extent AD induces disruption of the balance of local and global information processing in the human brain remains elusive. The main objective of this study is to explore the dynamic topological changes of AD networks in terms of brain network segregation and integration. We used electroencephalography (EEG) data recorded from 20 participants (10 AD patients and 10 healthy controls) during resting state. Functional brain networks were reconstructed using EEG source connectivity computed in different frequency bands. Graph theoretical analyses were performed assess differences between both groups. Results revealed that AD networks, compared to networks of age-matched healthy controls, are characterized by lower global information processing (integration) and higher local information processing (segregation). Results showed also significant correlation between the alterations in the AD patients' functional brain networks and their cognitive scores. These findings may contribute to the development of EEG network-based test that could strengthen results obtained from currently-used neurophysiological tests in neurodegenerative diseases.
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Bridging humans via agent networks
International Nuclear Information System (INIS)
Ishida, Toru
1994-01-01
Recent drastic advance in telecommunication networks enabled the human organization of new class, teleorganization, which differ from any existing organization in that the organization which is easy to create by using telecommunication networks is virtual and remote, that people can join multiple organizations simultaneously, and that the organization can involve people who may not know each other. In order to enjoy the recent advance in telecommunication, the agent networks to help people organize themselves are needed. In this paper, an architecture of agent networks, in which each agent learns the preference or the utility functioin of the owner, and acts on behalf of the owner in maintaining the organization, is proposed. When an agent networks supports a human organization, the conventional human interface is divided into personal and social interfaces. The functionalities of the social interface in teleconferencing and telelearning were investigated. In both cases, the existence of B-ISDN is assumed, and the extension to the business meeting scheduling using personal handy phone (PHS) networks with personal digital assistant (PDA) terminals is expected. These circumstances are described. Mutual selection protocols (MSP) and their dynamic properties are explained. (K.I.)
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Assessing neuronal networks: understanding Alzheimer's disease.
LENUS (Irish Health Repository)
Bokde, Arun L W
2012-02-01
Findings derived from neuroimaging of the structural and functional organization of the human brain have led to the widely supported hypothesis that neuronal networks of temporally coordinated brain activity across different regional brain structures underpin cognitive function. Failure of integration within a network leads to cognitive dysfunction. The current discussion on Alzheimer\\'s disease (AD) argues that it presents in part a disconnection syndrome. Studies using functional magnetic resonance imaging, positron emission tomography and electroencephalography demonstrate that synchronicity of brain activity is altered in AD and correlates with cognitive deficits. Moreover, recent advances in diffusion tensor imaging have made it possible to track axonal projections across the brain, revealing substantial regional impairment in fiber-tract integrity in AD. Accumulating evidence points towards a network breakdown reflecting disconnection at both the structural and functional system level. The exact relationship among these multiple mechanistic variables and their contribution to cognitive alterations and ultimately decline is yet unknown. Focused research efforts aimed at the integration of both function and structure hold great promise not only in improving our understanding of cognition but also of its characteristic progressive metamorphosis in complex chronic neurodegenerative disorders such as AD.
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Genome network medicine: innovation to overcome huge challenges in cancer therapy.
Roukos, Dimitrios H
2014-01-01
The post-ENCODE era shapes now a new biomedical research direction for understanding transcriptional and signaling networks driving gene expression and core cellular processes such as cell fate, survival, and apoptosis. Over the past half century, the Francis Crick 'central dogma' of single n gene/protein-phenotype (trait/disease) has defined biology, human physiology, disease, diagnostics, and drugs discovery. However, the ENCODE project and several other genomic studies using high-throughput sequencing technologies, computational strategies, and imaging techniques to visualize regulatory networks, provide evidence that transcriptional process and gene expression are regulated by highly complex dynamic molecular and signaling networks. This Focus article describes the linear experimentation-based limitations of diagnostics and therapeutics to cure advanced cancer and the need to move on from reductionist to network-based approaches. With evident a wide genomic heterogeneity, the power and challenges of next-generation sequencing (NGS) technologies to identify a patient's personal mutational landscape for tailoring the best target drugs in the individual patient are discussed. However, the available drugs are not capable of targeting aberrant signaling networks and research on functional transcriptional heterogeneity and functional genome organization is poorly understood. Therefore, the future clinical genome network medicine aiming at overcoming multiple problems in the new fields of regulatory DNA mapping, noncoding RNA, enhancer RNAs, and dynamic complexity of transcriptional circuitry are also discussed expecting in new innovation technology and strong appreciation of clinical data and evidence-based medicine. The problematic and potential solutions in the discovery of next-generation, molecular, and signaling circuitry-based biomarkers and drugs are explored. © 2013 Wiley Periodicals, Inc.
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The influence of personal networks on the use and abuse of alcohol and drugs.
Calafat, Amador; Cajal, Berta; Juan, Montse; Mendes, Fernando; Kokkevi, Anna; Blay, Nicole; Palmer, Alfonso; Duch, Maria Angels
2010-01-01
Party networks of young people are very important for socialization, but can also influence their involvement in risk behaviours or they can be protective. The influence of nightlife network of friends in using alcohol/ drugs is investigated through a survey. We explore the individual-centred networks (7.360 friends) of 1.363 recreational nightlife users in 9 European cities in 2006, through 22 friend characteristics. Statistical analysis utilised factorial analysis with varimax rotation and analysis of variance. The 69% of the sample had been drunk during the last month and more than half of them had used illicit drugs. Most of the respondents use to have a stable group of friends with whom to go out. Networks main characteristics were being more or less deviant and/or prosocial. Having not network or a less prosocial network is related to be low consumers. Having a non deviant, but prosocial network is related to being a person who gets drunk without using illegal drugs. Users of illegal drugs have a deviant and prosocial network. Finally ex users have less deviant networks, but at the same time a helper and prosocial network. Males drug use patterns appear to be less affected by the characteristics of their networks. Some preventive consequences coming from these results are already known as the importance of having less deviant friends. But some other issues are less known: to enhance certain prosocial skills may have counter preventive effects among recreational users and to influence the network for preventative purposes may be more effective among females.
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Mast Cell Stabilizers as Host Modulatory Drugs to Prevent and Control Periodontal Disease
Directory of Open Access Journals (Sweden)
Dhoom Singh Mehta
2011-01-01
Full Text Available Introduction: Mast cells are among the first cells to get in-volved in periodontal inflammation. Their numbers have been shown to be in-creased in cases of gingivitis and periodontal disease. The hypothesis: Since mast cell stabilizers like sodium cromogly-cate (SCG and nedocromil sodium (NS have been used in the prophylaxis of bronchial asthma without any significant adverse effects and also the fact that drugs like SCG show significant anti-inflammatory activities, it would be logical to use mast cell stabilizers as host modulating drugs for the treatment and prevention of peri-odontal disease. Evaluation of the hypothesis: Safety and efficacy of both SCG and NS are well documented. So, it will be systemically safe to use in humans. However, oral administration SCG or delivery of the drug by means local irrigation will not be very useful because SCG may not be secreted in the gingival crevicular fluid (GCF(as in the case of oral administraion or the drug may get washed out from periodontal pocket due to the constant flow of GCF(as in the case of irrigation. A local or targeted drug delivery of mast cell stabilizers can be used in patients with periodontal disease. Role of mast cells in periodontal disease has been dealt in-depth in many studies and articles. However, limited amount of research has been done on using mast cell stabilizers in the prevention and control of periodontal diseases. More studies are needed to study the efficacy and effective-ness of mast cell stabilizers as an adjunct to phase I therapy in the control of periodontal disease.
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Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?
Dobchev, Dimitar; Karelson, Mati
2016-07-01
Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably, in the past two decades, ANNs have been used widely in the process of drug discovery. In this review, the authors discuss advantages and disadvantages of ANNs in drug discovery as incorporated into the quantitative structure-activity relationships (QSAR) framework. Furthermore, the authors examine the recent studies, which span over a broad area with various diseases in drug discovery. In addition, the authors attempt to answer the question about the expectations of the ANNs in drug discovery and discuss the trends in this field. The old pitfalls of overtraining and interpretability are still present with ANNs. However, despite these pitfalls, the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR. It is likely that ANNs will continue to be used in drug development in the future.
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Directory of Open Access Journals (Sweden)
Ying Huang
2015-07-01
Full Text Available The rapid development of new and emerging science & technologies (NESTs brings unprecedented challenges, but also opportunities. In this paper, we use bibliometric and social network analyses, at country, institution, and individual levels, to explore the patterns of scientific networking for a key nano area – nano-enabled drug delivery (NEDD. NEDD has successfully been used clinically to modulate drug release and to target particular diseased tissues. The data for this research come from a global compilation of research publication information on NEDD directed at brain cancer. We derive a family of indicators that address multiple facets of research collaboration and knowledge transfer patterns. Results show that: (1 international cooperation is increasing, but networking characteristics change over time; (2 highly productive institutions also lead in influence, as measured by citation to their work, with American institutes leading; (3 research collaboration is dominated by local relationships, with interesting information available from authorship patterns that go well beyond journal impact factors. Results offer useful technical intelligence to help researchers identify potential collaborators and to help inform R&D management and science & innovation policy for such nanotechnologies.
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Cross-border drug injection relationships among injection drug users in Tijuana, Mexico
Wagner, Karla D.; Pollini, Robin A.; Patterson, Thomas L.; Lozada, Remedios; Ojeda, Victoria D.; Brouwer, Kimberly C.; Vera, Alicia; Volkmann, Tyson A.; Strathdee, Steffanie A.
2010-01-01
Background International borders are unique social and environmental contexts characterized by high levels of mobility. Among drug users, mobility increases risk for human immunodeficiency virus (HIV) in part through its effects on the social environment. However, the social dynamics of drug users living in border regions are understudied. Methods 1056 injection drug users (IDUs) residing in Tijuana, Mexico were recruited using respondent-driven sampling (RDS) from 2006 to 2007, and underwent surveys and testing for HIV, syphilis, and tuberculosis (TB). Using logistic regression on baseline data, we identified correlates of having ever injected drugs with someone from the US. Results Almost half (48%) reported ever injecting drugs with someone from the US. In RDS-adjusted logistic regression, factors independently associated with having ever injected with someone from the US included: having greater than middle school education (Adjusted Odds Ratio [AOR] 2.91; 95% Confidence Interval [C.I.] 1.52, 5.91), speaking English (AOR 3.24, 95% C.I. 1.96, 5.36), age (AOR 1.10 per year; 95% C.I. 1.07, 1.14), age at initiation of injection drug use (AOR 0.90 per year; 95% C.I. 0.86, 0.94), homelessness (AOR 2.61; 95% C.I. 1.27, 5.39), and having ever been incarcerated (AOR 11.82; 95% C.I., 5.22, 26.77). No associations with HIV, syphilis, TB, drug use, or injection risk behavior were detected. Conclusion Findings suggest that IDU networks in Mexico and the US may transcend international borders, with implications for cross-border transmission of infectious disease. Binational programs and policies need to consider the structure and geographic distribution of drug using networks. PMID:20889270
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An integrative -omics approach to identify functional sub-networks in human colorectal cancer.
Directory of Open Access Journals (Sweden)
Rod K Nibbe
2010-01-01
Full Text Available Emerging evidence indicates that gene products implicated in human cancers often cluster together in "hot spots" in protein-protein interaction (PPI networks. Additionally, small sub-networks within PPI networks that demonstrate synergistic differential expression with respect to tumorigenic phenotypes were recently shown to be more accurate classifiers of disease progression when compared to single targets identified by traditional approaches. However, many of these studies rely exclusively on mRNA expression data, a useful but limited measure of cellular activity. Proteomic profiling experiments provide information at the post-translational level, yet they generally screen only a limited fraction of the proteome. Here, we demonstrate that integration of these complementary data sources with a "proteomics-first" approach can enhance the discovery of candidate sub-networks in cancer that are well-suited for mechanistic validation in disease. We propose that small changes in the mRNA expression of multiple genes in the neighborhood of a protein-hub can be synergistically associated with significant changes in the activity of that protein and its network neighbors. Further, we hypothesize that proteomic targets with significant fold change between phenotype and control may be used to "seed" a search for small PPI sub-networks that are functionally associated with these targets. To test this hypothesis, we select proteomic targets having significant expression changes in human colorectal cancer (CRC from two independent 2-D gel-based screens. Then, we use random walk based models of network crosstalk and develop novel reference models to identify sub-networks that are statistically significant in terms of their functional association with these proteomic targets. Subsequently, using an information-theoretic measure, we evaluate synergistic changes in the activity of identified sub-networks based on genome-wide screens of mRNA expression in CRC
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Reconstruction and Analysis of Human Kidney-Specific Metabolic Network Based on Omics Data
Directory of Open Access Journals (Sweden)
Ai-Di Zhang
2013-01-01
Full Text Available With the advent of the high-throughput data production, recent studies of tissue-specific metabolic networks have largely advanced our understanding of the metabolic basis of various physiological and pathological processes. However, for kidney, which plays an essential role in the body, the available kidney-specific model remains incomplete. This paper reports the reconstruction and characterization of the human kidney metabolic network based on transcriptome and proteome data. In silico simulations revealed that house-keeping genes were more essential than kidney-specific genes in maintaining kidney metabolism. Importantly, a total of 267 potential metabolic biomarkers for kidney-related diseases were successfully explored using this model. Furthermore, we found that the discrepancies in metabolic processes of different tissues are directly corresponding to tissue's functions. Finally, the phenotypes of the differentially expressed genes in diabetic kidney disease were characterized, suggesting that these genes may affect disease development through altering kidney metabolism. Thus, the human kidney-specific model constructed in this study may provide valuable information for the metabolism of kidney and offer excellent insights into complex kidney diseases.
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Gómez, José M; Verdú, Miguel
2017-03-06
Epidemics can spread across large regions becoming pandemics by flowing along transportation and social networks. Two network attributes, transitivity (when a node is connected to two other nodes that are also directly connected between them) and centrality (the number and intensity of connections with the other nodes in the network), are widely associated with the dynamics of transmission of pathogens. Here we investigate how network centrality and transitivity influence vulnerability to diseases of human populations by examining one of the most devastating pandemic in human history, the fourteenth century plague pandemic called Black Death. We found that, after controlling for the city spatial location and the disease arrival time, cities with higher values of both centrality and transitivity were more severely affected by the plague. A simulation study indicates that this association was due to central cities with high transitivity undergo more exogenous re-infections. Our study provides an easy method to identify hotspots in epidemic networks. Focusing our effort in those vulnerable nodes may save time and resources by improving our ability of controlling deadly epidemics.
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Wan-Mamat, Wan Mohd Fahmi; Isa, Nor Ashidi Mat; Wahab, Habibah A; Wan-Mamat, Wan Mohd Fairuz
2009-01-01
An intelligent prediction system has been developed to discriminate drug-like and non drug-like molecules pattern. The system is constructed by using the application of advanced version of standard multilayer perceptron (MLP) neural network called Hybrid Multilayer Perceptron (HMLP) neural network and trained using Modified Recursive Prediction Error (MRPE) training algorithm. In this work, a well understood and easy excess Rule of Five + Veber filter properties are selected as the topological descriptor. The main idea behind the selection of this simple descriptor is to assure that the system could be used widely, beneficial and more advantageous regardless at all user level within a drug discovery organization.
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Integrative analysis for finding genes and networks involved in diabetes and other complex diseases
DEFF Research Database (Denmark)
Bergholdt, R.; Størling, Zenia, Marian; Hansen, Kasper Lage
2007-01-01
We have developed an integrative analysis method combining genetic interactions, identified using type 1 diabetes genome scan data, and a high-confidence human protein interaction network. Resulting networks were ranked by the significance of the enrichment of proteins from interacting regions. We...... identified a number of new protein network modules and novel candidate genes/proteins for type 1 diabetes. We propose this type of integrative analysis as a general method for the elucidation of genes and networks involved in diabetes and other complex diseases....
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Identification of illicit drugs by using SOM neural networks
Energy Technology Data Exchange (ETDEWEB)
Liang Meiyan; Shen Jingling; Wang Guangqin [Beijing Key Lab for Terahertz Spectroscopy and Imaging, Key Laboratory of Terahertz Optoelectronics, Ministry of Education, Department of Physics, Capital Normal University, Beijing 100037 (China)], E-mail: liangyan661982@163.com, E-mail: jinglingshen@gmail.com, E-mail: pywgq2004@163.com
2008-07-07
Absorption spectra of six illicit drugs were measured by using the terahertz time-domain spectroscopy technique in the range 0.2-2.6 THz and then clustered with self-organization feature map (SOM) artificial neural network. After the network training process, the spectra collected at another time were identified successfully by the well-trained SOM network. An effective distance was introduced as a quantitative criterion to decide which cluster the new spectra were affiliated with.
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Identification of illicit drugs by using SOM neural networks
International Nuclear Information System (INIS)
Liang Meiyan; Shen Jingling; Wang Guangqin
2008-01-01
Absorption spectra of six illicit drugs were measured by using the terahertz time-domain spectroscopy technique in the range 0.2-2.6 THz and then clustered with self-organization feature map (SOM) artificial neural network. After the network training process, the spectra collected at another time were identified successfully by the well-trained SOM network. An effective distance was introduced as a quantitative criterion to decide which cluster the new spectra were affiliated with
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iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking.
Fan, Yue-Nong; Xiao, Xuan; Min, Jian-Liang; Chou, Kuo-Chen
2014-03-19
Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called "iNR-Drug" was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional) vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine) algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.
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Accelerating Precision Drug Development and Drug Repurposing by Leveraging Human Genetics.
Pulley, Jill M; Shirey-Rice, Jana K; Lavieri, Robert R; Jerome, Rebecca N; Zaleski, Nicole M; Aronoff, David M; Bastarache, Lisa; Niu, Xinnan; Holroyd, Kenneth J; Roden, Dan M; Skaar, Eric P; Niswender, Colleen M; Marnett, Lawrence J; Lindsley, Craig W; Ekstrom, Leeland B; Bentley, Alan R; Bernard, Gordon R; Hong, Charles C; Denny, Joshua C
2017-04-01
The potential impact of using human genetic data linked to longitudinal electronic medical records on drug development is extraordinary; however, the practical application of these data necessitates some organizational innovations. Vanderbilt has created resources such as an easily queried database of >2.6 million de-identified electronic health records linked to BioVU, which is a DNA biobank with more than 230,000 unique samples. To ensure these data are used to maximally benefit and accelerate both de novo drug discovery and drug repurposing efforts, we created the Accelerating Drug Development and Repurposing Incubator, a multidisciplinary think tank of experts in various therapeutic areas within both basic and clinical science as well as experts in legal, business, and other operational domains. The Incubator supports a diverse pipeline of drug indication finding projects, leveraging the natural experiment of human genetics.
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Network analysis of translocated Takahe populations to identify disease surveillance targets.
Grange, Zoë L; VAN Andel, Mary; French, Nigel P; Gartrell, Brett D
2014-04-01
Social network analysis is being increasingly used in epidemiology and disease modeling in humans, domestic animals, and wildlife. We investigated this tool in describing a translocation network (area that allows movement of animals between geographically isolated locations) used for the conservation of an endangered flightless rail, the Takahe (Porphyrio hochstetteri). We collated records of Takahe translocations within New Zealand and used social network principles to describe the connectivity of the translocation network. That is, networks were constructed and analyzed using adjacency matrices with values based on the tie weights between nodes. Five annual network matrices were created using the Takahe data set, each incremental year included records of previous years. Weights of movements between connected locations were assigned by the number of Takahe moved. We calculated the number of nodes (i(total)) and the number of ties (t(total)) between the nodes. To quantify the small-world character of the networks, we compared the real networks to random graphs of the equivalent size, weighting, and node strength. Descriptive analysis of cumulative annual Takahe movement networks involved determination of node-level characteristics, including centrality descriptors of relevance to disease modeling such as weighted measures of in degree (k(i)(in)), out degree (k(i)(out)), and betweenness (B(i)). Key players were assigned according to the highest node measure of k(i)(in), k(i)(out), and B(i) per network. Networks increased in size throughout the time frame considered. The network had some degree small-world characteristics. Nodes with the highest cumulative tie weights connecting them were the captive breeding center, the Murchison Mountains and 2 offshore islands. The key player fluctuated between the captive breeding center and the Murchison Mountains. The cumulative networks identified the captive breeding center every year as the hub of the network until the final
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Issues surrounding orphan disease and orphan drug policies in Europe.
Denis, Alain; Mergaert, Lut; Fostier, Christel; Cleemput, Irina; Simoens, Steven
2010-01-01
An orphan disease is a disease with a very low prevalence. Although there are 5000-7000 orphan diseases, only 50 orphan drugs (i.e. drugs developed to treat orphan diseases) were marketed in the EU by the end of 2008. In 2000, the EU implemented policies specifically designed to stimulate the development of orphan drugs. While decisions on orphan designation and the marketing authorization of orphan drugs are made at the EU level, decisions on drug reimbursement are made at the member state level. The specific features of orphan diseases and orphan drugs make them a high-priority issue for policy makers. The aim of this article is to identify and discuss several issues surrounding orphan disease and drug policies in Europe. The present system of orphan designation allows for drugs for non-orphan diseases to be designated as orphan drugs. The economic factors underlying orphan designation can be questioned in some cases, as a low prevalence of a certain indication does not equal a low return on investment for the drug across its indications. High-quality evidence about the clinical added value of orphan drugs is rarely available at the time of marketing authorization, due to the low number of patients. A balance must be struck between ethical and economic concerns. To this effect, there is a need to initiate a societal dialogue on this issue, to clarify what society wants and accepts in terms of ethical and economic consequences. The growing budgetary impact of orphan drugs puts pressure on drug expenditure. Indications can be extended for an orphan drug and the total prevalence across indications is not considered. Finally, cooperation needs to be fostered in the EU, particularly through a standardized approach to the creation and use of registries. These issues require further attention from researchers, policy makers, health professionals, patients, pharmaceutical companies and other stakeholders with a view to optimizing orphan disease and drug policies in
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Genetic adaptation of the antibacterial human innate immunity network.
Casals, Ferran; Sikora, Martin; Laayouni, Hafid; Montanucci, Ludovica; Muntasell, Aura; Lazarus, Ross; Calafell, Francesc; Awadalla, Philip; Netea, Mihai G; Bertranpetit, Jaume
2011-07-11
Pathogens have represented an important selective force during the adaptation of modern human populations to changing social and other environmental conditions. The evolution of the immune system has therefore been influenced by these pressures. Genomic scans have revealed that immune system is one of the functions enriched with genes under adaptive selection. Here, we describe how the innate immune system has responded to these challenges, through the analysis of resequencing data for 132 innate immunity genes in two human populations. Results are interpreted in the context of the functional and interaction networks defined by these genes. Nucleotide diversity is lower in the adaptors and modulators functional classes, and is negatively correlated with the centrality of the proteins within the interaction network. We also produced a list of candidate genes under positive or balancing selection in each population detected by neutrality tests and showed that some functional classes are preferential targets for selection. We found evidence that the role of each gene in the network conditions the capacity to evolve or their evolvability: genes at the core of the network are more constrained, while adaptation mostly occurred at particular positions at the network edges. Interestingly, the functional classes containing most of the genes with signatures of balancing selection are involved in autoinflammatory and autoimmune diseases, suggesting a counterbalance between the beneficial and deleterious effects of the immune response.
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International Nuclear Information System (INIS)
Yanai, Kazuhiko
1999-01-01
Neuroimaging is a powerful and innovative tool for studying the pathology of psychiatric and neurological diseases and, more recently, for studying the drugs used in their treatment. Technological advances in imaging have made it possible to noninvasively extract information from the human brain regarding a drug's mechanism and site of action. Until now, our understanding of human brain pharmacology has depended primarily on indirect assessments or models derived from animal studies. However, the advent of multiple techniques for human brain imaging allows researchers to focus directly on human pharmacology and brain function. In this review article, our PET studies on the histaminergic neuron system were presented as an example. We have developed and used the PET techniques for 10 years in order to examine the H 1 receptors in the living human brain. This review outlines available PET techniques and examine how these various methods have already been applied to the drug development process and neuropharmacology in the living human brain. (author)
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Energy Technology Data Exchange (ETDEWEB)
Yanai, Kazuhiko [Tohoku Univ., Sendai (Japan). School of Medicine
1999-09-01
Neuroimaging is a powerful and innovative tool for studying the pathology of psychiatric and neurological diseases and, more recently, for studying the drugs used in their treatment. Technological advances in imaging have made it possible to noninvasively extract information from the human brain regarding a drug's mechanism and site of action. Until now, our understanding of human brain pharmacology has depended primarily on indirect assessments or models derived from animal studies. However, the advent of multiple techniques for human brain imaging allows researchers to focus directly on human pharmacology and brain function. In this review article, our PET studies on the histaminergic neuron system were presented as an example. We have developed and used the PET techniques for 10 years in order to examine the H{sub 1} receptors in the living human brain. This review outlines available PET techniques and examine how these various methods have already been applied to the drug development process and neuropharmacology in the living human brain. (author)
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Bulaj, Grzegorz; Ahern, Margaret M.; Kuhn, Alexis; Judkins, Zachary S.; Bowen, Randy C.; Chen, Yizhe
2016-01-01
Merging pharmaceutical and digital (mobile health, mHealth) ingredients to create new therapies for chronic diseases offers unique opportunities for natural products such as omega-3 polyunsaturated fatty acids (n-3 PUFA), curcumin, resveratrol, theanine, or α-lipoic acid. These compounds, when combined with pharmaceutical drugs, show improved efficacy and safety in preclinical and clinical studies of epilepsy, neuropathic pain, osteoarthritis, depression, schizophrenia, diabetes and cancer. Their additional clinical benefits include reducing levels of TNFα and other inflammatory cytokines. We describe how pleiotropic natural products can be developed as bioactive incentives within the network pharmacology together with pharmaceutical drugs and self-care interventions. Since approximately 50% of chronically-ill patients do not take pharmaceutical drugs as prescribed, psychobehavioral incentives may appeal to patients at risk for medication non-adherence. For epilepsy, the incentive-based network therapy comprises anticonvulsant drugs, antiseizure natural products (n-3 PUFA, curcumin or/and resveratrol) coupled with disease-specific behavioral interventions delivered by mobile medical apps. The add-on combination of antiseizure natural products and mHealth supports patient empowerment and intrinsic motivation by having a choice in self-care behaviors. The incentivized therapies offer opportunities: (1) to improve clinical efficacy and safety of existing drugs, (2) to catalyze patient-centered, disease self-management and behavior-changing habits, also improving health-related quality-of-life after reaching remission, and (3) merging copyrighted mHealth software with natural products, thus establishing an intellectual property protection of medical treatments comprising the natural products existing in public domain and currently promoted as dietary supplements. Taken together, clinical research on synergies between existing drugs and pleiotropic natural products
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Application of chimeric mice with humanized liver for study of human-specific drug metabolism.
Bateman, Thomas J; Reddy, Vijay G B; Kakuni, Masakazu; Morikawa, Yoshio; Kumar, Sanjeev
2014-06-01
Human-specific or disproportionately abundant human metabolites of drug candidates that are not adequately formed and qualified in preclinical safety assessment species pose an important drug development challenge. Furthermore, the overall metabolic profile of drug candidates in humans is an important determinant of their drug-drug interaction susceptibility. These risks can be effectively assessed and/or mitigated if human metabolic profile of the drug candidate could reliably be determined in early development. However, currently available in vitro human models (e.g., liver microsomes, hepatocytes) are often inadequate in this regard. Furthermore, the conduct of definitive radiolabeled human ADME studies is an expensive and time-consuming endeavor that is more suited for later in development when the risk of failure has been reduced. We evaluated a recently developed chimeric mouse model with humanized liver on uPA/SCID background for its ability to predict human disposition of four model drugs (lamotrigine, diclofenac, MRK-A, and propafenone) that are known to exhibit human-specific metabolism. The results from these studies demonstrate that chimeric mice were able to reproduce the human-specific metabolite profile for lamotrigine, diclofenac, and MRK-A. In the case of propafenone, however, the human-specific metabolism was not detected as a predominant pathway, and the metabolite profiles in native and humanized mice were similar; this was attributed to the presence of residual highly active propafenone-metabolizing mouse enzymes in chimeric mice. Overall, the data indicate that the chimeric mice with humanized liver have the potential to be a useful tool for the prediction of human-specific metabolism of xenobiotics and warrant further investigation.
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Zong, Nansu; Kim, Hyeoneui; Ngo, Victoria; Harismendy, Olivier
2017-08-01
A heterogeneous network topology possessing abundant interactions between biomedical entities has yet to be utilized in similarity-based methods for predicting drug-target associations based on the array of varying features of drugs and their targets. Deep learning reveals features of vertices of a large network that can be adapted in accommodating the similarity-based solutions to provide a flexible method of drug-target prediction. We propose a similarity-based drug-target prediction method that enhances existing association discovery methods by using a topology-based similarity measure. DeepWalk, a deep learning method, is adopted in this study to calculate the similarities within Linked Tripartite Network (LTN), a heterogeneous network generated from biomedical linked datasets. This proposed method shows promising results for drug-target association prediction: 98.96% AUC ROC score with a 10-fold cross-validation and 99.25% AUC ROC score with a Monte Carlo cross-validation with LTN. By utilizing DeepWalk, we demonstrate that: (i) this method outperforms other existing topology-based similarity computation methods, (ii) the performance is better for tripartite than with bipartite networks and (iii) the measure of similarity using network topology outperforms the ones derived from chemical structure (drugs) or genomic sequence (targets). Our proposed methodology proves to be capable of providing a promising solution for drug-target prediction based on topological similarity with a heterogeneous network, and may be readily re-purposed and adapted in the existing of similarity-based methodologies. The proposed method has been developed in JAVA and it is available, along with the data at the following URL: https://github.com/zongnansu1982/drug-target-prediction . nazong@ucsd.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
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Similarity-based search of model organism, disease and drug effect phenotypes
Hoehndorf, Robert
2015-02-19
Background: Semantic similarity measures over phenotype ontologies have been demonstrated to provide a powerful approach for the analysis of model organism phenotypes, the discovery of animal models of human disease, novel pathways, gene functions, druggable therapeutic targets, and determination of pathogenicity. Results: We have developed PhenomeNET 2, a system that enables similarity-based searches over a large repository of phenotypes in real-time. It can be used to identify strains of model organisms that are phenotypically similar to human patients, diseases that are phenotypically similar to model organism phenotypes, or drug effect profiles that are similar to the phenotypes observed in a patient or model organism. PhenomeNET 2 is available at http://aber-owl.net/phenomenet. Conclusions: Phenotype-similarity searches can provide a powerful tool for the discovery and investigation of molecular mechanisms underlying an observed phenotypic manifestation. PhenomeNET 2 facilitates user-defined similarity searches and allows researchers to analyze their data within a large repository of human, mouse and rat phenotypes.
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International Nuclear Information System (INIS)
Artursson, P.; Karlsson, J.
1991-01-01
Monolayers of a well differentiated human intestinal epithelial cell line, Caco-2, were used as a model to study passive drug absorption across the intestinal epithelium. Absorption rate constants (expressed as apparent permeability coefficients) were determined for 20 drugs and peptides with different structural properties. The permeability coefficients ranged from approximately 5 x 10 - 8 to 5 x 10 - 5 cm/s. A good correlation was obtained between data on oral absorption in humans and the results in the Caco-2 model. Drugs that are completely absorbed in humans had permeability coefficients greater than 1 x 10 - 6 cm/s. Drugs that are absorbed to greater than 1% but less than 100% had permeability coefficients of 0.1-1.0 x 10 - 6 cm/s while drugs and peptides that are absorbed to less than 1% had permeability coefficients of less than or equal to 1 x 10 - 7 cm/s. The results indicate that Caco-2 monolayers can be used as a model for studies on intestinal drug absorption
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Controlled delivery of antiangiogenic drug to human eye tissue using a MEMS device
Pirmoradi, Fatemeh Nazly
2013-01-01
We demonstrate an implantable MEMS drug delivery device to conduct controlled and on-demand, ex vivo drug transport to human eye tissue. Remotely operated drug delivery to human post-mortem eyes was performed via a MEMS device. The developed curved packaging cover conforms to the eyeball thereby preventing the eye tissue from contacting the actuating membrane. By pulsed operation of the device, using an externally applied magnetic field, the drug released from the device accumulates in a cavity adjacent to the tissue. As such, docetaxel (DTX), an antiangiogenic drug, diffuses through the eye tissue, from sclera and choroid to retina. DTX uptake by sclera and choroid were measured to be 1.93±0.66 and 7.24±0.37 μg/g tissue, respectively, after two hours in pulsed operation mode (10s on/off cycles) at 23°C. During this period, a total amount of 192 ng DTX diffused into the exposed tissue. This MEMS device shows great potential for the treatment of ocular posterior segment diseases such as diabetic retinopathy by introducing a novel way of drug administration to the eye. © 2013 IEEE.
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iNR-Drug: Predicting the Interaction of Drugs with Nuclear Receptors in Cellular Networking
Directory of Open Access Journals (Sweden)
Yue-Nong Fan
2014-03-01
Full Text Available Nuclear receptors (NRs are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called “iNR-Drug” was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.
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Hamada, Tomoyo; Nomura, Fumimasa; Kaneko, Tomoyuki; Yasuda, Kenji
2012-06-01
We have developed a three-dimensionally controlled in vitro human cardiomyocyte network assay for the measurements of drug-induced conductivity changes and the appearance of fatal arrhythmia such as ventricular tachycardia/fibrillation for more precise in vitro predictive cardiotoxicity. To construct an artificial conductance propagation model of a human cardiomyocyte network, first, we examined the cell concentration dependence of the cell network heights and found the existence of a height limit of cell networks, which was double-layer height, whereas the cardiomyocytes were effectively and homogeneously cultivated within the microchamber maintaining their spatial distribution constant and their electrophysiological conductance and propagation were successfully recorded using a microelectrode array set on the bottom of the microchamber. The pacing ability of a cardiomyocyte's electrophysiological response has been evaluated using microelectrode extracellular stimulation, and the stimulation for pacing also successfully regulated the beating frequencies of two-layered cardiomyocyte networks, whereas monolayered cardiomyocyte networks were hardly stimulated by the external electrodes using the two-layered cardiomyocyte stimulation condition. The stability of the lined-up shape of human cardiomyocytes within the rectangularly arranged agarose microchambers was limited for a two-layered cardiomyocyte network because their stronger force generation shrunk those cells after peeling off the substrate. The results indicate the importance of fabrication technology of thickness control of cellular networks for effective extracellular stimulation and the potential concerning thick cardiomyocyte networks for long-term cultivation.
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Directory of Open Access Journals (Sweden)
Xingli Guo
Full Text Available The identification of disease-causing genes is a fundamental challenge in human health and of great importance in improving medical care, and provides a better understanding of gene functions. Recent computational approaches based on the interactions among human proteins and disease similarities have shown their power in tackling the issue. In this paper, a novel systematic and global method that integrates two heterogeneous networks for prioritizing candidate disease-causing genes is provided, based on the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein interactions. In this method, the association score function between a query disease and a candidate gene is defined as the weighted sum of all the association scores between similar diseases and neighbouring genes. Moreover, the topological correlation of these two heterogeneous networks can be incorporated into the definition of the score function, and finally an iterative algorithm is designed for this issue. This method was tested with 10-fold cross-validation on all 1,126 diseases that have at least a known causal gene, and it ranked the correct gene as one of the top ten in 622 of all the 1,428 cases, significantly outperforming a state-of-the-art method called PRINCE. The results brought about by this method were applied to study three multi-factorial disorders: breast cancer, Alzheimer disease and diabetes mellitus type 2, and some suggestions of novel causal genes and candidate disease-causing subnetworks were provided for further investigation.
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In Vitro Drug Metabolism by Human Carboxylesterase 1
DEFF Research Database (Denmark)
Thomsen, Ragnar; Rasmussen, Henrik B; Linnet, Kristian
2014-01-01
Carboxylesterase 1 (CES1) is the major hydrolase in human liver. The enzyme is involved in the metabolism of several important therapeutic agents, drugs of abuse, and endogenous compounds. However, no studies have described the role of human CES1 in the activation of two commonly prescribed...... a panel of therapeutic drugs and drugs of abuse to assess their inhibition of the hydrolysis of p-nitrophenyl acetate by recombinant CES1 and human liver microsomes. The screening assay confirmed several known inhibitors of CES1 and identified two previously unreported inhibitors: the dihydropyridine...... calcium antagonist, isradipine, and the immunosuppressive agent, tacrolimus. CES1 plays a role in the metabolism of several drugs used in the treatment of common conditions, including hypertension, congestive heart failure, and diabetes mellitus; thus, there is a potential for clinically relevant drug-drug...
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Directory of Open Access Journals (Sweden)
Allan Peter Davis
Full Text Available Strategies for discovering common molecular events among disparate diseases hold promise for improving understanding of disease etiology and expanding treatment options. One technique is to leverage curated datasets found in the public domain. The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/ manually curates chemical-gene, chemical-disease, and gene-disease interactions from the scientific literature. The use of official gene symbols in CTD interactions enables this information to be combined with the Gene Ontology (GO file from NCBI Gene. By integrating these GO-gene annotations with CTD's gene-disease dataset, we produce 753,000 inferences between 15,700 GO terms and 4,200 diseases, providing opportunities to explore presumptive molecular underpinnings of diseases and identify biological similarities. Through a variety of applications, we demonstrate the utility of this novel resource. As a proof-of-concept, we first analyze known repositioned drugs (e.g., raloxifene and sildenafil and see that their target diseases have a greater degree of similarity when comparing GO terms vs. genes. Next, a computational analysis predicts seemingly non-intuitive diseases (e.g., stomach ulcers and atherosclerosis as being similar to bipolar disorder, and these are validated in the literature as reported co-diseases. Additionally, we leverage other CTD content to develop testable hypotheses about thalidomide-gene networks to treat seemingly disparate diseases. Finally, we illustrate how CTD tools can rank a series of drugs as potential candidates for repositioning against B-cell chronic lymphocytic leukemia and predict cisplatin and the small molecule inhibitor JQ1 as lead compounds. The CTD dataset is freely available for users to navigate pathologies within the context of extensive biological processes, molecular functions, and cellular components conferred by GO. This inference set should aid researchers, bioinformaticists, and
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Structure-based assessment of disease-related mutations in human voltage-gated sodium channels
Directory of Open Access Journals (Sweden)
Weiyun Huang
2017-02-01
Full Text Available ABSTRACT Voltage-gated sodium (Nav channels are essential for the rapid upstroke of action potentials and the propagation of electrical signals in nerves and muscles. Defects of Nav channels are associated with a variety of channelopathies. More than 1000 disease-related mutations have been identified in Nav channels, with Nav1.1 and Nav1.5 each harboring more than 400 mutations. Nav channels represent major targets for a wide array of neurotoxins and drugs. Atomic structures of Nav channels are required to understand their function and disease mechanisms. The recently determined atomic structure of the rabbit voltage-gated calcium (Cav channel Cav1.1 provides a template for homology-based structural modeling of the evolutionarily related Nav channels. In this Resource article, we summarized all the reported disease-related mutations in human Nav channels, generated a homologous model of human Nav1.7, and structurally mapped disease-associated mutations. Before the determination of structures of human Nav channels, the analysis presented here serves as the base framework for mechanistic investigation of Nav channelopathies and for potential structure-based drug discovery.
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Structure-based assessment of disease-related mutations in human voltage-gated sodium channels.
Huang, Weiyun; Liu, Minhao; Yan, S Frank; Yan, Nieng
2017-06-01
Voltage-gated sodium (Na v ) channels are essential for the rapid upstroke of action potentials and the propagation of electrical signals in nerves and muscles. Defects of Na v channels are associated with a variety of channelopathies. More than 1000 disease-related mutations have been identified in Na v channels, with Na v 1.1 and Na v 1.5 each harboring more than 400 mutations. Na v channels represent major targets for a wide array of neurotoxins and drugs. Atomic structures of Na v channels are required to understand their function and disease mechanisms. The recently determined atomic structure of the rabbit voltage-gated calcium (Ca v ) channel Ca v 1.1 provides a template for homology-based structural modeling of the evolutionarily related Na v channels. In this Resource article, we summarized all the reported disease-related mutations in human Na v channels, generated a homologous model of human Na v 1.7, and structurally mapped disease-associated mutations. Before the determination of structures of human Na v channels, the analysis presented here serves as the base framework for mechanistic investigation of Na v channelopathies and for potential structure-based drug discovery.
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Botanical drugs, synergy, and network pharmacology: forth and back to intelligent mixtures.
Gertsch, Jürg
2011-07-01
For centuries the science of pharmacognosy has dominated rational drug development until it was gradually substituted by target-based drug discovery in the last fifty years. Pharmacognosy stems from the different systems of traditional herbal medicine and its "reverse pharmacology" approach has led to the discovery of numerous pharmacologically active molecules and drug leads for humankind. But do botanical drugs also provide effective mixtures? Nature has evolved distinct strategies to modulate biological processes, either by selectively targeting biological macromolecules or by creating molecular promiscuity or polypharmacology (one molecule binds to different targets). Widely claimed to be superior over monosubstances, mixtures of bioactive compounds in botanical drugs allegedly exert synergistic therapeutic effects. Despite evolutionary clues to molecular synergism in nature, sound experimental data are still widely lacking to support this assumption. In this short review, the emerging concept of network pharmacology is highlighted, and the importance of studying ligand-target networks for botanical drugs is emphasized. Furthermore, problems associated with studying mixtures of molecules with distinctly different pharmacodynamic properties are addressed. It is concluded that a better understanding of the polypharmacology and potential network pharmacology of botanical drugs is fundamental in the ongoing rationalization of phytotherapy. © Georg Thieme Verlag KG Stuttgart · New York.
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Large-scale computational drug repositioning to find treatments for rare diseases.
Govindaraj, Rajiv Gandhi; Naderi, Misagh; Singha, Manali; Lemoine, Jeffrey; Brylinski, Michal
2018-01-01
Rare, or orphan, diseases are conditions afflicting a small subset of people in a population. Although these disorders collectively pose significant health care problems, drug companies require government incentives to develop drugs for rare diseases due to extremely limited individual markets. Computer-aided drug repositioning, i.e., finding new indications for existing drugs, is a cheaper and faster alternative to traditional drug discovery offering a promising venue for orphan drug research. Structure-based matching of drug-binding pockets is among the most promising computational techniques to inform drug repositioning. In order to find new targets for known drugs ultimately leading to drug repositioning, we recently developed e MatchSite, a new computer program to compare drug-binding sites. In this study, e MatchSite is combined with virtual screening to systematically explore opportunities to reposition known drugs to proteins associated with rare diseases. The effectiveness of this integrated approach is demonstrated for a kinase inhibitor, which is a confirmed candidate for repositioning to synapsin Ia. The resulting dataset comprises 31,142 putative drug-target complexes linked to 980 orphan diseases. The modeling accuracy is evaluated against the structural data recently released for tyrosine-protein kinase HCK. To illustrate how potential therapeutics for rare diseases can be identified, we discuss a possibility to repurpose a steroidal aromatase inhibitor to treat Niemann-Pick disease type C. Overall, the exhaustive exploration of the drug repositioning space exposes new opportunities to combat orphan diseases with existing drugs. DrugBank/Orphanet repositioning data are freely available to research community at https://osf.io/qdjup/.
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Saha, Sovan; Sengupta, Kaustav; Chatterjee, Piyali; Basu, Subhadip; Nasipuri, Mita
2017-09-23
Infection and disease progression is the outcome of protein interactions between pathogen and host. Pathogen, the role player of Infection, is becoming a severe threat to life as because of its adaptability toward drugs and evolutionary dynamism in nature. Identifying protein targets by analyzing protein interactions between host and pathogen is the key point. Proteins with higher degree and possessing some topologically significant graph theoretical measures are found to be drug targets. On the other hand, exceptional nodes may be involved in infection mechanism because of some pathway process and biologically unknown factors. In this article, we attempt to investigate characteristics of host-pathogen protein interactions by presenting a comprehensive review of computational approaches applied on different infectious diseases. As an illustration, we have analyzed a case study on infectious disease malaria, with its causative agent Plasmodium falciparum acting as 'Bait' and host, Homo sapiens/human acting as 'Prey'. In this pathogen-host interaction network based on some interconnectivity and centrality properties, proteins are viewed as central, peripheral, hub and non-hub nodes and their significance on infection process. Besides, it is observed that because of sparseness of the pathogen and host interaction network, there may be some topologically unimportant but biologically significant proteins, which can also act as Bait/Prey. So, functional similarity or gene ontology mapping can help us in this case to identify these proteins. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.
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[The trend of developing new disease-modifying drugs in Alzheimer's disease].
Arai, Hiroyuki; Furukawa, Katsutoshi; Tomita, Naoki; Ishiki, Aiko; Okamura, Nobuyuki; Kudo, Yukitsuka
2016-03-01
Development of symptomatic treatment of Alzheimer s disease by cholinesterase inhibitors like donepezil was successful. However, it is a disappointment that development of disease-modifying drugs such as anti-amyloid drug based on amyloid-cascade theory has been interrupted or unsuccessful. Therefore, we have to be more cautious regarding inclusion criteria for clinical trials of new drugs. We agree that potentially curative drugs should be started before symptoms begin as a preemptive therapy or prevention trial. The concept of personalized medicine also is important when ApoE4-related amyloid reducing therapy is considered. Unfortunately, Japanese-ADNI has suffered a setback since 2014. However, Ministry of Health, Labour and Welfare gave a final remark that there was nothing wrong in the data managing process in the J-ADNI data center. We should pay more attention to worldwide challenges of speeding up new drug development.
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Development and application of Human Genome Epidemiology
Xu, Jingwen
2017-12-01
Epidemiology is a science that studies distribution of diseases and health in population and its influencing factors, it also studies how to prevent and cure disease and promote health strategies and measures. Epidemiology has developed rapidly in recent years and it is an intercross subject with various other disciplines to form a series of branch disciplines such as Genetic epidemiology, molecular epidemiology, drug epidemiology and tumor epidemiology. With the implementation and completion of Human Genome Project (HGP), Human Genome Epidemiology (HuGE) has emerged at this historic moment. In this review, the development of Human Genome Epidemiology, research content, the construction and structure of relevant network, research standards, as well as the existing results and problems are briefly outlined.
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Dynamics of epidemic diseases on a growing adaptive network.
Demirel, Güven; Barter, Edmund; Gross, Thilo
2017-02-10
The study of epidemics on static networks has revealed important effects on disease prevalence of network topological features such as the variance of the degree distribution, i.e. the distribution of the number of neighbors of nodes, and the maximum degree. Here, we analyze an adaptive network where the degree distribution is not independent of epidemics but is shaped through disease-induced dynamics and mortality in a complex interplay. We study the dynamics of a network that grows according to a preferential attachment rule, while nodes are simultaneously removed from the network due to disease-induced mortality. We investigate the prevalence of the disease using individual-based simulations and a heterogeneous node approximation. Our results suggest that in this system in the thermodynamic limit no epidemic thresholds exist, while the interplay between network growth and epidemic spreading leads to exponential networks for any finite rate of infectiousness when the disease persists.
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Caberlotto, Laura; Lauria, Mario; Nguyen, Thanh-Phuong; Scotti, Marco
2013-01-01
Alzheimer's disease is the most common cause of dementia worldwide, affecting the elderly population. It is characterized by the hallmark pathology of amyloid-β deposition, neurofibrillary tangle formation, and extensive neuronal degeneration in the brain. Wealth of data related to Alzheimer's disease has been generated to date, nevertheless, the molecular mechanism underlying the etiology and pathophysiology of the disease is still unknown. Here we described a method for the combined analysis of multiple types of genome-wide data aimed at revealing convergent evidence interest that would not be captured by a standard molecular approach. Lists of Alzheimer-related genes (seed genes) were obtained from different sets of data on gene expression, SNPs, and molecular targets of drugs. Network analysis was applied for identifying the regions of the human protein-protein interaction network showing a significant enrichment in seed genes, and ultimately, in genes associated to Alzheimer's disease, due to the cumulative effect of different combinations of the starting data sets. The functional properties of these enriched modules were characterized, effectively considering the role of both Alzheimer-related seed genes and genes that closely interact with them. This approach allowed us to present evidence in favor of one of the competing theories about AD underlying processes, specifically evidence supporting a predominant role of metabolism-associated biological process terms, including autophagy, insulin and fatty acid metabolic processes in Alzheimer, with a focus on AMP-activated protein kinase. This central regulator of cellular energy homeostasis regulates a series of brain functions altered in Alzheimer's disease and could link genetic perturbation with neuronal transmission and energy regulation, representing a potential candidate to be targeted by therapy.
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Osafo, Charlotte; Raji, Yemi Raheem; Burke, David; Tayo, Bamidele O; Tiffin, Nicki; Moxey-Mims, Marva M; Rasooly, Rebekah S; Kimmel, Paul L; Ojo, Akinlolu; Adu, Dwomoa; Parekh, Rulan S
2015-12-07
CKD affects an estimated 14% of adults in sub-Saharan Africa, but very little research has been done on the cause, progression, and prevention of CKD there. As part of the Human Heredity and Health in Africa (H3Africa) Consortium, the H3Africa Kidney Disease Research Network was established to study prevalent forms of kidney disease in sub-Saharan Africa and increase the capacity for genetics and genomics research. The study is performing comprehensive phenotypic characterization and analyzing environmental and genetic factors from nine clinical centers in four African countries (Ghana, Nigeria, Ethiopia, and Kenya) over a 5-year period. Approximately 4000 participants with specified kidney disease diagnoses and 4000 control participants will be enrolled in the four African countries. In addition, approximately 50 families with hereditary glomerular disease will be enrolled. The study includes both pediatric and adult participants age research infrastructure can be successfully established in Africa. This study will provide clinical, biochemical, and genotypic data that will greatly increase the understanding of CKD in sub-Saharan Africa. Copyright © 2015 by the American Society of Nephrology.
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Receptor-mediated endocytosis of α-galactosidase A in human podocytes in Fabry disease.
Directory of Open Access Journals (Sweden)
Thaneas Prabakaran
Full Text Available Injury to the glomerular podocyte is a key mechanism in human glomerular disease and podocyte repair is an important therapeutic target. In Fabry disease, podocyte injury is caused by the intracellular accumulation of globotriaosylceramide. This study identifies in the human podocyte three endocytic receptors, mannose 6-phosphate/insulin-like growth II receptor, megalin, and sortilin and demonstrates their drug delivery capabilities for enzyme replacement therapy. Sortilin, a novel α-galactosidase A binding protein, reveals a predominant intracellular expression but also surface expression in the podocyte. The present study provides the rationale for the renal effect of treatment with α-galactosidase A and identifies potential pathways for future non-carbohydrate based drug delivery to the kidney podocyte and other potential affected organs.
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Directory of Open Access Journals (Sweden)
Bhavnani Suresh K
2010-11-01
Full Text Available Abstract Background In a recent study, two-dimensional (2D network layouts were used to visualize and quantitatively analyze the relationship between chronic renal diseases and regulated genes. The results revealed complex relationships between disease type, gene specificity, and gene regulation type, which led to important insights about the underlying biological pathways. Here we describe an attempt to extend our understanding of these complex relationships by reanalyzing the data using three-dimensional (3D network layouts, displayed through 2D and 3D viewing methods. Findings The 3D network layout (displayed through the 3D viewing method revealed that genes implicated in many diseases (non-specific genes tended to be predominantly down-regulated, whereas genes regulated in a few diseases (disease-specific genes tended to be up-regulated. This new global relationship was quantitatively validated through comparison to 1000 random permutations of networks of the same size and distribution. Our new finding appeared to be the result of using specific features of the 3D viewing method to analyze the 3D renal network. Conclusions The global relationship between gene regulation and gene specificity is the first clue from human studies that there exist common mechanisms across several renal diseases, which suggest hypotheses for the underlying mechanisms. Furthermore, the study suggests hypotheses for why the 3D visualization helped to make salient a new regularity that was difficult to detect in 2D. Future research that tests these hypotheses should enable a more systematic understanding of when and how to use 3D network visualizations to reveal complex regularities in biological networks.
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The persuasion network is modulated by drug-use risk and predicts anti-drug message effectiveness
Mangus, J Michael; Turner, Benjamin O
2017-01-01
Abstract While a persuasion network has been proposed, little is known about how network connections between brain regions contribute to attitude change. Two possible mechanisms have been advanced. One hypothesis predicts that attitude change results from increased connectivity between structures implicated in affective and executive processing in response to increases in argument strength. A second functional perspective suggests that highly arousing messages reduce connectivity between structures implicated in the encoding of sensory information, which disrupts message processing and thereby inhibits attitude change. However, persuasion is a multi-determined construct that results from both message features and audience characteristics. Therefore, persuasive messages should lead to specific functional connectivity patterns among a priori defined structures within the persuasion network. The present study exposed 28 subjects to anti-drug public service announcements where arousal, argument strength, and subject drug-use risk were systematically varied. Psychophysiological interaction analyses provide support for the affective-executive hypothesis but not for the encoding-disruption hypothesis. Secondary analyses show that video-level connectivity patterns among structures within the persuasion network predict audience responses in independent samples (one college-aged, one nationally representative). We propose that persuasion neuroscience research is best advanced by considering network-level effects while accounting for interactions between message features and target audience characteristics. PMID:29140500
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Directory of Open Access Journals (Sweden)
Naif Zaman
2013-10-01
Full Text Available Individual cancer cells carry a bewildering number of distinct genomic alterations (e.g., copy number variations and mutations, making it a challenge to uncover genomic-driven mechanisms governing tumorigenesis. Here, we performed exome sequencing on several breast cancer cell lines that represent two subtypes, luminal and basal. We integrated these sequencing data and functional RNAi screening data (for the identification of genes that are essential for cell proliferation and survival onto a human signaling network. Two subtype-specific networks that potentially represent core-signaling mechanisms underlying tumorigenesis were identified. Within both networks, we found that genes were differentially affected in different cell lines; i.e., in some cell lines a gene was identified through RNAi screening, whereas in others it was genomically altered. Interestingly, we found that highly connected network genes could be used to correctly classify breast tumors into subtypes on the basis of genomic alterations. Further, the networks effectively predicted subtype-specific drug targets, which were experimentally validated.
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Financing drug discovery for orphan diseases
Fagnan, David Erik; Gromatzky, Austin A.; Stein, Roger Mark; Fernandez, Jose-Maria; Lo, Andrew W.
2014-01-01
Recently proposed ‘megafund’ financing methods for funding translational medicine and drug development require billions of dollars in capital per megafund to de-risk the drug discovery process enough to issue long-term bonds. Here, we demonstrate that the same financing methods can be applied to orphan drug development but, because of the unique nature of orphan diseases and therapeutics (lower development costs, faster FDA approval times, lower failure rates and lower correlation of failures...
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Zilverstand, Anna; Huang, Anna S; Alia-Klein, Nelly; Goldstein, Rita Z
2018-06-06
The impaired response inhibition and salience attribution (iRISA) model proposes that impaired response inhibition and salience attribution underlie drug seeking and taking. To update this model, we systematically reviewed 105 task-related neuroimaging studies (n > 15/group) published since 2010. Results demonstrate specific impairments within six large-scale brain networks (reward, habit, salience, executive, memory, and self-directed networks) during drug cue exposure, decision making, inhibitory control, and social-emotional processing. Addicted individuals demonstrated increased recruitment of these networks during drug-related processing but a blunted response during non-drug-related processing, with the same networks also being implicated during resting state. Associations with real-life drug use, relapse, therapeutic interventions, and the relevance to initiation of drug use during adolescence support the clinical relevance of the results. Whereas the salience and executive networks showed impairments throughout the addiction cycle, the reward network was dysregulated at later stages of abuse. Effects were similar in alcohol, cannabis, and stimulant addiction. Copyright © 2018 Elsevier Inc. All rights reserved.
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Small vessel disease is linked to disrupted structural network covariance in Alzheimer's disease.
Nestor, Sean M; Mišić, Bratislav; Ramirez, Joel; Zhao, Jiali; Graham, Simon J; Verhoeff, Nicolaas P L G; Stuss, Donald T; Masellis, Mario; Black, Sandra E
2017-07-01
Cerebral small vessel disease (SVD) is thought to contribute to Alzheimer's disease (AD) through abnormalities in white matter networks. Gray matter (GM) hub covariance networks share only partial overlap with white matter connectivity, and their relationship with SVD has not been examined in AD. We developed a multivariate analytical pipeline to elucidate the cortical GM thickness systems that covary with major network hubs and assessed whether SVD and neurodegenerative pathologic markers were associated with attenuated covariance network integrity in mild AD and normal elderly control subjects. SVD burden was associated with reduced posterior cingulate corticocortical GM network integrity and subneocorticocortical hub network integrity in AD. These findings provide evidence that SVD is linked to the selective disruption of cortical hub GM networks in AD brains and point to the need to consider GM hub covariance networks when assessing network disruption in mixed disease. Copyright © 2017 the Alzheimer's Association. Published by Elsevier Inc. All rights reserved.
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The basics of preclinical drug development for neurodegenerative disease indications.
Steinmetz, Karen L; Spack, Edward G
2009-06-12
Preclinical development encompasses the activities that link drug discovery in the laboratory to initiation of human clinical trials. Preclinical studies can be designed to identify a lead candidate from several hits; develop the best procedure for new drug scale-up; select the best formulation; determine the route, frequency, and duration of exposure; and ultimately support the intended clinical trial design. The details of each preclinical development package can vary, but all have some common features. Rodent and nonrodent mammalian models are used to delineate the pharmacokinetic profile and general safety, as well as to identify toxicity patterns. One or more species may be used to determine the drug's mean residence time in the body, which depends on inherent absorption, distribution, metabolism, and excretion properties. For drugs intended to treat Alzheimer's disease or other brain-targeted diseases, the ability of a drug to cross the blood brain barrier may be a key issue. Toxicology and safety studies identify potential target organs for adverse effects and define the Therapeutic Index to set the initial starting doses in clinical trials. Pivotal preclinical safety studies generally require regulatory oversight as defined by US Food and Drug Administration (FDA) Good Laboratory Practices and international guidelines, including the International Conference on Harmonization. Concurrent preclinical development activities include developing the Clinical Plan and preparing the new drug product, including the associated documentation to meet stringent FDA Good Manufacturing Practices regulatory guidelines. A wide range of commercial and government contract options are available for investigators seeking to advance their candidate(s). Government programs such as the Small Business Innovative Research and Small Business Technology Transfer grants and the National Institutes of Health Rapid Access to Interventional Development Pilot Program provide funding and
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Directory of Open Access Journals (Sweden)
Jose A Santiago
Full Text Available Shared dysregulated pathways may contribute to Parkinson's disease and type 2 diabetes, chronic diseases that afflict millions of people worldwide. Despite the evidence provided by epidemiological and gene profiling studies, the molecular and functional networks implicated in both diseases, have not been fully explored. In this study, we used an integrated network approach to investigate the extent to which Parkinson's disease and type 2 diabetes are linked at the molecular level.Using a random walk algorithm within the human functional linkage network we identified a molecular cluster of 478 neighboring genes closely associated with confirmed Parkinson's disease and type 2 diabetes genes. Biological and functional analysis identified the protein serine-threonine kinase activity, MAPK cascade, activation of the immune response, and insulin receptor and lipid signaling as convergent pathways. Integration of results from microarrays studies identified a blood signature comprising seven genes whose expression is dysregulated in Parkinson's disease and type 2 diabetes. Among this group of genes, is the amyloid precursor protein (APP, previously associated with neurodegeneration and insulin regulation. Quantification of RNA from whole blood of 192 samples from two independent clinical trials, the Harvard Biomarker Study (HBS and the Prognostic Biomarker Study (PROBE, revealed that expression of APP is significantly upregulated in Parkinson's disease patients compared to healthy controls. Assessment of biomarker performance revealed that expression of APP could distinguish Parkinson's disease from healthy individuals with a diagnostic accuracy of 80% in both cohorts of patients.These results provide the first evidence that Parkinson's disease and diabetes are strongly linked at the molecular level and that shared molecular networks provide an additional source for identifying highly sensitive biomarkers. Further, these results suggest for the first
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Santiago, Jose A; Potashkin, Judith A
2013-01-01
Shared dysregulated pathways may contribute to Parkinson's disease and type 2 diabetes, chronic diseases that afflict millions of people worldwide. Despite the evidence provided by epidemiological and gene profiling studies, the molecular and functional networks implicated in both diseases, have not been fully explored. In this study, we used an integrated network approach to investigate the extent to which Parkinson's disease and type 2 diabetes are linked at the molecular level. Using a random walk algorithm within the human functional linkage network we identified a molecular cluster of 478 neighboring genes closely associated with confirmed Parkinson's disease and type 2 diabetes genes. Biological and functional analysis identified the protein serine-threonine kinase activity, MAPK cascade, activation of the immune response, and insulin receptor and lipid signaling as convergent pathways. Integration of results from microarrays studies identified a blood signature comprising seven genes whose expression is dysregulated in Parkinson's disease and type 2 diabetes. Among this group of genes, is the amyloid precursor protein (APP), previously associated with neurodegeneration and insulin regulation. Quantification of RNA from whole blood of 192 samples from two independent clinical trials, the Harvard Biomarker Study (HBS) and the Prognostic Biomarker Study (PROBE), revealed that expression of APP is significantly upregulated in Parkinson's disease patients compared to healthy controls. Assessment of biomarker performance revealed that expression of APP could distinguish Parkinson's disease from healthy individuals with a diagnostic accuracy of 80% in both cohorts of patients. These results provide the first evidence that Parkinson's disease and diabetes are strongly linked at the molecular level and that shared molecular networks provide an additional source for identifying highly sensitive biomarkers. Further, these results suggest for the first time that
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DEFF Research Database (Denmark)
Østergaard, Mikkel; Baslund, Bo; Rigby, William
2010-01-01
To investigate the safety and efficacy of ofatumumab, a novel human anti-CD20 monoclonal antibody (mAb), in patients with active rheumatoid arthritis (RA) whose disease did not respond to > or = 1 disease-modifying antirheumatic drug....
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Genetic adaptation of the antibacterial human innate immunity network
Directory of Open Access Journals (Sweden)
Lazarus Ross
2011-07-01
Full Text Available Abstract Background Pathogens have represented an important selective force during the adaptation of modern human populations to changing social and other environmental conditions. The evolution of the immune system has therefore been influenced by these pressures. Genomic scans have revealed that immune system is one of the functions enriched with genes under adaptive selection. Results Here, we describe how the innate immune system has responded to these challenges, through the analysis of resequencing data for 132 innate immunity genes in two human populations. Results are interpreted in the context of the functional and interaction networks defined by these genes. Nucleotide diversity is lower in the adaptors and modulators functional classes, and is negatively correlated with the centrality of the proteins within the interaction network. We also produced a list of candidate genes under positive or balancing selection in each population detected by neutrality tests and showed that some functional classes are preferential targets for selection. Conclusions We found evidence that the role of each gene in the network conditions the capacity to evolve or their evolvability: genes at the core of the network are more constrained, while adaptation mostly occurred at particular positions at the network edges. Interestingly, the functional classes containing most of the genes with signatures of balancing selection are involved in autoinflammatory and autoimmune diseases, suggesting a counterbalance between the beneficial and deleterious effects of the immune response.
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Li, Min; Zhang, Jiayi; Liu, Qing; Wang, Jianxin; Wu, Fang-Xiang
2014-01-01
Predicting disease-related genes is one of the most important tasks in bioinformatics and systems biology. With the advances in high-throughput techniques, a large number of protein-protein interactions are available, which make it possible to identify disease-related genes at the network level. However, network-based identification of disease-related genes is still a challenge as the considerable false-positives are still existed in the current available protein interaction networks (PIN). Considering the fact that the majority of genetic disorders tend to manifest only in a single or a few tissues, we constructed tissue-specific networks (TSN) by integrating PIN and tissue-specific data. We further weighed the constructed tissue-specific network (WTSN) by using DNA methylation as it plays an irreplaceable role in the development of complex diseases. A PageRank-based method was developed to identify disease-related genes from the constructed networks. To validate the effectiveness of the proposed method, we constructed PIN, weighted PIN (WPIN), TSN, WTSN for colon cancer and leukemia, respectively. The experimental results on colon cancer and leukemia show that the combination of tissue-specific data and DNA methylation can help to identify disease-related genes more accurately. Moreover, the PageRank-based method was effective to predict disease-related genes on the case studies of colon cancer and leukemia. Tissue-specific data and DNA methylation are two important factors to the study of human diseases. The same method implemented on the WTSN can achieve better results compared to those being implemented on original PIN, WPIN, or TSN. The PageRank-based method outperforms degree centrality-based method for identifying disease-related genes from WTSN.
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Yang, Hyun Mo
2015-12-01
Currently, discrete modellings are largely accepted due to the access to computers with huge storage capacity and high performance processors and easy implementation of algorithms, allowing to develop and simulate increasingly sophisticated models. Wang et al. [7] present a review of dynamics in complex networks, focusing on the interaction between disease dynamics and human behavioral and social dynamics. By doing an extensive review regarding to the human behavior responding to disease dynamics, the authors briefly describe the complex dynamics found in the literature: well-mixed populations networks, where spatial structure can be neglected, and other networks considering heterogeneity on spatially distributed populations. As controlling mechanisms are implemented, such as social distancing due 'social contagion', quarantine, non-pharmaceutical interventions and vaccination, adaptive behavior can occur in human population, which can be easily taken into account in the dynamics formulated by networked populations.
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Radiosensitivity of drug-resistant human tumour xenografts
International Nuclear Information System (INIS)
Mattern, J.; Bak, M. Jr.; Volm, M.; Hoever, K.H.
1989-01-01
The radiosensitivity of three drug-resistant sublines of a human epidermoid lung carcinoma growing as xenografts in nude mice was investigated. Drug resistance to vincristine, actinomycin D and cisplatin was developed in vivo by repeated drug treatment. It was found that all three drug-resistant tumour lines were not cross-resistant to irradiation. (orig.) [de
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Utility of humanized BLT mice for analysis of dengue virus infection and antiviral drug testing.
Frias-Staheli, Natalia; Dorner, Marcus; Marukian, Svetlana; Billerbeck, Eva; Labitt, Rachael N; Rice, Charles M; Ploss, Alexander
2014-02-01
Dengue virus (DENV) is the cause of a potentially life-threatening disease that affects millions of people worldwide. The lack of a small animal model that mimics the symptoms of DENV infection in humans has slowed the understanding of viral pathogenesis and the development of therapies and vaccines. Here, we investigated the use of humanized "bone marrow liver thymus" (BLT) mice as a model for immunological studies and assayed their applicability for preclinical testing of antiviral compounds. Human immune system (HIS) BLT-NOD/SCID mice were inoculated intravenously with a low-passage, clinical isolate of DENV-2, and this resulted in sustained viremia and infection of leukocytes in lymphoid and nonlymphoid organs. In addition, DENV infection increased serum cytokine levels and elicited DENV-2-neutralizing human IgM antibodies. Following restimulation with DENV-infected dendritic cells, in vivo-primed T cells became activated and acquired effector function. An adenosine nucleoside inhibitor of DENV decreased the circulating viral RNA when administered simultaneously or 2 days postinfection, simulating a potential treatment protocol for DENV infection in humans. In summary, we demonstrate that BLT mice are susceptible to infection with clinical DENV isolates, mount virus-specific adaptive immune responses, and respond to antiviral drug treatment. Although additional refinements to the model are required, BLT mice are a suitable platform to study aspects of DENV infection and pathogenesis and for preclinical testing of drug and vaccine candidates. IMPORTANCE Infection with dengue virus remains a major medical problem. Progress in our understanding of the disease and development of therapeutics has been hampered by the scarcity of small animal models. Here, we show that humanized mice, i.e., animals engrafted with components of a human immune system, that were infected with a patient-derived dengue virus strain developed clinical symptoms of the disease and mounted
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DEFF Research Database (Denmark)
Sørensen, Isabella S; Janfelt, Christian; Nielsen, Mette Marie B
2017-01-01
Study of skin penetration and distribution of the drug compounds in the skin is a major challenge in the development of topical drug products for treatment of skin diseases. It is crucial to have fast and efficacious screening methods which can provide information concerning the skin penetration ...... that combination of MALDI-MSI and cassette dosing can be used as a medium throughput screening tool at an early stage in the drug discovery/development process. Graphical abstract Investigation of drug distribution in human skin explant by MALDI-MSI after cassette dosing....
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Peng, Hui; Lan, Chaowang; Zheng, Yi; Hutvagner, Gyorgy; Tao, Dacheng; Li, Jinyan
2017-03-24
MicroRNAs always function cooperatively in their regulation of gene expression. Dysfunctions of these co-functional microRNAs can play significant roles in disease development. We are interested in those multi-disease associated co-functional microRNAs that regulate their common dysfunctional target genes cooperatively in the development of multiple diseases. The research is potentially useful for human disease studies at the transcriptional level and for the study of multi-purpose microRNA therapeutics. We designed a computational method to detect multi-disease associated co-functional microRNA pairs and conducted cross disease analysis on a reconstructed disease-gene-microRNA (DGR) tripartite network. The construction of the DGR tripartite network is by the integration of newly predicted disease-microRNA associations with those relationships of diseases, microRNAs and genes maintained by existing databases. The prediction method uses a set of reliable negative samples of disease-microRNA association and a pre-computed kernel matrix instead of kernel functions. From this reconstructed DGR tripartite network, multi-disease associated co-functional microRNA pairs are detected together with their common dysfunctional target genes and ranked by a novel scoring method. We also conducted proof-of-concept case studies on cancer-related co-functional microRNA pairs as well as on non-cancer disease-related microRNA pairs. With the prioritization of the co-functional microRNAs that relate to a series of diseases, we found that the co-function phenomenon is not unusual. We also confirmed that the regulation of the microRNAs for the development of cancers is more complex and have more unique properties than those of non-cancer diseases.
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Drugs and drug delivery systems targeting amyloid-β in Alzheimer's disease
Directory of Open Access Journals (Sweden)
Morgan Robinson
2015-07-01
Full Text Available Alzheimer's disease (AD is a devastating neurodegenerative disorder with no cure and limited treatment solutions that are unable to target any of the suspected causes. Increasing evidence suggests that one of the causes of neurodegeneration is the overproduction of amyloid beta (Aβ and the inability of Aβ peptides to be cleared from the brain, resulting in self-aggregation to form toxic oligomers, fibrils and plaques. One of the potential treatment options is to target Aβ and prevent self-aggregation to allow for a natural clearing of the brain. In this paper, we review the drugs and drug delivery systems that target Aβ in relation to Alzheimer's disease. Many attempts have been made to use anti-Aβ targeting molecules capable of targeting Aβ (with much success in vitro and in vivo animal models, but the major obstacle to this technique is the challenge posed by the blood brain barrier (BBB. This highly selective barrier protects the brain from toxic molecules and pathogens and prevents the delivery of most drugs. Therefore novel Aβ aggregation inhibitor drugs will require well thought-out drug delivery systems to deliver sufficient concentrations to the brain.
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The Impact of Disease and Drugs on Hip Fracture Risk.
Leavy, Breiffni; Michaëlsson, Karl; Åberg, Anna Cristina; Melhus, Håkan; Byberg, Liisa
2017-01-01
We report the risks of a comprehensive range of disease and drug categories on hip fracture occurrence using a strict population-based cohort design. Participants included the source population of a Swedish county, aged ≥50 years (n = 117,494) including all incident hip fractures during 1 year (n = 477). The outcome was hospitalization for hip fracture (ICD-10 codes S72.0-S72.2) during 1 year (2009-2010). Exposures included: prevalence of (1) inpatient diseases [International Classification of Diseases (ICD) codes A00-T98 in the National Patient Register 1987-2010] and (2) prescribed drugs dispensed in 2010 or the year prior to fracture. We present age- and sex-standardized risk ratios (RRs), risk differences (RDs) and population attributable risks (PARs) of disease and drug categories in relation to hip fracture risk. All disease categories were associated with increased risk of hip fracture. Largest risk ratios and differences were for mental and behavioral disorders, diseases of the blood and previous fracture (RRs between 2.44 and 3.00; RDs (per 1000 person-years) between 5.0 and 6.9). For specific drugs, strongest associations were seen for antiparkinson (RR 2.32 [95 % CI 1.48-1.65]; RD 5.2 [1.1-9.4]) and antidepressive drugs (RR 1.90 [1.55-2.32]; RD 3.1 [2.0-4.3]). Being prescribed ≥10 drugs during 1 year incurred an increased risk of hip fracture, whereas prescription of cardiovascular drugs or ≤5 drugs did not appear to increase risk. Diseases inferring the greatest PARs included: cardiovascular diseases PAR 22 % (95 % CI 14-29) and previous injuries (PAR 21 % [95 % CI 16-25]; for specific drugs, antidepressants posed the greatest risk (PAR 16 % [95 % CI 12.0-19.3]).
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The dynamics of injection drug users' personal networks and HIV risk behaviors.
Costenbader, Elizabeth C; Astone, Nan M; Latkin, Carl A
2006-07-01
While studies of the social networks of injection drug users (IDUs) have provided insight into how the structures of interpersonal relationships among IDUs affect HIV risk behaviors, the majority of these studies have been cross-sectional. The present study examined the dynamics of IDUs' social networks and HIV risk behaviors over time. Using data from a longitudinal HIV-intervention study conducted in Baltimore, MD, this study assessed changes in the composition of the personal networks of 409 IDUs. We used a multi-nomial logistic regression analysis to assess the association between changes in network composition and simultaneous changes in levels of injection HIV risk behaviors. Using the regression parameters generated by the multi-nomial model, we estimated the predicted probability of being in each of four HIV risk behavior change groups. Compared to the base case, individuals who reported an entirely new set of drug-using network contacts at follow-up were more than three times as likely to be in the increasing risk group. In contrast, reporting all new non-drug-using contacts at follow-up increased the likelihood of being in the stable low-risk group by almost 50% and decreased the probability of being in the consistently high-risk group by more than 70%. The findings from this study show that, over and above IDUs' baseline characteristics, changes in their personal networks are associated with changes in individuals' risky injection behaviors. They also suggest that interventions aimed at reducing HIV risk among IDUs might benefit from increasing IDUs' social contacts with individuals who are not drug users.
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Sato, Yasuhiko; Oba, Takuma; Danjo, Kazumi
2013-01-01
We have discussed the essential property for periodontal disease medication using protein, such as recombinant human basic fibroblast growth factor (rhbFGF). In our previous study, the criteria of thickener for the medication, viscosity, flowability etc., were set. The aim of this study was to evaluate the physical and chemical effect of concomitant use of general dental drug or device on thickener properties for the clinical use of viscous rhbFGF formulation. Viscous formulation was prepared with six cellulose derivatives, two types hydroxy propyl cellulose (HPC), three types hydroxy ethyl cellulose (HEC) and methyl cellulose (MC). Antibiotic ointment, local anesthetic, bone graft substitute, agent for gargle and mouthwashes, were chosen as general dental drug and device. These drugs and device were mixed with the viscous formulations and the change of viscosity and flowability, the remaining ratio of rhbFGF were evaluated. When the various thickener solutions were mixed with the liquid drugs, viscosity and flowability did not changed much. However, in the case of MC solution, viscous property declined greatly when MC solution was mixed with cationic surfactant for gargle. The flowabilities of thickener solutions were declined with insoluble bone graft. The stabilities of rhbFGF in thickener solutions were no problem for 24 hours even in the case of mixing with dental drug or device. Our findings suggested that the viscous rhbFGF formulations prepared in this research were not substantially affected by the concomitant use of dental drug or device, especially the formulation with HPC or HEC was useful.
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Reduced integration and improved segregation of functional brain networks in Alzheimer’s disease
Kabbara, A.; Eid, H.; El Falou, W.; Khalil, M.; Wendling, F.; Hassan, M.
2018-04-01
Objective. Emerging evidence shows that cognitive deficits in Alzheimer’s disease (AD) are associated with disruptions in brain functional connectivity. Thus, the identification of alterations in AD functional networks has become a topic of increasing interest. However, to what extent AD induces disruption of the balance of local and global information processing in the human brain remains elusive. The main objective of this study is to explore the dynamic topological changes of AD networks in terms of brain network segregation and integration. Approach. We used electroencephalography (EEG) data recorded from 20 participants (10 AD patients and 10 healthy controls) during resting state. Functional brain networks were reconstructed using EEG source connectivity computed in different frequency bands. Graph theoretical analyses were performed assess differences between both groups. Main results. Results revealed that AD networks, compared to networks of age-matched healthy controls, are characterized by lower global information processing (integration) and higher local information processing (segregation). Results showed also significant correlation between the alterations in the AD patients’ functional brain networks and their cognitive scores. Significance. These findings may contribute to the development of EEG network-based test that could strengthen results obtained from currently-used neurophysiological tests in neurodegenerative diseases.
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Garbayo, E; Ansorena, E; Blanco-Prieto, M J
2013-11-01
Current treatments for Parkinson's disease (PD) are aimed at addressing motor symptoms but there is no therapy focused on modifying the course of the disease. Successful treatment strategies have been so far limited and brain drug delivery remains a major challenge that restricts its treatment. This review provides an overview of the most promising emerging agents in the field of PD drug discovery, discussing improvements that have been made in brain drug delivery for PD. It will be shown that new approaches able to extend the length of the treatment, to release the drug in a continuous manner or to cross the blood-brain barrier and target a specific region are still needed. Overall, the results reviewed here show that there is an urgent need to develop both symptomatic and disease-modifying treatments, giving priority to neuroprotective treatments. Promising perspectives are being provided in this field by rasagiline and by neurotrophic factors like glial cell line-derived neurotrophic factor. The identification of disease-relevant genes has also encouraged the search for disease-modifying therapies that function by identifying molecularly targeted drugs. The advent of new molecular and cellular targets like α-synuclein, leucine-rich repeat serine/threonine protein kinase 2 or parkin, among others, will require innovative delivery therapies. In this regard, drug delivery systems (DDS) have shown great potential for improving the efficacy of conventional and new PD therapy and reducing its side effects. The new DDS discussed here, which include microparticles, nanoparticles and hydrogels among others, will probably open up possibilities that extend beyond symptomatic relief. However, further work needs to be done before DDS become a therapeutic option for PD patients. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
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Drug development for airway diseases: looking forward
Holgate, Stephen; Agusti, Alvar; Strieter, Robert M.; Anderson, Gary P.; Fogel, Robert; Bel, Elisabeth; Martin, Thomas R.; Reiss, Theodore F.
2015-01-01
Advancing drug development for airway diseases beyond the established mechanisms and symptomatic therapies requires redefining the classifications of airway diseases, considering systemic manifestations, developing new tools and encouraging collaborations
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A drug-sensitive genetic network masks fungi from the immune system.
Directory of Open Access Journals (Sweden)
Robert T Wheeler
2006-04-01
Full Text Available Fungal pathogens can be recognized by the immune system via their beta-glucan, a potent proinflammatory molecule that is present at high levels but is predominantly buried beneath a mannoprotein coat and invisible to the host. To investigate the nature and significance of "masking" this molecule, we characterized the mechanism of masking and consequences of unmasking for immune recognition. We found that the underlying beta-glucan in the cell wall of Candida albicans is unmasked by subinhibitory doses of the antifungal drug caspofungin, causing the exposed fungi to elicit a stronger immune response. Using a library of bakers' yeast (Saccharomyces cerevisiae mutants, we uncovered a conserved genetic network that is required for concealing beta-glucan from the immune system and limiting the host response. Perturbation of parts of this network in the pathogen C. albicans caused unmasking of its beta-glucan, leading to increased beta-glucan receptor-dependent elicitation of key proinflammatory cytokines from primary mouse macrophages. By creating an anti-inflammatory barrier to mask beta-glucan, opportunistic fungi may promote commensal colonization and have an increased propensity for causing disease. Targeting the widely conserved gene network required for creating and maintaining this barrier may lead to novel broad-spectrum antimycotics.
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Drugs of abuse and Parkinson's disease.
Mursaleen, Leah R; Stamford, Jonathan A
2016-01-04
The term "drug of abuse" is highly contextual. What constitutes a drug of abuse for one population of patients does not for another. It is therefore important to examine the needs of the patient population to properly assess the status of drugs of abuse. The focus of this article is on the bidirectional relationship between patients and drug abuse. In this paper we will introduce the dopaminergic systems of the brain in Parkinson's and the influence of antiparkinsonian drugs upon them before discussing this synergy of condition and medication as fertile ground for drug abuse. We will then examine the relationship between drugs of abuse and Parkinson's, both beneficial and deleterious. In summary we will draw the different strands together and speculate on the future merit of current drugs of abuse as treatments for Parkinson's disease. Copyright © 2015 Elsevier Inc. All rights reserved.
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Network biology concepts in complex disease comorbidities
DEFF Research Database (Denmark)
Hu, Jessica Xin; Thomas, Cecilia Engel; Brunak, Søren
2016-01-01
collected electronically, disease co-occurrences are starting to be quantitatively characterized. Linking network dynamics to the real-life, non-ideal patient in whom diseases co-occur and interact provides a valuable basis for generating hypotheses on molecular disease mechanisms, and provides knowledge......The co-occurrence of diseases can inform the underlying network biology of shared and multifunctional genes and pathways. In addition, comorbidities help to elucidate the effects of external exposures, such as diet, lifestyle and patient care. With worldwide health transaction data now often being...
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Discovering disease-associated genes in weighted protein-protein interaction networks
Cui, Ying; Cai, Meng; Stanley, H. Eugene
2018-04-01
Although there have been many network-based attempts to discover disease-associated genes, most of them have not taken edge weight - which quantifies their relative strength - into consideration. We use connection weights in a protein-protein interaction (PPI) network to locate disease-related genes. We analyze the topological properties of both weighted and unweighted PPI networks and design an improved random forest classifier to distinguish disease genes from non-disease genes. We use a cross-validation test to confirm that weighted networks are better able to discover disease-associated genes than unweighted networks, which indicates that including link weight in the analysis of network properties provides a better model of complex genotype-phenotype associations.
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Decision Support System for Hepatitis Disease Diagnosis using Bayesian Network
Directory of Open Access Journals (Sweden)
Shamshad Lakho
2017-12-01
Full Text Available Medical judgments are tough and challenging as the decisions are often based on the deficient and ambiguous information. Moreover, the result of decision process has direct effects on human lives. The act of human decision declines in emergency situations due to the complication, time limit, and high risks. Therefore, provision of medical diagnosis plays a dynamic role, specifically in the preliminary stage when a physician has limited diagnosis experience and identifies the directions to be taken for the treatment process. Computerized Decision Support Systems have brought a revolution in the medical diagnosis. These automatic systems support the diagnosticians in the course of diagnosis. The major role of Decision Support Systems is to support the medical personnel in decision-making procedures regarding disease diagnosis and treatment recommendation. The proposed system provides easy support in Hepatitis disease recognition. The system is developed using the Bayesian network model. The physician provides the input to the system in the form of symptoms stated by the patient. These signs and symptoms match with the casual relationships present in the knowledge model. The Bayesian network infers conclusion from the knowledge model and calculates the probability of occurrence of Hepatitis B, C and D disorders.
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Applying genetics in inflammatory disease drug discovery
DEFF Research Database (Denmark)
Folkersen, Lasse; Biswas, Shameek; Frederiksen, Klaus Stensgaard
2015-01-01
, with several notable exceptions, the journey from a small-effect genetic variant to a functional drug has proven arduous, and few examples of actual contributions to drug discovery exist. Here, we discuss novel approaches of overcoming this hurdle by using instead public genetics resources as a pragmatic guide...... alongside existing drug discovery methods. Our aim is to evaluate human genetic confidence as a rationale for drug target selection....
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Spread of epidemic disease on networks
Newman, M. E.
2002-07-01
The study of social networks, and in particular the spread of disease on networks, has attracted considerable recent attention in the physics community. In this paper, we show that a large class of standard epidemiological models, the so-called susceptible/infective/removed (SIR) models can be solved exactly on a wide variety of networks. In addition to the standard but unrealistic case of fixed infectiveness time and fixed and uncorrelated probability of transmission between all pairs of individuals, we solve cases in which times and probabilities are nonuniform and correlated. We also consider one simple case of an epidemic in a structured population, that of a sexually transmitted disease in a population divided into men and women. We confirm the correctness of our exact solutions with numerical simulations of SIR epidemics on networks.
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Modeling human diseases: an education in interactions and interdisciplinary approaches
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Leonard Zon
2016-06-01
Full Text Available Traditionally, most investigators in the biomedical arena exploit one model system in the course of their careers. Occasionally, an investigator will switch models. The selection of a suitable model system is a crucial step in research design. Factors to consider include the accuracy of the model as a reflection of the human disease under investigation, the numbers of animals needed and ease of husbandry, its physiology and developmental biology, and the ability to apply genetics and harness the model for drug discovery. In my lab, we have primarily used the zebrafish but combined it with other animal models and provided a framework for others to consider the application of developmental biology for therapeutic discovery. Our interdisciplinary approach has led to many insights into human diseases and to the advancement of candidate drugs to clinical trials. Here, I draw on my experiences to highlight the importance of combining multiple models, establishing infrastructure and genetic tools, forming collaborations, and interfacing with the medical community for successful translation of basic findings to the clinic.
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Neural networks for perception human and machine perception
Wechsler, Harry
1991-01-01
Neural Networks for Perception, Volume 1: Human and Machine Perception focuses on models for understanding human perception in terms of distributed computation and examples of PDP models for machine perception. This book addresses both theoretical and practical issues related to the feasibility of both explaining human perception and implementing machine perception in terms of neural network models. The book is organized into two parts. The first part focuses on human perception. Topics on network model ofobject recognition in human vision, the self-organization of functional architecture in t
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Cardiovascular safety of non-steroidal anti-inflammatory drugs: network meta-analysis.
Trelle, Sven; Reichenbach, Stephan; Wandel, Simon; Hildebrand, Pius; Tschannen, Beatrice; Villiger, Peter M; Egger, Matthias; Jüni, Peter
2011-01-11
To analyse the available evidence on cardiovascular safety of non-steroidal anti-inflammatory drugs. Network meta-analysis. Bibliographic databases, conference proceedings, study registers, the Food and Drug Administration website, reference lists of relevant articles, and reports citing relevant articles through the Science Citation Index (last update July 2009). Manufacturers of celecoxib and lumiracoxib provided additional data. All large scale randomised controlled trials comparing any non-steroidal anti-inflammatory drug with other non-steroidal anti-inflammatory drugs or placebo. Two investigators independently assessed eligibility. The primary outcome was myocardial infarction. Secondary outcomes included stroke, death from cardiovascular disease, and death from any cause. Two investigators independently extracted data. 31 trials in 116 429 patients with more than 115 000 patient years of follow-up were included. Patients were allocated to naproxen, ibuprofen, diclofenac, celecoxib, etoricoxib, rofecoxib, lumiracoxib, or placebo. Compared with placebo, rofecoxib was associated with the highest risk of myocardial infarction (rate ratio 2.12, 95% credibility interval 1.26 to 3.56), followed by lumiracoxib (2.00, 0.71 to 6.21). Ibuprofen was associated with the highest risk of stroke (3.36, 1.00 to 11.6), followed by diclofenac (2.86, 1.09 to 8.36). Etoricoxib (4.07, 1.23 to 15.7) and diclofenac (3.98, 1.48 to 12.7) were associated with the highest risk of cardiovascular death. Although uncertainty remains, little evidence exists to suggest that any of the investigated drugs are safe in cardiovascular terms. Naproxen seemed least harmful. Cardiovascular risk needs to be taken into account when prescribing any non-steroidal anti-inflammatory drug.
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EDUCATIONAL NETWORKING: HUMAN VIEW TO CYBER DEFENSE
Directory of Open Access Journals (Sweden)
Oleksandr Yu. Burov
2016-05-01
Full Text Available Networks play more and more important role for human life and activity, both in critical occupations (aviation, power industry, military missions etc., and in everyday life (home computers, education, leisure. Interaction between human and other elements of human-machine system have changed, because they coincide in the information habitat. Human-system integration has reached new level of defense needs. The paper will introduce features of information society in respect of a human and corresponding changes in HF/E: (1 information becomes a tool, goal, mean and environment of a human activity, (2 it becomes a part of the human nature and this makes him/her unprotected, (3 human psycho-physiological status becomes not only a basis of effective performance, but an object of control and support, and means of a human security and safety should be a part of information habitat, (4 networking environment becomes an independent actor in a human activity. Accompanying cyber-security challenges and tasks are discussed, as well as types of networking threats and Human View regarding the cyber security challenges.
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The persuasion network is modulated by drug-use risk and predicts anti-drug message effectiveness.
Huskey, Richard; Mangus, J Michael; Turner, Benjamin O; Weber, René
2017-12-01
While a persuasion network has been proposed, little is known about how network connections between brain regions contribute to attitude change. Two possible mechanisms have been advanced. One hypothesis predicts that attitude change results from increased connectivity between structures implicated in affective and executive processing in response to increases in argument strength. A second functional perspective suggests that highly arousing messages reduce connectivity between structures implicated in the encoding of sensory information, which disrupts message processing and thereby inhibits attitude change. However, persuasion is a multi-determined construct that results from both message features and audience characteristics. Therefore, persuasive messages should lead to specific functional connectivity patterns among a priori defined structures within the persuasion network. The present study exposed 28 subjects to anti-drug public service announcements where arousal, argument strength, and subject drug-use risk were systematically varied. Psychophysiological interaction analyses provide support for the affective-executive hypothesis but not for the encoding-disruption hypothesis. Secondary analyses show that video-level connectivity patterns among structures within the persuasion network predict audience responses in independent samples (one college-aged, one nationally representative). We propose that persuasion neuroscience research is best advanced by considering network-level effects while accounting for interactions between message features and target audience characteristics. © The Author (2017). Published by Oxford University Press.
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Melak, Tilahun; Gakkhar, Sunita
2015-12-01
In spite of the implementations of several strategies, tuberculosis (TB) is overwhelmingly a serious global public health problem causing millions of infections and deaths every year. This is mainly due to the emergence of drug-resistance varieties of TB. The current treatment strategies for the drug-resistance TB are of longer duration, more expensive and have side effects. This highlights the importance of identification and prioritization of targets for new drugs. This study has been carried out to prioritize potential drug targets of Mycobacterium tuberculosis H37Rv based on their flow to resistance genes. The weighted proteome interaction network of the pathogen was constructed using a dataset from STRING database. Only a subset of the dataset with interactions that have a combined score value ≥770 was considered. Maximum flow approach has been used to prioritize potential drug targets. The potential drug targets were obtained through comparative genome and network centrality analysis. The curated set of resistance genes was retrieved from literatures. Detail literature review and additional assessment of the method were also carried out for validation. A list of 537 proteins which are essential to the pathogen and non-homologous with human was obtained from the comparative genome analysis. Through network centrality measures, 131 of them were found within the close neighborhood of the centre of gravity of the proteome network. These proteins were further prioritized based on their maximum flow value to resistance genes and they are proposed as reliable drug targets of the pathogen. Proteins which interact with the host were also identified in order to understand the infection mechanism. Potential drug targets of Mycobacterium tuberculosis H37Rv were successfully prioritized based on their flow to resistance genes of existing drugs which is believed to increase the druggability of the targets since inhibition of a protein that has a maximum flow to
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Directory of Open Access Journals (Sweden)
Wan Li
Full Text Available The cardiomyopathies are a group of heart muscle diseases which can be inherited (familial. Identifying potential disease-related proteins is important to understand mechanisms of cardiomyopathies. Experimental identification of cardiomyophthies is costly and labour-intensive. In contrast, bioinformatics approach has a competitive advantage over experimental method. Based on "guilt by association" analysis, we prioritized candidate proteins involving in human cardiomyopathies. We first built weighted human cardiomyopathy-specific protein-protein interaction networks for three subtypes of cardiomyopathies using the known disease proteins from Online Mendelian Inheritance in Man as seeds. We then developed a method in prioritizing disease candidate proteins to rank candidate proteins in the network based on "guilt by association" analysis. It was found that most candidate proteins with high scores shared disease-related pathways with disease seed proteins. These top ranked candidate proteins were related with the corresponding disease subtypes, and were potential disease-related proteins. Cross-validation and comparison with other methods indicated that our approach could be used for the identification of potentially novel disease proteins, which may provide insights into cardiomyopathy-related mechanisms in a more comprehensive and integrated way.
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Systematic synergy modeling: understanding drug synergy from a systems biology perspective.
Chen, Di; Liu, Xi; Yang, Yiping; Yang, Hongjun; Lu, Peng
2015-09-16
Owing to drug synergy effects, drug combinations have become a new trend in combating complex diseases like cancer, HIV and cardiovascular diseases. However, conventional synergy quantification methods often depend on experimental dose-response data which are quite resource-demanding. In addition, these methods are unable to interpret the explicit synergy mechanism. In this review, we give representative examples of how systems biology modeling offers strategies toward better understanding of drug synergy, including the protein-protein interaction (PPI) network-based methods, pathway dynamic simulations, synergy network motif recognitions, integrative drug feature calculations, and "omic"-supported analyses. Although partially successful in drug synergy exploration and interpretation, more efforts should be put on a holistic understanding of drug-disease interactions, considering integrative pharmacology and toxicology factors. With a comprehensive and deep insight into the mechanism of drug synergy, systems biology opens a novel avenue for rational design of effective drug combinations.
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Norbutas, Lukas
2018-06-01
Cryptomarkets, or illegal anonymizing online platforms that facilitate drug trade, have been analyzed in a rapidly growing body of research. Previous research has found that, despite increased risks, cryptomarket sellers are often willing to ship illegal drugs internationally. There is little to no information, however, about the extent to which uncertainty and risk related to geographic constraints shapes buyers' behavior and, in turn, the structure of the global online drug trade network. In this paper, we analyze the structure of a complete cryptomarket trade network with a focus on the role of geographic clustering of buyers and sellers. We use publicly available crawls of the cryptomarket Abraxas, encompassing market transactions between 463 sellers and 3542 buyers of drugs in 2015. We use descriptive social network analysis and Exponential Random Graph Models (ERGM) to analyze the structure of the trade network. The structure of the online drug trade network is primarily shaped by geographical boundaries. Buyers are more likely to buy from multiple sellers within a single country, and avoid buying from sellers in different countries, which leads to strong geographic clustering. The effect is especially strong between continents and weaker for countries within Europe. A small fraction of buyers (10%) account for more than a half of all drug purchases, while most buyers only buy once. Online drug trade networks might still be heavily shaped by offline (geographic) constraints, despite their ability to provide access for end-users to large international supply. Cryptomarkets might be more "localized" and less international than thought before. We discuss potential explanations for such geographical clustering and implications of the findings. Copyright © 2018 The Author(s). Published by Elsevier B.V. All rights reserved.
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Systematic evaluation of drug-disease relationships to identify leads for novel drug uses.
Chiang, A P; Butte, A J
2009-11-01
Drug repositioning refers to the discovery of alternative uses for drugs--uses that are different from that for which the drugs were originally intended. One challenge in this effort lies in choosing the indication for which a drug of interest could be prospectively tested. We systematically evaluated a drug treatment-based view of diseases in order to address this challenge. Suggestions for novel drug uses were generated using a "guilt by association" approach. When compared with a control group of drug uses, the suggested novel drug uses generated by this approach were significantly enriched with respect to previous and ongoing clinical trials.
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Directory of Open Access Journals (Sweden)
Russell Bell
2009-03-01
Full Text Available We have mapped a protein interaction network of human homologs of proteins that modify longevity in invertebrate species. This network is derived from a proteome-scale human protein interaction Core Network generated through unbiased high-throughput yeast two-hybrid searches. The longevity network is composed of 175 human homologs of proteins known to confer increased longevity through loss of function in yeast, nematode, or fly, and 2,163 additional human proteins that interact with these homologs. Overall, the network consists of 3,271 binary interactions among 2,338 unique proteins. A comparison of the average node degree of the human longevity homologs with random sets of proteins in the Core Network indicates that human homologs of longevity proteins are highly connected hubs with a mean node degree of 18.8 partners. Shortest path length analysis shows that proteins in this network are significantly more connected than would be expected by chance. To examine the relationship of this network to human aging phenotypes, we compared the genes encoding longevity network proteins to genes known to be changed transcriptionally during aging in human muscle. In the case of both the longevity protein homologs and their interactors, we observed enrichments for differentially expressed genes in the network. To determine whether homologs of human longevity interacting proteins can modulate life span in invertebrates, homologs of 18 human FRAP1 interacting proteins showing significant changes in human aging muscle were tested for effects on nematode life span using RNAi. Of 18 genes tested, 33% extended life span when knocked-down in Caenorhabditis elegans. These observations indicate that a broad class of longevity genes identified in invertebrate models of aging have relevance to human aging. They also indicate that the longevity protein interaction network presented here is enriched for novel conserved longevity proteins.
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The Role of Natural Products in Drug Discovery and Development against Neglected Tropical Diseases
Directory of Open Access Journals (Sweden)
Peter Mubanga Cheuka
2016-12-01
Full Text Available Endemic in 149 tropical and subtropical countries, neglected tropical diseases (NTDs affect more than 1 billion people annually, including 875 million children in developing economies. These diseases are also responsible for over 500,000 deaths per year and are characterized by long-term disability and severe pain. The impact of the combined NTDs closely rivals that of malaria and tuberculosis. Current treatment options are associated with various limitations including widespread drug resistance, severe adverse effects, lengthy treatment duration, unfavorable toxicity profiles, and complicated drug administration procedures. Natural products have been a valuable source of drug regimens that form the cornerstone of modern pharmaceutical care. In this review, we highlight the potential that remains untapped in natural products as drug leads for NTDs. We cover natural products from plant, marine, and microbial sources including natural-product-inspired semi-synthetic derivatives which have been evaluated against the various causative agents of NTDs. Our coverage is limited to four major NTDs which include human African trypanosomiasis (sleeping sickness, leishmaniasis, schistosomiasis and lymphatic filariasis.
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Macrophage models of Gaucher disease for evaluating disease pathogenesis and candidate drugs.
Aflaki, Elma; Stubblefield, Barbara K; Maniwang, Emerson; Lopez, Grisel; Moaven, Nima; Goldin, Ehud; Marugan, Juan; Patnaik, Samarjit; Dutra, Amalia; Southall, Noel; Zheng, Wei; Tayebi, Nahid; Sidransky, Ellen
2014-06-11
Gaucher disease is caused by an inherited deficiency of glucocerebrosidase that manifests with storage of glycolipids in lysosomes, particularly in macrophages. Available cell lines modeling Gaucher disease do not demonstrate lysosomal storage of glycolipids; therefore, we set out to develop two macrophage models of Gaucher disease that exhibit appropriate substrate accumulation. We used these cellular models both to investigate altered macrophage biology in Gaucher disease and to evaluate candidate drugs for its treatment. We generated and characterized monocyte-derived macrophages from 20 patients carrying different Gaucher disease mutations. In addition, we created induced pluripotent stem cell (iPSC)-derived macrophages from five fibroblast lines taken from patients with type 1 or type 2 Gaucher disease. Macrophages derived from patient monocytes or iPSCs showed reduced glucocerebrosidase activity and increased storage of glucocerebroside and glucosylsphingosine in lysosomes. These macrophages showed efficient phagocytosis of bacteria but reduced production of intracellular reactive oxygen species and impaired chemotaxis. The disease phenotype was reversed with a noninhibitory small-molecule chaperone drug that enhanced glucocerebrosidase activity in the macrophages, reduced glycolipid storage, and normalized chemotaxis and production of reactive oxygen species. Macrophages differentiated from patient monocytes or patient-derived iPSCs provide cellular models that can be used to investigate disease pathogenesis and facilitate drug development. Copyright © 2014, American Association for the Advancement of Science.
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Human skeletal muscle drug transporters determine local exposure and toxicity of statins.
Knauer, Michael J; Urquhart, Bradley L; Meyer zu Schwabedissen, Henriette E; Schwarz, Ute I; Lemke, Christopher J; Leake, Brenda F; Kim, Richard B; Tirona, Rommel G
2010-02-05
The 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors, or statins, are important drugs used in the treatment and prevention of cardiovascular disease. Although statins are well tolerated, many patients develop myopathy manifesting as muscle aches and pain. Rhabdomyolysis is a rare but severe toxicity of statins. Interindividual differences in the activities of hepatic membrane drug transporters and metabolic enzymes are known to influence statin plasma pharmacokinetics and risk for myopathy. Interestingly, little is known regarding the molecular determinants of statin distribution into skeletal muscle and its relevance to toxicity. We sought to identify statin transporters in human skeletal muscle and determine their impact on statin toxicity in vitro. We demonstrate that the uptake transporter OATP2B1 (human organic anion transporting polypeptide 2B1) and the efflux transporters, multidrug resistance-associated protein (MRP)1, MRP4, and MRP5 are expressed on the sarcolemmal membrane of human skeletal muscle fibers and that atorvastatin and rosuvastatin are substrates of these transporters when assessed using a heterologous expression system. In an in vitro model of differentiated, primary human skeletal muscle myoblast cells, we demonstrate basal membrane expression and drug efflux activity of MRP1, which contributes to reducing intracellular statin accumulation. Furthermore, we show that expression of human OATP2B1 in human skeletal muscle myoblast cells by adenoviral vectors increases intracellular accumulation and toxicity of statins and such effects were abrogated when cells overexpressed MRP1. These results identify key membrane transporters as modulators of skeletal muscle statin exposure and toxicity.
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Disease network of mental disorders in Korea.
Choi, Myoungje; Lee, Dong-Woo; Cho, Maeng Je; Park, Jee Eun; Gim, Minsook
2015-12-01
Network medicine considers networks among genes, diseases, and individuals. Networks of mental disorders remain poorly understood, despite their high comorbidity. In this study, a network of mental disorders in Korea was constructed to offer a complementary approach to treatment. Data on the prevalence and morbidity of mental disorders were obtained from the 2006 and 2011 Korean Epidemiologic Catchment Area Study, including 22 psychiatric disorders. Nodes in the network were disease phenotypes identified by Diagnostic and Statistical Manual of Mental Disorders-IV, and the links connected phenotypes showing significant comorbidity. Odds ratios were used to quantify the distance between disease pairs. Network centrality was analyzed with and without weighting of the links between disorders. Degree centrality was correlated with suicidal behaviors and use of mental health services. In 2011 and 2006, degree centrality was highest for major depressive disorder, followed by nicotine dependence and generalized anxiety disorder (2011) or alcohol dependence (2006). Weighted degree centrality was highest in conversion disorder in both years. Therefore, major depressive disorder and nicotine dependence are highly connected to other mental disorders in Korea, indicating their comorbidity and possibility of shared biological mechanisms. The use of networks could enhance the understanding of mental disorders to provide effective mental health services.
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Scaling drug indication curation through crowdsourcing.
Khare, Ritu; Burger, John D; Aberdeen, John S; Tresner-Kirsch, David W; Corrales, Theodore J; Hirchman, Lynette; Lu, Zhiyong
2015-01-01
Motivated by the high cost of human curation of biological databases, there is an increasing interest in using computational approaches to assist human curators and accelerate the manual curation process. Towards the goal of cataloging drug indications from FDA drug labels, we recently developed LabeledIn, a human-curated drug indication resource for 250 clinical drugs. Its development required over 40 h of human effort across 20 weeks, despite using well-defined annotation guidelines. In this study, we aim to investigate the feasibility of scaling drug indication annotation through a crowdsourcing technique where an unknown network of workers can be recruited through the technical environment of Amazon Mechanical Turk (MTurk). To translate the expert-curation task of cataloging indications into human intelligence tasks (HITs) suitable for the average workers on MTurk, we first simplify the complex task such that each HIT only involves a worker making a binary judgment of whether a highlighted disease, in context of a given drug label, is an indication. In addition, this study is novel in the crowdsourcing interface design where the annotation guidelines are encoded into user options. For evaluation, we assess the ability of our proposed method to achieve high-quality annotations in a time-efficient and cost-effective manner. We posted over 3000 HITs drawn from 706 drug labels on MTurk. Within 8 h of posting, we collected 18 775 judgments from 74 workers, and achieved an aggregated accuracy of 96% on 450 control HITs (where gold-standard answers are known), at a cost of $1.75 per drug label. On the basis of these results, we conclude that our crowdsourcing approach not only results in significant cost and time saving, but also leads to accuracy comparable to that of domain experts. Published by Oxford University Press 2015. This work is written by US Government employees and is in the public domain in the US.
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Mordwinkin, Nicholas M.; Burridge, Paul W.; Wu, Joseph C.
2012-01-01
Drug attrition rates have increased in past years, resulting in growing costs for the pharmaceutical industry and consumers. The reasons for this include the lack of in vitro models that correlate with clinical results, and poor preclinical toxicity screening assays. The in vitro production of human cardiac progenitor cells and cardiomyocytes from human pluripotent stem cells provides an amenable source of cells for applications in drug discovery, disease modeling, regenerative medicine, and ...
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The Sociospatial Network: Risk and the Role of Place in the Transmission of Infectious Diseases.
Directory of Open Access Journals (Sweden)
James J Logan
Full Text Available Control of sexually transmitted infections and blood-borne pathogens is challenging due to their presence in groups exhibiting complex social interactions. In particular, sharing injection drug use equipment and selling sex (prostitution puts people at high risk. Previous work examining the involvement of risk behaviours in social networks has suggested that social and geographic distance of persons within a group contributes to these pathogens' endemicity. In this study, we examine the role of place in the connectedness of street people, selected by respondent driven sampling, in the transmission of blood-borne and sexually transmitted pathogens. A sample of 600 injection drug users, men who have sex with men, street youth and homeless people were recruited in Winnipeg, Canada from January to December, 2009. The residences of participants and those of their social connections were linked to each other and to locations where they engaged in risk activity. Survey responses identified 101 unique sites where respondents participated in injection drug use or sex transactions. Risk sites and respondents' residences were geocoded, with residence representing the individuals. The sociospatial network and estimations of geographic areas most likely to be frequented were mapped with network graphs and spatially using a Geographic Information System (GIS. The network with the most nodes connected 7.7% of respondents; consideration of the sociospatial network increased this to 49.7%. The mean distance between any two locations in the network was within 3.5 kilometres. Kernel density estimation revealed key activity spaces where the five largest networks overlapped. Here, the combination of spatial and social entities in network analysis defines the overlap of vulnerable populations in risk space, over and above the person to person links. Implications of this work are far reaching, not just for understanding transmission dynamics of sexually transmitted
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The Sociospatial Network: Risk and the Role of Place in the Transmission of Infectious Diseases.
Logan, James J; Jolly, Ann M; Blanford, Justine I
2016-01-01
Control of sexually transmitted infections and blood-borne pathogens is challenging due to their presence in groups exhibiting complex social interactions. In particular, sharing injection drug use equipment and selling sex (prostitution) puts people at high risk. Previous work examining the involvement of risk behaviours in social networks has suggested that social and geographic distance of persons within a group contributes to these pathogens' endemicity. In this study, we examine the role of place in the connectedness of street people, selected by respondent driven sampling, in the transmission of blood-borne and sexually transmitted pathogens. A sample of 600 injection drug users, men who have sex with men, street youth and homeless people were recruited in Winnipeg, Canada from January to December, 2009. The residences of participants and those of their social connections were linked to each other and to locations where they engaged in risk activity. Survey responses identified 101 unique sites where respondents participated in injection drug use or sex transactions. Risk sites and respondents' residences were geocoded, with residence representing the individuals. The sociospatial network and estimations of geographic areas most likely to be frequented were mapped with network graphs and spatially using a Geographic Information System (GIS). The network with the most nodes connected 7.7% of respondents; consideration of the sociospatial network increased this to 49.7%. The mean distance between any two locations in the network was within 3.5 kilometres. Kernel density estimation revealed key activity spaces where the five largest networks overlapped. Here, the combination of spatial and social entities in network analysis defines the overlap of vulnerable populations in risk space, over and above the person to person links. Implications of this work are far reaching, not just for understanding transmission dynamics of sexually transmitted infections by
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Directory of Open Access Journals (Sweden)
Cheng-Wei Li
2016-10-01
Full Text Available Tuberculosis is caused by Mycobacterium tuberculosis (Mtb infection. Mtb is one of the oldest human pathogens, and evolves mechanisms implied in human evolution. The lungs are the first organ exposed to aerosol-transmitted Mtb during gaseous exchange. Therefore, the guards of the immune system in the lungs, such as macrophages (Mϕs and dendritic cells (DCs, are the most important defense against Mtb infection. There have been several studies discussing the functions of Mϕs and DCs during Mtb infection, but the genome-wide pathways and networks are still incomplete. Furthermore, the immune response induced by Mϕs and DCs varies. Therefore, we analyzed the cross-talk genome-wide genetic-and-epigenetic interspecies networks (GWGEINs between Mϕs vs. Mtb and DCs vs. Mtb to determine the varying mechanisms of both the host and pathogen as it relates to Mϕs and DCs during early Mtb infection.First, we performed database mining to construct candidate cross-talk GWGEIN between human cells and Mtb. Then we constructed dynamic models to characterize the molecular mechanisms, including intraspecies gene/microRNA (miRNA regulation networks (GRNs, intraspecies protein-protein interaction networks (PPINs, and the interspecies PPIN of the cross-talk GWGEIN. We applied a system identification method and a system order detection scheme to dynamic models to identify the real cross-talk GWGEINs using the microarray data of Mϕs, DCs and Mtb.After identifying the real cross-talk GWGEINs, the principal network projection (PNP method was employed to construct host-pathogen core networks (HPCNs between Mϕs vs. Mtb and DCs vs. Mtb during infection process. Thus, we investigated the underlying cross-talk mechanisms between the host and the pathogen to determine how the pathogen counteracts host defense mechanisms in Mϕs and DCs during Mtb H37Rv early infection. Based on our findings, we propose Rv1675c as a potential drug target because of its important defensive
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From Network Analysis to Functional Metabolic Modeling of the Human Gut Microbiota.
Bauer, Eugen; Thiele, Ines
2018-01-01
An important hallmark of the human gut microbiota is its species diversity and complexity. Various diseases have been associated with a decreased diversity leading to reduced metabolic functionalities. Common approaches to investigate the human microbiota include high-throughput sequencing with subsequent correlative analyses. However, to understand the ecology of the human gut microbiota and consequently design novel treatments for diseases, it is important to represent the different interactions between microbes with their associated metabolites. Computational systems biology approaches can give further mechanistic insights by constructing data- or knowledge-driven networks that represent microbe interactions. In this minireview, we will discuss current approaches in systems biology to analyze the human gut microbiota, with a particular focus on constraint-based modeling. We will discuss various community modeling techniques with their advantages and differences, as well as their application to predict the metabolic mechanisms of intestinal microbial communities. Finally, we will discuss future perspectives and current challenges of simulating realistic and comprehensive models of the human gut microbiota.
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People who use drugs, HIV, and human rights.
Jürgens, Ralf; Csete, Joanne; Amon, Joseph J; Baral, Stefan; Beyrer, Chris
2010-08-07
We reviewed evidence from more than 900 studies and reports on the link between human rights abuses experienced by people who use drugs and vulnerability to HIV infection and access to services. Published work documents widespread abuses of human rights, which increase vulnerability to HIV infection and negatively affect delivery of HIV programmes. These abuses include denial of harm-reduction services, discriminatory access to antiretroviral therapy, abusive law enforcement practices, and coercion in the guise of treatment for drug dependence. Protection of the human rights of people who use drugs therefore is important not only because their rights must be respected, protected, and fulfilled, but also because it is an essential precondition to improving the health of people who use drugs. Rights-based responses to HIV and drug use have had good outcomes where they have been implemented, and they should be replicated in other countries. Copyright 2010 Elsevier Ltd. All rights reserved.
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Kitamura, Shigeyuki; Sugihara, Kazumi
2014-01-01
1. Human-chimeric mice with humanized liver have been constructed by transplantation of human hepatocytes into several types of mice having genetic modifications that injure endogenous liver cells. Here, we focus on liver urokinase-type plasminogen activator-transgenic severe combined immunodeficiency (uPA/SCID) mice, which are the most widely used human-chimeric mice. Studies so far indicate that drug metabolism, drug transport, pharmacological effects and toxicological action in these mice are broadly similar to those in humans. 2. Expression of various drug-metabolizing enzymes is known to be different between humans and rodents. However, the expression pattern of cytochrome P450, aldehyde oxidase and phase II enzymes in the liver of human-chimeric mice resembles that in humans, not that in the host mice. 3. Metabolism of various drugs, including S-warfarin, zaleplon, ibuprofen, naproxen, coumarin, troglitazone and midazolam, in human-chimeric mice is mediated by human drug-metabolizing enzymes, not by host mouse enzymes, and thus resembles that in humans. 4. Pharmacological and toxicological effects of various drugs in human-chimeric mice are also similar to those in humans. 5. The current consensus is that chimeric mice with humanized liver are useful to predict drug metabolism catalyzed by cytochrome P450, aldehyde oxidase and phase II enzymes in humans in vivo and in vitro. Some remaining issues are discussed in this review.
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Human drug metabolism: an introduction
National Research Council Canada - National Science Library
Coleman, Michael D
2010-01-01
Human Drug Metabolism, An Introduction, Second Edition provides an accessible introduction to the subject and will be particularly invaluable to those who already have some understanding of the life sciences...
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A common brain network links development, aging, and vulnerability to disease.
Douaud, Gwenaëlle; Groves, Adrian R; Tamnes, Christian K; Westlye, Lars Tjelta; Duff, Eugene P; Engvig, Andreas; Walhovd, Kristine B; James, Anthony; Gass, Achim; Monsch, Andreas U; Matthews, Paul M; Fjell, Anders M; Smith, Stephen M; Johansen-Berg, Heidi
2014-12-09
Several theories link processes of development and aging in humans. In neuroscience, one model posits for instance that healthy age-related brain degeneration mirrors development, with the areas of the brain thought to develop later also degenerating earlier. However, intrinsic evidence for such a link between healthy aging and development in brain structure remains elusive. Here, we show that a data-driven analysis of brain structural variation across 484 healthy participants (8-85 y) reveals a largely--but not only--transmodal network whose lifespan pattern of age-related change intrinsically supports this model of mirroring development and aging. We further demonstrate that this network of brain regions, which develops relatively late during adolescence and shows accelerated degeneration in old age compared with the rest of the brain, characterizes areas of heightened vulnerability to unhealthy developmental and aging processes, as exemplified by schizophrenia and Alzheimer's disease, respectively. Specifically, this network, while derived solely from healthy subjects, spatially recapitulates the pattern of brain abnormalities observed in both schizophrenia and Alzheimer's disease. This network is further associated in our large-scale healthy population with intellectual ability and episodic memory, whose impairment contributes to key symptoms of schizophrenia and Alzheimer's disease. Taken together, our results suggest that the common spatial pattern of abnormalities observed in these two disorders, which emerge at opposite ends of the life spectrum, might be influenced by the timing of their separate and distinct pathological processes in disrupting healthy cerebral development and aging, respectively.
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Crawford, Natalie D; Ford, Chandra; Rudolph, Abby; Kim, BoRin; Lewis, Crystal M
2017-09-01
Experiences of discrimination, or social marginalization and ostracism, may lead to the formation of social networks characterized by inequality. For example, those who experience discrimination may be more likely to develop drug use and sexual partnerships with others who are at increased risk for HIV compared to those without experiences of discrimination. This is critical as engaging in risk behaviors with others who are more likely to be HIV positive can increase one's risk of HIV. We used log-binomial regression models to examine the relationship between drug use, racial and incarceration discrimination with changes in the composition of one's risk network among 502 persons who use drugs. We examined both absolute and proportional changes with respect to sex partners, drug use partners, and injecting partners, after accounting for individual risk behaviors. At baseline, participants were predominately male (70%), black or Latino (91%), un-married (85%), and used crack (64%). Among those followed-up (67%), having experienced discrimination due to drug use was significantly related to increases in the absolute number of sex networks and drug networks over time. No types of discrimination were related to changes in the proportion of high-risk network members. Discrimination may increase one's risk of HIV acquisition by leading them to preferentially form risk relationships with higher-risk individuals, thereby perpetuating racial and ethnic inequities in HIV. Future social network studies and behavioral interventions should consider whether social discrimination plays a role in HIV transmission.
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Szalay, Kristóf Z; Nussinov, Ruth; Csermely, Peter
2014-06-01
Conformational barcodes tag functional sites of proteins and are decoded by interacting molecules transmitting the incoming signal. Conformational barcodes are modified by all co-occurring allosteric events induced by post-translational modifications, pathogen, drug binding, etc. We argue that fuzziness (plasticity) of conformational barcodes may be increased by disordered protein structures, by integrative plasticity of multi-phosphorylation events, by increased intracellular water content (decreased molecular crowding) and by increased action of molecular chaperones. This leads to increased plasticity of signaling and cellular networks. Increased plasticity is both substantiated by and inducing an increased noise level. Using the versatile network dynamics tool, Turbine (www.turbine.linkgroup.hu), here we show that the 10 % noise level expected in cellular systems shifts a cancer-related signaling network of human cells from its proliferative attractors to its largest, apoptotic attractor representing their health-preserving response in the carcinogen containing and tumor suppressor deficient environment modeled in our study. Thus, fuzzy conformational barcodes may not only make the cellular system more plastic, and therefore more adaptable, but may also stabilize the complex system allowing better access to its largest attractor. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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Directory of Open Access Journals (Sweden)
Milutinović Jelena D.
2015-01-01
Full Text Available Introduction: Drug-related problems are frequent in almost all therapeutic areas. Aims: The aim of this paper was to detect drug - related problems in patients with gastroesophageal reflux and to analyze their possible association with the patient characteristics. Material and methods: The study was designed as descriptive, retrospective, crosssectional study aiming to determine the most common drug - related problems in patients with gastro-esophageal reflux disease treated with proton-pump inhibitors. The survey was conducted at the Department of Gastroenterology, Clinical Centre in Kragujevac. The study enrolled all patients treated from gastroesophageal reflux disease with proton pump inhibitors during the time period from 1.1.2014 until 1.1.2015. The study used descriptive statistics (percentage distribution, mean and standard deviation. The correlation between the number of adverse events and patient characteristics was also calculated. Results: The average age of the patients was 55.97±15.811 years, and 43 of the patients (60.6 % were male. The average hospitalization duration was 12.30±8.89 days. Based on the Pharmaceutical Care Network Europe classification, there were 182 Drug-Related Problems which was, on average, 2.56 problems per patient. Only 5 patients (7% did not report any problem while 11 patients (15.49% had over 10 possible drug-drug interactions. The most common problems which occurred were erroneous drug choice, inappropriate administration and possible interactions between medications. Conclusions: Based on the results of this study, one must pay attention to possible drug interactions and other problems which may occur with proton-pump inhibitors. Recognition of different sub-types of drug-related problems and of factors associated with drug related problems may reduce risk from adverse outcomes of gastro-esophageal reflux disease treatment with proton pump inhibitors.
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Discovering network behind infectious disease outbreak
Maeno, Yoshiharu
2010-11-01
Stochasticity and spatial heterogeneity are of great interest recently in studying the spread of an infectious disease. The presented method solves an inverse problem to discover the effectively decisive topology of a heterogeneous network and reveal the transmission parameters which govern the stochastic spreads over the network from a dataset on an infectious disease outbreak in the early growth phase. Populations in a combination of epidemiological compartment models and a meta-population network model are described by stochastic differential equations. Probability density functions are derived from the equations and used for the maximal likelihood estimation of the topology and parameters. The method is tested with computationally synthesized datasets and the WHO dataset on the SARS outbreak.
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Drug-Induced Liver Injury Network Causality Assessment: Criteria and Experience in the United States
Directory of Open Access Journals (Sweden)
Paul H. Hayashi
2016-02-01
Full Text Available Hepatotoxicity due to drugs, herbal or dietary supplements remains largely a clinical diagnosis based on meticulous history taking and exclusion of other causes of liver injury. In 2004, the U.S. Drug-Induced Liver Injury Network (DILIN was created under the auspices of the U.S. National Institute of Diabetes and Digestive and Kidney Diseases with the aims of establishing a large registry of cases for clinical, epidemiological and mechanistic study. From inception, the DILIN has used an expert opinion process that incorporates consensus amongst three different DILIN hepatologists assigned to each case. It is the most well-established, well-described and vigorous expert opinion process for DILI to date, and yet it is an imperfect standard. This review will discuss the DILIN expert opinion process, its strengths and weaknesses, psychometric performance and future.
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G protein-coupled receptor mutations and human genetic disease.
Thompson, Miles D; Hendy, Geoffrey N; Percy, Maire E; Bichet, Daniel G; Cole, David E C
2014-01-01
Genetic variations in G protein-coupled receptor genes (GPCRs) disrupt GPCR function in a wide variety of human genetic diseases. In vitro strategies and animal models have been used to identify the molecular pathologies underlying naturally occurring GPCR mutations. Inactive, overactive, or constitutively active receptors have been identified that result in pathology. These receptor variants may alter ligand binding, G protein coupling, receptor desensitization and receptor recycling. Receptor systems discussed include rhodopsin, thyrotropin, parathyroid hormone, melanocortin, follicle-stimulating hormone (FSH), luteinizing hormone, gonadotropin-releasing hormone (GNRHR), adrenocorticotropic hormone, vasopressin, endothelin-β, purinergic, and the G protein associated with asthma (GPRA or neuropeptide S receptor 1 (NPSR1)). The role of activating and inactivating calcium-sensing receptor (CaSR) mutations is discussed in detail with respect to familial hypocalciuric hypercalcemia (FHH) and autosomal dominant hypocalemia (ADH). The CASR mutations have been associated with epilepsy. Diseases caused by the genetic disruption of GPCR functions are discussed in the context of their potential to be selectively targeted by drugs that rescue altered receptors. Examples of drugs developed as a result of targeting GPCRs mutated in disease include: calcimimetics and calcilytics, therapeutics targeting melanocortin receptors in obesity, interventions that alter GNRHR loss from the cell surface in idiopathic hypogonadotropic hypogonadism and novel drugs that might rescue the P2RY12 receptor congenital bleeding phenotype. De-orphanization projects have identified novel disease-associated receptors, such as NPSR1 and GPR35. The identification of variants in these receptors provides genetic reagents useful in drug screens. Discussion of the variety of GPCRs that are disrupted in monogenic Mendelian disorders provides the basis for examining the significance of common
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Identifying co-targets to fight drug resistance based on a random walk model
Directory of Open Access Journals (Sweden)
Chen Liang-Chun
2012-01-01
Full Text Available Abstract Background Drug resistance has now posed more severe and emergent threats to human health and infectious disease treatment. However, wet-lab approaches alone to counter drug resistance have so far still achieved limited success due to less knowledge about the underlying mechanisms of drug resistance. Our approach apply a heuristic search algorithm in order to extract active network under drug treatment and use a random walk model to identify potential co-targets for effective antibacterial drugs. Results We use interactome network of Mycobacterium tuberculosis and gene expression data which are treated with two kinds of antibiotic, Isoniazid and Ethionamide as our test data. Our analysis shows that the active drug-treated networks are associated with the trigger of fatty acid metabolism and synthesis and nicotinamide adenine dinucleotide (NADH-related processes and those results are consistent with the recent experimental findings. Efflux pumps processes appear to be the major mechanisms of resistance but SOS response is significantly up-regulation under Isoniazid treatment. We also successfully identify the potential co-targets with literature confirmed evidences which are related to the glycine-rich membrane, adenosine triphosphate energy and cell wall processes. Conclusions With gene expression and interactome data supported, our study points out possible pathways leading to the emergence of drug resistance under drug treatment. We develop a computational workflow for giving new insights to bacterial drug resistance which can be gained by a systematic and global analysis of the bacterial regulation network. Our study also discovers the potential co-targets with good properties in biological and graph theory aspects to overcome the problem of drug resistance.
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Structural Genomics and Drug Discovery for Infectious Diseases
International Nuclear Information System (INIS)
Anderson, W.F.
2009-01-01
The application of structural genomics methods and approaches to proteins from organisms causing infectious diseases is making available the three dimensional structures of many proteins that are potential drug targets and laying the groundwork for structure aided drug discovery efforts. There are a number of structural genomics projects with a focus on pathogens that have been initiated worldwide. The Center for Structural Genomics of Infectious Diseases (CSGID) was recently established to apply state-of-the-art high throughput structural biology technologies to the characterization of proteins from the National Institute for Allergy and Infectious Diseases (NIAID) category A-C pathogens and organisms causing emerging, or re-emerging infectious diseases. The target selection process emphasizes potential biomedical benefits. Selected proteins include known drug targets and their homologs, essential enzymes, virulence factors and vaccine candidates. The Center also provides a structure determination service for the infectious disease scientific community. The ultimate goal is to generate a library of structures that are available to the scientific community and can serve as a starting point for further research and structure aided drug discovery for infectious diseases. To achieve this goal, the CSGID will determine protein crystal structures of 400 proteins and protein-ligand complexes using proven, rapid, highly integrated, and cost-effective methods for such determination, primarily by X-ray crystallography. High throughput crystallographic structure determination is greatly aided by frequent, convenient access to high-performance beamlines at third-generation synchrotron X-ray sources.
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Comprehensive Reconstruction and Visualization of Non-Coding Regulatory Networks in Human
Bonnici, Vincenzo; Russo, Francesco; Bombieri, Nicola; Pulvirenti, Alfredo; Giugno, Rosalba
2014-01-01
Research attention has been powered to understand the functional roles of non-coding RNAs (ncRNAs). Many studies have demonstrated their deregulation in cancer and other human disorders. ncRNAs are also present in extracellular human body fluids such as serum and plasma, giving them a great potential as non-invasive biomarkers. However, non-coding RNAs have been relatively recently discovered and a comprehensive database including all of them is still missing. Reconstructing and visualizing the network of ncRNAs interactions are important steps to understand their regulatory mechanism in complex systems. This work presents ncRNA-DB, a NoSQL database that integrates ncRNAs data interactions from a large number of well established on-line repositories. The interactions involve RNA, DNA, proteins, and diseases. ncRNA-DB is available at http://ncrnadb.scienze.univr.it/ncrnadb/. It is equipped with three interfaces: web based, command-line, and a Cytoscape app called ncINetView. By accessing only one resource, users can search for ncRNAs and their interactions, build a network annotated with all known ncRNAs and associated diseases, and use all visual and mining features available in Cytoscape. PMID:25540777
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Comprehensive reconstruction and visualization of non-coding regulatory networks in human.
Bonnici, Vincenzo; Russo, Francesco; Bombieri, Nicola; Pulvirenti, Alfredo; Giugno, Rosalba
2014-01-01
Research attention has been powered to understand the functional roles of non-coding RNAs (ncRNAs). Many studies have demonstrated their deregulation in cancer and other human disorders. ncRNAs are also present in extracellular human body fluids such as serum and plasma, giving them a great potential as non-invasive biomarkers. However, non-coding RNAs have been relatively recently discovered and a comprehensive database including all of them is still missing. Reconstructing and visualizing the network of ncRNAs interactions are important steps to understand their regulatory mechanism in complex systems. This work presents ncRNA-DB, a NoSQL database that integrates ncRNAs data interactions from a large number of well established on-line repositories. The interactions involve RNA, DNA, proteins, and diseases. ncRNA-DB is available at http://ncrnadb.scienze.univr.it/ncrnadb/. It is equipped with three interfaces: web based, command-line, and a Cytoscape app called ncINetView. By accessing only one resource, users can search for ncRNAs and their interactions, build a network annotated with all known ncRNAs and associated diseases, and use all visual and mining features available in Cytoscape.
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The basics of preclinical drug development for neurodegenerative disease indications
Directory of Open Access Journals (Sweden)
Spack Edward G
2009-06-01
Full Text Available Abstract Preclinical development encompasses the activities that link drug discovery in the laboratory to initiation of human clinical trials. Preclinical studies can be designed to identify a lead candidate from several hits; develop the best procedure for new drug scale-up; select the best formulation; determine the route, frequency, and duration of exposure; and ultimately support the intended clinical trial design. The details of each preclinical development package can vary, but all have some common features. Rodent and nonrodent mammalian models are used to delineate the pharmacokinetic profile and general safety, as well as to identify toxicity patterns. One or more species may be used to determine the drug's mean residence time in the body, which depends on inherent absorption, distribution, metabolism, and excretion properties. For drugs intended to treat Alzheimer's disease or other brain-targeted diseases, the ability of a drug to cross the blood brain barrier may be a key issue. Toxicology and safety studies identify potential target organs for adverse effects and define the Therapeutic Index to set the initial starting doses in clinical trials. Pivotal preclinical safety studies generally require regulatory oversight as defined by US Food and Drug Administration (FDA Good Laboratory Practices and international guidelines, including the International Conference on Harmonisation. Concurrent preclinical development activities include developing the Clinical Plan and preparing the new drug product, including the associated documentation to meet stringent FDA Good Manufacturing Practices regulatory guidelines. A wide range of commercial and government contract options are available for investigators seeking to advance their candidate(s. Government programs such as the Small Business Innovative Research and Small Business Technology Transfer grants and the National Institutes of Health Rapid Access to Interventional Development Pilot
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Novel approaches to develop community-built biological network models for potential drug discovery.
Talikka, Marja; Bukharov, Natalia; Hayes, William S; Hofmann-Apitius, Martin; Alexopoulos, Leonidas; Peitsch, Manuel C; Hoeng, Julia
2017-08-01
Hundreds of thousands of data points are now routinely generated in clinical trials by molecular profiling and NGS technologies. A true translation of this data into knowledge is not possible without analysis and interpretation in a well-defined biology context. Currently, there are many public and commercial pathway tools and network models that can facilitate such analysis. At the same time, insights and knowledge that can be gained is highly dependent on the underlying biological content of these resources. Crowdsourcing can be employed to guarantee the accuracy and transparency of the biological content underlining the tools used to interpret rich molecular data. Areas covered: In this review, the authors describe crowdsourcing in drug discovery. The focal point is the efforts that have successfully used the crowdsourcing approach to verify and augment pathway tools and biological network models. Technologies that enable the building of biological networks with the community are also described. Expert opinion: A crowd of experts can be leveraged for the entire development process of biological network models, from ontologies to the evaluation of their mechanistic completeness. The ultimate goal is to facilitate biomarker discovery and personalized medicine by mechanistically explaining patients' differences with respect to disease prevention, diagnosis, and therapy outcome.
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Preciados, Mark; Yoo, Changwon; Roy, Deodutta
2016-12-13
these genes are involved with brain diseases, such as Alzheimer's Disease (AD), Parkinson's Disease, Huntington's Disease, Amyotrophic Lateral Sclerosis, Autism Spectrum Disorder, and Brain Neoplasms. For example, the search of enriched pathways showed that top ten E2 interacting genes in AD- APOE , APP , ATP5A1 , CALM1 , CASP3 , GSK3B , IL1B , MAPT , PSEN2 and TNF- underlie the enrichment of the Kyoto Encyclopedia of Genes and Genomes (KEGG) AD pathway. With AD, the six E2-responsive genes are NRF1 target genes: APBB2 , DPYSL2 , EIF2S1 , ENO1 , MAPT , and PAXIP1 . These genes are also responsive to the following EEDs: ethinyl estradiol ( APBB2 , DPYSL2 , EIF2S1 , ENO1 , MAPT , and PAXIP1 ), BPA ( APBB2 , EIF2S1 , ENO1 , MAPT , and PAXIP1 ), dibutyl phthalate (DPYSL2, EIF2S1, and ENO1), diethylhexyl phthalate ( DPYSL2 and MAPT ). To validate findings from Comparative Toxicogenomics Database (CTD) curated data, we used Bayesian network (BN) analysis on microarray data of AD patients. We observed that both gender and NRF1 were associated with AD. The female NRF1 gene network is completely different from male human AD patients. AD-associated NRF1 target genes- APLP1 , APP , GRIN1 , GRIN2B , MAPT , PSEN2 , PEN2 , and IDE -are also regulated by E2. NRF1 regulates targets genes with diverse functions, including cell growth, apoptosis/autophagy, mitochondrial biogenesis, genomic instability, neurogenesis, neuroplasticity, synaptogenesis, and senescence. By activating or repressing the genes involved in cell proliferation, growth suppression, DNA damage/repair, apoptosis/autophagy, angiogenesis, estrogen signaling, neurogenesis, synaptogenesis, and senescence, and inducing a wide range of DNA damage, genomic instability and DNA methylation and transcriptional repression, NRF1 may act as a major regulator of EEDs-induced brain health deficits. In summary, estrogenic endocrine disrupting chemicals-modified genes in brain health deficits are part of both estrogen and NRF1
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Directory of Open Access Journals (Sweden)
Mark Preciados
2016-12-01
NRF1. Some of these genes are involved with brain diseases, such as Alzheimer’s Disease (AD, Parkinson’s Disease, Huntington’s Disease, Amyotrophic Lateral Sclerosis, Autism Spectrum Disorder, and Brain Neoplasms. For example, the search of enriched pathways showed that top ten E2 interacting genes in AD—APOE, APP, ATP5A1, CALM1, CASP3, GSK3B, IL1B, MAPT, PSEN2 and TNF—underlie the enrichment of the Kyoto Encyclopedia of Genes and Genomes (KEGG AD pathway. With AD, the six E2-responsive genes are NRF1 target genes: APBB2, DPYSL2, EIF2S1, ENO1, MAPT, and PAXIP1. These genes are also responsive to the following EEDs: ethinyl estradiol (APBB2, DPYSL2, EIF2S1, ENO1, MAPT, and PAXIP1, BPA (APBB2, EIF2S1, ENO1, MAPT, and PAXIP1, dibutyl phthalate (DPYSL2, EIF2S1, and ENO1, diethylhexyl phthalate (DPYSL2 and MAPT. To validate findings from Comparative Toxicogenomics Database (CTD curated data, we used Bayesian network (BN analysis on microarray data of AD patients. We observed that both gender and NRF1 were associated with AD. The female NRF1 gene network is completely different from male human AD patients. AD-associated NRF1 target genes—APLP1, APP, GRIN1, GRIN2B, MAPT, PSEN2, PEN2, and IDE—are also regulated by E2. NRF1 regulates targets genes with diverse functions, including cell growth, apoptosis/autophagy, mitochondrial biogenesis, genomic instability, neurogenesis, neuroplasticity, synaptogenesis, and senescence. By activating or repressing the genes involved in cell proliferation, growth suppression, DNA damage/repair, apoptosis/autophagy, angiogenesis, estrogen signaling, neurogenesis, synaptogenesis, and senescence, and inducing a wide range of DNA damage, genomic instability and DNA methylation and transcriptional repression, NRF1 may act as a major regulator of EEDs-induced brain health deficits. In summary, estrogenic endocrine disrupting chemicals-modified genes in brain health deficits are part of both estrogen and NRF1 signaling pathways. Our
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DR2DI: a powerful computational tool for predicting novel drug-disease associations
Lu, Lu; Yu, Hua
2018-05-01
Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.
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DR2DI: a powerful computational tool for predicting novel drug-disease associations
Lu, Lu; Yu, Hua
2018-04-01
Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.
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Systems pharmacology - Towards the modeling of network interactions.
Danhof, Meindert
2016-10-30
Mechanism-based pharmacokinetic and pharmacodynamics (PKPD) and disease system (DS) models have been introduced in drug discovery and development research, to predict in a quantitative manner the effect of drug treatment in vivo in health and disease. This requires consideration of several fundamental properties of biological systems behavior including: hysteresis, non-linearity, variability, interdependency, convergence, resilience, and multi-stationarity. Classical physiology-based PKPD models consider linear transduction pathways, connecting processes on the causal path between drug administration and effect, as the basis of drug action. Depending on the drug and its biological target, such models may contain expressions to characterize i) the disposition and the target site distribution kinetics of the drug under investigation, ii) the kinetics of target binding and activation and iii) the kinetics of transduction. When connected to physiology-based DS models, PKPD models can characterize the effect on disease progression in a mechanistic manner. These models have been found useful to characterize hysteresis and non-linearity, yet they fail to explain the effects of the other fundamental properties of biological systems behavior. Recently systems pharmacology has been introduced as novel approach to predict in vivo drug effects, in which biological networks rather than single transduction pathways are considered as the basis of drug action and disease progression. These models contain expressions to characterize the functional interactions within a biological network. Such interactions are relevant when drugs act at multiple targets in the network or when homeostatic feedback mechanisms are operative. As a result systems pharmacology models are particularly useful to describe complex patterns of drug action (i.e. synergy, oscillatory behavior) and disease progression (i.e. episodic disorders). In this contribution it is shown how physiology-based PKPD and
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Public acceptance of drug use for non-disease conditions
DEFF Research Database (Denmark)
Møldrup, Claus; Hansen, Rikke Rie
2006-01-01
OBJECTIVE: This article deals with the issue of ordinary healthy people using drugs to improve or enhance non-disease conditions. The objective is to illuminate the extent of public acceptance of this practice. RESEARCH DESIGN AND METHODS: The results are based on two studies: a classically...... of drugs for non-disease conditions. Men in particular look favourably on the use of drugs by healthy individuals. People with less education find this type of drug use unacceptable to a greater extent than those with more education, who are more positive. If we look at political affiliation, a pattern...
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Endangered species: mitochondrial DNA loss as a mechanism of human disease.
Herrera, Alan; Garcia, Iraselia; Gaytan, Norma; Jones, Edith; Maldonado, Alicia; Gilkerson, Robert
2015-06-01
Human mitochondrial DNA (mtDNA) is a small maternally inherited DNA, typically present in hundreds of copies in a single human cell. Thus, despite its small size, the mitochondrial genome plays a crucial role in the metabolic homeostasis of the cell. Our understanding of mtDNA genotype-phenotype relationships is derived largely from studies of the classical mitochondrial neuromuscular diseases, in which mutations of mtDNA lead to compromised mitochondrial bioenergetic function, with devastating pathological consequences. Emerging research suggests that loss, rather than mutation, of mtDNA plays a major role across a range of prevalent human diseases, including diabetes mellitus, cardiovascular disease, and aging. Here, we examine the 'rules' of mitochondrial genetics and function, the clinical settings in which loss of mtDNA is an emerging pathogenic mechanism, and explore mtDNA damage and its consequences for the organellar network and cell at large. As extranuclear genetic material arrayed throughout the cell to support metabolism, mtDNA is increasingly implicated in a host of disease conditions, opening a range of exciting questions regarding mtDNA and its role in cellular homeostasis.
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Papoian, Thomas; Jagadeesh, Gowraganahalli; Saulnier, Muriel; Simpson, Natalie; Ravindran, Arippa; Yang, Baichun; Laniyonu, Adebayo A; Khan, Imran; Szarfman, Ana
2017-04-01
Drug-induced valvular heart disease (VHD) is a serious side effect linked to long-term treatment with 5-hydroxytryptamine (serotonin) receptor 2B (5-HT 2B ) agonists. Safety assessment for off-target pharmacodynamic activity is a common approach used to screen drugs for this undesired property. Such studies include in vitro assays to determine whether the drug is a 5-HT 2B agonist, a necessary pharmacological property for development of VHD. Measures of in vitro binding affinity (IC 50 , K i ) or cellular functional activity (EC 50 ) are often compared to maximum therapeutic free plasma drug levels ( fC max ) from which safety margins (SMs) can be derived. However, there is no clear consensus on what constitutes an appropriate SM under various therapeutic conditions of use. The strengths and limitations of SM determinations and current risk assessment methodology are reviewed and evaluated. It is concluded that the use of SMs based on K i values, or those relative to serotonin (5-HT), appears to be a better predictor than the use of EC 50 or EC 50 /human fC max values for determining whether known 5-HT 2B agonists have resulted in VHD. It is hoped that such a discussion will improve efforts to reduce this preventable serious drug-induced toxicity from occurring and lead to more informed risk assessment strategies.
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The aerosphere as a network connector of organisms and their diseases
Ross, Jeremy D.; Bridge, Eli S.; Prosser, Diann J.; Takekawa, John Y.
2018-01-01
Aeroecological processes, especially powered flight of animals, can rapidly connect biological communities across the globe. This can have profound consequences for evolutionary diversification, energy and nutrient transfers, and the spread of infectious diseases. The latter is of particular consequence for human populations, since migratory birds are known to host diseases which have a history of transmission into domestic poultry or even jumping to human hosts. In this chapter, we present a scenario under which a highly pathogenic avian influenza (HPAI) strain enters North America from East Asia via post-molting waterfowl migration. We use an agent-based model (ABM) to simulate the movement and disease transmission among 106 generalized waterfowl agents originating from ten molting locations in eastern Siberia, with the HPAI seeded in only ~102 agents at one of these locations. Our ABM tracked the disease dynamics across a very large grid of sites as well as individual agents, allowing us to examine the spatiotemporal patterns of change in virulence of the HPAI infection as well as waterfowl host susceptibility to the disease. We concurrently simulated a 12-station disease monitoring network in the northwest USA and Canada in order to assess the potential efficacy of these sites to detect and confirm the arrival of HPAI. Our findings indicated that HPAI spread was initially facilitated but eventually subdued by the migration of host agents. Yet, during the 90-day simulation, selective pressures appeared to have distilled the HPAI strain to its most virulent form (i.e., through natural selection), which was counterbalanced by the host susceptibility being conversely reduced (i.e., through genetic predisposition and acquired immunity). The monitoring network demonstrated wide variation in the utility of sites; some were clearly better at providing early warnings of HPAI arrival, while sites further from the disease origin exposed the selective dynamics which
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Mallick, Pankajini; Taneja, Guncha; Moorthy, Bhagavatula; Ghose, Romi
2017-06-01
Drug-metabolizing enzymes (DMEs) are primarily down-regulated during infectious and inflammatory diseases, leading to disruption in the metabolism of small molecule drugs (smds), which are increasingly being prescribed therapeutically in combination with biologics for a number of chronic diseases. The biologics may exert pro- or anti-inflammatory effect, which may in turn affect the expression/activity of DMEs. Thus, patients with infectious/inflammatory diseases undergoing biologic/smd treatment can have complex changes in DMEs due to combined effects of the disease and treatment. Areas covered: We will discuss clinical biologics-SMD interaction and regulation of DMEs during infection and inflammatory diseases. Mechanistic studies will be discussed and consequences on biologic-small molecule combination therapy on disease outcome due to changes in drug metabolism will be highlighted. Expert opinion: The involvement of immunomodulatory mediators in biologic-SMDs is well known. Regulatory guidelines recommend appropriate in vitro or in vivo assessments for possible interactions. The role of cytokines in biologic-SMDs has been documented. However, the mechanisms of drug-drug interactions is much more complex, and is probably multi-factorial. Studies aimed at understanding the mechanism by which biologics effect the DMEs during inflammation/infection are clinically important.
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Social network members' roles and use of mental health services among drug users in New York City.
Sapra, Katherine J; Crawford, Natalie D; Rudolph, Abby E; Jones, Kandice C; Benjamin, Ebele O; Fuller, Crystal M
2013-10-01
Depression is more common among drug users (15-63 %) than the general population (5-16 %). Lack of social support network members may be associated with low mental health service (MHS) use rates observed among drug users. We investigated the relationship between social network members' roles and MHS use among frequent drug users using Social Ties Associated with Risk of Transition into Injection Drug Use data (NYC 2006-2009). Surveys assessed depression, MHS use, demographics, drug use and treatment, and social network members' roles. Participants reporting lifetime depressive episode with start/end dates and information on social/risk network members were included (n = 152). Adjusting for emotional support and HIV status, having one or more informational support network members remained associated with MHS use at last depressive episode (adjusted odds ratio (AOR) 3.37, 95 % confidence interval (CI) 1.38-8.19), as did history of drug treatment (AOR 2.75, 95 % CI 1.02-7.41) and no legal income (AOR 0.23, 95 % CI 0.08-0.64). These data suggest that informational support is associated with MHS utilization among depressed drug users.
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Network analysis of microRNAs and their regulation in human ovarian cancer
Schmeier, Sebastian
2011-11-03
Background: MicroRNAs (miRNAs) are small non-coding RNA molecules that repress the translation of messenger RNAs (mRNAs) or degrade mRNAs. These functions of miRNAs allow them to control key cellular processes such as development, differentiation and apoptosis, and they have also been implicated in several cancers such as leukaemia, lung, pancreatic and ovarian cancer (OC). Unfortunately, the specific machinery of miRNA regulation, involving transcription factors (TFs) and transcription co-factors (TcoFs), is not well understood. In the present study we focus on computationally deciphering the underlying network of miRNAs, their targets, and their control mechanisms that have an influence on OC development.Results: We analysed experimentally verified data from multiple sources that describe miRNA influence on diseases, miRNA targeting of mRNAs, and on protein-protein interactions, and combined this data with ab initio transcription factor binding site predictions within miRNA promoter regions. From these analyses, we derived a network that describes the influence of miRNAs and their regulation in human OC. We developed a methodology to analyse the network in order to find the nodes that have the largest potential of influencing the network\\'s behaviour (network hubs). We further show the potentially most influential miRNAs, TFs and TcoFs, showing subnetworks illustrating the involved mechanisms as well as regulatory miRNA network motifs in OC. We find an enrichment of miRNA targeted OC genes in the highly relevant pathways cell cycle regulation and apoptosis.Conclusions: We combined several sources of interaction and association data to analyse and place miRNAs within regulatory pathways that influence human OC. These results represent the first comprehensive miRNA regulatory network analysis for human OC. This suggests that miRNAs and their regulation may play a major role in OC and that further directed research in this area is of utmost importance to enhance
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Yu, Lianchun; De Mazancourt, Marine; Hess, Agathe; Ashadi, Fakhrul R; Klein, Isabelle; Mal, Hervé; Courbage, Maurice; Mangin, Laurence
2016-08-01
Breathing involves a complex interplay between the brainstem automatic network and cortical voluntary command. How these brain regions communicate at rest or during inspiratory loading is unknown. This issue is crucial for several reasons: (i) increased respiratory loading is a major feature of several respiratory diseases, (ii) failure of the voluntary motor and cortical sensory processing drives is among the mechanisms that precede acute respiratory failure, (iii) several cerebral structures involved in responding to inspiratory loading participate in the perception of dyspnea, a distressing symptom in many disease. We studied functional connectivity and Granger causality of the respiratory network in controls and patients with chronic obstructive pulmonary disease (COPD), at rest and during inspiratory loading. Compared with those of controls, the motor cortex area of patients exhibited decreased connectivity with their contralateral counterparts and no connectivity with the brainstem. In the patients, the information flow was reversed at rest with the source of the network shifted from the medulla towards the motor cortex. During inspiratory loading, the system was overwhelmed and the motor cortex became the sink of the network. This major finding may help to understand why some patients with COPD are prone to acute respiratory failure. Network connectivity and causality were related to lung function and illness severity. We validated our connectivity and causality results with a mathematical model of neural network. Our findings suggest a new therapeutic strategy involving the modulation of brain activity to increase motor cortex functional connectivity and improve respiratory muscles performance in patients. Hum Brain Mapp 37:2736-2754, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
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Interpenetrating Polymer Networks as Innovative Drug Delivery Systems
Directory of Open Access Journals (Sweden)
Alka Lohani
2014-01-01
Full Text Available Polymers have always been valuable excipients in conventional dosage forms, also have shown excellent performance into the parenteral arena, and are now capable of offering advanced and sophisticated functions such as controlled drug release and drug targeting. Advances in polymer science have led to the development of several novel drug delivery systems. Interpenetrating polymer networks (IPNs have shown superior performances over the conventional individual polymers and, consequently, the ranges of applications have grown rapidly for such class of materials. The advanced properties of IPNs like swelling capacity, stability, biocompatibility, nontoxicity and biodegradability have attracted considerable attention in pharmaceutical field especially in delivering bioactive molecules to the target site. In the past few years various research reports on the IPN based delivery systems showed that these carriers have emerged as a novel carrier in controlled drug delivery. The present review encompasses IPNs, their types, method of synthesis, factors which affects the morphology of IPNs, extensively studied IPN based drug delivery systems, and some natural polymers widely used for IPNs.
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Bakker, Barbara M; van Eunen, Karen; Jeneson, Jeroen A L; van Riel, Natal A W; Bruggeman, Frank J; Teusink, Bas
2010-10-01
Human metabolic diseases are typically network diseases. This holds not only for multifactorial diseases, such as metabolic syndrome or Type 2 diabetes, but even when a single gene defect is the primary cause, where the adaptive response of the entire network determines the severity of disease. The latter may differ between individuals carrying the same mutation. Understanding the adaptive responses of human metabolism naturally requires a systems biology approach. Modelling of metabolic pathways in micro-organisms and some mammalian tissues has yielded many insights, qualitative as well as quantitative, into their control and regulation. Yet, even for a well-known pathway such as glycolysis, precise predictions of metabolite dynamics from experimentally determined enzyme kinetics have been only moderately successful. In the present review, we compare kinetic models of glycolysis in three cell types (African trypanosomes, yeast and skeletal muscle), evaluate their predictive power and identify limitations in our understanding. Although each of these models has its own merits and shortcomings, they also share common features. For example, in each case independently measured enzyme kinetic parameters were used as input. Based on these 'lessons from glycolysis', we will discuss how to make best use of kinetic computer models to advance our understanding of human metabolic diseases.
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Yamamoto, Syunsuke; Karashima, Masatoshi; Arai, Yuta; Tohyama, Kimio; Amano, Nobuyuki
2017-09-01
Although several mathematical models have been reported for the estimation of human plasma concentration profiles of drug substances after dermal application, the successful cases that can predict human pharmacokinetic profiles are limited. Therefore, the aim of this study is to investigate the prediction of human plasma concentrations after dermal application using in vitro permeation parameters obtained from excised human skin. The in vitro skin permeability of 7 marketed drug products was evaluated. The plasma concentration-time profiles of the drug substances in humans after their dermal application were simulated using compartment models and the clinical pharmacokinetic parameters. The transdermal process was simulated using the in vitro skin permeation rate and lag time assuming a zero-order absorption. These simulated plasma concentration profiles were compared with the clinical data. The result revealed that the steady-state plasma concentration of diclofenac and the maximum concentrations of nicotine, bisoprolol, rivastigmine, and lidocaine after topical application were within 2-fold of the clinical data. Furthermore, the simulated concentration profiles of bisoprolol, nicotine, and rivastigmine reproduced the decrease in absorption due to drug depletion from the formulation. In conclusion, this simple compartment model using in vitro human skin permeation parameters as zero-order absorption predicted the human plasma concentrations accurately. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.
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Construction and Deciphering of Human Phosphorylation-Mediated Signaling Transduction Networks.
Zhang, Menghuan; Li, Hong; He, Ying; Sun, Han; Xia, Li; Wang, Lishun; Sun, Bo; Ma, Liangxiao; Zhang, Guoqing; Li, Jing; Li, Yixue; Xie, Lu
2015-07-02
Protein phosphorylation is the most abundant reversible covalent modification. Human protein kinases participate in almost all biological pathways, and approximately half of the kinases are associated with disease. PhoSigNet was designed to store and display human phosphorylation-mediated signal transduction networks, with additional information related to cancer. It contains 11 976 experimentally validated directed edges and 216 871 phosphorylation sites. Moreover, 3491 differentially expressed proteins in human cancer from dbDEPC, 18 907 human cancer variation sites from CanProVar, and 388 hyperphosphorylation sites from PhosphoSitePlus were collected as annotation information. Compared with other phosphorylation-related databases, PhoSigNet not only takes the kinase-substrate regulatory relationship pairs into account, but also extends regulatory relationships up- and downstream (e.g., from ligand to receptor, from G protein to kinase, and from transcription factor to targets). Furthermore, PhoSigNet allows the user to investigate the impact of phosphorylation modifications on cancer. By using one set of in-house time series phosphoproteomics data, the reconstruction of a conditional and dynamic phosphorylation-mediated signaling network was exemplified. We expect PhoSigNet to be a useful database and analysis platform benefiting both proteomics and cancer studies.
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HOLA: Human-like Orthogonal Network Layout.
Kieffer, Steve; Dwyer, Tim; Marriott, Kim; Wybrow, Michael
2016-01-01
Over the last 50 years a wide variety of automatic network layout algorithms have been developed. Some are fast heuristic techniques suitable for networks with hundreds of thousands of nodes while others are multi-stage frameworks for higher-quality layout of smaller networks. However, despite decades of research currently no algorithm produces layout of comparable quality to that of a human. We give a new "human-centred" methodology for automatic network layout algorithm design that is intended to overcome this deficiency. User studies are first used to identify the aesthetic criteria algorithms should encode, then an algorithm is developed that is informed by these criteria and finally, a follow-up study evaluates the algorithm output. We have used this new methodology to develop an automatic orthogonal network layout method, HOLA, that achieves measurably better (by user study) layout than the best available orthogonal layout algorithm and which produces layouts of comparable quality to those produced by hand.
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Integrating Data and Networks: Human Factors
Chen, R. S.
2012-12-01
The development of technical linkages and interoperability between scientific networks is a necessary but not sufficient step towards integrated use and application of networked data and information for scientific and societal benefit. A range of "human factors" must also be addressed to ensure the long-term integration, sustainability, and utility of both the interoperable networks themselves and the scientific data and information to which they provide access. These human factors encompass the behavior of both individual humans and human institutions, and include system governance, a common framework for intellectual property rights and data sharing, consensus on terminology, metadata, and quality control processes, agreement on key system metrics and milestones, the compatibility of "business models" in the short and long term, harmonization of incentives for cooperation, and minimization of disincentives. Experience with several national and international initiatives and research programs such as the International Polar Year, the Group on Earth Observations, the NASA Earth Observing Data and Information System, the U.S. National Spatial Data Infrastructure, the Global Earthquake Model, and the United Nations Spatial Data Infrastructure provide a range of lessons regarding these human factors. Ongoing changes in science, technology, institutions, relationships, and even culture are creating both opportunities and challenges for expanded interoperability of scientific networks and significant improvement in data integration to advance science and the use of scientific data and information to achieve benefits for society as a whole.
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[Development of anti-Alzheimer's disease drug based on beta-amyloid hypothesis].
Sugimoto, Hachiro
2010-04-01
Currently, there are five anti-Alzheimer's disease drugs approved. These are tacrine, donepezil, rivastigmine, galantamine, and memantine. The mechanism of the first four drugs is acetylcholinesterase inhibition, while memantine is an NMDA-receptor antagonist. However, these drugs do not cure Alzheimer's, but are only symptomatic treatments. Therefore, a cure for Alzheimer's disease is truly needed. Alzheimer's disease is a progressive neurodegenerative disease characterized by cognitive deficits. The cause of the disease is not well understood, but research indicates that the aggregation of beta-amyloid is the fundamental cause. This theory suggests that beta-amyloid aggregation causes neurotoxicity. Therefore, development of the next anti-Alzheimer's disease drug is based on the beta-amyloid theory. We are now studying natural products, such as mulberry leaf extracts and curcumin derivatives, as potential cure for Alzheimer's disease. In this report, we describe some data about these natural products and derivatives.
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Health Technology Assessment Of Orphan Drugs : The example of Pompe disease
T.A. Kanters (Tim A.)
2016-01-01
markdownabstractIn recent decades, the development of orphan drugs, i.e. drugs for rare diseases, is stimulated by regulations in various countries. However, the generally high prices of orphan drugs confront policy makers with difficult reimbursement decisions. The orphan disease investigated in
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Mello, Juliana da Fonseca Rezende E; Gomes, Renan Augusto; Vital-Fujii, Drielli Gomes; Ferreira, Glaucio Monteiro; Trossini, Gustavo Henrique Goulart
2017-12-01
Neglected diseases (NDs) affect large populations and almost whole continents, representing 12% of the global health burden. In contrast, the treatment available today is limited and sometimes ineffective. Under this scenery, the Fragment-Based Drug Discovery emerged as one of the most promising alternatives to the traditional methods of drug development. This method allows achieving new lead compounds with smaller size of fragment libraries. Even with the wide Fragment-Based Drug Discovery success resulting in new effective therapeutic agents against different diseases, until this moment few studies have been applied this approach for NDs area. In this article, we discuss the basic Fragment-Based Drug Discovery process, brief successful ideas of general applications and show a landscape of its use in NDs, encouraging the implementation of this strategy as an interesting way to optimize the development of new drugs to NDs. © 2017 John Wiley & Sons A/S.
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Proteins aggregation and human diseases
International Nuclear Information System (INIS)
Hu, Chin-Kun
2015-01-01
Many human diseases and the death of most supercentenarians are related to protein aggregation. Neurodegenerative diseases include Alzheimer's disease (AD), Huntington's disease (HD), Parkinson's disease (PD), frontotemporallobar degeneration, etc. Such diseases are due to progressive loss of structure or function of neurons caused by protein aggregation. For example, AD is considered to be related to aggregation of Aβ40 (peptide with 40 amino acids) and Aβ42 (peptide with 42 amino acids) and HD is considered to be related to aggregation of polyQ (polyglutamine) peptides. In this paper, we briefly review our recent discovery of key factors for protein aggregation. We used a lattice model to study the aggregation rates of proteins and found that the probability for a protein sequence to appear in the conformation of the aggregated state can be used to determine the temperature at which proteins can aggregate most quickly. We used molecular dynamics and simple models of polymer chains to study relaxation and aggregation of proteins under various conditions and found that when the bending-angle dependent and torsion-angle dependent interactions are zero or very small, then protein chains tend to aggregate at lower temperatures. All atom models were used to identify a key peptide chain for the aggregation of insulin chains and to find that two polyQ chains prefer anti-parallel conformation. It is pointed out that in many cases, protein aggregation does not result from protein mis-folding. A potential drug from Chinese medicine was found for Alzheimer's disease. (paper)
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Proteins aggregation and human diseases
Hu, Chin-Kun
2015-04-01
Many human diseases and the death of most supercentenarians are related to protein aggregation. Neurodegenerative diseases include Alzheimer's disease (AD), Huntington's disease (HD), Parkinson's disease (PD), frontotemporallobar degeneration, etc. Such diseases are due to progressive loss of structure or function of neurons caused by protein aggregation. For example, AD is considered to be related to aggregation of Aβ40 (peptide with 40 amino acids) and Aβ42 (peptide with 42 amino acids) and HD is considered to be related to aggregation of polyQ (polyglutamine) peptides. In this paper, we briefly review our recent discovery of key factors for protein aggregation. We used a lattice model to study the aggregation rates of proteins and found that the probability for a protein sequence to appear in the conformation of the aggregated state can be used to determine the temperature at which proteins can aggregate most quickly. We used molecular dynamics and simple models of polymer chains to study relaxation and aggregation of proteins under various conditions and found that when the bending-angle dependent and torsion-angle dependent interactions are zero or very small, then protein chains tend to aggregate at lower temperatures. All atom models were used to identify a key peptide chain for the aggregation of insulin chains and to find that two polyQ chains prefer anti-parallel conformation. It is pointed out that in many cases, protein aggregation does not result from protein mis-folding. A potential drug from Chinese medicine was found for Alzheimer's disease.
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Talwar, Puneet; Gupta, Renu; Kushwaha, Suman; Agarwal, Rachna; Saso, Luciano; Kukreti, Shrikant; Kukreti, Ritushree
2018-04-19
Alzheimer's disease (AD) is genetically complex with multifactorial etiology. Here, we aim to identify the potential viral pathogens leading to aberrant inflammatory and oxidative stress response in AD along with potential drug candidates using systems biology approach. We retrieved protein interactions of amyloid precursor protein (APP) and tau protein (MAPT) from NCBI and genes for oxidative stress from NetAge, for inflammation from NetAge and InnateDB databases. Genes implicated in aging were retrieved from GenAge database and two GEO expression datasets. These genes were individually used to create protein-protein interaction network using STRING database (score≥0.7). The interactions of candidate genes with known viruses were mapped using virhostnet v2.0 database. Drug molecules targeting candidate genes were retrieved using the Drug-Gene Interaction Database (DGIdb). Data mining resulted in 2095 APP, 116 MAPT, 214 oxidative stress, 1269 inflammatory genes. After STRING PPIN analysis, 404 APP, 109 MAPT, 204 oxidative stress and 1014 inflammation related high confidence proteins were identified. The overlap among all datasets yielded eight common markers (AKT1, GSK3B, APP, APOE, EGFR, PIN1, CASP8 and SNCA). These genes showed association with hepatitis C virus (HCV), Epstein-Barr virus (EBV), human herpes virus 8 and Human papillomavirus (HPV). Further, screening of drugs targeting candidate genes, and possessing anti-inflammatory property, antiviral activity along with suggested role in AD pathophysiology yielded 12 potential drug candidates. Our study demonstrated the role of viral etiology in AD pathogenesis by elucidating interaction of oxidative stress and inflammation causing candidate genes with common viruses along with the identification of potential AD drug candidates. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
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Hyper-connectivity of functional networks for brain disease diagnosis.
Jie, Biao; Wee, Chong-Yaw; Shen, Dinggang; Zhang, Daoqiang
2016-08-01
Exploring structural and functional interactions among various brain regions enables better understanding of pathological underpinnings of neurological disorders. Brain connectivity network, as a simplified representation of those structural and functional interactions, has been widely used for diagnosis and classification of neurodegenerative diseases, especially for Alzheimer's disease (AD) and its early stage - mild cognitive impairment (MCI). However, the conventional functional connectivity network is usually constructed based on the pairwise correlation among different brain regions and thus ignores their higher-order relationships. Such loss of high-order information could be important for disease diagnosis, since neurologically a brain region predominantly interacts with more than one other brain regions. Accordingly, in this paper, we propose a novel framework for estimating the hyper-connectivity network of brain functions and then use this hyper-network for brain disease diagnosis. Here, the functional connectivity hyper-network denotes a network where each of its edges representing the interactions among multiple brain regions (i.e., an edge can connect with more than two brain regions), which can be naturally represented by a hyper-graph. Specifically, we first construct connectivity hyper-networks from the resting-state fMRI (R-fMRI) time series by using sparse representation. Then, we extract three sets of brain-region specific features from the connectivity hyper-networks, and further exploit a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Finally, we use multi-kernel support vector machine (SVM) for classification. The experimental results on both MCI dataset and attention deficit hyperactivity disorder (ADHD) dataset demonstrate that, compared with the conventional connectivity network-based methods, the proposed method can not only improve the classification performance, but also help
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Advanced and controlled drug delivery systems in clinical disease management
Brouwers, JRBJ
1996-01-01
Advanced and controlled drug delivery systems are important for clinical disease management. In this review the most important new systems which have reached clinical application are highlighted. Microbiologically controlled drug delivery is important for gastrointestinal diseases like ulcerative
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[Non steroidal anti-inflammatory drugs and rheumatic diseases].
Cossermelli, W; Pastor, E H
1995-01-01
Nonsteroidal anti-inflammatory drugs (NSAID) comprise an important class of medicaments that reduced the symptoms of inflamation in rheumatic disease. This article emphasizes similarities and class characteristics of the NSAID, mechanisms of action, and drug-interactions.
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River Networks As Ecological Corridors for Species, Populations and Pathogens of Water-Borne Disease
Rinaldo, A.
2014-12-01
River basins are a natural laboratory for the study of the integration of hydrological, ecological and geomorphological processes. Moving from morphological and functional analyses of dendritic geometries observed in Nature over a wide range of scales, this Lecture addresses essential ecological processes that take place along dendritic structures, hydrology-driven and controlled, like e.g.: population migrations and human settlements, that historically proceeded along river networks to follow water supply routes; riparian ecosystems composition that owing to their positioning along streams play crucial roles in their watersheds and in the loss of biodiversity proceeding at unprecedented rates; waterborne disease spreading, like epidemic cholera that exhibits epidemic patterns that mirror those of watercourses and of human mobility and resurgences upon heavy rainfall. Moreover, the regional incidence of Schistosomiasis, a parasitic waterborne disease, and water resources developments prove tightly related, and proliferative kidney disease in fish thrives differently in pristine and engineered watercourses: can we establish quantitatively the critical linkages with hydrologic drivers and controls? How does connectivity within a river network affect community composition or the spreading mechanisms? Does the river basin act as a template for biodiversity or for species' persistence? Are there hydrologic controls on epidemics of water-borne disease? Here, I shall focus on the noteworthy scientific perspectives provided by spatially explicit eco-hydrological studies centered on river networks viewed as ecological corridors for species, populations and pathogens of waterborne disease. A notable methodological coherence is granted by the mathematical description of river networks as the support for reactive transport. The Lecture overviews a number of topics idiosyncratically related to my own research work but ideally aimed at a coherent body of materials and methods. A
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Analyzing the genes related to Alzheimer's disease via a network and pathway-based approach.
Hu, Yan-Shi; Xin, Juncai; Hu, Ying; Zhang, Lei; Wang, Ju
2017-04-27
Our understanding of the molecular mechanisms underlying Alzheimer's disease (AD) remains incomplete. Previous studies have revealed that genetic factors provide a significant contribution to the pathogenesis and development of AD. In the past years, numerous genes implicated in this disease have been identified via genetic association studies on candidate genes or at the genome-wide level. However, in many cases, the roles of these genes and their interactions in AD are still unclear. A comprehensive and systematic analysis focusing on the biological function and interactions of these genes in the context of AD will therefore provide valuable insights to understand the molecular features of the disease. In this study, we collected genes potentially associated with AD by screening publications on genetic association studies deposited in PubMed. The major biological themes linked with these genes were then revealed by function and biochemical pathway enrichment analysis, and the relation between the pathways was explored by pathway crosstalk analysis. Furthermore, the network features of these AD-related genes were analyzed in the context of human interactome and an AD-specific network was inferred using the Steiner minimal tree algorithm. We compiled 430 human genes reported to be associated with AD from 823 publications. Biological theme analysis indicated that the biological processes and biochemical pathways related to neurodevelopment, metabolism, cell growth and/or survival, and immunology were enriched in these genes. Pathway crosstalk analysis then revealed that the significantly enriched pathways could be grouped into three interlinked modules-neuronal and metabolic module, cell growth/survival and neuroendocrine pathway module, and immune response-related module-indicating an AD-specific immune-endocrine-neuronal regulatory network. Furthermore, an AD-specific protein network was inferred and novel genes potentially associated with AD were identified. By
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Dopamine and the Brainstem Locomotor Networks: From Lamprey to Human
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Dimitri Ryczko
2017-05-01
Full Text Available In vertebrates, dopamine neurons are classically known to modulate locomotion via their ascending projections to the basal ganglia that project to brainstem locomotor networks. An increased dopaminergic tone is associated with increase in locomotor activity. In pathological conditions where dopamine cells are lost, such as in Parkinson's disease, locomotor deficits are traditionally associated with the reduced ascending dopaminergic input to the basal ganglia. However, a descending dopaminergic pathway originating from the substantia nigra pars compacta was recently discovered. It innervates the mesencephalic locomotor region (MLR from basal vertebrates to mammals. This pathway was shown to increase locomotor output in lampreys, and could very well play an important role in mammals. Here, we provide a detailed account on the newly found dopaminergic pathway in lamprey, salamander, rat, monkey, and human. In lampreys and salamanders, dopamine release in the MLR is associated with the activation of reticulospinal neurons that carry the locomotor command to the spinal cord. Dopamine release in the MLR potentiates locomotor movements through a D1-receptor mechanism in lampreys. In rats, stimulation of the substantia nigra pars compacta elicited dopamine release in the pedunculopontine nucleus, a known part of the MLR. In a monkey model of Parkinson's disease, a reduced dopaminergic innervation of the brainstem locomotor networks was reported. Dopaminergic fibers are also present in human pedunculopontine nucleus. We discuss the conserved locomotor role of this pathway from lamprey to mammals, and the hypothesis that this pathway could play a role in the locomotor deficits reported in Parkinson's disease.
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Drug repurposing based on drug-drug interaction.
Zhou, Bin; Wang, Rong; Wu, Ping; Kong, De-Xin
2015-02-01
Given the high risk and lengthy procedure of traditional drug development, drug repurposing is gaining more and more attention. Although many types of drug information have been used to repurpose drugs, drug-drug interaction data, which imply possible physiological effects or targets of drugs, remain unexploited. In this work, similarity of drug interaction was employed to infer similarity of the physiological effects or targets for the drugs. We collected 10,835 drug-drug interactions concerning 1074 drugs, and for 700 of them, drug similarity scores based on drug interaction profiles were computed and rendered using a drug association network with 589 nodes (drugs) and 2375 edges (drug similarity scores). The 589 drugs were clustered into 98 groups with Markov Clustering Algorithm, most of which were significantly correlated with certain drug functions. This indicates that the network can be used to infer the physiological effects of drugs. Furthermore, we evaluated the ability of this drug association network to predict drug targets. The results show that the method is effective for 317 of 561 drugs that have known targets. Comparison of this method with the structure-based approach shows that they are complementary. In summary, this study demonstrates the feasibility of drug repurposing based on drug-drug interaction data. © 2014 John Wiley & Sons A/S.
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International Nuclear Information System (INIS)
2003-01-01
The rising incidence of malaria and tuberculosis in sub-Saharan Africa is causing great hardship, not only to the individuals affected but also to the economies of the countries where they are rife. Both diseases are becoming more resistant to the drugs that are currently available for treatment and drug resistant strains are posing a global threat. The International Atomic Energy Agency (IAEA) is responding by sponsoring a programme to build technical competency in molecular and radioisotope-based techniques. (IAEA)
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Directory of Open Access Journals (Sweden)
Sepideh Parsi
2015-01-01
Full Text Available Alzheimer's disease (AD is a multifactorial, fatal neurodegenerative disorder characterized by the abnormal accumulation of Aβ and Tau deposits in the brain. There is no cure for AD, and failure at different clinical trials emphasizes the need for new treatments. In recent years, significant progress has been made toward the development of miRNA-based therapeutics for human disorders. This study was designed to evaluate the efficiency and potential safety of miRNA replacement therapy in AD, using miR-15/107 paralogues as candidate drug targets. We identified miR-16 as a potent inhibitor of amyloid precursor protein (APP and BACE1 expression, Aβ peptide production, and Tau phosphorylation in cells. Brain delivery of miR-16 mimics in mice resulted in a reduction of AD-related genes APP, BACE1, and Tau in a region-dependent manner. We further identified Nicastrin, a γ-secretase component involved in Aβ generation, as a target of miR-16. Proteomics analysis identified a number of additional putative miR-16 targets in vivo, including α-Synuclein and Transferrin receptor 1. Top-ranking biological networks associated with miR-16 delivery included AD and oxidative stress. Collectively, our data suggest that miR-16 is a good candidate for future drug development by targeting simultaneously endogenous regulators of AD biomarkers (i.e., Aβ and Tau, inflammation, and oxidative stress.
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Pharmacological approaches for Alzheimer's disease: neurotransmitter as drug targets.
Prakash, Atish; Kalra, Jaspreet; Mani, Vasudevan; Ramasamy, Kalavathy; Majeed, Abu Bakar Abdul
2015-01-01
Alzheimer's disease (AD) is the most common CNS disorder occurring worldwide. There is neither proven effective prevention for AD nor a cure for patients with this disorder. Hence, there is an urgent need to develop safer and more efficacious drugs to help combat the tremendous increase in disease progression. The present review is an attempt at discussing the treatment strategies and drugs under clinical trials governing the modulation of neurotransmitter. Therefore, looking at neurotransmitter abnormalities, there is an urge for developing the pharmacological approaches aimed at correcting those abnormalities and dysfunctioning. In addition, this review also discusses the drugs that are in Phase III trials for the treatment of AD. Despite advances in treatment strategies aimed at correcting neurotransmitter abnormalities, there exists a need for the development of drug therapies focusing on the attempts to remove the pathogenomic protein deposits, thus combating the disease progression.
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Systems Pharmacology in Small Molecular Drug Discovery
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Wei Zhou
2016-02-01
Full Text Available Drug discovery is a risky, costly and time-consuming process depending on multidisciplinary methods to create safe and effective medicines. Although considerable progress has been made by high-throughput screening methods in drug design, the cost of developing contemporary approved drugs did not match that in the past decade. The major reason is the late-stage clinical failures in Phases II and III because of the complicated interactions between drug-specific, human body and environmental aspects affecting the safety and efficacy of a drug. There is a growing hope that systems-level consideration may provide a new perspective to overcome such current difficulties of drug discovery and development. The systems pharmacology method emerged as a holistic approach and has attracted more and more attention recently. The applications of systems pharmacology not only provide the pharmacodynamic evaluation and target identification of drug molecules, but also give a systems-level of understanding the interaction mechanism between drugs and complex disease. Therefore, the present review is an attempt to introduce how holistic systems pharmacology that integrated in silico ADME/T (i.e., absorption, distribution, metabolism, excretion and toxicity, target fishing and network pharmacology facilitates the discovery of small molecular drugs at the system level.
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Structural genomics of infectious disease drug targets: the SSGCID
International Nuclear Information System (INIS)
Stacy, Robin; Begley, Darren W.; Phan, Isabelle; Staker, Bart L.; Van Voorhis, Wesley C.; Varani, Gabriele; Buchko, Garry W.; Stewart, Lance J.; Myler, Peter J.
2011-01-01
An introduction and overview of the focus, goals and overall mission of the Seattle Structural Genomics Center for Infectious Disease (SSGCID) is given. The Seattle Structural Genomics Center for Infectious Disease (SSGCID) is a consortium of researchers at Seattle BioMed, Emerald BioStructures, the University of Washington and Pacific Northwest National Laboratory that was established to apply structural genomics approaches to drug targets from infectious disease organisms. The SSGCID is currently funded over a five-year period by the National Institute of Allergy and Infectious Diseases (NIAID) to determine the three-dimensional structures of 400 proteins from a variety of Category A, B and C pathogens. Target selection engages the infectious disease research and drug-therapy communities to identify drug targets, essential enzymes, virulence factors and vaccine candidates of biomedical relevance to combat infectious diseases. The protein-expression systems, purified proteins, ligand screens and three-dimensional structures produced by SSGCID constitute a valuable resource for drug-discovery research, all of which is made freely available to the greater scientific community. This issue of Acta Crystallographica Section F, entirely devoted to the work of the SSGCID, covers the details of the high-throughput pipeline and presents a series of structures from a broad array of pathogenic organisms. Here, a background is provided on the structural genomics of infectious disease, the essential components of the SSGCID pipeline are discussed and a survey of progress to date is presented
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2014-01-01
Background The demand for novel molecularly targeted drugs will continue to rise as we move forward toward the goal of personalizing cancer treatment to the molecular signature of individual tumors. However, the identification of targets and combinations of targets that can be safely and effectively modulated is one of the greatest challenges facing the drug discovery process. A promising approach is to use biological networks to prioritize targets based on their relative positions to one another, a property that affects their ability to maintain network integrity and propagate information-flow. Here, we introduce influence networks and demonstrate how they can be used to generate influence scores as a network-based metric to rank genes as potential drug targets. Results We use this approach to prioritize genes as drug target candidates in a set of ER + breast tumor samples collected during the course of neoadjuvant treatment with the aromatase inhibitor letrozole. We show that influential genes, those with high influence scores, tend to be essential and include a higher proportion of essential genes than those prioritized based on their position (i.e. hubs or bottlenecks) within the same network. Additionally, we show that influential genes represent novel biologically relevant drug targets for the treatment of ER + breast cancers. Moreover, we demonstrate that gene influence differs between untreated tumors and residual tumors that have adapted to drug treatment. In this way, influence scores capture the context-dependent functions of genes and present the opportunity to design combination treatment strategies that take advantage of the tumor adaptation process. Conclusions Influence networks efficiently find essential genes as promising drug targets and combinations of targets to inform the development of molecularly targeted drugs and their use. PMID:24495353
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Chen, Yang; Gao, Zhen; Wang, Bingcheng; Xu, Rong
2016-08-22
Glioblastoma (GBM) is the most common and aggressive brain tumors. It has poor prognosis even with optimal radio- and chemo-therapies. Since GBM is highly heterogeneous, drugs that target on specific molecular profiles of individual tumors may achieve maximized efficacy. Currently, the Cancer Genome Atlas (TCGA) projects have identified hundreds of GBM-associated genes. We develop a drug repositioning approach combining disease genomics and mouse phenotype data towards predicting targeted therapies for GBM. We first identified disease specific mouse phenotypes using the most recently discovered GBM genes. Then we systematically searched all FDA-approved drugs for candidates that share similar mouse phenotype profiles with GBM. We evaluated the ranks for approved and novel GBM drugs, and compared with an existing approach, which also use the mouse phenotype data but not the disease genomics data. We achieved significantly higher ranks for the approved and novel GBM drugs than the earlier approach. For all positive examples of GBM drugs, we achieved a median rank of 9.2 45.6 of the top predictions have been demonstrated effective in inhibiting the growth of human GBM cells. We developed a computational drug repositioning approach based on both genomic and phenotypic data. Our approach prioritized existing GBM drugs and outperformed a recent approach. Overall, our approach shows potential in discovering new targeted therapies for GBM.
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One For All? Hitting multiple Alzheimer’s Disease targets with one drug
Directory of Open Access Journals (Sweden)
Rebecca Ellen Hughes
2016-04-01
Full Text Available Alzheimer’s disease is a complex and multifactorial disease for which the mechanism is still not fully understood. As new insights into disease progression are discovered, new drugs must be designed to target those aspects of the disease that cause neuronal damage rather than just the symptoms currently addressed by single target drugs. It is becoming possible to target several aspects of the disease pathology at once using multi-target drugs. Intended as a introduction for non-experts, this review describes the key multi-target drug design approaches, namely structure-based, in silico, and data-mining, to evaluate what is preventing compounds progressing through the clinic to the market. Repurposing current drugs using their off-target effects reduces the cost of development, time to launch and also the uncertainty associated with safety and pharmacokinetics. The most promising drugs currently being investigated for repurposing to Alzheimer’s Disease are rasagiline, originally developed for the treatment of Parkinson’s Disease, and liraglutide, an antidiabetic. Rational drug design can combine pharmacophores of multiple drugs, systematically change functional groups, and rank them by virtual screening. Hits confirmed experimentally are rationally modified to generate an effective multi-potent lead compound. Examples from this approach are ASS234 with properties similar to rasagiline, and donecopride, a hybrid of an acetylcholinesterase inhibitor and a 5-HT4 receptor agonist with pro-cognitive effects. Exploiting these interdisciplinary approaches, public-private collaborative lead factories promise faster delivery of new drugs to the clinic.
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Directory of Open Access Journals (Sweden)
Meng-Lu Liu
2016-01-01
Full Text Available Subtype-specific neurons obtained from adult humans will be critical to modeling neurodegenerative diseases, such as amyotrophic lateral sclerosis (ALS. Here, we show that adult human skin fibroblasts can be directly and efficiently converted into highly pure motor neurons without passing through an induced pluripotent stem cell stage. These adult human induced motor neurons (hiMNs exhibit the cytological and electrophysiological features of spinal motor neurons and form functional neuromuscular junctions (NMJs with skeletal muscles. Importantly, hiMNs converted from ALS patient fibroblasts show disease-specific degeneration manifested through poor survival, soma shrinkage, hypoactivity, and an inability to form NMJs. A chemical screen revealed that the degenerative features of ALS hiMNs can be remarkably rescued by the small molecule kenpaullone. Taken together, our results define a direct and efficient strategy to obtain disease-relevant neuronal subtypes from adult human patients and reveal their promising value in disease modeling and drug identification.
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Herrera-Ibatá, Diana María; Pazos, Alejandro; Orbegozo-Medina, Ricardo Alfredo; Romero-Durán, Francisco Javier; González-Díaz, Humberto
2015-06-01
Using computational algorithms to design tailored drug cocktails for highly active antiretroviral therapy (HAART) on specific populations is a goal of major importance for both pharmaceutical industry and public health policy institutions. New combinations of compounds need to be predicted in order to design HAART cocktails. On the one hand, there are the biomolecular factors related to the drugs in the cocktail (experimental measure, chemical structure, drug target, assay organisms, etc.); on the other hand, there are the socioeconomic factors of the specific population (income inequalities, employment levels, fiscal pressure, education, migration, population structure, etc.) to study the relationship between the socioeconomic status and the disease. In this context, machine learning algorithms, able to seek models for problems with multi-source data, have to be used. In this work, the first artificial neural network (ANN) model is proposed for the prediction of HAART cocktails, to halt AIDS on epidemic networks of U.S. counties using information indices that codify both biomolecular and several socioeconomic factors. The data was obtained from at least three major sources. The first dataset included assays of anti-HIV chemical compounds released to ChEMBL. The second dataset is the AIDSVu database of Emory University. AIDSVu compiled AIDS prevalence for >2300 U.S. counties. The third data set included socioeconomic data from the U.S. Census Bureau. Three scales or levels were employed to group the counties according to the location or population structure codes: state, rural urban continuum code (RUCC) and urban influence code (UIC). An analysis of >130,000 pairs (network links) was performed, corresponding to AIDS prevalence in 2310 counties in U.S. vs. drug cocktails made up of combinations of ChEMBL results for 21,582 unique drugs, 9 viral or human protein targets, 4856 protocols, and 10 possible experimental measures. The best model found with the original
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Network analysis of microRNAs and their regulation in human ovarian cancer
Schmeier, Sebastian; Schaefer, Ulf; Essack, Magbubah; Bajic, Vladimir B.
2011-01-01
Background: MicroRNAs (miRNAs) are small non-coding RNA molecules that repress the translation of messenger RNAs (mRNAs) or degrade mRNAs. These functions of miRNAs allow them to control key cellular processes such as development, differentiation and apoptosis, and they have also been implicated in several cancers such as leukaemia, lung, pancreatic and ovarian cancer (OC). Unfortunately, the specific machinery of miRNA regulation, involving transcription factors (TFs) and transcription co-factors (TcoFs), is not well understood. In the present study we focus on computationally deciphering the underlying network of miRNAs, their targets, and their control mechanisms that have an influence on OC development.Results: We analysed experimentally verified data from multiple sources that describe miRNA influence on diseases, miRNA targeting of mRNAs, and on protein-protein interactions, and combined this data with ab initio transcription factor binding site predictions within miRNA promoter regions. From these analyses, we derived a network that describes the influence of miRNAs and their regulation in human OC. We developed a methodology to analyse the network in order to find the nodes that have the largest potential of influencing the network's behaviour (network hubs). We further show the potentially most influential miRNAs, TFs and TcoFs, showing subnetworks illustrating the involved mechanisms as well as regulatory miRNA network motifs in OC. We find an enrichment of miRNA targeted OC genes in the highly relevant pathways cell cycle regulation and apoptosis.Conclusions: We combined several sources of interaction and association data to analyse and place miRNAs within regulatory pathways that influence human OC. These results represent the first comprehensive miRNA regulatory network analysis for human OC. This suggests that miRNAs and their regulation may play a major role in OC and that further directed research in this area is of utmost importance to enhance our
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Use of genome editing tools in human stem cell-based disease modeling and precision medicine.
Wei, Yu-da; Li, Shuang; Liu, Gai-gai; Zhang, Yong-xian; Ding, Qiu-rong
2015-10-01
Precision medicine emerges as a new approach that takes into account individual variability. The successful conduct of precision medicine requires the use of precise disease models. Human pluripotent stem cells (hPSCs), as well as adult stem cells, can be differentiated into a variety of human somatic cell types that can be used for research and drug screening. The development of genome editing technology over the past few years, especially the CRISPR/Cas system, has made it feasible to precisely and efficiently edit the genetic background. Therefore, disease modeling by using a combination of human stem cells and genome editing technology has offered a new platform to generate " personalized " disease models, which allow the study of the contribution of individual genetic variabilities to disease progression and the development of precise treatments. In this review, recent advances in the use of genome editing in human stem cells and the generation of stem cell models for rare diseases and cancers are discussed.
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Improving drug delivery technology for treating neurodegenerative diseases.
Choonara, Yahya E; Kumar, Pradeep; Modi, Girish; Pillay, Viness
2016-07-01
Neurodegenerative diseases (NDs) represent intricate challenges for efficient uptake and transport of drugs to the brain mainly due to the restrictive blood-brain barrier (BBB). NDs are characterized by the loss of neuronal subtypes as sporadic and/or familial and several mechanisms of neurodegeneration have been identified. This review attempts to recap, organize and concisely evaluate the advanced drug delivery systems designed for treating common NDs. It highlights key research gaps and opinionates on new neurotherapies to overcome the BBB as an addition to the current treatments of countering oxidative stress, inflammation and apoptotic mechanisms. Current treatments do not fully address the biological, drug and therapeutic factors faced. This has led to the development of vogue treatments such as nose-to-brain technologies, bio-engineered systems, fusion protein chaperones, stem cells, gene therapy, use of natural compounds, neuroprotectants and even vaccines. However, failure of these treatments is mainly due to the BBB and non-specific delivery in the brain. In order to increase neuroavailability various advanced drug delivery systems provide promising alternatives that are able to augment the treatment of Alzheimer's disease and Parkinson's disease. However, much work is still required in this field beyond the preclinical testing phase.
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In silico studies in drug research against neurodegenerative diseases.
Makhouri, Farahnaz Rezaei; Ghasemi, Jahan B
2017-08-22
Neurodegenerative diseases such as Alzheimer's disease (AD), progressive neurodegenerative forms of Huntington's disease, Parkinson's disease (PD), amyotrophic lateral sclerosis, spinal cerebellar ataxias, and spinal and bulbar muscular atrophy are described by slow and selective dysfunction and degeneration of neurons and axons in the central nervous system (CNS). Computer-aided or in silico design methods have matured into powerful tools for reducing the number of ligands that should be screened in experimental assays. In the present review, the authors provide a basic background about neurodegenerative diseases and in silico techniques in the drug research. Furthermore, they review the various in silico studies reported against various targets in neurodegenerative diseases, including homology modeling, molecular docking, virtual high-throughput screening, quantitative structure activity relationship (QSAR), hologram quantitative structure activity relationship (HQSAR), 3D pharmacophore mapping, proteochemometrics modeling (PCM), fingerprints, fragment-based drug discovery, Monte Carlo simulation, molecular dynamic (MD) simulation, quantum-mechanical methods for drug design, support vector machines, and machine learning approaches. Neurodegenerative diseases have a multifactorial pathoetiological origin, so scientists have become persuaded that a multi-target therapeutic strategy aimed at the simultaneous targeting of multiple proteins (and therefore etiologies) involved in the development of a disease is recommended in future. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
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Hydrogen-bond network and pH sensitivity in human transthyretin
Energy Technology Data Exchange (ETDEWEB)
Yokoyama, Takeshi, E-mail: tyokoya3@pha.u-toyama.ac.jp; Mizuguchi, Mineyuki; Nabeshima, Yuko [University of Toyama, 2630 Sugitani, Toyama 930-0914 (Japan); Kusaka, Katsuhiro; Yamada, Taro [Ibaraki University, 162-1 Shirakata, Tokai, Ibaraki 319-1106 (Japan); Hosoya, Takaaki [Ibaraki University, 162-1 Shirakata, Tokai, Ibaraki 319-1106 (Japan); Ibaraki University, 4-12-1 Naka-Narusawa, Hitachi, Ibaraki 316-8511 (Japan); Ohhara, Takashi [Comprehensive Research Organization for Science and Society, 162-1 Shirakata, Tokai, Ibaraki 319-1106 (Japan); Kurihara, Kazuo [Japan Atomic Energy Agency, 2-4 Shirakata, Tokai, Ibaraki 319-1195 (Japan); Tanaka, Ichiro [Ibaraki University, 162-1 Shirakata, Tokai, Ibaraki 319-1106 (Japan); Ibaraki University, 4-12-1 Naka-Narusawa, Hitachi, Ibaraki 316-8511 (Japan); Niimura, Nobuo [Ibaraki University, 162-1 Shirakata, Tokai, Ibaraki 319-1106 (Japan)
2013-11-01
The neutron crystal structure of human transthyretin is presented. Transthyretin (TTR) is a tetrameric protein. TTR misfolding and aggregation are associated with human amyloid diseases. Dissociation of the TTR tetramer is believed to be the rate-limiting step in the amyloid fibril formation cascade. Low pH is known to promote dissociation into monomer and the formation of amyloid fibrils. In order to reveal the molecular mechanisms underlying pH sensitivity and structural stabilities of TTR, neutron diffraction studies were conducted using the IBARAKI Biological Crystal Diffractometer with the time-of-flight method. Crystals for the neutron diffraction experiments were grown up to 2.5 mm{sup 3} for four months. The neutron crystal structure solved at 2.0 Å revealed the protonation states of His88 and the detailed hydrogen-bond network depending on the protonation states of His88. This hydrogen-bond network is involved in monomer–monomer and dimer–dimer interactions, suggesting that the double protonation of His88 by acidification breaks the hydrogen-bond network and causes the destabilization of the TTR tetramer. Structural comparison with the X-ray crystal structure at acidic pH identified the three amino acid residues responsible for the pH sensitivity of TTR. Our neutron model provides insights into the molecular stability related to amyloidosis.
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Atomoxetine Enhances Connectivity of Prefrontal Networks in Parkinson's Disease.
Borchert, Robin J; Rittman, Timothy; Passamonti, Luca; Ye, Zheng; Sami, Saber; Jones, Simon P; Nombela, Cristina; Vázquez Rodríguez, Patricia; Vatansever, Deniz; Rae, Charlotte L; Hughes, Laura E; Robbins, Trevor W; Rowe, James B
2016-07-01
Cognitive impairment is common in Parkinson's disease (PD), but often not improved by dopaminergic treatment. New treatment strategies targeting other neurotransmitter deficits are therefore of growing interest. Imaging the brain at rest ('task-free') provides the opportunity to examine the impact of a candidate drug on many of the brain networks that underpin cognition, while minimizing task-related performance confounds. We test this approach using atomoxetine, a selective noradrenaline reuptake inhibitor that modulates the prefrontal cortical activity and can facilitate some executive functions and response inhibition. Thirty-three patients with idiopathic PD underwent task-free fMRI. Patients were scanned twice in a double-blind, placebo-controlled crossover design, following either placebo or 40-mg oral atomoxetine. Seventy-six controls were scanned once without medication to provide normative data. Seed-based correlation analyses were used to measure changes in functional connectivity, with the right inferior frontal gyrus (IFG) a critical region for executive function. Patients on placebo had reduced connectivity relative to controls from right IFG to dorsal anterior cingulate cortex and to left IFG and dorsolateral prefrontal cortex. Atomoxetine increased connectivity from the right IFG to the dorsal anterior cingulate. In addition, the atomoxetine-induced change in connectivity from right IFG to dorsolateral prefrontal cortex was proportional to the change in verbal fluency, a simple index of executive function. The results support the hypothesis that atomoxetine may restore prefrontal networks related to executive functions. We suggest that task-free imaging can support translational pharmacological studies of new drug therapies and provide evidence for engagement of the relevant neurocognitive systems.
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Dissecting the Molecular Mechanisms of Neurodegenerative Diseases through Network Biology
Directory of Open Access Journals (Sweden)
Jose A. Santiago
2017-05-01
Full Text Available Neurodegenerative diseases are rarely caused by a mutation in a single gene but rather influenced by a combination of genetic, epigenetic and environmental factors. Emerging high-throughput technologies such as RNA sequencing have been instrumental in deciphering the molecular landscape of neurodegenerative diseases, however, the interpretation of such large amounts of data remains a challenge. Network biology has become a powerful platform to integrate multiple omics data to comprehensively explore the molecular networks in the context of health and disease. In this review article, we highlight recent advances in network biology approaches with an emphasis in brain-networks that have provided insights into the molecular mechanisms leading to the most prevalent neurodegenerative diseases including Alzheimer’s (AD, Parkinson’s (PD and Huntington’s diseases (HD. We discuss how integrative approaches using multi-omics data from different tissues have been valuable for identifying biomarkers and therapeutic targets. In addition, we discuss the challenges the field of network medicine faces toward the translation of network-based findings into clinically actionable tools for personalized medicine applications.
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Petrović, Jelena; Ibrić, Svetlana; Betz, Gabriele; Đurić, Zorica
2012-05-30
The main objective of the study was to develop artificial intelligence methods for optimization of drug release from matrix tablets regardless of the matrix type. Static and dynamic artificial neural networks of the same topology were developed to model dissolution profiles of different matrix tablets types (hydrophilic/lipid) using formulation composition, compression force used for tableting and tablets porosity and tensile strength as input data. Potential application of decision trees in discovering knowledge from experimental data was also investigated. Polyethylene oxide polymer and glyceryl palmitostearate were used as matrix forming materials for hydrophilic and lipid matrix tablets, respectively whereas selected model drugs were diclofenac sodium and caffeine. Matrix tablets were prepared by direct compression method and tested for in vitro dissolution profiles. Optimization of static and dynamic neural networks used for modeling of drug release was performed using Monte Carlo simulations or genetic algorithms optimizer. Decision trees were constructed following discretization of data. Calculated difference (f(1)) and similarity (f(2)) factors for predicted and experimentally obtained dissolution profiles of test matrix tablets formulations indicate that Elman dynamic neural networks as well as decision trees are capable of accurate predictions of both hydrophilic and lipid matrix tablets dissolution profiles. Elman neural networks were compared to most frequently used static network, Multi-layered perceptron, and superiority of Elman networks have been demonstrated. Developed methods allow simple, yet very precise way of drug release predictions for both hydrophilic and lipid matrix tablets having controlled drug release. Copyright © 2012 Elsevier B.V. All rights reserved.
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Human brain networks function in connectome-specific harmonic waves.
Atasoy, Selen; Donnelly, Isaac; Pearson, Joel
2016-01-21
A key characteristic of human brain activity is coherent, spatially distributed oscillations forming behaviour-dependent brain networks. However, a fundamental principle underlying these networks remains unknown. Here we report that functional networks of the human brain are predicted by harmonic patterns, ubiquitous throughout nature, steered by the anatomy of the human cerebral cortex, the human connectome. We introduce a new technique extending the Fourier basis to the human connectome. In this new frequency-specific representation of cortical activity, that we call 'connectome harmonics', oscillatory networks of the human brain at rest match harmonic wave patterns of certain frequencies. We demonstrate a neural mechanism behind the self-organization of connectome harmonics with a continuous neural field model of excitatory-inhibitory interactions on the connectome. Remarkably, the critical relation between the neural field patterns and the delicate excitation-inhibition balance fits the neurophysiological changes observed during the loss and recovery of consciousness.
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Advanced Therapeutic Strategies for Chronic Lung Disease Using Nanoparticle-Based Drug Delivery
Directory of Open Access Journals (Sweden)
Ji Young Yhee
2016-09-01
Full Text Available Chronic lung diseases include a variety of obstinate and fatal diseases, including asthma, chronic obstructive pulmonary disease (COPD, cystic fibrosis (CF, idiopathic pulmonary fibrosis (IPF, and lung cancers. Pharmacotherapy is important for the treatment of chronic lung diseases, and current progress in nanoparticles offers great potential as an advanced strategy for drug delivery. Based on their biophysical properties, nanoparticles have shown improved pharmacokinetics of therapeutics and controlled drug delivery, gaining great attention. Herein, we will review the nanoparticle-based drug delivery system for the treatment of chronic lung diseases. Various types of nanoparticles will be introduced, and recent innovative efforts to utilize the nanoparticles as novel drug carriers for the effective treatment of chronic lung diseases will also be discussed.
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Yakovenko, Oleksandr; Jones, Steven J. M.
2018-01-01
We report the implementation of molecular modeling approaches developed as a part of the 2016 Grand Challenge 2, the blinded competition of computer aided drug design technologies held by the D3R Drug Design Data Resource (https://drugdesigndata.org/). The challenge was focused on the ligands of the farnesoid X receptor (FXR), a highly flexible nuclear receptor of the cholesterol derivative chenodeoxycholic acid. FXR is considered an important therapeutic target for metabolic, inflammatory, bowel and obesity related diseases (Expert Opin Drug Metab Toxicol 4:523-532, 2015), but in the context of this competition it is also interesting due to the significant ligand-induced conformational changes displayed by the protein. To deal with these conformational changes we employed multiple simulations of molecular dynamics (MD). Our MD-based protocols were top-ranked in estimating the free energy of binding of the ligands and FXR protein. Our approach was ranked second in the prediction of the binding poses where we also combined MD with molecular docking and artificial neural networks. Our approach showed mediocre results for high-throughput scoring of interactions.
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Directory of Open Access Journals (Sweden)
Matthew D Dyer
2010-08-01
Full Text Available Bacillus anthracis, Francisella tularensis, and Yersinia pestis are bacterial pathogens that can cause anthrax, lethal acute pneumonic disease, and bubonic plague, respectively, and are listed as NIAID Category A priority pathogens for possible use as biological weapons. However, the interactions between human proteins and proteins in these bacteria remain poorly characterized leading to an incomplete understanding of their pathogenesis and mechanisms of immune evasion.In this study, we used a high-throughput yeast two-hybrid assay to identify physical interactions between human proteins and proteins from each of these three pathogens. From more than 250,000 screens performed, we identified 3,073 human-B. anthracis, 1,383 human-F. tularensis, and 4,059 human-Y. pestis protein-protein interactions including interactions involving 304 B. anthracis, 52 F. tularensis, and 330 Y. pestis proteins that are uncharacterized. Computational analysis revealed that pathogen proteins preferentially interact with human proteins that are hubs and bottlenecks in the human PPI network. In addition, we computed modules of human-pathogen PPIs that are conserved amongst the three networks. Functionally, such conserved modules reveal commonalities between how the different pathogens interact with crucial host pathways involved in inflammation and immunity.These data constitute the first extensive protein interaction networks constructed for bacterial pathogens and their human hosts. This study provides novel insights into host-pathogen interactions.
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Drug Delivery Systems for Imaging and Therapy of Parkinson's Disease.
Gunay, Mine Silindir; Ozer, A Yekta; Chalon, Sylvie
2016-01-01
Although a variety of therapeutic approaches are available for the treatment of Parkinson's disease, challenges limit effective therapy. Among these challenges are delivery of drugs through the blood brain barier to the target brain tissue and the side effects observed during long term administration of antiparkinsonian drugs. The use of drug delivery systems such as liposomes, niosomes, micelles, nanoparticles, nanocapsules, gold nanoparticles, microspheres, microcapsules, nanobubbles, microbubbles and dendrimers is being investigated for diagnosis and therapy. This review focuses on formulation, development and advantages of nanosized drug delivery systems which can penetrate the central nervous system for the therapy and/or diagnosis of PD, and highlights future nanotechnological approaches. It is esential to deliver a sufficient amount of either therapeutic or radiocontrast agents to the brain in order to provide the best possible efficacy or imaging without undesired degradation of the agent. Current treatments focus on motor symptoms, but these treatments generally do not deal with modifying the course of Parkinson's disease. Beyond pharmacological therapy, the identification of abnormal proteins such as α -synuclein, parkin or leucine-rich repeat serine/threonine protein kinase 2 could represent promising alternative targets for molecular imaging and therapy of Parkinson's disease. Nanotechnology and nanosized drug delivery systems are being investigated intensely and could have potential effect for Parkinson's disease. The improvement of drug delivery systems could dramatically enhance the effectiveness of Parkinson's Disease therapy and reduce its side effects.
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Directory of Open Access Journals (Sweden)
Tingting Zheng
2017-09-01
Full Text Available Background: A range of computational methods that rely on the analysis of genome-wide expression datasets have been developed and successfully used for drug repositioning. The success of these methods is based on the hypothesis that introducing a factor (in this case, a drug molecule that could reverse the disease gene expression signature will lead to a therapeutic effect. However, it has also been shown that globally reversing the disease expression signature is not a prerequisite for drug activity. On the other hand, the basic idea of significant anti-correlation in expression profiles could have great value for establishing diet-disease associations and could provide new insights into the role of dietary interventions in disease.Methods: We performed an integrated analysis of publicly available gene expression profiles for foods, diseases and drugs, by calculating pairwise similarity scores for diet and disease gene expression signatures and characterizing their topological features in protein-protein interaction networks.Results: We identified 485 diet-disease pairs where diet could positively influence disease development and 472 pairs where specific diets should be avoided in a disease state. Multiple evidence suggests that orange, whey and coconut fat could be beneficial for psoriasis, lung adenocarcinoma and macular degeneration, respectively. On the other hand, fructose-rich diet should be restricted in patients with chronic intermittent hypoxia and ovarian cancer. Since humans normally do not consume foods in isolation, we also applied different algorithms to predict synergism; as a result, 58 food pairs were predicted. Interestingly, the diets identified as anti-correlated with diseases showed a topological proximity to the disease proteins similar to that of the corresponding drugs.Conclusions: In conclusion, we provide a computational framework for establishing diet-disease associations and additional information on the role of
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Directory of Open Access Journals (Sweden)
Kelly L. Johnston
2014-12-01
Full Text Available Lymphatic filariasis and onchocerciasis are debilitating diseases caused by parasitic filarial nematodes infecting around 150 million people throughout the tropics with more than 1.5 billion at risk. As with other neglected tropical diseases, classical drug-discovery and development is lacking and a 50 year programme of macrofilaricidal discovery failed to deliver a drug which can be used as a public health tool. Recently, antibiotic targeting of filarial Wolbachia, an essential bacterial symbiont, has provided a novel drug treatment for filariasis with macrofilaricidal activity, although the current gold-standard, doxycycline, is unsuitable for use in mass drug administration (MDA. The anti-Wolbachia (A·WOL Consortium aims to identify novel anti-Wolbachia drugs, compounds or combinations that are suitable for use in MDA. Development of a Wolbachia cell-based assay has enabled the screening of the approved human drug-pharmacopoeia (∼2600 drugs for a potential repurposing. This screening strategy has revealed that approved drugs from various classes show significant bacterial load reduction equal to or superior to the gold-standard doxycycline, with 69 orally available hits from different drug categories being identified. Based on our defined hit criteria, 15 compounds were then selectively screened in a Litomosoides sigmodontis mouse model, 4 of which were active. These came from the tetracycline, fluoroquinolone and rifamycin classes. This strategy of repurposing approved drugs is a promising development in the goal of finding a novel treatment against filariasis and could also be a strategy applicable for other neglected tropical diseases.
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Human abuse liability evaluation of CNS stimulant drugs.
Romach, Myroslava K; Schoedel, Kerri A; Sellers, Edward M
2014-12-01
Psychoactive drugs that increase alertness, attention and concentration and energy, while also elevating mood, heart rate and blood pressure are referred to as stimulants. Despite some overlapping similarities, stimulants cannot be easily categorized by their chemical structure, mechanism of action, receptor binding profile, effects on monoamine uptake, behavioral pharmacology (e.g., effects on locomotion, temperature, and blood pressure), therapeutic indication or efficacy. Because of their abuse liability, a pre-market assessment of abuse potential is required for drugs that show stimulant properties; this review article focuses on the clinical aspects of this evaluation. This includes clinical trial adverse events, evidence of diversion or tampering, overdoses and the results of a human abuse potential study. While there are different types of human experimental studies that can be employed to evaluate stimulant abuse potential (e.g., drug discrimination, self-administration), only the human abuse potential study and clinical trial adverse event data are required for drug approval. The principal advances that have improved human abuse potential studies include using study enrichment strategies (pharmacologic qualification), larger sample sizes, better selection of endpoints and measurement strategies and more carefully considered interpretation of data. Because of the methodological advances, comparisons of newer studies with historical data is problematic and may contribute to a biased regulatory framework for the evaluation of newer stimulant-like drugs, such as A2 antagonists. This article is part of the Special Issue entitled 'CNS Stimulants'. Copyright © 2014 Elsevier Ltd. All rights reserved.
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Comparative genomics allowed the identification of drug targets against human fungal pathogens
Directory of Open Access Journals (Sweden)
Martins Natalia F
2011-01-01
Full Text Available Abstract Background The prevalence of invasive fungal infections (IFIs has increased steadily worldwide in the last few decades. Particularly, there has been a global rise in the number of infections among immunosuppressed people. These patients present severe clinical forms of the infections, which are commonly fatal, and they are more susceptible to opportunistic fungal infections than non-immunocompromised people. IFIs have historically been associated with high morbidity and mortality, partly because of the limitations of available antifungal therapies, including side effects, toxicities, drug interactions and antifungal resistance. Thus, the search for alternative therapies and/or the development of more specific drugs is a challenge that needs to be met. Genomics has created new ways of examining genes, which open new strategies for drug development and control of human diseases. Results In silico analyses and manual mining selected initially 57 potential drug targets, based on 55 genes experimentally confirmed as essential for Candida albicans or Aspergillus fumigatus and other 2 genes (kre2 and erg6 relevant for fungal survival within the host. Orthologs for those 57 potential targets were also identified in eight human fungal pathogens (C. albicans, A. fumigatus, Blastomyces dermatitidis, Paracoccidioides brasiliensis, Paracoccidioides lutzii, Coccidioides immitis, Cryptococcus neoformans and Histoplasma capsulatum. Of those, 10 genes were present in all pathogenic fungi analyzed and absent in the human genome. We focused on four candidates: trr1 that encodes for thioredoxin reductase, rim8 that encodes for a protein involved in the proteolytic activation of a transcriptional factor in response to alkaline pH, kre2 that encodes for α-1,2-mannosyltransferase and erg6 that encodes for Δ(24-sterol C-methyltransferase. Conclusions Our data show that the comparative genomics analysis of eight fungal pathogens enabled the identification of
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3D Miniaturization of Human Organs for Drug Discovery.
Park, Joseph; Wetzel, Isaac; Dréau, Didier; Cho, Hansang
2018-01-01
"Engineered human organs" hold promises for predicting the effectiveness and accuracy of drug responses while reducing cost, time, and failure rates in clinical trials. Multiorgan human models utilize many aspects of currently available technologies including self-organized spherical 3D human organoids, microfabricated 3D human organ chips, and 3D bioprinted human organ constructs to mimic key structural and functional properties of human organs. They enable precise control of multicellular activities, extracellular matrix (ECM) compositions, spatial distributions of cells, architectural organizations of ECM, and environmental cues. Thus, engineered human organs can provide the microstructures and biological functions of target organs and advantageously substitute multiscaled drug-testing platforms including the current in vitro molecular assays, cell platforms, and in vivo models. This review provides an overview of advanced innovative designs based on the three main technologies used for organ construction leading to single and multiorgan systems useable for drug development. Current technological challenges and future perspectives are also discussed. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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González-Díaz, Humberto; Herrera-Ibatá, Diana María; Duardo-Sánchez, Aliuska; Munteanu, Cristian R; Orbegozo-Medina, Ricardo Alfredo; Pazos, Alejandro
2014-03-24
This work is aimed at describing the workflow for a methodology that combines chemoinformatics and pharmacoepidemiology methods and at reporting the first predictive model developed with this methodology. The new model is able to predict complex networks of AIDS prevalence in the US counties, taking into consideration the social determinants and activity/structure of anti-HIV drugs in preclinical assays. We trained different Artificial Neural Networks (ANNs) using as input information indices of social networks and molecular graphs. We used a Shannon information index based on the Gini coefficient to quantify the effect of income inequality in the social network. We obtained the data on AIDS prevalence and the Gini coefficient from the AIDSVu database of Emory University. We also used the Balaban information indices to quantify changes in the chemical structure of anti-HIV drugs. We obtained the data on anti-HIV drug activity and structure (SMILE codes) from the ChEMBL database. Last, we used Box-Jenkins moving average operators to quantify information about the deviations of drugs with respect to data subsets of reference (targets, organisms, experimental parameters, protocols). The best model found was a Linear Neural Network (LNN) with values of Accuracy, Specificity, and Sensitivity above 0.76 and AUROC > 0.80 in training and external validation series. This model generates a complex network of AIDS prevalence in the US at county level with respect to the preclinical activity of anti-HIV drugs in preclinical assays. To train/validate the model and predict the complex network we needed to analyze 43,249 data points including values of AIDS prevalence in 2,310 counties in the US vs ChEMBL results for 21,582 unique drugs, 9 viral or human protein targets, 4,856 protocols, and 10 possible experimental measures.
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Nagatsu, Toshiharu; Nagatsu, Ikuko
2016-11-01
Tyrosine hydroxylase (TH), which was discovered at the National Institutes of Health (NIH) in 1964, is a tetrahydrobiopterin (BH4)-requiring monooxygenase that catalyzes the first and rate-limiting step in the biosynthesis of catecholamines (CAs), such as dopamine, noradrenaline, and adrenaline. Since deficiencies of dopamine and noradrenaline in the brain stem, caused by neurodegeneration of dopamine and noradrenaline neurons, are mainly related to non-motor and motor symptoms of Parkinson's disease (PD), we have studied human CA-synthesizing enzymes [TH; BH4-related enzymes, especially GTP-cyclohydrolase I (GCH1); aromatic L-amino acid decarboxylase (AADC); dopamine β-hydroxylase (DBH); and phenylethanolamine N-methyltransferase (PNMT)] and their genes in relation to PD in postmortem brains from PD patients, patients with CA-related genetic diseases, mice with genetically engineered CA neurons, and animal models of PD. We purified all human CA-synthesizing enzymes, produced their antibodies for immunohistochemistry and immunoassay, and cloned all human genes, especially the human TH gene and the human gene for GCH1, which synthesizes BH4 as a cofactor of TH. This review discusses the historical overview of TH, BH4-, and other CA-related enzymes and their genes in relation to the pathophysiology of PD, the development of drugs, such as L-DOPA, and future prospects for drug and gene therapy for PD, especially the potential of induced pluripotent stem (iPS) cells.
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He, Yongqun
2016-06-01
Compared with controlled terminologies ( e.g. , MedDRA, CTCAE, and WHO-ART), the community-based Ontology of AEs (OAE) has many advantages in adverse event (AE) classifications. The OAE-derived Ontology of Vaccine AEs (OVAE) and Ontology of Drug Neuropathy AEs (ODNAE) serve as AE knowledge bases and support data integration and analysis. The Immune Response Gene Network Theory explains molecular mechanisms of vaccine-related AEs. The OneNet Theory of Life treats the whole process of a life of an organism as a single complex and dynamic network ( i.e. , OneNet). A new "OneNet effectiveness" tenet is proposed here to expand the OneNet theory. Derived from the OneNet theory, the author hypothesizes that one human uses one single genotype-rooted mechanism to respond to different vaccinations and drug treatments, and experimentally identified mechanisms are manifestations of the OneNet blueprint mechanism under specific conditions. The theories and ontologies interact together as semantic frameworks to support integrative pharmacovigilance research.
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Autophagy as a Molecular Target of Flavonoids Underlying their Protective Effects in Human Disease.
Prieto-Domínguez, Nestor; Garcia-Mediavilla, Maria V; Sanchez-Campos, Sonia; Mauriz, Jose L; Gonzalez-Gallego, Javier
2018-01-01
Autophagy is a cellular pathway with the ability to maintain cell homeostasis through the elimination of damaged or useless cellular components, and its deregulation may initiate or aggravate different human diseases. Flavonoids, a group of plant metabolites, are able to modulate different molecular and cellular processes including autophagy. To review the effects of flavonoids on autophagy pathway in both invasive and noninvasive human diseases, focusing on the global outcomes in their progression. Moreover, the efficacy of the combination of flavonoids with drugs or other natural nontoxic compounds was also reviewed. A literature search was performed to identify and analyze peer-reviewed publications containing in vitro and in vivo studies focused on autophagy deregulation in different proliferative and non-proliferative pathologies and the potential protective effects of flavonoids. Analyzed publications indicated that imbalance between cell death and survival induced by changes in autophagy play an important role in the pathophysiology of a number of human diseases. The use of different flavonoids as autophagy modulators, alone or in combination with other molecules, might be a worthy strategy in the treatment of cancer, neurodegenerative disorders, cardiovascular diseases, hepatic diseases, leishmaniasis, influenza, gastric ulcers produced by Helicobacter pylori infection, diabetes, asthma, age-related macular degeneration or osteoporosis. Flavonoids could potentially constitute important adjuvant agents of conventional therapies in the treatment of autophagy deregulation-related diseases. Moreover, combined therapy may help to diminish the doses of those conventional treatments, leading to reduced drug-derivative side effects and to improved patients' survival. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
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Support network of adolescents with chronic disease: adolescents' perspective.
Kyngäs, Helvi
2004-12-01
The purpose of this study was to describe the support network of adolescents with a chronic disease from their own perspective. Data were collected by interviewing adolescents with asthma, epilepsy, juvenile rheumatoid arthritis (JRA) and insulin-dependent diabetes mellitus (IDDM). The sample consisted of 40 adolescents aged between 13 and 17 years. Interview data were examined using content analysis. Six main categories were established to describe the support network of adolescents with a chronic disease: parents, peers, school, health care providers, technology and pets. Peers were divided into two groups: fellow sufferers and peers without a chronic disease. At school, teachers, school nurses and classmates were part of the support network. Health care providers included nurses, physicians and physiotherapists. Technology was also part of the support network and included four techniques that may be used to communicate: computers, mobile telephones, television and videos. The results provided a useful insight into the social network of adolescents with chronic disease and serve to raise awareness of the problems and opinions experienced by adolescents with this condition.
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Multimodal drugs and their future for Alzheimer's and Parkinson's disease.
Van der Schyf, Cornelis J; Geldenhuys, Werner J
2011-01-01
This chapter discusses the rationale for developing multimodal or multifunctional drugs (also called designed multiple ligands or DMLs) aimed at disease-modifying treatment strategies for the most common neurodegenerative diseases Alzheimer's and Parkinson's disease (AD and PD). Both the prevalence and incidence of AD and PD have seen consistent and dramatic increases, a disconcerting phenomenon which, ironically, has been attributed to extended life expectancy brought about by better health care globally. In spite of these statistics, the development and introduction to the clinic of new therapies proven to prevent or delay the onset of AD and PD have been disappointing. Evidence has accumulated to suggest that the etiopathology of these diseases is extremely complex, with an array of potential drug targets located within a number of deleterious biochemical pathways. Therefore, in these diseases, it is unlikely that the complex pathoetiological cascade leading to disease initiation or progression will be mitigated by any one drug acting on a single pathway or target. The pursuit of novel DMLs may offer far better outcomes. Although certainly not the only, and perhaps not even the best, approach but farthest along the drug development pipeline in the DML paradigm are drugs that combine inhibition of monoamine oxidase with associated etiological targets unique to either AD or PD. These compounds will constitute the major focus of this chapter, which will also explore radically new paradigms that seek to combine cognitive enhancers with proneurogenesis compounds. Copyright © 2011 Elsevier Inc. All rights reserved.
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Bile Acid Signaling in Metabolic Disease and Drug Therapy
Li, Tiangang
2014-01-01
Bile acids are the end products of cholesterol catabolism. Hepatic bile acid synthesis accounts for a major fraction of daily cholesterol turnover in humans. Biliary secretion of bile acids generates bile flow and facilitates hepatobiliary secretion of lipids, lipophilic metabolites, and xenobiotics. In the intestine, bile acids are essential for the absorption, transport, and metabolism of dietary fats and lipid-soluble vitamins. Extensive research in the last 2 decades has unveiled new functions of bile acids as signaling molecules and metabolic integrators. The bile acid–activated nuclear receptors farnesoid X receptor, pregnane X receptor, constitutive androstane receptor, vitamin D receptor, and G protein–coupled bile acid receptor play critical roles in the regulation of lipid, glucose, and energy metabolism, inflammation, and drug metabolism and detoxification. Bile acid synthesis exhibits a strong diurnal rhythm, which is entrained by fasting and refeeding as well as nutrient status and plays an important role for maintaining metabolic homeostasis. Recent research revealed an interaction of liver bile acids and gut microbiota in the regulation of liver metabolism. Circadian disturbance and altered gut microbiota contribute to the pathogenesis of liver diseases, inflammatory bowel diseases, nonalcoholic fatty liver disease, diabetes, and obesity. Bile acids and their derivatives are potential therapeutic agents for treating metabolic diseases of the liver. PMID:25073467
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Mukhopadhyay, Sayak; Saha, Rohini; Palanisamy, Anbarasi; Ghosh, Madhurima; Biswas, Anupriya; Roy, Saheli; Pal, Arijit; Sarkar, Kathakali; Bagh, Sangram
2016-05-17
Microgravity is a prominent health hazard for astronauts, yet we understand little about its effect at the molecular systems level. In this study, we have integrated a set of systems-biology tools and databases and have analysed more than 8000 molecular pathways on published global gene expression datasets of human cells in microgravity. Hundreds of new pathways have been identified with statistical confidence for each dataset and despite the difference in cell types and experiments, around 100 of the new pathways are appeared common across the datasets. They are related to reduced inflammation, autoimmunity, diabetes and asthma. We have identified downregulation of NfκB pathway via Notch1 signalling as new pathway for reduced immunity in microgravity. Induction of few cancer types including liver cancer and leukaemia and increased drug response to cancer in microgravity are also found. Increase in olfactory signal transduction is also identified. Genes, based on their expression pattern, are clustered and mathematically stable clusters are identified. The network mapping of genes within a cluster indicates the plausible functional connections in microgravity. This pipeline gives a new systems level picture of human cells under microgravity, generates testable hypothesis and may help estimating risk and developing medicine for space missions.
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Mukhopadhyay, Sayak; Saha, Rohini; Palanisamy, Anbarasi; Ghosh, Madhurima; Biswas, Anupriya; Roy, Saheli; Pal, Arijit; Sarkar, Kathakali; Bagh, Sangram
2016-05-01
Microgravity is a prominent health hazard for astronauts, yet we understand little about its effect at the molecular systems level. In this study, we have integrated a set of systems-biology tools and databases and have analysed more than 8000 molecular pathways on published global gene expression datasets of human cells in microgravity. Hundreds of new pathways have been identified with statistical confidence for each dataset and despite the difference in cell types and experiments, around 100 of the new pathways are appeared common across the datasets. They are related to reduced inflammation, autoimmunity, diabetes and asthma. We have identified downregulation of NfκB pathway via Notch1 signalling as new pathway for reduced immunity in microgravity. Induction of few cancer types including liver cancer and leukaemia and increased drug response to cancer in microgravity are also found. Increase in olfactory signal transduction is also identified. Genes, based on their expression pattern, are clustered and mathematically stable clusters are identified. The network mapping of genes within a cluster indicates the plausible functional connections in microgravity. This pipeline gives a new systems level picture of human cells under microgravity, generates testable hypothesis and may help estimating risk and developing medicine for space missions.
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Liby, Karen T; Sporn, Michael B
2012-10-01
We review the rationale for the use of synthetic oleanane triterpenoids (SOs) for prevention and treatment of disease, as well as extensive biological data on this topic resulting from both cell culture and in vivo studies. Emphasis is placed on understanding mechanisms of action. SOs are noncytotoxic drugs with an excellent safety profile. Several hundred SOs have now been synthesized and in vitro have been shown to: 1) suppress inflammation and oxidative stress and therefore be cytoprotective, especially at low nanomolar doses, 2) induce differentiation, and 3) block cell proliferation and induce apoptosis at higher micromolar doses. Animal data on the use of SOs in neurodegenerative diseases and in diseases of the eye, lung, cardiovascular system, liver, gastrointestinal tract, and kidney, as well as in cancer and in metabolic and inflammatory/autoimmune disorders, are reviewed. The importance of the cytoprotective Kelch-like erythroid cell-derived protein with CNC homology-associated protein 1/nuclear factor (erythroid-derived 2)-like 2/antioxidant response element (Keap1/Nrf2/ARE) pathway as a mechanism of action is explained, but interactions with peroxisome proliferator-activated receptor γ (PARPγ), inhibitor of nuclear factor-κB kinase complex (IKK), janus tyrosine kinase/signal transducer and activator of transcription (JAK/STAT), human epidermal growth factor receptor 2 (HER2)/ErbB2/neu, phosphatase and tensin homolog (PTEN), the phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) pathway, mammalian target of rapamycin (mTOR), and the thiol proteome are also described. In these interactions, Michael addition of SOs to reactive cysteine residues in specific molecular targets triggers biological activity. Ultimately, SOs are multifunctional drugs that regulate the activity of entire networks. Recent progress in the earliest clinical trials with 2-cyano-3,12-dioxooleana-1,9(11)-dien-28-oic acid (CDDO) methyl ester (bardoxolone methyl) is also
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Boivin, Rémi
2014-03-01
Illegal drug prices are extremely high, compared to similar goods. There is, however, considerable variation in value depending on place, market level and type of drugs. A prominent framework for the study of illegal drugs is the "risks and prices" model (Reuter & Kleiman, 1986). Enforcement is seen as a "tax" added to the regular price. In this paper, it is argued that such economic models are not sufficient to explain price variations at country-level. Drug markets are analysed as global trade networks in which a country's position has an impact on various features, including illegal drug prices. This paper uses social network analysis (SNA) to explain price markups between pairs of countries involved in the trafficking of illegal drugs between 1998 and 2007. It aims to explore a simple question: why do prices increase between two countries? Using relational data from various international organizations, separate trade networks were built for cocaine, heroin and cannabis. Wholesale price markups are predicted with measures of supply, demand, risks of seizures, geographic distance and global positioning within the networks. Reported prices (in $US) and purchasing power parity-adjusted values are analysed. Drug prices increase more sharply when drugs are headed to countries where law enforcement imposes higher costs on traffickers. The position and role of a country in global drug markets are also closely associated with the value of drugs. Price markups are lower if the destination country is a transit to large potential markets. Furthermore, price markups for cocaine and heroin are more pronounced when drugs are exported to countries that are better positioned in the legitimate world-economy, suggesting that relations in legal and illegal markets are directed in opposite directions. Consistent with the world-system perspective, evidence is found of coherent world drug markets driven by both local realities and international relations. Copyright © 2013 Elsevier B
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Mohana, Krishnamoorthy; Achary, Anant
2017-08-01
Glutathione-S-transferase (GST) inhibition is a strategy to overcome drug resistance. Several isoforms of human GSTs are present and they are expressed in almost all the organs. Specific expression levels of GSTs in various organs are collected from the human transcriptome data and analysis of the organ-specific expression of GST isoforms is carried out. The variations in the level of expressions of GST isoforms are statistically significant. The GST expression differs in diseased conditions as reported by many investigators and some of the isoforms of GSTs are disease markers or drug targets. Structure analysis of various isoforms is carried out and literature mining has been performed to identify the differences in the active sites of the GSTs. The xenobiotic binding H site is classified into H1, H2, and H3 and the differences in the amino acid composition, the hydrophobicity and other structural features of H site of GSTs are discussed. The existing inhibition strategies are compared. The advent of rational drug design, mechanism-based inhibition strategies, availability of high-throughput screening, target specific, and selective inhibition of GST isoforms involved in drug resistance could be achieved for the reversal of drug resistance and aid in the treatment of diseases.
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Vaccine and Drug Ontology Studies (VDOS 2014).
Tao, Cui; He, Yongqun; Arabandi, Sivaram
2016-01-01
The "Vaccine and Drug Ontology Studies" (VDOS) international workshop series focuses on vaccine- and drug-related ontology modeling and applications. Drugs and vaccines have been critical to prevent and treat human and animal diseases. Work in both (drugs and vaccines) areas is closely related - from preclinical research and development to manufacturing, clinical trials, government approval and regulation, and post-licensure usage surveillance and monitoring. Over the last decade, tremendous efforts have been made in the biomedical ontology community to ontologically represent various areas associated with vaccines and drugs - extending existing clinical terminology systems such as SNOMED, RxNorm, NDF-RT, and MedDRA, developing new models such as the Vaccine Ontology (VO) and Ontology of Adverse Events (OAE), vernacular medical terminologies such as the Consumer Health Vocabulary (CHV). The VDOS workshop series provides a platform for discussing innovative solutions as well as the challenges in the development and applications of biomedical ontologies for representing and analyzing drugs and vaccines, their administration, host immune responses, adverse events, and other related topics. The five full-length papers included in this 2014 thematic issue focus on two main themes: (i) General vaccine/drug-related ontology development and exploration, and (ii) Interaction and network-related ontology studies.
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Eggo, Rosalind M; Lenczner, Michael
2015-01-01
Background Multiple waves of transmission during infectious disease epidemics represent a major public health challenge, but the ecological and behavioral drivers of epidemic resurgence are poorly understood. In theory, community structure—aggregation into highly intraconnected and loosely interconnected social groups—within human populations may lead to punctuated outbreaks as diseases progress from one community to the next. However, this explanation has been largely overlooked in favor of temporal shifts in environmental conditions and human behavior and because of the difficulties associated with estimating large-scale contact patterns. Objective The aim was to characterize naturally arising patterns of human contact that are capable of producing simulated epidemics with multiple wave structures. Methods We used an extensive dataset of proximal physical contacts between users of a public Wi-Fi Internet system to evaluate the epidemiological implications of an empirical urban contact network. We characterized the modularity (community structure) of the network and then estimated epidemic dynamics under a percolation-based model of infectious disease spread on the network. We classified simulated epidemics as multiwave using a novel metric and we identified network structures that were critical to the network’s ability to produce multiwave epidemics. Results We identified robust community structure in a large, empirical urban contact network from which multiwave epidemics may emerge naturally. This pattern was fueled by a special kind of insularity in which locally popular individuals were not the ones forging contacts with more distant social groups. Conclusions Our results suggest that ordinary contact patterns can produce multiwave epidemics at the scale of a single urban area without the temporal shifts that are usually assumed to be responsible. Understanding the role of community structure in epidemic dynamics allows officials to anticipate epidemic
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[Application of network biology on study of traditional Chinese medicine].
Tian, Sai-Sai; Yang, Jian; Zhao, Jing; Zhang, Wei-Dong
2018-01-01
With the completion of the human genome project, people have gradually recognized that the functions of the biological system are fulfilled through network-type interaction between genes, proteins and small molecules, while complex diseases are caused by the imbalance of biological processes due to a number of gene expression disorders. These have contributed to the rise of the concept of the "multi-target" drug discovery. Treatment and diagnosis of traditional Chinese medicine are based on holism and syndrome differentiation. At the molecular level, traditional Chinese medicine is characterized by multi-component and multi-target prescriptions, which is expected to provide a reference for the development of multi-target drugs. This paper reviews the application of network biology in traditional Chinese medicine in six aspects, in expectation to provide a reference to the modernized study of traditional Chinese medicine. Copyright© by the Chinese Pharmaceutical Association.
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Desquesnes, M.; Yangtara, S.; Kunphukhieo, P.; Jittapalapong, S.; Herder, Stéphane
2016-01-01
Beside typical human trypanosomes responsible of sleeping sickness in Africa and Chagas disease in Latin America, there is a growing number of reported atypical human infections due to Trypanosoma evansi, a livestock parasite, or Trypanosoma lewisi, a rat parasite, especially in Asia. Drugs available for the treatment of T. brucei ssp. in humans are obviously of choice for the control of T. evansi because it is derived from T. brucei. However, concerning T. lewisi, there is an urgent need to ...
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Selection in the social network: effects of chronic diseases.
Tijhuis, M.A.R.; Flap, H.D.; Foets, M.; Groenewegen, P.P.
1998-01-01
Background: this article deals with the consequences of disease for someone's personal social network. It is hypothesized that the duration of a socially severe disease will affect the social network in such a way that the proportions of women, kin, long-standing relationships and people living
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Linton, Sabriya L; Haley, Danielle F; Hunter-Jones, Josalin; Ross, Zev; Cooper, Hannah L F
2017-07-01
Theories of social causation and social influence, which posit that neighborhood and social network characteristics are distal causes of substance use, are frequently used to interpret associations among neighborhood characteristics, social network characteristics and substance use. These associations are also hypothesized to result from selection processes, in which substance use determines where people live and who they interact with. The potential for these competing selection mechanisms to co-occur has been underexplored among adults. This study utilizes path analysis to determine the paths that relate census tract characteristics (e.g., economic deprivation), social network characteristics (i.e., having ≥ 1 illicit drug-using network member) and illicit drug use, among 172 African American adults relocated from public housing in Atlanta, Georgia and followed from 2009 to 2014 (7 waves). Individual and network-level characteristics were captured using surveys. Census tract characteristics were created using administrative data. Waves 1 (pre-relocation), 2 (1st wave post-relocation), and 7 were analyzed. When controlling for individual-level sociodemographic factors, residing in census tracts with prior economic disadvantage was significantly associated with illicit drug use at wave 1; illicit drug use at wave 1 was significantly associated with living in economically-disadvantaged census tracts at wave 2; and violent crime at wave 2 was associated with illicit drug-using social network members at wave 7. Findings from this study support theories that describe social causation and neighborhood selection processes as explaining relationships of neighborhood characteristics with illicit drug use and illicit drug-using social networks. Policies that improve local economic and social conditions of neighborhoods may discourage substance use. Future studies should further identify the barriers that prevent substance users from obtaining housing in less
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Drugs to foster kidney regeneration in experimental animals and humans.
Gagliardini, Elena; Benigni, Ariela
2014-01-01
The incidence of kidney diseases is increasing worldwide and they are emerging as a major public health problem. Once mostly considered inexorable, renal disease progression can now be halted and lesions can even regress with drugs such as angiotensin-converting enzyme inhibitors (ACEi) and angiotensin II type I receptor blockers, indicating the possibility of kidney repair. The discovery of renal progenitor cells lining the Bowman capsule of adult rat and human kidneys has shed light on the mechanism of repair by ACEi. Parietal progenitors are a reservoir of cells that contribute to podocyte turnover in physiological conditions. In the early phases of renal disease these progenitors migrate chaotically and subsequently proliferate, accumulating in Bowman's space. The abnormal behavior of parietal progenitors is sustained by the activation of CXCR4 receptors in response to an increased production of the chemokine SDF-1 by podocytes activated by the inflammatory environment. Ang II, via the AT1 receptor, also contributes to progenitor cell proliferation. The CXCR4/SDF-1 and Ang II/AT1 receptor pathogenic pathways both pave the way for lesion formation and subsequent sclerosis. ACEi normalize the CXCR4 and AT1 receptor expression on progenitors, limiting their proliferation, concomitant with the regression of hyperplastic lesions in animals, and in a patient with crescentic glomerulopathy. Understanding the molecular and cellular determinants of regeneration triggered by renoprotective drugs will reveal novel pathways that might be challenged or targeted by pharmacological therapy. © 2014 S. Karger AG, Basel.
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Fractal scale-free networks resistant to disease spread
International Nuclear Information System (INIS)
Zhang, Zhongzhi; Zhou, Shuigeng; Zou, Tao; Chen, Guisheng
2008-01-01
The conventional wisdom is that scale-free networks are prone to epidemic propagation; in the paper we demonstrate that, on the contrary, disease spreading is inhibited in fractal scale-free networks. We first propose a novel network model and show that it simultaneously has the following rich topological properties: scale-free degree distribution, tunable clustering coefficient, 'large-world' behavior, and fractal scaling. Existing network models do not display these characteristics. Then, we investigate the susceptible–infected–removed (SIR) model of the propagation of diseases in our fractal scale-free networks by mapping it to the bond percolation process. We establish the existence of non-zero tunable epidemic thresholds by making use of the renormalization group technique, which implies that power law degree distribution does not suffice to characterize the epidemic dynamics on top of scale-free networks. We argue that the epidemic dynamics are determined by the topological properties, especially the fractality and its accompanying 'large-world' behavior
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[Influence of the social network on consumption in drug addicts exhibiting psychiatric comorbidity].
Acier, D; Nadeau, L; Landry, M
2011-09-01
This research used a qualitative methodology and was conducted on a sample of 22 participants with concomitant substance-related and mental health disorders. Today, dual diagnosis patients represent the standard rather than the exception. Our objectives were to consider the elements and processes of the social network to explain variations in consumption of alcohol and drugs. The social network refers to all bonds established by patients, mainly family, couple, friends and therapist relationships. The 22 patients have used a specialized addiction treatment in Montreal (Canada). A focused qualitative interview was conducted with each participant using an audionumeric recording. The analysis follows the method of the mixed approach of Miles and Huberman, which combines the objectives of the grounded theory and the ethnography. All the interviews were transcribed then coded and analyzed with QSR N' Vivo 2.0. The method uses an iterative process making a constant return between verbatim and codes. The qualitative analyses present patients' perceptions on the increases and reductions in alcohol and drug consumption. Family network refers to participants where the family is named as supporting a decrease in drug consumption: couple network refers to intimate relations supporting a decrease in consumption. Mutual help network refers to alcoholics anonymous (AA) or other self-help groups. Several verbatim have been included. We propose strategies for the substance abuse treatment centers based on: (1) the paradox influence of the social network and the importance of clinical evaluation of patients of social networks; (2) emotions management, especially negative feelings, which include training of feeling, recognizing and naming, ability to the express and communicate to others; (3) importance of groups of mutual aid providing periods of sharing, validating individual experiences and pushing away loneliness; (4) function of social support of the clinical professionals as
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Directory of Open Access Journals (Sweden)
Antoinesha L. Hollman
2016-03-01
Full Text Available Exposure to environmental hazards has been associated with diseases in humans. The identification of single nucleotide polymorphisms (SNPs in human populations exposed to different environmental hazards, is vital for detecting the genetic risks of some important human diseases. Several studies in this field have been conducted on glutathione S-transferases (GSTs, a phase II detoxification superfamily, to investigate its role in the occurrence of diseases. Human GSTs consist of cytosolic and microsomal superfamilies that are further divided into subfamilies. Based on scientific search engines and a review of the literature, we have found a large amount of published articles on human GST super- and subfamilies that have greatly assisted in our efforts to examine their role in health and disease. Because of its polymorphic variations in relation to environmental hazards such as air pollutants, cigarette smoke, pesticides, heavy metals, carcinogens, pharmaceutical drugs, and xenobiotics, GST is considered as a significant biomarker. This review examines the studies on gene-environment interactions related to various diseases with respect to single nucleotide polymorphisms (SNPs found in the GST superfamily. Overall, it can be concluded that interactions between GST genes and environmental factors play an important role in human diseases.
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Small-world human brain networks: Perspectives and challenges.
Liao, Xuhong; Vasilakos, Athanasios V; He, Yong
2017-06-01
Modelling the human brain as a complex network has provided a powerful mathematical framework to characterize the structural and functional architectures of the brain. In the past decade, the combination of non-invasive neuroimaging techniques and graph theoretical approaches enable us to map human structural and functional connectivity patterns (i.e., connectome) at the macroscopic level. One of the most influential findings is that human brain networks exhibit prominent small-world organization. Such a network architecture in the human brain facilitates efficient information segregation and integration at low wiring and energy costs, which presumably results from natural selection under the pressure of a cost-efficiency balance. Moreover, the small-world organization undergoes continuous changes during normal development and ageing and exhibits dramatic alterations in neurological and psychiatric disorders. In this review, we survey recent advances regarding the small-world architecture in human brain networks and highlight the potential implications and applications in multidisciplinary fields, including cognitive neuroscience, medicine and engineering. Finally, we highlight several challenging issues and areas for future research in this rapidly growing field. Copyright © 2017 Elsevier Ltd. All rights reserved.
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2012-09-24
... disease areas to consider. FDA also welcomes public comment on the criteria for disease area selection... DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration [Docket No. FDA-2012-N-0967...: Food and Drug Administration, HHS. ACTION: Notice of public meeting; request for comments. SUMMARY: The...
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Emerging drugs for gastroesophageal reflux disease
Boeckxstaens, G. E.
2009-01-01
Proton pump inhibitors (PPIs) are very effective and safe drugs for the treatment of erosive and non-erosive gastroesophageal reflux disease (GERD). Nevertheless, a significant proportion of GERD patients (30 - 40%) continue to suffer from symptoms during PPI treatment, which has stimulated the
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Directory of Open Access Journals (Sweden)
Hugo Geerts
Full Text Available The tremendous advances in understanding the neurobiological circuits involved in schizophrenia have not translated into more effective treatments. An alternative strategy is to use a recently published 'Quantitative Systems Pharmacology' computer-based mechanistic disease model of cortical/subcortical and striatal circuits based upon preclinical physiology, human pathology and pharmacology. The physiology of 27 relevant dopamine, serotonin, acetylcholine, norepinephrine, gamma-aminobutyric acid (GABA and glutamate-mediated targets is calibrated using retrospective clinical data on 24 different antipsychotics. The model was challenged to predict quantitatively the clinical outcome in a blinded fashion of two experimental antipsychotic drugs; JNJ37822681, a highly selective low-affinity dopamine D(2 antagonist and ocaperidone, a very high affinity dopamine D(2 antagonist, using only pharmacology and human positron emission tomography (PET imaging data. The model correctly predicted the lower performance of JNJ37822681 on the positive and negative syndrome scale (PANSS total score and the higher extra-pyramidal symptom (EPS liability compared to olanzapine and the relative performance of ocaperidone against olanzapine, but did not predict the absolute PANSS total score outcome and EPS liability for ocaperidone, possibly due to placebo responses and EPS assessment methods. Because of its virtual nature, this modeling approach can support central nervous system research and development by accounting for unique human drug properties, such as human metabolites, exposure, genotypes and off-target effects and can be a helpful tool for drug discovery and development.
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Greenblum, Sharon; Turnbaugh, Peter J; Borenstein, Elhanan
2012-01-10
The human microbiome plays a key role in a wide range of host-related processes and has a profound effect on human health. Comparative analyses of the human microbiome have revealed substantial variation in species and gene composition associated with a variety of disease states but may fall short of providing a comprehensive understanding of the impact of this variation on the community and on the host. Here, we introduce a metagenomic systems biology computational framework, integrating metagenomic data with an in silico systems-level analysis of metabolic networks. Focusing on the gut microbiome, we analyze fecal metagenomic data from 124 unrelated individuals, as well as six monozygotic twin pairs and their mothers, and generate community-level metabolic networks of the microbiome. Placing variations in gene abundance in the context of these networks, we identify both gene-level and network-level topological differences associated with obesity and inflammatory bowel disease (IBD). We show that genes associated with either of these host states tend to be located at the periphery of the metabolic network and are enriched for topologically derived metabolic "inputs." These findings may indicate that lean and obese microbiomes differ primarily in their interface with the host and in the way they interact with host metabolism. We further demonstrate that obese microbiomes are less modular, a hallmark of adaptation to low-diversity environments. We additionally link these topological variations to community species composition. The system-level approach presented here lays the foundation for a unique framework for studying the human microbiome, its organization, and its impact on human health.
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West, Brooke S; Choo, Martin; El-Bassel, Nabila; Gilbert, Louisa; Wu, Elwin; Kamarulzaman, Adeeba
2014-05-01
HIV prevalence among Malaysian fishermen is ten times that of the general population. Fishing boats are a key place where drug use occurs, but we know little about how these environments shape HIV risk behaviour. Utilizing Rhodes' 'risk environment' framework, we assessed drug use contexts and how characteristics of place associated with fishing and fishermen's social networks served as key axes along which drug use and HIV risk behaviour occurred. Data were collected during 2009-2011 in Kuantan, a fishing port on the eastern coast of Malaysia, and include 28 in-depth interviews and 398 surveys collected using RDS. Logistic regression was used to determine the effect of occupational, network and risk environment characteristics on unsafe injection behaviour and access to clean needles/syringes; qualitative data were coded and analyzed thematically. Drug injecting was common and occurred on boats, often with other crewmembers. Captains and crewmembers were aware of drug use. Unsafe injection practices were significantly associated with having a larger proportion of drug injectors in network (OR=3.510, 95% CI=1.053-11.700) and having a captain provide drugs for work (OR=2.777, 95% CI=1.018-7.576). Size of fishermen network (OR=0.987, 95% CI=0.977-0.996), crewmembers' knowledge of drug use (OR=7.234, 95% CI=1.430-36.604), and having a captain provide drugs for work (OR=0.134, 95% CI=0.025-0.720) predicted access to clean needles/syringes. Qualitative analyses revealed that occupational culture and social relationships on boats drove drug use and HIV risk. While marginalized in broader society, the acceptance of drug use within the fishing community created occupational networks of risk. Fishing boats were spaces of both risk and safety; where drug users participated in the formal economy, but also where HIV risk behaviour occurred. Understanding the interplay between social networks and place is essential for developing HIV prevention and harm reduction policies
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Repurposing of Copper(II)-chelating Drugs for the Treatment of Neurodegenerative Diseases.
Lanza, Valeria; Milardi, Danilo; Di Natale, Giuseppe; Pappalardo, Giuseppe
2018-02-12
There is mounting urgency to find new drugs for the treatment of neurodegenerative disorders. A large number of reviews have exhaustively described either the molecular or clinical aspects of neurodegenerative diseases such as Alzheimer's (AD) and Parkinson's (PD). Conversely, reports outlining how known drugs in use for other diseases can also be effective as therapeutic agents in neurodegenerative diseases are less reported. This review focuses on the current uses of some copper(II) chelating molecules as potential drug candidates in neurodegeneration. Starting from the well-known harmful relationships existing between the dyshomeostasis and mis-management of metals and AD onset, we surveyed the experimental work reported in the literature, which deals with the repositioning of metal-chelating drugs in the field of neurodegenerative diseases. The reviewed papers were retrieved from common literature and their selection was limited to those describing the biomolecular aspects associated with neuroprotection. In particular, we emphasized the copper(II) coordination abilities of the selected drugs. Copper, together with zinc and iron, are known to play a key role in regulating neuronal functions. Changes in copper homeostasis are crucial for several neurodegenerative disorders. The studies included in this review may provide an overview on the current strategies aimed at repurposing copper (II) chelating drugs for the treatment of neurodegenerative disorders. Starting from the exemplary case of clioquinol repurposing, we discuss the challenge and the opportunities that repurposing of other metal-chelating drugs may provide (e.g. PBT-2, metformin and cyclodipeptides) in the treatment of neurodegenerative disease. In order to improve the success rate of drug repositioning, comprehensive studies on the molecular mechanism and therapeutic efficacy are still required. The present review upholds that drug repurposing makes significant advantages over drug discovery since
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Directory of Open Access Journals (Sweden)
Martin Florian
2012-05-01
Full Text Available Abstract Background High-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus. Results Four complementary methods were devised to quantify treatment-induced activity changes in processes described by network models. In addition, companion statistics were developed to qualify significance and specificity of the results. This approach is called Network Perturbation Amplitude (NPA scoring because the amplitudes of treatment-induced perturbations are computed for biological network models. The NPA methods were tested on two transcriptomic data sets: normal human bronchial epithelial (NHBE cells treated with the pro-inflammatory signaling mediator TNFα, and HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against network models representing different aspects of inflammatory signaling and cell cycle progression, and these scores were compared with independent measures of pathway activity in NHBE cells to verify the approach. The NPA scoring method successfully quantified the amplitude of TNFα-induced perturbation for each network model when compared against NF-κB nuclear localization and cell number. In addition, the degree and specificity to which CDK
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IgY antibodies in human nutrition for disease prevention.
Müller, Sandra; Schubert, Andreas; Zajac, Julia; Dyck, Terry; Oelkrug, Christopher
2015-10-20
Oral administration of preformed specific antibodies is an attractive approach against infections of the digestive system in humans and animals in times of increasing antibiotic resistances. Previous studies showed a positive effect of egg yolk IgY antibodies on bacterial intoxications in animals and humans. Immunization of chickens with specific antigens offers the possibility to create various forms of antibodies. Research shows that orally applied IgY's isolated from egg yolks can passively cure or prevent diseases of the digestive system. The use of these alternative therapeutic drugs provides further advantages: (1) The production of IgY's is a non-invasive alternative to current methods; (2) The keeping of chickens is inexpensive; (3) The animals are easy to handle; (4) It avoids repetitive bleeding of laboratory animals; (5) It is also very cost effective regarding the high IgY concentration within the egg yolk. Novel targets of these antigen specific antibodies are Helicobacter pylori and also molecules involved in signaling pathways in gastric cancer. Furthermore, also dental caries causing bacteria like Streptococcus mutans or opportunistic Pseudomonas aeruginosa in cystic fibrosis patients are possible targets. Therefore, IgY's included in food for human consumption may be able to prevent or cure human diseases.
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Centralized Networks to Generate Human Body Motions.
Vakulenko, Sergei; Radulescu, Ovidiu; Morozov, Ivan; Weber, Andres
2017-12-14
We consider continuous-time recurrent neural networks as dynamical models for the simulation of human body motions. These networks consist of a few centers and many satellites connected to them. The centers evolve in time as periodical oscillators with different frequencies. The center states define the satellite neurons' states by a radial basis function (RBF) network. To simulate different motions, we adjust the parameters of the RBF networks. Our network includes a switching module that allows for turning from one motion to another. Simulations show that this model allows us to simulate complicated motions consisting of many different dynamical primitives. We also use the model for learning human body motion from markers' trajectories. We find that center frequencies can be learned from a small number of markers and can be transferred to other markers, such that our technique seems to be capable of correcting for missing information resulting from sparse control marker settings.
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Directory of Open Access Journals (Sweden)
Gao XF
2017-05-01
Full Text Available Xiao-Fei Gao,1 Jun-Jie Zhang,1,2 Xiao-Min Jiang,1 Zhen Ge,1,2 Zhi-Mei Wang,1 Bing Li,1 Wen-Xing Mao,1 Shao-Liang Chen1,2 1Department of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, 2Department of Cardiology, Nanjing Heart Center, Nanjing, People’s Republic of China Background: Pulmonary arterial hypertension (PAH is a devastating disease and ultimately leads to right heart failure and premature death. A total of four classical targeted drugs, prostanoids, endothelin receptor antagonists (ERAs, phosphodiesterase 5 inhibitors (PDE-5Is, and soluble guanylate cyclase stimulator (sGCS, have been proved to improve exercise capacity and hemodynamics compared to placebo; however, direct head-to-head comparisons of these drugs are lacking. This network meta-analysis was conducted to comprehensively compare the efficacy of these targeted drugs for PAH.Methods: Medline, the Cochrane Library, and other Internet sources were searched for randomized clinical trials exploring the efficacy of targeted drugs for patients with PAH. The primary effective end point of this network meta-analysis was a 6-minute walk distance (6MWD.Results: Thirty-two eligible trials including 6,758 patients were identified. There was a statistically significant improvement in 6MWD, mean pulmonary arterial pressure, pulmonary vascular resistance, and clinical worsening events associated with each of the four targeted drugs compared with placebo. Combination therapy improved 6MWD by 20.94 m (95% confidence interval [CI]: 6.94, 34.94; P=0.003 vs prostanoids, and 16.94 m (95% CI: 4.41, 29.47; P=0.008 vs ERAs. PDE-5Is improved 6MWD by 17.28 m (95% CI: 1.91, 32.65; P=0.028 vs prostanoids, with a similar result with combination therapy. In addition, combination therapy reduced mean pulmonary artery pressure by 3.97 mmHg (95% CI: -6.06, -1.88; P<0.001 vs prostanoids, 8.24 mmHg (95% CI: -10.71, -5.76; P<0.001 vs ERAs, 3.38 mmHg (95% CI: -6.30, -0.47; P=0.023 vs
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The drug target genes show higher evolutionary conservation than non-target genes.
Lv, Wenhua; Xu, Yongdeng; Guo, Yiying; Yu, Ziqi; Feng, Guanglong; Liu, Panpan; Luan, Meiwei; Zhu, Hongjie; Liu, Guiyou; Zhang, Mingming; Lv, Hongchao; Duan, Lian; Shang, Zhenwei; Li, Jin; Jiang, Yongshuai; Zhang, Ruijie
2016-01-26
Although evidence indicates that drug target genes share some common evolutionary features, there have been few studies analyzing evolutionary features of drug targets from an overall level. Therefore, we conducted an analysis which aimed to investigate the evolutionary characteristics of drug target genes. We compared the evolutionary conservation between human drug target genes and non-target genes by combining both the evolutionary features and network topological properties in human protein-protein interaction network. The evolution rate, conservation score and the percentage of orthologous genes of 21 species were included in our study. Meanwhile, four topological features including the average shortest path length, betweenness centrality, clustering coefficient and degree were considered for comparison analysis. Then we got four results as following: compared with non-drug target genes, 1) drug target genes had lower evolutionary rates; 2) drug target genes had higher conservation scores; 3) drug target genes had higher percentages of orthologous genes and 4) drug target genes had a tighter network structure including higher degrees, betweenness centrality, clustering coefficients and lower average shortest path lengths. These results demonstrate that drug target genes are more evolutionarily conserved than non-drug target genes. We hope that our study will provide valuable information for other researchers who are interested in evolutionary conservation of drug targets.
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Pharmacogenomics to Revive Drug Development in Cardiovascular Disease.
Dubé, Marie-Pierre; de Denus, Simon; Tardif, Jean-Claude
2016-02-01
Investment in cardiovascular drug development is on the decline as large cardiovascular outcomes trials require considerable investments in time, efforts and financial resources. Pharmacogenomics has the potential to help revive the cardiovascular drug development pipeline by providing new and better drug targets at an earlier stage and by enabling more efficient outcomes trials. This article will review some of the recent developments highlighting the value of pharmacogenomics for drug development. We discuss how genetic biomarkers can enable the conduct of more efficient clinical outcomes trials by enriching patient populations for good responders to the medication. In addition, we assess past drug development programs which support the added value of selecting drug targets that have established genetic evidence supporting the targeted mechanism of disease. Finally, we discuss how pharmacogenomics can provide valuable evidence linking a drug target to clinically relevant outcomes, enabling novel drug discovery and drug repositioning opportunities.
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Chansanroj, Krisanin; Petrović, Jelena; Ibrić, Svetlana; Betz, Gabriele
2011-10-09
Artificial neural networks (ANNs) were applied for system understanding and prediction of drug release properties from direct compacted matrix tablets using sucrose esters (SEs) as matrix-forming agents for controlled release of a highly water soluble drug, metoprolol tartrate. Complexity of the system was presented through the effects of SE concentration and tablet porosity at various hydrophilic-lipophilic balance (HLB) values of SEs ranging from 0 to 16. Both effects contributed to release behaviors especially in the system containing hydrophilic SEs where swelling phenomena occurred. A self-organizing map neural network (SOM) was applied for visualizing interrelation among the variables and multilayer perceptron neural networks (MLPs) were employed to generalize the system and predict the drug release properties based on HLB value and concentration of SEs and tablet properties, i.e., tablet porosity, volume and tensile strength. Accurate prediction was obtained after systematically optimizing network performance based on learning algorithm of MLP. Drug release was mainly attributed to the effects of SEs, tablet volume and tensile strength in multi-dimensional interrelation whereas tablet porosity gave a small impact. Ability of system generalization and accurate prediction of the drug release properties proves the validity of SOM and MLPs for the formulation modeling of direct compacted matrix tablets containing controlled release agents of different material properties. Copyright © 2011 Elsevier B.V. All rights reserved.
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Murphy, Patricia; Kabir, Md Humayun; Srivastava, Tarini; Mason, Michele E; Dewi, Chitra U; Lim, Seakcheng; Yang, Andrian; Djordjevic, Djordje; Killingsworth, Murray C; Ho, Joshua W K; Harman, David G; O'Connor, Michael D
2018-01-09
Cataracts cause vision loss and blindness by impairing the ability of the ocular lens to focus light onto the retina. Various cataract risk factors have been identified, including drug treatments, age, smoking and diabetes. However, the molecular events responsible for these different forms of cataract are ill-defined, and the advent of modern cataract surgery in the 1960s virtually eliminated access to human lenses for research. Here, we demonstrate large-scale production of light-focusing human micro-lenses from spheroidal masses of human lens epithelial cells purified from differentiating pluripotent stem cells. The purified lens cells and micro-lenses display similar morphology, cellular arrangement, mRNA expression and protein expression to human lens cells and lenses. Exposing the micro-lenses to the emergent cystic fibrosis drug Vx-770 reduces micro-lens transparency and focusing ability. These human micro-lenses provide a powerful and large-scale platform for defining molecular disease mechanisms caused by cataract risk factors, for anti-cataract drug screening and for clinically relevant toxicity assays. © 2018. Published by The Company of Biologists Ltd.
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DEFF Research Database (Denmark)
Agerskov, Claus; Mortensen, Rasmus M.; Bohr, Henrik G.
2015-01-01
A study is presented on how well possible drug-molecules can be predicted with respect to their function and binding to a selection of neuro-receptors by the use of artificial neural networks. The ligands investigated in this study are chosen to be corresponding to the G protein-coupled receptors...... computational tools, able to aid in drug-design in a fast and cheap fashion, compared to conventional pharmacological techniques....... mu-opioid, serotonin 2B (5-HT2B) and metabotropic glutamate D5. They are selected due to the availability of pharmacological drug-molecule binding data for these receptors. Feedback and deep belief artificial neural network architectures (NNs) were chosen to perform the task of aiding drug-design.......925. The performance of 8 category networks (8 output classes for binding strength) obtained a prediction accuracy of above 60 %. After training the networks, tests were done on how well the systems could be used as an aid in designing candidate drug molecules. Specifically, it was shown how a selection of chemical...
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Gender and images of heart disease in Scandinavian drug advertising.
Riska, Elianne; Heikell, Thomas
2007-01-01
This study examines the construction of the "heart disease candidate" in advertisements for cardiovascular drugs in Scandinavian medical journals. All advertisements for cardiovascular drugs (n = 603) in Scandinavian medical journals (Denmark, Finland, Norway, and Sweden) in 2005 were collected. Only advertisements that portray users (n = 289, 48% of the advertisements) were analyzed. The results show that coronary candidacy is constructed as a male condition in half of the advertisements for cardiovascular drugs. The advertisements suggest a gendering of heart disease: men are the major victims of heart failure and cardiac insufficiency, and women are in need of cholesterol-lowering drugs. The cardiovascular drug advertisements portray a restoration of men's hyperactive agency, valorized by means of sporty images, by drawing on masculinity as a fixed trait and behavior. Hypercholesterolemia as a woman's disease reproduces the tyranny of slimness for women: Only women's stoutness is medicalized, and there are no pictures of heavy men. The findings point to the public health implications of gendered images of coronary candidacy in medical advertising.
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A scored human protein-protein interaction network to catalyze genomic interpretation
DEFF Research Database (Denmark)
Li, Taibo; Wernersson, Rasmus; Hansen, Rasmus B
2017-01-01
Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (InWeb_InBioMap,......Genome-scale human protein-protein interaction networks are critical to understanding cell biology and interpreting genomic data, but challenging to produce experimentally. Through data integration and quality control, we provide a scored human protein-protein interaction network (In...
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Cardiovascular disease among people with drug use disorders
DEFF Research Database (Denmark)
Thylstrup, Birgitte; Clausen, Thomas; Hesse, Morten
2015-01-01
Objectives To present the prevalence and incidence of cardiovascular disease (CVD) in a national cohort of patients seeking treatment for drug use disorders (DUD). Methods This is a longitudinal record linkage study of consecutive DUD treatment admissions between 2000 and 2006 from Denmark. Results...... treatment (SHR = 1.15, p = 0.022). The use of amphetamines was negatively associated with the risk of CVD within this cohort (SHR = 0.75, p = 0.001). Conclusions Patients injecting drugs using prescribed methadone were at elevated risk for cardiovascular disease and should be monitored for CVD. Opioid...... medications should be evaluated in terms of their cardiovascular sequelae....
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Tong, Rui; Zhou, Wei-Min; Liu, Xi-Jun; Wang, Yue; Lou, Yong-Liang; Tan, Wen-Jie
2013-04-01
To analyze the infection of human parvovirus B19, human bocavirus (HBoV) and human parvovirus 4 (PARV4) in blood samples among patients with liver disease in Nanjing by molecular detection. Nested PCR assays were designed and validated to detect B19, HBoV and PARV4, respectively. The assays were used to screen three parvoviruses in blood samples from 95 patients with different liver disease in Nanjing. The parvovirus infection was analyzed statistically. The detection limits were 10 copies of genomic DNA equivalents per reaction for each assays and the good specificity were observed. The frequency of B19 and HBoV were 2/95 (2.1%) and 9/95 (9.5%) in blood samples respectively. No PARV4 was detected. HBoV was detected in 3/5 patients with drug-induced hepatitis. Both B19 and HBoV infection were detected in blood from patients with liver disease.
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Gong, Tao; Shuai, Lan; Wu, Yicheng
2014-12-01
By analyzing complex networks constructed from authentic language data, Cong and Liu [1] advance linguistics research into the big data era. The network approach has revealed many intrinsic generalities and crucial differences at both the macro and micro scales between human languages. The axiom behind this research is that language is a complex adaptive system [2]. Although many lexical, semantic, or syntactic features have been discovered by means of analyzing the static and dynamic linguistic networks of world languages, available network-based language studies have not explicitly addressed the evolutionary dynamics of language systems and the correlations between language and human cognition. This commentary aims to provide some insights on how to use the network approach to study these issues.
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Directory of Open Access Journals (Sweden)
Deepu Mathew
2017-12-01
Full Text Available Molecular mechanism behind the therapeutic potential of pigeon pea over the human diseases such as rheumatoid arthritis, breast cancer, type II diabetes, malaria, measles and sickle cell disease were revealed through docking of GCâMS identified phyto-compound ligands with candidate disease proteins. Of the 242 ligands, three dimensional structures of 47 compounds had to be drawn using ChemSketch and the remaining structures were retrieved from PubChem and docked with the active sites of candidate proteins. The molecules identified through docking were further subjected to ADMET analysis and promising drug candidates were identified for each disease. This paper presents a precise account of the chemoprofile of pigeon pea leaves, stems and seeds, interaction of these molecules with target proteins and suggests 26 highly potential molecules which are drug candidates for multiple human diseases. Pigeon pea seeds are especially proven as invaluable source for therapeutic molecules. Keywords: Breast cancer, Drug discovery, Herbal medicine, In silico, Malaria, Measles, Phyto-compounds, Rheumatoid arthritis, Sickle cell disease, Type II diabetes
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Disease spreading in real-life networks
Gallos, Lazaros; Argyrakis, Panos
2002-08-01
In recent years the scientific community has shown a vivid interest in the network structure and dynamics of real-life organized systems. Many such systems, covering an extremely wide range of applications, have been recently shown to exhibit scale-free character in their connectivity distribution, meaning that they obey a power law. Modeling of epidemics on lattices and small-world networks suffers from the presence of a critical infection threshold, above which the entire population is infected. For scale-free networks, the original assumption was that the formation of a giant cluster would lead to an epidemic spreading in the same way as in simpler networks. Here we show that modeling epidemics on a scale-free network can greatly improve the predictions on the rate and efficiency of spreading, as compared to lattice models and small-world networks. We also show that the dynamics of a disease are greatly influenced by the underlying population structure. The exact same model can describe a plethora of networks, such as social networks, virus spreading in the Web, rumor spreading, signal transmission etc.
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Directory of Open Access Journals (Sweden)
Daniel R. Bayzigitov
2016-01-01
Full Text Available Fundamental studies of molecular and cellular mechanisms of cardiovascular disease pathogenesis are required to create more effective and safer methods of their therapy. The studies can be carried out only when model systems that fully recapitulate pathological phenotype seen in patients are used. Application of laboratory animals for cardiovascular disease modeling is limited because of physiological differences with humans. Since discovery of induced pluripotency generating induced pluripotent stem cells has become a breakthrough technology in human disease modeling. In this review, we discuss a progress that has been made in modeling inherited arrhythmias and cardiomyopathies, studying molecular mechanisms of the diseases, and searching for and testing drug compounds using patient-specific induced pluripotent stem cell-derived cardiomyocytes.
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Correia, Rion Brattig; Li, Lang; Rocha, Luis M
2016-01-01
Much recent research aims to identify evidence for Drug-Drug Interactions (DDI) and Adverse Drug reactions (ADR) from the biomedical scientific literature. In addition to this "Bibliome", the universe of social media provides a very promising source of large-scale data that can help identify DDI and ADR in ways that have not been hitherto possible. Given the large number of users, analysis of social media data may be useful to identify under-reported, population-level pathology associated with DDI, thus further contributing to improvements in population health. Moreover, tapping into this data allows us to infer drug interactions with natural products-including cannabis-which constitute an array of DDI very poorly explored by biomedical research thus far. Our goal is to determine the potential of Instagram for public health monitoring and surveillance for DDI, ADR, and behavioral pathology at large. Most social media analysis focuses on Twitter and Facebook, but Instagram is an increasingly important platform, especially among teens, with unrestricted access of public posts, high availability of posts with geolocation coordinates, and images to supplement textual analysis. Using drug, symptom, and natural product dictionaries for identification of the various types of DDI and ADR evidence, we have collected close to 7000 user timelines spanning from October 2010 to June 2015.We report on 1) the development of a monitoring tool to easily observe user-level timelines associated with drug and symptom terms of interest, and 2) population-level behavior via the analysis of co-occurrence networks computed from user timelines at three different scales: monthly, weekly, and daily occurrences. Analysis of these networks further reveals 3) drug and symptom direct and indirect associations with greater support in user timelines, as well as 4) clusters of symptoms and drugs revealed by the collective behavior of the observed population. This demonstrates that Instagram
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Humanized Mouse Model of Ebola Virus Disease Mimics the Immune Responses in Human Disease.
Bird, Brian H; Spengler, Jessica R; Chakrabarti, Ayan K; Khristova, Marina L; Sealy, Tara K; Coleman-McCray, JoAnn D; Martin, Brock E; Dodd, Kimberly A; Goldsmith, Cynthia S; Sanders, Jeanine; Zaki, Sherif R; Nichol, Stuart T; Spiropoulou, Christina F
2016-03-01
Animal models recapitulating human Ebola virus disease (EVD) are critical for insights into virus pathogenesis. Ebola virus (EBOV) isolates derived directly from human specimens do not, without adaptation, cause disease in immunocompetent adult rodents. Here, we describe EVD in mice engrafted with human immune cells (hu-BLT). hu-BLT mice developed EVD following wild-type EBOV infection. Infection with high-dose EBOV resulted in rapid, lethal EVD with high viral loads, alterations in key human antiviral immune cytokines and chemokines, and severe histopathologic findings similar to those shown in the limited human postmortem data available. A dose- and donor-dependent clinical course was observed in hu-BLT mice infected with lower doses of either Mayinga (1976) or Makona (2014) isolates derived from human EBOV cases. Engraftment of the human cellular immune system appeared to be essential for the observed virulence, as nonengrafted mice did not support productive EBOV replication or develop lethal disease. hu-BLT mice offer a unique model for investigating the human immune response in EVD and an alternative animal model for EVD pathogenesis studies and therapeutic screening. Published by Oxford University Press for the Infectious Diseases Society of America 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.
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Mordwinkin, Nicholas M; Burridge, Paul W; Wu, Joseph C
2013-02-01
Drug attrition rates have increased in past years, resulting in growing costs for the pharmaceutical industry and consumers. The reasons for this include the lack of in vitro models that correlate with clinical results and poor preclinical toxicity screening assays. The in vitro production of human cardiac progenitor cells and cardiomyocytes from human pluripotent stem cells provides an amenable source of cells for applications in drug discovery, disease modeling, regenerative medicine, and cardiotoxicity screening. In addition, the ability to derive human-induced pluripotent stem cells from somatic tissues, combined with current high-throughput screening and pharmacogenomics, may help realize the use of these cells to fulfill the potential of personalized medicine. In this review, we discuss the use of pluripotent stem cell-derived cardiomyocytes for drug discovery and cardiotoxicity screening, as well as current hurdles that must be overcome for wider clinical applications of this promising approach.
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Monkey-based research on human disease: the implications of genetic differences.
Bailey, Jarrod
2014-11-01
Assertions that the use of monkeys to investigate human diseases is valid scientifically are frequently based on a reported 90-93% genetic similarity between the species. Critical analyses of the relevance of monkey studies to human biology, however, indicate that this genetic similarity does not result in sufficient physiological similarity for monkeys to constitute good models for research, and that monkey data do not translate well to progress in clinical practice for humans. Salient examples include the failure of new drugs in clinical trials, the highly different infectivity and pathology of SIV/HIV, and poor extrapolation of research on Alzheimer's disease, Parkinson's disease and stroke. The major molecular differences underlying these inter-species phenotypic disparities have been revealed by comparative genomics and molecular biology - there are key differences in all aspects of gene expression and protein function, from chromosome and chromatin structure to post-translational modification. The collective effects of these differences are striking, extensive and widespread, and they show that the superficial similarity between human and monkey genetic sequences is of little benefit for biomedical research. The extrapolation of biomedical data from monkeys to humans is therefore highly unreliable, and the use of monkeys must be considered of questionable value, particularly given the breadth and potential of alternative methods of enquiry that are currently available to scientists. 2014 FRAME.
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Chen, Zhifen; Xian, Wenying; Bellin, Milena; Dorn, Tatjana; Tian, Qinghai; Goedel, Alexander; Dreizehnter, Lisa; Schneider, Christine M.; Ward-van Oostwaard, Dorien; Ng, Judy King Man; Hinkel, Rabea; Pane, Luna Simona; Mummery, Christine L.; Lipp, Peter; Moretti, Alessandra; Laugwitz, Karl-Ludwig; Sinnecker, Daniel
2016-01-01
AIMS: Cardiomyocytes (CMs) generated from human induced pluripotent stem cells (hiPSCs) are increasingly used in disease modelling and drug evaluation. However, they are typically a heterogeneous mix of ventricular-, atrial-, and nodal-like cells based on action potentials (APs) and gene expression.
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How Can We Treat Cancer Disease Not Cancer Cells?
Kim, Kyu-Won; Lee, Su-Jae; Kim, Woo-Young; Seo, Ji Hae; Lee, Ho-Young
2017-01-01
Since molecular biology studies began, researches in biological science have centered on proteins and genes at molecular level of a single cell. Cancer research has also focused on various functions of proteins and genes that distinguish cancer cells from normal cells. Accordingly, most contemporary anticancer drugs have been developed to target abnormal characteristics of cancer cells. Despite the great advances in the development of anticancer drugs, vast majority of patients with advanced cancer have shown grim prognosis and high rate of relapse. To resolve this problem, we must reevaluate our focuses in current cancer research. Cancer should be considered as a systemic disease because cancer cells undergo a complex interaction with various surrounding cells in cancer tissue and spread to whole body through metastasis under the control of the systemic modulation. Human body relies on the cooperative interaction between various tissues and organs, and each organ performs its specialized function through tissue-specific cell networks. Therefore, investigation of the tumor-specific cell networks can provide novel strategy to overcome the limitation of current cancer research. This review presents the limitations of the current cancer research, emphasizing the necessity of studying tissue-specific cell network which could be a new perspective on treating cancer disease, not cancer cells.
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Dopamine in the medial amygdala network mediates human bonding.
Atzil, Shir; Touroutoglou, Alexandra; Rudy, Tali; Salcedo, Stephanie; Feldman, Ruth; Hooker, Jacob M; Dickerson, Bradford C; Catana, Ciprian; Barrett, Lisa Feldman
2017-02-28
Research in humans and nonhuman animals indicates that social affiliation, and particularly maternal bonding, depends on reward circuitry. Although numerous mechanistic studies in rodents demonstrated that maternal bonding depends on striatal dopamine transmission, the neurochemistry supporting maternal behavior in humans has not been described so far. In this study, we tested the role of central dopamine in human bonding. We applied a combined functional MRI-PET scanner to simultaneously probe mothers' dopamine responses to their infants and the connectivity between the nucleus accumbens (NAcc), the amygdala, and the medial prefrontal cortex (mPFC), which form an intrinsic network (referred to as the "medial amygdala network") that supports social functioning. We also measured the mothers' behavioral synchrony with their infants and plasma oxytocin. The results of this study suggest that synchronous maternal behavior is associated with increased dopamine responses to the mother's infant and stronger intrinsic connectivity within the medial amygdala network. Moreover, stronger network connectivity is associated with increased dopamine responses within the network and decreased plasma oxytocin. Together, these data indicate that dopamine is involved in human bonding. Compared with other mammals, humans have an unusually complex social life. The complexity of human bonding cannot be fully captured in nonhuman animal models, particularly in pathological bonding, such as that in autistic spectrum disorder or postpartum depression. Thus, investigations of the neurochemistry of social bonding in humans, for which this study provides initial evidence, are warranted.
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Brenner, Frank; Marwan, Norbert; Hoffmann, Peter
2017-06-01
In this study we combined a wide range of data sets to simulate the outbreak of an airborne infectious disease that is directly transmitted from human to human. The basis is a complex network whose structures are inspired by global air traffic data (from openflights.org) containing information about airports, airport locations, direct flight connections and airplane types. Disease spreading inside every node is realized with a Susceptible-Exposed-Infected-Recovered (SEIR) compartmental model. Disease transmission rates in our model are depending on the climate environment and therefore vary in time and from node to node. To implement the correlation between water vapor pressure and influenza transmission rate [J. Shaman, M. Kohn, Proc. Natl. Acad. Sci. 106, 3243 (2009)], we use global available climate reanalysis data (WATCH-Forcing-Data-ERA-Interim, WFDEI). During our sensitivity analysis we found that disease spreading dynamics are strongly depending on network properties, the climatic environment of the epidemic outbreak location, and the season during the year in which the outbreak is happening.
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Uno, Narumi; Abe, Satoshi; Oshimura, Mitsuo; Kazuki, Yasuhiro
2018-02-01
Chromosome transfer technology, including chromosome modification, enables the introduction of Mb-sized or multiple genes to desired cells or animals. This technology has allowed innovative developments to be made for models of human disease and humanized animals, including Down syndrome model mice and humanized transchromosomic (Tc) immunoglobulin mice. Genome editing techniques are developing rapidly, and permit modifications such as gene knockout and knockin to be performed in various cell lines and animals. This review summarizes chromosome transfer-related technologies and the combined technologies of chromosome transfer and genome editing mainly for the production of cell/animal models of human disease and humanized animal models. Specifically, these include: (1) chromosome modification with genome editing in Chinese hamster ovary cells and mouse A9 cells for efficient transfer to desired cell types; (2) single-nucleotide polymorphism modification in humanized Tc mice with genome editing; and (3) generation of a disease model of Down syndrome-associated hematopoiesis abnormalities by the transfer of human chromosome 21 to normal human embryonic stem cells and the induction of mutation(s) in the endogenous gene(s) with genome editing. These combinations of chromosome transfer and genome editing open up new avenues for drug development and therapy as well as for basic research.
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Yang, Hyun-Jin; Ratnapriya, Rinki; Cogliati, Tiziana; Kim, Jung-Woong; Swaroop, Anand
2015-05-01
Genomics and genetics have invaded all aspects of biology and medicine, opening uncharted territory for scientific exploration. The definition of "gene" itself has become ambiguous, and the central dogma is continuously being revised and expanded. Computational biology and computational medicine are no longer intellectual domains of the chosen few. Next generation sequencing (NGS) technology, together with novel methods of pattern recognition and network analyses, has revolutionized the way we think about fundamental biological mechanisms and cellular pathways. In this review, we discuss NGS-based genome-wide approaches that can provide deeper insights into retinal development, aging and disease pathogenesis. We first focus on gene regulatory networks (GRNs) that govern the differentiation of retinal photoreceptors and modulate adaptive response during aging. Then, we discuss NGS technology in the context of retinal disease and develop a vision for therapies based on network biology. We should emphasize that basic strategies for network construction and analyses can be transported to any tissue or cell type. We believe that specific and uniform guidelines are required for generation of genome, transcriptome and epigenome data to facilitate comparative analysis and integration of multi-dimensional data sets, and for constructing networks underlying complex biological processes. As cellular homeostasis and organismal survival are dependent on gene-gene and gene-environment interactions, we believe that network-based biology will provide the foundation for deciphering disease mechanisms and discovering novel drug targets for retinal neurodegenerative diseases. Published by Elsevier Ltd.
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Regulatory Role of Small Nucleolar RNAs in Human Diseases
Directory of Open Access Journals (Sweden)
Grigory A. Stepanov
2015-01-01
Full Text Available Small nucleolar RNAs (snoRNAs are appreciable players in gene expression regulation in human cells. The canonical function of box C/D and box H/ACA snoRNAs is posttranscriptional modification of ribosomal RNAs (rRNAs, namely, 2′-O-methylation and pseudouridylation, respectively. A series of independent studies demonstrated that snoRNAs, as well as other noncoding RNAs, serve as the source of various short regulatory RNAs. Some snoRNAs and their fragments can also participate in the regulation of alternative splicing and posttranscriptional modification of mRNA. Alterations in snoRNA expression in human cells can affect numerous vital cellular processes. SnoRNA level in human cells, blood serum, and plasma presents a promising target for diagnostics and treatment of human pathologies. Here we discuss the relation between snoRNAs and oncological, neurodegenerative, and viral diseases and also describe changes in snoRNA level in response to artificial stress and some drugs.
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PHD fingers in human diseases: Disorders arising from misinterpreting epigenetic marks
Energy Technology Data Exchange (ETDEWEB)
Baker, Lindsey A. [Rockefeller University, Laboratory of Chromatin Biology and Epigenetics, 1230 York Avenue, Box 78, New York, NY 10065 (United States); Allis, C. David [Rockefeller University, Laboratory of Chromatin Biology and Epigenetics, 1230 York Avenue, Box 78, New York, NY 10065 (United States)], E-mail: alliscd@rockefeller.edu; Wang, Gang G. [Rockefeller University, Laboratory of Chromatin Biology and Epigenetics, 1230 York Avenue, Box 78, New York, NY 10065 (United States)], E-mail: gwang@rockefeller.edu
2008-12-01
Histone covalent modifications regulate many, if not all, DNA-templated processes, including gene expression and DNA damage response. The biological consequences of histone modifications are mediated partially by evolutionarily conserved 'reader/effector' modules that bind to histone marks in a modification- and context-specific fashion and subsequently enact chromatin changes or recruit other proteins to do so. Recently, the Plant Homeodomain (PHD) finger has emerged as a class of specialized 'reader' modules that, in some instances, recognize the methylation status of histone lysine residues, such as histone H3 lysine 4 (H3K4). While mutations in catalytic enzymes that mediate the addition or removal of histone modifications (i.e., 'writers' and 'erasers') are already known to be involved in various human diseases, mutations in the modification-specific 'reader' proteins are only beginning to be recognized as contributing to human diseases. For instance, point mutations, deletions or chromosomal translocations that target PHD fingers encoded by many genes (such as recombination activating gene 2 (RAG2), Inhibitor of Growth (ING), nuclear receptor-binding SET domain-containing 1 (NSD1) and Alpha Thalassaemia and Mental Retardation Syndrome, X-linked (ATRX)) have been associated with a wide range of human pathologies including immunological disorders, cancers, and neurological diseases. In this review, we will discuss the structural features of PHD fingers as well as the diseases for which direct mutation or dysregulation of the PHD finger has been reported. We propose that misinterpretation of the epigenetic marks may serve as a general mechanism for human diseases of this category. Determining the regulatory roles of histone covalent modifications in the context of human disease will allow for a more thorough understanding of normal and pathological development, and may provide innovative therapeutic strategies
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PHD fingers in human diseases: Disorders arising from misinterpreting epigenetic marks
International Nuclear Information System (INIS)
Baker, Lindsey A.; Allis, C. David; Wang, Gang G.
2008-01-01
Histone covalent modifications regulate many, if not all, DNA-templated processes, including gene expression and DNA damage response. The biological consequences of histone modifications are mediated partially by evolutionarily conserved 'reader/effector' modules that bind to histone marks in a modification- and context-specific fashion and subsequently enact chromatin changes or recruit other proteins to do so. Recently, the Plant Homeodomain (PHD) finger has emerged as a class of specialized 'reader' modules that, in some instances, recognize the methylation status of histone lysine residues, such as histone H3 lysine 4 (H3K4). While mutations in catalytic enzymes that mediate the addition or removal of histone modifications (i.e., 'writers' and 'erasers') are already known to be involved in various human diseases, mutations in the modification-specific 'reader' proteins are only beginning to be recognized as contributing to human diseases. For instance, point mutations, deletions or chromosomal translocations that target PHD fingers encoded by many genes (such as recombination activating gene 2 (RAG2), Inhibitor of Growth (ING), nuclear receptor-binding SET domain-containing 1 (NSD1) and Alpha Thalassaemia and Mental Retardation Syndrome, X-linked (ATRX)) have been associated with a wide range of human pathologies including immunological disorders, cancers, and neurological diseases. In this review, we will discuss the structural features of PHD fingers as well as the diseases for which direct mutation or dysregulation of the PHD finger has been reported. We propose that misinterpretation of the epigenetic marks may serve as a general mechanism for human diseases of this category. Determining the regulatory roles of histone covalent modifications in the context of human disease will allow for a more thorough understanding of normal and pathological development, and may provide innovative therapeutic strategies wherein 'chromatin readers' stand as potential drug
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Huang, Hui; Li, Yuyu; Huang, Bo; Pi, Xing
2015-07-09
In order to recycle and dispose of all people's expired drugs, the government should design a subsidy policy to stimulate users to return their expired drugs, and drug-stores should take the responsibility of recycling expired drugs, in other words, to be recycling stations. For this purpose it is necessary for the government to select the right recycling stations and treatment stations to optimize the expired drug recycling logistics network and minimize the total costs of recycling and disposal. This paper establishes a tri-level programming model to study how the government can optimize an expired drug recycling logistics network and the appropriate subsidy policies. Furthermore, a Hybrid Genetic Simulated Annealing Algorithm (HGSAA) is proposed to search for the optimal solution of the model. An experiment is discussed to illustrate the good quality of the recycling logistics network and government subsides obtained by the HGSAA. The HGSAA is proven to have the ability to converge on the global optimal solution, and to act as an effective algorithm for solving the optimization problem of expired drug recycling logistics network and government subsidies.
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In vitro production of huperzine A, a promising drug candidate for Alzheimer's disease.
Ma, Xiaoqiang; Gang, David R
2008-07-01
Alzheimer's disease (AD) is growing in impact on human health. With no known cure, AD is one of the most expensive diseases in the world to treat. Huperzine A (HupA), a anti-AD drug candidate from the traditional Chinese medicine Qian Ceng Ta (Huperzia serrata), has been shown to be a powerful and selective inhibitor of acetylcholinesterase and has attracted widespread attention because of its unique pharmacological activities and low toxicity. As a result, HupA is becoming an important lead compound for drugs to treat AD. HupA is obtained naturally from very limited and slowly growing natural resources, members of the Huperziaceae. Unfortunately, the content of HupA is very low in the raw plant material. This has led to strong interest in developing sources of HupA. We have developed a method to propagate in vitro tissues of Phlegmariurus squarrosus, a member of the Huperziaceae, that produce high levels of HupA. The in vitro propagated tissues produce even higher levels of HupA than the natural plant, and may represent an excellent source for HupA.
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[Experience of rapid drug desensitization therapy in the treatment of mycobacterial disease].
Sasaki, Yuka; Kurashima, Atsuyuki; Morimoto, Kozo; Okumura, Masao; Watanabe, Masato; Yoshiyama, Takashi; Ogata, Hideo; Gotoh, Hajime; Kudoh, Shoji; Suzuki, Hiroaki
2014-11-01
Drugs for tuberculosis and non-tuberculosis mycobacterial diseases are limited. In particular, no new drugs for non-tuberculosis mycobacterial disease have been developed in recent years. Antimycobacterial drugs have many adverse reactions, for which drug desensitization therapy has been used. Rapid drug desensitization (RDD) therapy, including antituberculosis drugs and clarithromycin, has been implemented in many regions in Europe and the United States. We investigated the validity of RDD therapy in Japan. We report our experience with RDD therapy in 13 patients who developed severe drug allergy to antimycobacterial treatment. The desensitization protocol reported by Holland and Cernandas was adapted. The underlying diseases were 7 cases of pulmonary Mycobacterium avium complex disease and 6 cases of pulmonary tuberculosis. Isoniazid was readministered in 2 (100%) of 2 patients; rifampicin, in 8 (67.7%) of 12 patients; ethambutol, in 4 (67.7%) of 6 patients; and clarithromycin, in 2 (100%) of 2 patients. In Japan, the desensitization therapy recommended by the Treatment Committee of the Japanese Society for Tuberculosis have been implemented generally. We think RDD therapy is effective and safe as the other desensitization therapy. We will continue to investigate the efficiency of RDD therapy in patients who had discontinued antimycobacterial treatment because of the drug allergic reaction.
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Gyarmathy, V Anna; Neaigus, Alan; Li, Nan; Ujhelyi, Eszter; Caplinskiene, Irma; Caplinskas, Saulius; Latkin, Carl A
2011-01-01
The aim of this study was to assess the prevalence and correlates of disclosure to network members of being hepatitis C virus (HCV)- or human immunodeficiency virus (HIV)-infected among injecting dyads of infected injection drug users (IDUs) in Budapest, Hungary and Vilnius, Lithuania,. Multivariate generalized estimating equations (GEE) were used to assess associations. Very strong infection disclosure norms exist in Hungary, and HCV disclosure was associated with using drugs and having sex within the dyad. Non-ethnic Russian IDUs in Lithuania were more likely to disclose HCV infection to non-Roma, emotionally close and HCV-infected network members, and to those with whom they shared cookers, filters, drug solutions or rinse water or got used syringes from, and if they had fewer non-IDU or IDU network members. Ethnic Russian Lithuanian IDUs were more likely to disclose HCV if they had higher disclosure attitude and knowledge scores, 'trusted' network members, and had lower non-injecting network density and higher injecting network density. HIV-infected Lithuanian IDUs were more likely to disclose to 'trusted' network members. Disclosure norms matched disclosure behaviour in Hungary, while disclosure in Lithuania to 'trusted' network members suggests possible stigmatization. Ongoing free and confidential HCV/HIV testing services for IDUs are needed to emphasize and strengthen disclosure norms, and to decrease stigma.
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Drug therapy in patients with Parkinson’s disease
Directory of Open Access Journals (Sweden)
Müller Thomas
2012-05-01
Full Text Available Abstract Parkinson`s disease (PD is a progressive, disabling neurodegenerative disorder with onset of motor and non-motor features. Both reduce quality of life of PD patients and cause caregiver burden. This review aims to provide a survey of possible therapeutic options for treatment of motor and non motor symptoms of PD and to discuss their relation to each other. MAO-B-Inhibitors, NMDA antagonists, dopamine agonists and levodopa with its various application modes mainly improve the dopamine associated motor symptoms in PD. This armentarium of PD drugs only partially influences the onset and occurrence of non motor symptoms. These PD features predominantly result from non dopaminergic neurodegeneration. Autonomic features, such as seborrhea, hyperhidrosis, orthostatic syndrome, salivation, bladder dysfunction, gastrointestinal disturbances, and neuropsychiatric symptoms, such as depression, sleep disorders, psychosis, cognitive dysfunction with impaired execution and impulse control may appear. Drug therapy of these non motor symptoms complicates long-term PD drug therapy due to possible occurrence of drug interactions, - side effects, and altered pharmacokinetic behaviour of applied compounds. Dopamine substituting compounds themselves may contribute to onset of these non motor symptoms. This complicates the differentiation from the disease process itself and influences therapeutic options, which are often limited because of additional morbidity with necessary concomitant drug therapy.
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Guo, Yingying; Ding, Yan; Xu, Feifei; Liu, Baoyue; Kou, Zinong; Xiao, Wei; Zhu, Jingbo
2015-05-13
Sea cucumber, a kind of marine animal, have long been utilized as tonic and traditional remedies in the Middle East and Asia because of its effectiveness against hypertension, asthma, rheumatism, cuts and burns, impotence, and constipation. In this study, an overall study performed on sea cucumber was used as an example to show drug discovery from marine resource by using systems pharmacology model. The value of marine natural resources has been extensively considered because these resources can be potentially used to treat and prevent human diseases. However, the discovery of drugs from oceans is difficult, because of complex environments in terms of composition and active mechanisms. Thus, a comprehensive systems approach which could discover active constituents and their targets from marine resource, understand the biological basis for their pharmacological properties is necessary. In this study, a feasible pharmacological model based on systems pharmacology was established to investigate marine medicine by incorporating active compound screening, target identification, and network and pathway analysis. As a result, 106 candidate components of sea cucumber and 26 potential targets were identified. Furthermore, the functions of sea cucumber in health improvement and disease treatment were elucidated in a holistic way based on the established compound-target and target-disease networks, and incorporated pathways. This study established a novel strategy that could be used to explore specific active mechanisms and discover new drugs from marine sources. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
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Pringle, Abbie; Harmer, Catherine J
2015-12-01
Human models of emotional processing suggest that the direct effect of successful antidepressant drug treatment may be to modify biases in the processing of emotional information. Negative biases in emotional processing are documented in depression, and single or short-term dosing with conventional antidepressant drugs reverses these biases in depressed patients prior to any subjective change in mood. Antidepressant drug treatments also modulate emotional processing in healthy volunteers, which allows the consideration of the psychological effects of these drugs without the confound of changes in mood. As such, human models of emotional processing may prove to be useful for testing the efficacy of novel treatments and for matching treatments to individual patients or subgroups of patients.
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Salganik, Matthew J; Fazito, Dimitri; Bertoni, Neilane; Abdo, Alexandre H; Mello, Maeve B; Bastos, Francisco I
2011-11-15
One of the many challenges hindering the global response to the human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) epidemic is the difficulty of collecting reliable information about the populations most at risk for the disease. Thus, the authors empirically assessed a promising new method for estimating the sizes of most at-risk populations: the network scale-up method. Using 4 different data sources, 2 of which were from other researchers, the authors produced 5 estimates of the number of heavy drug users in Curitiba, Brazil. The authors found that the network scale-up and generalized network scale-up estimators produced estimates 5-10 times higher than estimates made using standard methods (the multiplier method and the direct estimation method using data from 2004 and 2010). Given that equally plausible methods produced such a wide range of results, the authors recommend that additional studies be undertaken to compare estimates based on the scale-up method with those made using other methods. If scale-up-based methods routinely produce higher estimates, this would suggest that scale-up-based methods are inappropriate for populations most at risk of HIV/AIDS or that standard methods may tend to underestimate the sizes of these populations.
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Sensitivity analysis of human brain structural network construction
Directory of Open Access Journals (Sweden)
Kuang Wei
2017-12-01
Full Text Available Network neuroscience leverages diffusion-weighted magnetic resonance imaging and tractography to quantify structural connectivity of the human brain. However, scientists and practitioners lack a clear understanding of the effects of varying tractography parameters on the constructed structural networks. With diffusion images from the Human Connectome Project (HCP, we characterize how structural networks are impacted by the spatial resolution of brain atlases, total number of tractography streamlines, and grey matter dilation with various graph metrics. We demonstrate how injudicious combinations of highly refined brain parcellations and low numbers of streamlines may inadvertently lead to disconnected network models with isolated nodes. Furthermore, we provide solutions to significantly reduce the likelihood of generating disconnected networks. In addition, for different tractography parameters, we investigate the distributions of values taken by various graph metrics across the population of HCP subjects. Analyzing the ranks of individual subjects within the graph metric distributions, we find that the ranks of individuals are affected differently by atlas scale changes. Our work serves as a guideline for researchers to optimize the selection of tractography parameters and illustrates how biological characteristics of the brain derived in network neuroscience studies can be affected by the choice of atlas parcellation schemes. Diffusion tractography has been proven to be a promising noninvasive technique to study the network properties of the human brain. However, how various tractography and network construction parameters affect network properties has not been studied using a large cohort of high-quality data. We utilize data provided by the Human Connectome Project to characterize the changes to network properties induced by varying the brain parcellation atlas scales, the number of reconstructed tractography tracks, and the degree of grey
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Systematic integration of biomedical knowledge prioritizes drugs for repurposing.
Himmelstein, Daniel Scott; Lizee, Antoine; Hessler, Christine; Brueggeman, Leo; Chen, Sabrina L; Hadley, Dexter; Green, Ari; Khankhanian, Pouya; Baranzini, Sergio E
2017-09-22
The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data were integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then, we predicted the probability of treatment for 209,168 compound-disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.
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Global Considerations in Human Immunodeficiency Virus-Associated Respiratory Disease.
Rylance, Jamie; Meghji, Jamilah; Miller, Robert F; Ferrand, Rashida A
2016-04-01
Respiratory tract infection, particularly tuberculosis, is a major cause of mortality among human immunodeficiency virus (HIV)-infected individuals. Antiretroviral therapy (ART) has resulted in a dramatic increase in survival, although coverage of HIV treatment remains low in many parts of the world. There is a concurrent growing burden of chronic noninfectious respiratory disease as a result of increased survival. Many risk factors associated with the development of respiratory disease, such as cigarette smoking and intravenous drug use, are overrepresented among people living with HIV. In addition, there is emerging evidence that HIV infection may directly cause or accelerate the course of chronic lung disease. This review summarizes the clinical spectrum and epidemiology of respiratory tract infections and noninfectious pulmonary pathologies, and factors that explain the global variation in HIV-associated respiratory disease. The potential for enhancing diagnoses of noninfective chronic conditions through the use of clinical algorithms is discussed. We also consider issues in assessment and management of HIV-related respiratory disease in view of the increasing global scale up of ART. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.
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Non-monotonic reorganization of brain networks with Alzheimer’s disease progression
Directory of Open Access Journals (Sweden)
Hyoungkyu eKim
2015-06-01
Full Text Available Background: Identification of stage-specific changes in brain network of patients with Alzheimer’s disease (AD is critical for rationally designed therapeutics that delays the progression of the disease. However, pathological neural processes and their resulting changes in brain network topology with disease progression are not clearly known. Methods: The current study was designed to investigate the alterations in network topology of resting state fMRI among patients in three different clinical dementia rating (CDR groups (i.e., CDR = 0.5, 1, 2 and amnestic mild cognitive impairment (aMCI and age-matched healthy subject groups. We constructed cost networks from these 5 groups and analyzed their network properties using graph theoretical measures.Results: The topological properties of AD brain networks differed in a non-monotonic, stage-specific manner. Interestingly, local and global efficiency and betweenness of the network were rather higher in the aMCI and AD (CDR 1 groups than those of prior stage groups. The number, location, and structure of rich-clubs changed dynamically as the disease progressed.Conclusions: The alterations in network topology of the brain are quite dynamic with AD progression, and these dynamic changes in network patterns should be considered meticulously for efficient therapeutic interventions of AD.
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Non-steroidal anti-inflammatory drug use and the risk of Parkinson's disease
DEFF Research Database (Denmark)
Manthripragada, Angelika D; Schernhammer, Eva S; Qiu, Jiaheng
2011-01-01
Experimental evidence supports a preventative role for non-steroidal anti-inflammatory drugs (NSAIDs) in Parkinson's disease (PD).......Experimental evidence supports a preventative role for non-steroidal anti-inflammatory drugs (NSAIDs) in Parkinson's disease (PD)....
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Directory of Open Access Journals (Sweden)
Hailin Chen
2013-01-01
Full Text Available Increasing evidence has revealed that microRNAs (miRNAs play important roles in the development and progression of human diseases. However, efforts made to uncover OMIM disease-miRNA associations are lacking and the majority of diseases in the OMIM database are not associated with any miRNA. Therefore, there is a strong incentive to develop computational methods to detect potential OMIM disease-miRNA associations. In this paper, random walk on OMIM disease similarity network is applied to predict potential OMIM disease-miRNA associations under the assumption that functionally related miRNAs are often associated with phenotypically similar diseases. Our method makes full use of global disease similarity values. We tested our method on 1226 known OMIM disease-miRNA associations in the framework of leave-one-out cross-validation and achieved an area under the ROC curve of 71.42%. Excellent performance enables us to predict a number of new potential OMIM disease-miRNA associations and the newly predicted associations are publicly released to facilitate future studies. Some predicted associations with high ranks were manually checked and were confirmed from the publicly available databases, which was a strong evidence for the practical relevance of our method.
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Omer, Ankur; Singh, Poonam
2017-05-01
A serious challenge in cancer treatment is to reposition the activity of various already known drug candidates against cancer. There is a need to rewrite and systematically analyze the detailed mechanistic aspect of cellular networks to gain insight into the novel role played by various molecules. Most Human Immunodeficiency Virus infection-associated cancers are caused by oncogenic viruses like Human Papilloma Viruses and Epstein-Bar Virus. As the onset of AIDS-associated cancers marks the severity of AIDS, there might be possible interconnections between the targets and mechanism of both the diseases. We have explored the possibility of certain antiviral compounds to act against major AIDS-associated cancers: Kaposi's Sarcoma, Non-Hodgkin Lymphoma, and Cervical Cancer with the help of systems pharmacology approach that includes screening for targets and molecules through the construction of a series of drug-target and drug-target-diseases network. Two molecules (Calanolide A and Chaetochromin B) and the target "HRAS" were finally screened with the help of molecular docking and molecular dynamics simulation. The results provide novel antiviral molecules against HRAS target to treat AIDS defining cancers and an insight for understanding the pharmacological, therapeutic aspects of similar unexplored molecules against various cancers.
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Directory of Open Access Journals (Sweden)
Zhiwei Hu
2018-01-01
Full Text Available Accumulating evidence suggests that tissue factor (TF is selectively expressed in pathological angiogenesis-dependent as well as macrophage-associated human diseases. Pathological angiogenesis, the formation of neovasculature, is involved in many clinically significant human diseases, notably cancer, age-related macular degeneration (AMD, endometriosis and rheumatoid arthritis (RA. Macrophage is involved in the progression of a variety of human diseases, such as atherosclerosis and viral infections (human immunodeficiency virus, HIV and Ebola. It is well documented that TF is selectively expressed on angiogenic vascular endothelial cells (VECs in these pathological angiogenesis-dependent human diseases and on disease-associated macrophages. Under physiology condition, TF is not expressed by quiescent VECs and monocytes but is solely restricted on some cells (such as pericytes that are located outside of blood circulation and the inner layer of blood vessel walls. Here, we summarize TF expression on angiogenic VECs, macrophages and other diseased cell types in these human diseases. In cancer, for example, the cancer cells also overexpress TF in solid cancers and leukemia. Moreover, our group recently reported that TF is also expressed by cancer-initiating stem cells (CSCs and can serve as a novel oncotarget for eradication of CSCs without drug resistance. Furthermore, we review and discuss two generations of TF-targeting therapeutic antibody-like immunoconjugates (ICON and L-ICON1 and antibody-drug conjugates that are currently being tested in preclinical and clinical studies for the treatment of some of these human diseases. If efficacy and safety are proven in current and future clinical trials, TF-targeting immunoconjugates may provide novel therapeutic approaches with potential to broadly impact the treatment regimen of these significant angiogenesis-dependent, as well as macrophage-associated, human diseases.
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Drug discovery for Chagas disease should consider Trypanosoma cruzi strain diversity
Directory of Open Access Journals (Sweden)
Bianca Zingales
2014-09-01
Full Text Available This opinion piece presents an approach to standardisation of an important aspect of Chagas disease drug discovery and development: selecting Trypanosoma cruzi strains for in vitro screening. We discuss the rationale for strain selection representing T. cruzi diversity and provide recommendations on the preferred parasite stage for drug discovery, T. cruzi discrete typing units to include in the panel of strains and the number of strains/clones for primary screens and lead compounds. We also consider experimental approaches for in vitro drug assays. The Figure illustrates the current Chagas disease drug-discovery and development landscape.
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Highly dynamic animal contact network and implications on disease transmission
Shi Chen; Brad J. White; Michael W. Sanderson; David E. Amrine; Amiyaal Ilany; Cristina Lanzas
2014-01-01
Contact patterns among hosts are considered as one of the most critical factors contributing to unequal pathogen transmission. Consequently, networks have been widely applied in infectious disease modeling. However most studies assume static network structure due to lack of accurate observation and appropriate analytic tools. In this study we used high temporal and spatial resolution animal position data to construct a high-resolution contact network relevant to infectious disease transmissio...
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Temporal stability in human interaction networks
Fabbri, Renato; Fabbri, Ricardo; Antunes, Deborah Christina; Pisani, Marilia Mello; de Oliveira, Osvaldo Novais
2017-11-01
This paper reports on stable (or invariant) properties of human interaction networks, with benchmarks derived from public email lists. Activity, recognized through messages sent, along time and topology were observed in snapshots in a timeline, and at different scales. Our analysis shows that activity is practically the same for all networks across timescales ranging from seconds to months. The principal components of the participants in the topological metrics space remain practically unchanged as different sets of messages are considered. The activity of participants follows the expected scale-free trace, thus yielding the hub, intermediary and peripheral classes of vertices by comparison against the Erdös-Rényi model. The relative sizes of these three sectors are essentially the same for all email lists and the same along time. Typically, 45% are peripheral vertices. Similar results for the distribution of participants in the three sectors and for the relative importance of the topological metrics were obtained for 12 additional networks from Facebook, Twitter and ParticipaBR. These properties are consistent with the literature and may be general for human interaction networks, which has important implications for establishing a typology of participants based on quantitative criteria.
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Control of human parasitic diseases: Context and overview.
Molyneux, David H
2006-01-01
The control of parasitic diseases of humans has been undertaken since the aetiology and natural history of the infections was recognized and the deleterious effects on human health and well-being appreciated by policy makers, medical practitioners and public health specialists. However, while some parasitic infections such as malaria have proved difficult to control, as defined by a sustained reduction in incidence, others, particularly helminth infections can be effectively controlled. The different approaches to control from diagnosis, to treatment and cure of the clinically sick patient, to control the transmission within the community by preventative chemotherapy and vector control are outlined. The concepts of eradication, elimination and control are defined and examples of success summarized. Overviews of the health policy and financing environment in which programmes to control or eliminate parasitic diseases are positioned and the development of public-private partnerships as vehicles for product development or access to drugs for parasite disease control are discussed. Failure to sustain control of parasites may be due to development of drug resistance or the failure to implement proven strategies as a result of decreased resources within the health system, decentralization of health management through health-sector reform and the lack of financial and human resources in settings where per capita government expenditure on health may be less than $US 5 per year. However, success has been achieved in several large-scale programmes through sustained national government investment and/or committed donor support. It is also widely accepted that the level of investment in drug development for the parasitic diseases of poor populations is an unattractive option for pharmaceutical companies. The development of partnerships to specifically address this need provides some hope that the intractable problems of the treatment regimens for the trypanosomiases and
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Unraveling the disease consequences and mechanisms of modular structure in animal social networks
Sah, Pratha; Leu, Stephan T.; Cross, Paul C.; Hudson, Peter J.; Bansal, Shweta
2017-01-01
Disease risk is a potential cost of group living. Although modular organization is thought to reduce this cost in animal societies, empirical evidence toward this hypothesis has been conflicting. We analyzed empirical social networks from 43 animal species to motivate our study of the epidemiological consequences of modular structure in animal societies. From these empirical studies, we identified the features of interaction patterns associated with network modularity and developed a theoretical network model to investigate when and how subdivisions in social networks influence disease dynamics. Contrary to prior work, we found that disease risk is largely unaffected by modular structure, although social networks beyond a modular threshold experience smaller disease burden and longer disease duration. Our results illustrate that the lowering of disease burden in highly modular social networks is driven by two mechanisms of modular organization: network fragmentation and subgroup cohesion. Highly fragmented social networks with cohesive subgroups are able to structurally trap infections within a few subgroups and also cause a structural delay to the spread of disease outbreaks. Finally, we show that network models incorporating modular structure are necessary only when prior knowledge suggests that interactions within the population are highly subdivided. Otherwise, null networks based on basic knowledge about group size and local contact heterogeneity may be sufficient when data-limited estimates of epidemic consequences are necessary. Overall, our work does not support the hypothesis that modular structure universally mitigates the disease impact of group living.
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Ivabradine: An Intelligent Drug for the Treatment of Ischemic Heart Disease
Directory of Open Access Journals (Sweden)
Graziano Riccioni
2012-11-01
Full Text Available Heart rate (HR is a precisely regulated variable, which plays a critical role in health and disease. Elevated resting HR is a significant predictor of all-cause and cardiovascular mortality in the general population and patients with cardiovascular disease (CVD. β-blocking drugs exert negative effects on regional myocardial blood flow and function when HR reduction is eliminated by atrial pacing; calcium channel antagonists (CCAs functionally antagonize coronary vasoconstriction mediated through α-adreno-receptors and are thus devoid of this undesired effect, but the compounds are nevertheless negative inotropes. From these observations derives the necessity to find alternative, more selective drugs to reduce HR through inhibition of specific electrical current (If. Ivabradine (IVA is a novel specific HR-lowering agent that acts in sinus atrial node (SAN cells by selectively inhibiting the pacemaker If current in a dose-dependent manner by slowing the diastolic depolarization slope of SAN cells, and by reducing HR at rest during exercise in humans. Coronary artery diseases (CAD represent the most common cause of death in middle–aged and older adults in European Countries. Most ischemic episodes are triggered by an increase in HR, that induces an imbalance between myocardial oxygen delivery and consumption. IVA, a selective and specific inhibitor of the If current which reduced HR without adverse hemodynamic effects, has clearly and unequivocally demonstrated its efficacy in the treatment of chronic stable angina pectoris (CSAP and myocardial ischemia with optimal tolerability profile due to selective interaction with If channels. The aim of this review is to point out the usefulness of IVA in the treatment of ischemic heart disease.
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Orphan drugs for rare diseases: is it time to revisit their special market access status?
Simoens, Steven; Cassiman, David; Dooms, Marc; Picavet, Eline
2012-07-30
Orphan drugs are intended for diseases with a very low prevalence, and many countries have implemented legislation to support market access of orphan drugs. We argue that it is time to revisit the special market access status of orphan drugs. Indeed, evidence suggests that there is no societal preference for treating rare diseases. Although society appears to assign a greater value to severity of disease, this criterion is equally relevant to many common diseases. Furthermore, the criterion of equity in access to treatment, which underpins orphan drug legislation, puts more value on health improvement in rare diseases than in common diseases and implies that population health is not maximized. Finally, incentives for the development, pricing and reimbursement of orphan drugs have created market failures, including monopolistic prices and the artificial creation of rare diseases. We argue that, instead of awarding special market access status to orphan drugs, there is scope to optimize research and development (R&D) of orphan drugs and to control prices of orphan drugs by means of, for example, patent auctions, advance purchase commitments, pay-as-you-go schemes and dose-modification studies. Governments should consider carefully the right incentive strategy for R&D of orphan drugs in rare diseases.
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Survey of Network-Based Approaches to Research of Cardiovascular Diseases
Directory of Open Access Journals (Sweden)
Anida Sarajlić
2014-01-01
Full Text Available Cardiovascular diseases (CVDs are the leading health problem worldwide. Investigating causes and mechanisms of CVDs calls for an integrative approach that would take into account its complex etiology. Biological networks generated from available data on biomolecular interactions are an excellent platform for understanding interconnectedness of all processes within a living cell, including processes that underlie diseases. Consequently, topology of biological networks has successfully been used for identifying genes, pathways, and modules that govern molecular actions underlying various complex diseases. Here, we review approaches that explore and use relationships between topological properties of biological networks and mechanisms underlying CVDs.
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Drug Induced Steatohepatitis: An Uncommon Culprit of a Common Disease
Directory of Open Access Journals (Sweden)
Liane Rabinowich
2015-01-01
Full Text Available Nonalcoholic fatty liver disease (NAFLD is a leading cause of liver disease in developed countries. Its frequency is increasing in the general population mostly due to the widespread occurrence of obesity and the metabolic syndrome. Although drugs and dietary supplements are viewed as a major cause of acute liver injury, drug induced steatosis and steatohepatitis are considered a rare form of drug induced liver injury (DILI. The complex mechanism leading to hepatic steatosis caused by commonly used drugs such as amiodarone, methotrexate, tamoxifen, valproic acid, glucocorticoids, and others is not fully understood. It relates not only to induction of the metabolic syndrome by some drugs but also to their impact on important molecular pathways including increased hepatocytes lipogenesis, decreased secretion of fatty acids, and interruption of mitochondrial β-oxidation as well as altered expression of genes responsible for drug metabolism. Better familiarity with this type of liver injury is important for early recognition of drug hepatotoxicity and crucial for preventing severe forms of liver injury and cirrhosis. Moreover, understanding the mechanisms leading to drug induced hepatic steatosis may provide much needed clues to the mechanism and potential prevention of the more common form of metabolic steatohepatitis.
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In Silico Chemogenomics Drug Repositioning Strategies for Neglected Tropical Diseases.
Andrade, Carolina Horta; Neves, Bruno Junior; Melo-Filho, Cleber Camilo; Rodrigues, Juliana; Silva, Diego Cabral; Braga, Rodolpho Campos; Cravo, Pedro Vitor Lemos
2018-03-08
Only ~1% of all drug candidates against Neglected Tropical Diseases (NTDs) have reached clinical trials in the last decades, underscoring the need for new, safe and effective treatments. In such context, drug repositioning, which allows finding novel indications for approved drugs whose pharmacokinetic and safety profiles are already known, is emerging as a promising strategy for tackling NTDs. Chemogenomics is a direct descendent of the typical drug discovery process that involves the systematic screening of chemical compounds against drug targets in high-throughput screening (HTS) efforts, for the identification of lead compounds. However, different to the one-drug-one-target paradigm, chemogenomics attempts to identify all potential ligands for all possible targets and diseases. In this review, we summarize current methodological development efforts in drug repositioning that use state-of-the-art computational ligand- and structure-based chemogenomics approaches. Furthermore, we highlighted the recent progress in computational drug repositioning for some NTDs, based on curation and modeling of genomic, biological, and chemical data. Additionally, we also present in-house and other successful examples and suggest possible solutions to existing pitfalls. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
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Comparison of minipig, dog, monkey and human drug metabolism and disposition.
Dalgaard, Lars
2015-01-01
This article gives an overview of the drug metabolism and disposition (ADME) characteristics of the most common non-rodent species used in toxicity testing of drugs (minipigs, dogs, and monkeys) and compares these to human characteristics with regard to enzymes mediating the metabolism of drugs and the transport proteins which contribute to the absorption, distribution and excretion of drugs. Literature on ADME and regulatory guidelines of relevance in drug development of small molecules has been gathered. Non-human primates (monkeys) are the species that is closest to humans in terms of genetic homology. Dogs have an advantage due to the ready availability of comprehensive background data for toxicological safety assessment and dogs are easy to handle. Pigs have been used less than dogs and monkeys as a model in safety assessment of drug candidates. However, when a drug candidate is metabolised by aldehyde oxidase (AOX1), N-acetyltransferases (NAT1 and NAT2) or cytochrome (CYP2C9-like) enzymes which are not expressed in dogs, but are present in pigs, this species may be a better choice than dogs, provided that adequate exposure can be obtained in pigs. Conversely, pigs might not be the right choice if sulfation, involving 3-phospho-adenosyl-5-phosphosulphate sulphotransferase (PAPS) is an important pathway in the human metabolism of a drug candidate. In general, the species selection should be based on comparison between in vitro studies with human cell-based systems and animal-cell-based systems. Results from pharmacokinetic studies are also important for decision-making by establishing the obtainable exposure level in the species. Access to genetically humanized mouse models and highly sensitive analytical methods (accelerator mass spectrometry) makes it possible to improve the chance of finding all metabolites relevant for humans before clinical trials have been initiated and, if necessary, to include another animal species before long term toxicity studies are
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Multi-target drugs: the trend of drug research and development.
Lu, Jin-Jian; Pan, Wei; Hu, Yuan-Jia; Wang, Yi-Tao
2012-01-01
Summarizing the status of drugs in the market and examining the trend of drug research and development is important in drug discovery. In this study, we compared the drug targets and the market sales of the new molecular entities approved by the U.S. Food and Drug Administration from January 2000 to December 2009. Two networks, namely, the target-target and drug-drug networks, have been set up using the network analysis tools. The multi-target drugs have much more potential, as shown by the network visualization and the market trends. We discussed the possible reasons and proposed the rational strategies for drug research and development in the future.
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Navigating cancer network attractors for tumor-specific therapy
DEFF Research Database (Denmark)
Creixell, Pau; Schoof, Erwin; Erler, Janine Terra
2012-01-01
understanding of the processes by which genetic lesions perturb these networks and lead to disease phenotypes. Network biology will help circumvent fundamental obstacles in cancer treatment, such as drug resistance and metastasis, empowering personalized and tumor-specific cancer therapies....
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Coupling effects on turning points of infectious diseases epidemics in scale-free networks.
Kim, Kiseong; Lee, Sangyeon; Lee, Doheon; Lee, Kwang Hyung
2017-05-31
Pandemic is a typical spreading phenomenon that can be observed in the human society and is dependent on the structure of the social network. The Susceptible-Infective-Recovered (SIR) model describes spreading phenomena using two spreading factors; contagiousness (β) and recovery rate (γ). Some network models are trying to reflect the social network, but the real structure is difficult to uncover. We have developed a spreading phenomenon simulator that can input the epidemic parameters and network parameters and performed the experiment of disease propagation. The simulation result was analyzed to construct a new marker VRTP distribution. We also induced the VRTP formula for three of the network mathematical models. We suggest new marker VRTP (value of recovered on turning point) to describe the coupling between the SIR spreading and the Scale-free (SF) network and observe the aspects of the coupling effects with the various of spreading and network parameters. We also derive the analytic formulation of VRTP in the fully mixed model, the configuration model, and the degree-based model respectively in the mathematical function form for the insights on the relationship between experimental simulation and theoretical consideration. We discover the coupling effect between SIR spreading and SF network through devising novel marker VRTP which reflects the shifting effect and relates to entropy.
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ChemProt: a disease chemical biology database
DEFF Research Database (Denmark)
Taboureau, Olivier; Nielsen, Sonny Kim; Audouze, Karine Marie Laure
2011-01-01
Systems pharmacology is an emergent area that studies drug action across multiple scales of complexity, from molecular and cellular to tissue and organism levels. There is a critical need to develop network-based approaches to integrate the growing body of chemical biology knowledge with network...... biology. Here, we report ChemProt, a disease chemical biology database, which is based on a compilation of multiple chemical-protein annotation resources, as well as disease-associated protein-protein interactions (PPIs). We assembled more than 700 000 unique chemicals with biological annotation for 30...... evaluation of environmental chemicals, natural products and approved drugs, as well as the selection of new compounds based on their activity profile against most known biological targets, including those related to adverse drug events. Results from the disease chemical biology database associate citalopram...
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Directory of Open Access Journals (Sweden)
Hui Huang
2015-07-01
Full Text Available In order to recycle and dispose of all people’s expired drugs, the government should design a subsidy policy to stimulate users to return their expired drugs, and drug-stores should take the responsibility of recycling expired drugs, in other words, to be recycling stations. For this purpose it is necessary for the government to select the right recycling stations and treatment stations to optimize the expired drug recycling logistics network and minimize the total costs of recycling and disposal. This paper establishes a tri-level programming model to study how the government can optimize an expired drug recycling logistics network and the appropriate subsidy policies. Furthermore, a Hybrid Genetic Simulated Annealing Algorithm (HGSAA is proposed to search for the optimal solution of the model. An experiment is discussed to illustrate the good quality of the recycling logistics network and government subsides obtained by the HGSAA. The HGSAA is proven to have the ability to converge on the global optimal solution, and to act as an effective algorithm for solving the optimization problem of expired drug recycling logistics network and government subsidies.
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Choosing a Drug to Prevent Malaria
... Malaria About Malaria FAQs Fast Facts Disease Biology Ecology Human Factors Sickle Cell Mosquitoes Parasites Where Malaria ... medicines, also consider the possibility of drug-drug interactions with other medicines that the person might be ...
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Wilson, Kris; Mole, Damian J; Homer, Natalie Z M; Iredale, John P; Auer, Manfred; Webster, Scott P
2015-02-01
Human kynurenine 3-monooxygenase (KMO) is emerging as an important drug target enzyme in a number of inflammatory and neurodegenerative disease states. Recombinant protein production of KMO, and therefore discovery of KMO ligands, is challenging due to a large membrane targeting domain at the C-terminus of the enzyme that causes stability, solubility, and purification difficulties. The purpose of our investigation was to develop a suitable screening method for targeting human KMO and other similarly challenging drug targets. Here, we report the development of a magnetic bead-based binding assay using mass spectrometry detection for human KMO protein. The assay incorporates isolation of FLAG-tagged KMO enzyme on protein A magnetic beads. The protein-bound beads are incubated with potential binding compounds before specific cleavage of the protein-compound complexes from the beads. Mass spectrometry analysis is used to identify the compounds that demonstrate specific binding affinity for the target protein. The technique was validated using known inhibitors of KMO. This assay is a robust alternative to traditional ligand-binding assays for challenging protein targets, and it overcomes specific difficulties associated with isolating human KMO. © 2014 Society for Laboratory Automation and Screening.
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Mobilizing Drug Consumption Rooms: inter-place networks and harm reduction drug policy.
McCann, Eugene; Temenos, Cristina
2015-01-01
This article discusses the learning and politics involved in spreading Drug Consumption Rooms (DCRs) globally. DCRs are health facilities, operating under a harm reduction philosophy, where people consume illicit drugs in a supervised setting. Approximately 90 are located in almost 60 cities in 11 countries. They are intensely local attempts to improve the lives of specific populations and urban neighborhoods. DCRs are also global models that travel. This article examines the relationship between DCRs as facilities that are fixed in place and DCRs as globally-mobilized models of drug policy and public health practice. Drawing on research from seven countries, we apply concepts from the policy mobilities literature to analyze the travels of the DCR model and the political strategies involved in the siting of these public health service facilities. We detail the networked mobilization of the DCR model from Europe to Canada and Australia, the learning among facilities, the strategies used to mold the DCR model to local contexts, and the role of DCR staff in promoting continued proliferation of DCRs. We conclude by identifying some immobilities of DCRs to identify questions about practices, principles and future directions of harm reduction. Copyright © 2014 Elsevier Ltd. All rights reserved.
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DEFF Research Database (Denmark)
Oprea, Tudor; Nielsen, Sonny Kim; Ursu, Oleg
2011-01-01
benefit from an integrated, semantic-web compliant computer-aided drug repurposing (CADR) effort, one that would enable deep data mining of associations between approved drugs (D), targets (T), clinical outcomes (CO) and SE. We report preliminary results from text mining and multivariate statistics, based...... on 7684 approved drug labels, ADL (Dailymed) via text mining. From the ADL corresponding to 988 unique drugs, the "adverse reactions" section was mapped onto 174 SE, then clustered via principal component analysis into a 5 x 5 self-organizing map that was integrated into a Cytoscape network of SE......Finding new uses for old drugs is a strategy embraced by the pharmaceutical industry, with increasing participation from the academic sector. Drug repurposing efforts focus on identifying novel modes of action, but not in a systematic manner. With intensive data mining and curation, we aim to apply...
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eRepo-ORP: Exploring the Opportunity Space to Combat Orphan Diseases with Existing Drugs.
Brylinski, Michal; Naderi, Misagh; Govindaraj, Rajiv Gandhi; Lemoine, Jeffrey
2017-12-10
About 7000 rare, or orphan, diseases affect more than 350 million people worldwide. Although these conditions collectively pose significant health care problems, drug companies seldom develop drugs for orphan diseases due to extremely limited individual markets. Consequently, developing new treatments for often life-threatening orphan diseases is primarily contingent on financial incentives from governments, special research grants, and private philanthropy. Computer-aided drug repositioning is a cheaper and faster alternative to traditional drug discovery offering a promising venue for orphan drug research. Here, we present eRepo-ORP, a comprehensive resource constructed by a large-scale repositioning of existing drugs to orphan diseases with a collection of structural bioinformatics tools, including eThread, eFindSite, and eMatchSite. Specifically, a systematic exploration of 320,856 possible links between known drugs in DrugBank and orphan proteins obtained from Orphanet reveals as many as 18,145 candidates for repurposing. In order to illustrate how potential therapeutics for rare diseases can be identified with eRepo-ORP, we discuss the repositioning of a kinase inhibitor for Ras-associated autoimmune leukoproliferative disease. The eRepo-ORP data set is available through the Open Science Framework at https://osf.io/qdjup/. Copyright © 2017. Published by Elsevier Ltd.
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Does human migration affect international trade? A complex-network perspective.
Fagiolo, Giorgio; Mastrorillo, Marina
2014-01-01
This paper explores the relationships between international human migration and merchandise trade, using a complex-network approach. We firstly compare the topological structure of worldwide networks of human migration and bilateral trade over the period 1960-2000. Next, we ask whether the position of any pair of countries in the migration network affects their bilateral trade flows. We show that: (i) both weighted and binary versions of the networks of international migration and trade are strongly correlated; (ii) such correlations can be mostly explained by country economic/demographic size and geographical distance; and (iii) pairs of countries that are more central in the international-migration network trade more. Our findings suggest that bilateral trade between any two countries is not only affected by the presence of migrants from either countries but also by their relative embeddedness in the complex web of corridors making up the network of international human migration.
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A novel neurotrophic drug for cognitive enhancement and Alzheimer's disease.
Directory of Open Access Journals (Sweden)
Qi Chen
Full Text Available Currently, the major drug discovery paradigm for neurodegenerative diseases is based upon high affinity ligands for single disease-specific targets. For Alzheimer's disease (AD, the focus is the amyloid beta peptide (Aß that mediates familial Alzheimer's disease pathology. However, given that age is the greatest risk factor for AD, we explored an alternative drug discovery scheme that is based upon efficacy in multiple cell culture models of age-associated pathologies rather than exclusively amyloid metabolism. Using this approach, we identified an exceptionally potent, orally active, neurotrophic molecule that facilitates memory in normal rodents, and prevents the loss of synaptic proteins and cognitive decline in a transgenic AD mouse model.
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Glicksberg, Benjamin S.; Li, Li; Badgeley, Marcus A.; Shameer, Khader; Kosoy, Roman; Beckmann, Noam D.; Pho, Nam; Hakenberg, Jörg; Ma, Meng; Ayers, Kristin L.; Hoffman, Gabriel E.; Dan Li, Shuyu; Schadt, Eric E.; Patel, Chirag J.; Chen, Rong; Dudley, Joel T.
2016-01-01
Motivation: Underrepresentation of racial groups represents an important challenge and major gap in phenomics research. Most of the current human phenomics research is based primarily on European populations; hence it is an important challenge to expand it to consider other population groups. One approach is to utilize data from EMR databases that contain patient data from diverse demographics and ancestries. The implications of this racial underrepresentation of data can be profound regarding effects on the healthcare delivery and actionability. To the best of our knowledge, our work is the first attempt to perform comparative, population-scale analyses of disease networks across three different populations, namely Caucasian (EA), African American (AA) and Hispanic/Latino (HL). Results: We compared susceptibility profiles and temporal connectivity patterns for 1988 diseases and 37 282 disease pairs represented in a clinical population of 1 025 573 patients. Accordingly, we revealed appreciable differences in disease susceptibility, temporal patterns, network structure and underlying disease connections between EA, AA and HL populations. We found 2158 significantly comorbid diseases for the EA cohort, 3265 for AA and 672 for HL. We further outlined key disease pair associations unique to each population as well as categorical enrichments of these pairs. Finally, we identified 51 key ‘hub’ diseases that are the focal points in the race-centric networks and of particular clinical importance. Incorporating race-specific disease comorbidity patterns will produce a more accurate and complete picture of the disease landscape overall and could support more precise understanding of disease relationships and patient management towards improved clinical outcomes. Contacts: rong.chen@mssm.edu or joel.dudley@mssm.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307606
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Implications of prion adaptation and evolution paradigm for human neurodegenerative diseases.
Kabir, M Enamul; Safar, Jiri G
2014-01-01
There is a growing body of evidence indicating that number of human neurodegenerative diseases, including Alzheimer disease, Parkinson disease, fronto-temporal dementias, and amyotrophic lateral sclerosis, propagate in the brain via prion-like intercellular induction of protein misfolding. Prions cause lethal neurodegenerative diseases in humans, the most prevalent being sporadic Creutzfeldt-Jakob disease (sCJD); they self-replicate and spread by converting the cellular form of prion protein (PrP(C)) to a misfolded pathogenic conformer (PrP(Sc)). The extensive phenotypic heterogeneity of human prion diseases is determined by polymorphisms in the prion protein gene, and by prion strain-specific conformation of PrP(Sc). Remarkably, even though informative nucleic acid is absent, prions may undergo rapid adaptation and evolution in cloned cells and upon crossing the species barrier. In the course of our investigation of this process, we isolated distinct populations of PrP(Sc) particles that frequently co-exist in sCJD. The human prion particles replicate independently and undergo competitive selection of those with lower initial conformational stability. Exposed to mutant substrate, the winning PrP(Sc) conformers are subject to further evolution by natural selection of the subpopulation with the highest replication rate due to the lowest stability. Thus, the evolution and adaptation of human prions is enabled by a dynamic collection of distinct populations of particles, whose evolution is governed by the selection of progressively less stable, faster replicating PrP(Sc) conformers. This fundamental biological mechanism may explain the drug resistance that some prions gained after exposure to compounds targeting PrP(Sc). Whether the phenotypic heterogeneity of other neurodegenerative diseases caused by protein misfolding is determined by the spectrum of misfolded conformers (strains) remains to be established. However, the prospect that these conformers may evolve and
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CORREIA, RION BRATTIG; LI, LANG; ROCHA, LUIS M.
2015-01-01
Much recent research aims to identify evidence for Drug-Drug Interactions (DDI) and Adverse Drug reactions (ADR) from the biomedical scientific literature. In addition to this “Bibliome”, the universe of social media provides a very promising source of large-scale data that can help identify DDI and ADR in ways that have not been hitherto possible. Given the large number of users, analysis of social media data may be useful to identify under-reported, population-level pathology associated with DDI, thus further contributing to improvements in population health. Moreover, tapping into this data allows us to infer drug interactions with natural products—including cannabis—which constitute an array of DDI very poorly explored by biomedical research thus far. Our goal is to determine the potential of Instagram for public health monitoring and surveillance for DDI, ADR, and behavioral pathology at large. Most social media analysis focuses on Twitter and Facebook, but Instagram is an increasingly important platform, especially among teens, with unrestricted access of public posts, high availability of posts with geolocation coordinates, and images to supplement textual analysis. Using drug, symptom, and natural product dictionaries for identification of the various types of DDI and ADR evidence, we have collected close to 7000 user timelines spanning from October 2010 to June 2015. We report on 1) the development of a monitoring tool to easily observe user-level timelines associated with drug and symptom terms of interest, and 2) population-level behavior via the analysis of co-occurrence networks computed from user timelines at three different scales: monthly, weekly, and daily occurrences. Analysis of these networks further reveals 3) drug and symptom direct and indirect associations with greater support in user timelines, as well as 4) clusters of symptoms and drugs revealed by the collective behavior of the observed population. This demonstrates that
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Electrochemical studies of ropinirole, an anti-Parkinson's disease drug
Indian Academy of Sciences (India)
The oxidation behaviour of a potent anti-Parkinson's disease drug ropinirole hydrochloride was investigated over a wide pH range in aqueous solution at glassy carbon electrode using cyclic and square-wave voltammetry. The oxidation of drug is a pH dependent irreversible process and occurs in two steps.
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Linking Microbiota to Human Diseases
DEFF Research Database (Denmark)
Wu, Hao; Tremaroli, Valentina; Bäckhed, F
2015-01-01
The human gut microbiota encompasses a densely populated ecosystem that provides essential functions for host development, immune maturation, and metabolism. Alterations to the gut microbiota have been observed in numerous diseases, including human metabolic diseases such as obesity, type 2...
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Generation of human pluripotent stem cell-derived hepatocyte-like cells for drug toxicity screening.
Takayama, Kazuo; Mizuguchi, Hiroyuki
2017-02-01
Because drug-induced liver injury is one of the main reasons for drug development failures, it is important to perform drug toxicity screening in the early phase of pharmaceutical development. Currently, primary human hepatocytes are most widely used for the prediction of drug-induced liver injury. However, the sources of primary human hepatocytes are limited, making it difficult to supply the abundant quantities required for large-scale drug toxicity screening. Therefore, there is an urgent need for a novel unlimited, efficient, inexpensive, and predictive model which can be applied for large-scale drug toxicity screening. Human embryonic stem (ES) cells and induced pluripotent stem (iPS) cells are able to replicate indefinitely and differentiate into most of the body's cell types, including hepatocytes. It is expected that hepatocyte-like cells generated from human ES/iPS cells (human ES/iPS-HLCs) will be a useful tool for drug toxicity screening. To apply human ES/iPS-HLCs to various applications including drug toxicity screening, homogenous and functional HLCs must be differentiated from human ES/iPS cells. In this review, we will introduce the current status of hepatocyte differentiation technology from human ES/iPS cells and a novel method to predict drug-induced liver injury using human ES/iPS-HLCs. Copyright © 2016 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.
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Directory of Open Access Journals (Sweden)
Feixiong Cheng
2016-09-01
Full Text Available Viruses require host cellular factors for successful replication. A comprehensive systems-level investigation of the virus-host interactome is critical for understanding the roles of host factors with the end goal of discovering new druggable antiviral targets. Gene-trap insertional mutagenesis is a high-throughput forward genetics approach to randomly disrupt (trap host genes and discover host genes that are essential for viral replication, but not for host cell survival. In this study, we used libraries of randomly mutagenized cells to discover cellular genes that are essential for the replication of 10 distinct cytotoxic mammalian viruses, 1 gram-negative bacterium, and 5 toxins. We herein reported 712 candidate cellular genes, characterizing distinct topological network and evolutionary signatures, and occupying central hubs in the human interactome. Cell cycle phase-specific network analysis showed that host cell cycle programs played critical roles during viral replication (e.g. MYC and TAF4 regulating G0/1 phase. Moreover, the viral perturbation of host cellular networks reflected disease etiology in that host genes (e.g. CTCF, RHOA, and CDKN1B identified were frequently essential and significantly associated with Mendelian and orphan diseases, or somatic mutations in cancer. Computational drug repositioning framework via incorporating drug-gene signatures from the Connectivity Map into the virus-host interactome identified 110 putative druggable antiviral targets and prioritized several existing drugs (e.g. ajmaline that may be potential for antiviral indication (e.g. anti-Ebola. In summary, this work provides a powerful methodology with a tight integration of gene-trap insertional mutagenesis testing and systems biology to identify new antiviral targets and drugs for the development of broadly acting and targeted clinical antiviral therapeutics.
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Danshen extract circumvents drug resistance and represses cell growth in human oral cancer cells.
Yang, Cheng-Yu; Hsieh, Cheng-Chih; Lin, Chih-Kung; Lin, Chun-Shu; Peng, Bo; Lin, Gu-Jiun; Sytwu, Huey-Kang; Chang, Wen-Liang; Chen, Yuan-Wu
2017-12-29
Danshen is a common traditional Chinese medicine used to treat neoplastic and chronic inflammatory diseases in China. However, the effects of Danshen on human oral cancer cells remain relatively unknown. This study investigated the antiproliferative effects of a Danshen extract on human oral cancer SAS, SCC25, OEC-M1, and KB drug-resistant cell lines and elucidated the possible underlying mechanism. We investigated the anticancer potential of the Danshen extract in human oral cancer cell lines and an in vivo oral cancer xenograft mouse model. The expression of apoptosis-related molecules was evaluated through Western blotting, and the concentration of in vivo apoptotic markers was measured using immunohistochemical staining. The antitumor effects of 5-fluorouracil and the Danshen extract were compared. Cell proliferation assays revealed that the Danshen extract strongly inhibited oral cancer cell proliferation. Cell morphology studies revealed that the Danshen extract inhibited the growth of SAS, SCC25, and OEC-M1 cells by inducing apoptosis. The Flow cytometric analysis indicated that the Danshen extract induced cell cycle G0/G1 arrest. Immunoblotting analysis for the expression of active caspase-3 and X-linked inhibitor of apoptosis protein indicated that Danshen extract-induced apoptosis in human oral cancer SAS cells was mediated through the caspase pathway. Moreover, the Danshen extract significantly inhibited growth in the SAS xenograft mouse model. Furthermore, the Danshen extract circumvented drug resistance in KB drug-resistant oral cancer cells. The study results suggest that the Danshen extract could be a potential anticancer agent in oral cancer treatment.
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Bahramali, Golnaz; Goliaei, Bahram; Minuchehr, Zarrin; Marashi, Sayed-Amir
2017-02-01
Chameleon proteins are proteins which include sequences that can adopt α-helix-β-strand (HE-chameleon) or α-helix-coil (HC-chameleon) or β-strand-coil (CE-chameleon) structures to operate their crucial biological functions. In this study, using a network-based approach, we examined the chameleon proteins to give a better knowledge on these proteins. We focused on proteins with identical chameleon sequences with more than or equal to seven residues long in different PDB entries, which adopt HE-chameleon, HC-chameleon, and CE-chameleon structures in the same protein. One hundred and ninety-one human chameleon proteins were identified via our in-house program. Then, protein-protein interaction (PPI) networks, Gene ontology (GO) enrichment, disease network, and pathway enrichment analyses were performed for our derived data set. We discovered that there are chameleon sequences which reside in protein-protein interaction regions between two proteins critical for their dual function. Analysis of the PPI networks for chameleon proteins introduced five hub proteins, namely TP53, EGFR, HSP90AA1, PPARA, and HIF1A, which were presented in four PPI clusters. The outcomes demonstrate that the chameleon regions are in critical domains of these proteins and are important in the development and treatment of human cancers. The present report is the first network-based functional study of chameleon proteins using computational approaches and might provide a new perspective for understanding the mechanisms of diseases helping us in developing new medical therapies along with discovering new proteins with chameleon properties which are highly important in cancer.
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Drug policy, harm and human rights: a rationalist approach.
Stevens, Alex
2011-05-01
It has recently been argued that drug-related harms cannot be compared, so making it impossible to choose rationally between various drug policy options. Attempts to apply international human rights law to this area are valid, but have found it difficult to overcome the problems in applying codified human rights to issues of drug policy. This article applies the rationalist ethical argument of Gewirth (1978) to this issue. It outlines his argument to the 'principle of generic consistency' and the hierarchy of basic, nonsubtractive and additive rights that it entails. It then applies these ideas to drug policy issues, such as whether there is a right to use drugs, whether the rights of drug 'addicts' can be limited, and how different harms can be compared in choosing between policies. There is an additive right to use drugs, but only insofar as this right does not conflict with the basic and nonsubtractive rights of others. People whose freedom to choose whether to use drugs is compromised by compulsion have a right to receive treatment. They retain enforceable duties not to inflict harms on others. Policies which reduce harms to basic and nonsubtractive rights should be pursued, even if they lead to harms to additive rights. There exists a sound, rational, extra-legal basis for the discussion of drug policy and related harms which enables commensurable discussion of drug policy options. Copyright © 2011 Elsevier B.V. All rights reserved.
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EDUCATIONAL NETWORKING: HUMAN VIEW TO CYBER DEFENSE
Oleksandr Yu. Burov
2016-01-01
Networks play more and more important role for human life and activity, both in critical occupations (aviation, power industry, military missions etc.), and in everyday life (home computers, education, leisure). Interaction between human and other elements of human-machine system have changed, because they coincide in the information habitat. Human-system integration has reached new level of defense needs. The paper will introduce features of information society in respect of a human and corr...
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Functional evolution of new and expanded attention networks in humans.
Patel, Gaurav H; Yang, Danica; Jamerson, Emery C; Snyder, Lawrence H; Corbetta, Maurizio; Ferrera, Vincent P
2015-07-28
Macaques are often used as a model system for invasive investigations of the neural substrates of cognition. However, 25 million years of evolution separate humans and macaques from their last common ancestor, and this has likely substantially impacted the function of the cortical networks underlying cognitive processes, such as attention. We examined the homology of frontoparietal networks underlying attention by comparing functional MRI data from macaques and humans performing the same visual search task. Although there are broad similarities, we found fundamental differences between the species. First, humans have more dorsal attention network areas than macaques, indicating that in the course of evolution the human attention system has expanded compared with macaques. Second, potentially homologous areas in the dorsal attention network have markedly different biases toward representing the contralateral hemifield, indicating that the underlying neural architecture of these areas may differ in the most basic of properties, such as receptive field distribution. Third, despite clear evidence of the temporoparietal junction node of the ventral attention network in humans as elicited by this visual search task, we did not find functional evidence of a temporoparietal junction in macaques. None of these differences were the result of differences in training, experimental power, or anatomical variability between the two species. The results of this study indicate that macaque data should be applied to human models of cognition cautiously, and demonstrate how evolution may shape cortical networks.
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Experimental psychiatric illness and drug abuse models: from human to animal, an overview.
Edwards, Scott; Koob, George F
2012-01-01
Preclinical animal models have supported much of the recent rapid expansion of neuroscience research and have facilitated critical discoveries that undoubtedly benefit patients suffering from psychiatric disorders. This overview serves as an introduction for the following chapters describing both in vivo and in vitro preclinical models of psychiatric disease components and briefly describes models related to drug dependence and affective disorders. Although there are no perfect animal models of any psychiatric disorder, models do exist for many elements of each disease state or stage. In many cases, the development of certain models is essentially restricted to the human clinical laboratory domain for the purpose of maximizing validity, whereas the use of in vitro models may best represent an adjunctive, well-controlled means to model specific signaling mechanisms associated with psychiatric disease states. The data generated by preclinical models are only as valid as the model itself, and the development and refinement of animal models for human psychiatric disorders continues to be an important challenge. Collaborative relationships between basic neuroscience and clinical modeling could greatly benefit the development of new and better models, in addition to facilitating medications development.
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Prioritizing chronic obstructive pulmonary disease (COPD) candidate genes in COPD-related networks.
Zhang, Yihua; Li, Wan; Feng, Yuyan; Guo, Shanshan; Zhao, Xilei; Wang, Yahui; He, Yuehan; He, Weiming; Chen, Lina
2017-11-28
Chronic obstructive pulmonary disease (COPD) is a multi-factor disease, which could be caused by many factors, including disturbances of metabolism and protein-protein interactions (PPIs). In this paper, a weighted COPD-related metabolic network and a weighted COPD-related PPI network were constructed base on COPD disease genes and functional information. Candidate genes in these weighted COPD-related networks were prioritized by making use of a gene prioritization method, respectively. Literature review and functional enrichment analysis of the top 100 genes in these two networks suggested the correlation of COPD and these genes. The performance of our gene prioritization method was superior to that of ToppGene and ToppNet for genes from the COPD-related metabolic network or the COPD-related PPI network after assessing using leave-one-out cross-validation, literature validation and functional enrichment analysis. The top-ranked genes prioritized from COPD-related metabolic and PPI networks could promote the better understanding about the molecular mechanism of this disease from different perspectives. The top 100 genes in COPD-related metabolic network or COPD-related PPI network might be potential markers for the diagnosis and treatment of COPD.
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Xu, B; Charlton, F; Makris, A; Hennessy, A
2012-07-01
Preeclampsia is an exaggerated maternal inflammatory state with over-expression of placental soluble fms-like tyrosine kinase 1 (sFlt-1). It is also associated with shallow trophoblast invasion and inadequate transformation of uterine spiral arteries. Antihypertensive drugs administrated in preeclampsia to control blood pressure have been reported to regulate placental and circulating cytokine production from women with preeclampsia. Whether they could modulate the interaction between trophoblast and endothelial cells are not investigated. This study is to examine the effect of pharmacological dose of anti-hypertensive hydralazine, clonidine and labetalol on trophoblast cell integration into inflammatory TNF-a pre-exposed endothelial cellular networks. Human uterine myometrial microvascular endothelial cells (UtMVECs) were pre-incubated with (or without) low dose (0.5ng/ml) inflammatory TNF-a or TNF-a plus sFlt-1 (100ng/ml) for 24hours. These cells were labelled with red fluorescence and seeded on a 24-well culture plate coated with Matrigel. Endothelial tubular structures appeared within 4hours. Green fluorescent-labelled HTR-8/SVneo trophoblast cells were then co-cultured with endothelial cells, with (or without) hydralazine (10μg/ml), clonidine (1.0μg/ml) or labetalol (0.5μg/ml). Red and green fluorescent images were captured after 24hours. Drug effect on HTR-8 cells integration into endothelial cellular networks was quantified by Image Analysis software. The conditioned media were also collected to measure concentrations of free VEGF, PLGF and sFlt-1 by ELISA. When HTR-8/SVneo trophoblast cells were co-cultured with TNF-a pre-incubated endothelial cells, hydralazine and clonidine can significantly increase the trophoblast integration into endothelial cellular networks. This increase was not seen if co-cultured with normal endothelial cells (without TNF-a pre-incubation) or with TNF-a plus sFlt-1 treated endothelial cells. Labetalol could increase the HTR-8
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Grothe, Michel J; Teipel, Stefan J
2016-01-01
Recent neuroimaging studies of Alzheimer's disease (AD) have emphasized topographical similarities between AD-related brain changes and a prominent cortical association network called the default-mode network (DMN). However, the specificity of distinct imaging abnormalities for the DMN compared to other intrinsic connectivity networks (ICNs) of the limbic and heteromodal association cortex has not yet been examined systematically. We assessed regional amyloid load using AV45-PET, neuronal metabolism using FDG-PET, and gray matter volume using structural MRI in 473 participants from the Alzheimer's Disease Neuroimaging Initiative, including preclinical, predementia, and clinically manifest AD stages. Complementary region-of-interest and voxel-based analyses were used to assess disease stage- and modality-specific changes within seven principle ICNs of the human brain as defined by a standardized functional connectivity atlas. Amyloid deposition in AD dementia showed a preference for the DMN, but high effect sizes were also observed for other neocortical ICNs, most notably the frontoparietal-control network. Atrophic changes were most specific for an anterior limbic network, followed by the DMN, whereas other neocortical networks were relatively spared. Hypometabolism appeared to be a mixture of both amyloid- and atrophy-related profiles. Similar patterns of modality-dependent network specificity were also observed in the predementia and, for amyloid deposition, in the preclinical stage. These quantitative data confirm a high vulnerability of the DMN for multimodal imaging abnormalities in AD. However, rather than being selective for the DMN, imaging abnormalities more generally affect higher order cognitive networks and, importantly, the vulnerability profiles of these networks markedly differ for distinct aspects of AD pathology. © 2015 Wiley Periodicals, Inc.
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Coronado-García, Mayra; Thrash, Courtney R; Welch-Lazoritz, Melissa; Gauthier, Robin; Reyes, Juan Carlos; Khan, Bilal; Dombrowski, Kirk
2017-06-01
This research examined the social network and recruitment patterns of a sample of people who inject drugs (PWIDs) in rural Puerto Rico, in an attempt to uncover systematic clustering and between-group social boundaries that potentially influence disease spread. Respondent driven sampling was utilized to obtain a sample of PWID in rural Puerto Rico. Through eight initial "seeds", 317 injection drug users were recruited. Using recruitment patterns of this sample, estimates of homophily and affiliation were calculated using RDSAT. Analyses showed clustering within the social network of PWID in rural Puerto Rico. In particular, females showed a very high tendency to recruit male PWID, which suggests low social cohesion among female PWID. Results for (believed) HCV status at the time of interview indicate that HCV+ individuals were less likely to interact with HCV- individuals or those who were unaware of their status, and may be acting as "gatekeepers" to prevent disease spread. Individuals who participated in a substance use program were more likely to affiliate with one another. The use of speedballs was related to clustering within the network, in which individuals who injected this mixture were more likely to affiliate with other speedball users. Social clustering based on several characteristics and behaviors were found within the IDU population in rural Puerto Rico. RDS was effective in not only garnering a sample of PWID in rural Puerto Rico, but also in uncovering social clustering that can potentially influence disease spread among this population.
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Brain anatomical networks in early human brain development.
Fan, Yong; Shi, Feng; Smith, Jeffrey Keith; Lin, Weili; Gilmore, John H; Shen, Dinggang
2011-02-01
Recent neuroimaging studies have demonstrated that human brain networks have economic small-world topology and modular organization, enabling efficient information transfer among brain regions. However, it remains largely unknown how the small-world topology and modular organization of human brain networks emerge and develop. Using longitudinal MRI data of 28 healthy pediatric subjects, collected at their ages of 1 month, 1 year, and 2 years, we analyzed development patterns of brain anatomical networks derived from morphological correlations of brain regional volumes. The results show that the brain network of 1-month-olds has the characteristically economic small-world topology and nonrandom modular organization. The network's cost efficiency increases with the brain development to 1 year and 2 years, so does the modularity, providing supportive evidence for the hypothesis that the small-world topology and the modular organization of brain networks are established during early brain development to support rapid synchronization and information transfer with minimal rewiring cost, as well as to balance between local processing and global integration of information. Copyright © 2010. Published by Elsevier Inc.
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Static human face recognition using artificial neural networks
International Nuclear Information System (INIS)
Qamar, R.; Shah, S.H.; Javed-ur-Rehman
2003-01-01
This paper presents a novel method of human face recognition using digital computers. A digital PC camera is used to take the BMP images of the human faces. An artificial neural network using Back Propagation Algorithm is developed as a recognition engine. The BMP images of the faces serve as the input patterns for this engine. A software 'Face Recognition' has been developed to recognize the human faces for which it is trained. Once the neural network is trained for patterns of the faces, the software is able to detect and recognize them with success rate of about 97%. (author)
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Indian Academy of Sciences (India)
preventing disease in human beings or in animals. In the process ... of requirement. In the process, they may cause toxic side effects. .... the liver to release the physiologically active drug. Similarly ... patients addicted to alcohol. However, it is a ...
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Directory of Open Access Journals (Sweden)
Hugo eGeerts
2013-04-01
Full Text Available Successful disease-modifying drug development for Alzheimer’s disease has hit a roadblock with the recent failures of amyloid-based therapies, highlighting the translational disconnect between preclinical animal models and clinical outcome. Although disease-modifying therapies are the Holy Grail to pursue, symptomatic therapies addressing cognitive and neuropsychiatric aspects of the disease are also extremely important for the quality of life of patients and caregivers. Despite the fact that neuropsychiatric problems in Alzheimer patients are the major driver for costs associated with institutionalization, no good preclinical animal models with predictive validity have been documented. We propose a combination of quantitative systems pharmacology (QSP, phenotypic screening and preclinical animal models as a novel strategy for addressing the bottleneck in both cognitive and neuropsychiatric drug discovery and development for Alzheimer’s disease. Preclinical animal models such as transgene rats documenting changes in neurotransmitters with tau and amyloid pathology will provide key information that together with human imaging, pathology and clinical data will inform the virtual patient model. In this way QSP modeling can partially overcome the translational disconnect and reduce the attrition of drug programs in the clinical setting. This approach is different from target driven drug discovery as it aims to restore emergent properties of the networks and therefore likely will identify multitarget drugs. We review examples on how this hybrid humanized QSP approach has been helpful in predicting clinical outcomes in schizophrenia treatment and cognitive impairment in Alzheimer’s disease and expand on how this strategy could be applied to neuropsychiatric symptoms in dementia. We believe such an innovative approach when used carefully could change the R&D paradigm for symptomatic treatment in Alzheimer’s disease.
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Holme, Petter
2017-01-01
This book covers recent developments in epidemic process models and related data on temporally varying networks. It is widely recognized that contact networks are indispensable for describing, understanding, and intervening to stop the spread of infectious diseases in human and animal populations; “network epidemiology” is an umbrella term to describe this research field. More recently, contact networks have been recognized as being highly dynamic. This observation, also supported by an increasing amount of new data, has led to research on temporal networks, a rapidly growing area. Changes in network structure are often informed by epidemic (or other) dynamics, in which case they are referred to as adaptive networks. This volume gathers contributions by prominent authors working in temporal and adaptive network epidemiology, a field essential to understanding infectious diseases in real society.